Authors: Ji-A Byeon Han-Byul Kim Seo-Young Ban Hyun-Woo Kim Ye-Eun Bae Dong-Ho Kang Bo-Ae Kim Se-Gie Kim Jin-Tae Lee Yong-Jin Kwon
Skin hyperpigmentation caused by excessive melanin production has led to increasing interest in the development of effective anti-melanogenic agents. In this study, the anti-melanogenic effects and underlying mechanisms of nicotinamide mononucleotide (NMN) were investigated using α-MSH-induced B16F10 melanoma cells. NMN maintained over 80% cell viability at concentrations below 0.5% and significantly reduced α-MSH-induced melanin accumulation and melanin content. In addition, in situ tyrosinase assay demonstrated that NMN effectively suppressed intracellular melanogenesis. However, NMN did not exhibit direct inhibitory activity against mushroom tyrosinase. Molecular docking analysis further revealed that NMN did not directly interact with the catalytic copper-binding site of tyrosinase. Furthermore, tyrosinase zymography and Western blot analyses demonstrated that NMN suppressed melanogenesis primarily through downregulation of tyrosinase protein expression rather than direct inhibition of tyrosinase catalytic activity. Collectively, these findings suggest that NMN suppresses melanogenesis through regulation of melanogenic signaling pathways, indicating its potential utility for hyperpigmentation-related applications.
]]>Authors: Jure Margeta
The study of the conceptual framework examined climate change, water civilization, and socio-economic development in ancient Dalmatia (Croatia). The study examined the cultural and social development centered on managing and depending on water from 350 BCE to 900 CE. Significance was highlighted on water resources and water civilization as the basis of human society, influencing culture, governance, and survival. This research utilized a thermodynamic framework to illustrate the dynamic interrelationships that affect the development of civilization, establishing continuous cycles of reciprocal influence and adjustment. The findings indicate that the main external dangers to civilizations in Dalmatia were significant alterations in climate patterns and abrupt variations in water cycles brought on by solar radiation and cooling due to volcanic eruptions. Within the organization, there was an insufficient water management expertise required to manage and regulate water resource systems. With the failure of water networks and dissipative structures, local communities were deprived of water and food resources. Hunger, warfare, and illness frequently ensued, resulting in large-scale movement or the desertion of cities. The results suggest that the connection among climate change, water societies, and socio-economic elements underpins both human history and contemporary global issues.
]]>Authors: Arezoo Miri Hossein Sabouri Habiballah Soughi
Chlorophyll fluorescence parameters are sensitive indicators of photosystem II (PSII) functionality and overall photosynthetic performance; however, their genetic architecture in wheat remains poorly understood. This study aimed to elucidate the gene action governing chlorophyll fluorescence traits in two bread wheat (Triticum aestivum L.) crosses: N-92-9 × Ehsan and Kohdasht × Ehsan. Six generations (P1, P2, F1, F2, BC1, and BC2) were evaluated under a randomized complete block design (RCBD) with three replications. Sixteen OJIP-based fluorescence parameters were recorded, including Fo, Fm, Fv, Fv/Fm, ABS/RC, TRo/RC, ETo/RC, REo/RC, φ(Po), ψ(Eo), ABS/CSo, DIo/CSo, TRo/CSo, ETo/CSo, ABS/CSm, and DIo/CSm. Significant generation effects were detected for 14 of the 16 parameters in N-92-9 × Ehsan and for 12 parameters in Kohdasht × Ehsan. These statistical patterns and the subsequent generation mean analyses indicated cross- and trait-dependent contributions of additive, dominance, and epistatic effects. The ratio |F|/√(D×H) was less than unity for the majority of traits in both crosses, indicating an asymmetrical distribution of dominant and recessive alleles between the parents and confirming the importance of dominance in controlling these traits. Broad-sense heritability ranged from moderate to high, reaching 0.991 for Fv/Fm in N-92-9 × Ehsan and 0.697 for ABS/CSm in Kohdasht × Ehsan. Dominance gene effects were predominant for most traits in both crosses, suggesting that hybrid breeding strategies would be more effective for improving these traits. An exception was TRo/CSo in Kohdasht × Ehsan, where additive effects were more prominent, making it amenable to improvement through selection-based methods. These findings provide a cross-specific quantitative-genetic assessment of gene action for PSII fluorescence parameters and illustrate the potential value of combining generation mean analysis with marker-based approaches when integrating photosynthetic efficiency into wheat breeding programs.
]]>Authors: Oluwatoyin Kode Michin Hong Long Nguyen Soo-Yeon Ji
Dementia-related disorders, particularly Alzheimer’s disease (AD), represent a growing global health challenge, increasing the need for early and reliable detection. Electroencephalography (EEG), a non-invasive and cost-effective neurophysiological modality, has emerged as a promising tool for identifying neural signatures associated with cognitive decline. Recent advances in machine learning (ML) and deep learning (DL) have enabled more effective analysis of complex EEG signals for automated dementia prediction. This survey provides a comprehensive synthesis of EEG-based dementia studies published between 2020 and 2025, with a primary focus on Alzheimer’s disease (AD), frontotemporal dementia (FTD), mild cognitive impairment (MCI), and related dementia disorders. Unlike previous reviews that emphasize multimodal approaches or specific methodologies, this work exclusively focuses on EEG and presents a systematic comparison of ML and DL approaches, preprocessing pipelines, feature extraction techniques, publicly available datasets, validation strategies, performance metrics, and explainable methods. We further examine current research trends, identify methodological limitations such as small dataset sizes, subject-level data leakage, class imbalance, inconsistent preprocessing protocols, and limited multicenter validation, and discuss their implications for model generalizability and clinical adoption. We conclude by outlining future research directions toward developing robust, interpretable, and scalable EEG-based dementia prediction systems suitable for real-world clinical applications.
]]>Authors: Jia Yang Yongqi Yin Weiming Fang
Rapid and accurate detection of pathogenic bacteria is of critical concern in the food and pharmaceutical sectors. In this study, a gene membrane chip detection method combining multiplex PCR with reverse dot blot hybridization was developed to enable visual, high-throughput simultaneous identification of 12 common pathogenic bacteria. Specific primers and probes were designed targeting Klebsiella pneumoniae, Proteus mirabilis, Vibrio parahaemolyticus, Enterobacter cloacae, Pseudomonas putida, Listeria monocytogenes, Salmonella spp., Pseudomonas aeruginosa, Escherichia coli, Acinetobacter baumannii, Clostridium perfringens, and Staphylococcus aureus. The multiplex PCR reaction system, hybridization temperature, and color development conditions were systematically optimized to construct the gene membrane chip detection platform. The specificity, limit of detection, and stability of the method were rigorously evaluated. Results demonstrated that the 12 selected specific primer pairs effectively amplified the corresponding targets, yielding amplicon lengths ranging from 81 to 298 bp, with no nonspecific amplification observed. Under the optimized detection system, each probe produced clear, visually distinct color spots exclusively for its target, with no cross-reactivity. The method achieved a limit of detection as low as 0.01 ng/μL for mixed templates, and exhibited excellent intra- and inter-assay reproducibility. The established visual gene membrane chip assay successfully enables simultaneous differentiation of 12 control bacteria in a single reaction, providing a robust technical foundation and promising potential for rapid screening in food and drug safety, but its practical application requires further validation through authentic or spiked food samples.
]]>Authors: Juliana dos Santos Fernandes Rodrigo Pires do Nascimento Ivaldo Itabaiana
This study integrates microbial biotechnology, bioprocess optimization, and circular bioeconomy principles to valorize oilseed agro-industrial coproducts as low-cost inducing substrates for lipase production. Palm kernel cake (PKC), canola cake, andiroba cake, and brewer’s spent grain were evaluated as carbon sources for extracellular lipase production by the wild-type strain Trichoderma sp. LEPM-711 under submerged fermentation. Qualitative screening in olive oil/rhodamine B medium confirmed the strain’s lipolytic potential. Among the substrates tested, PKC showed the strongest induction, reaching 4.68 U mL−1, and was therefore selected for nitrogen supplementation and statistical optimization. Peptone supplementation increased lipolytic activity to 8.41 U mL−1 after 72 h. A Plackett–Burman design identified CaCl2·2H2O, CoCl2·6H2O, and peptone as significant medium components, while response surface methodology established the optimal operational conditions as pH 6.0, 106 spores mL−1, and 2% (w/v) PKC. Under these conditions, the crude enzymatic extract reached 18.31 U mL−1 at pH 7.0 and 45 °C, corresponding to an approximately 3.9-fold increase over the initial PKC fermentation. These findings support the use of Trichoderma sp. LEPM-711 and oilseed coproducts as a sustainable platform for enzyme production and residue upgrading within applied natural sciences.
]]>Authors: Murat Sariyar
Background: Removing explicit protected health information (PHI) does not necessarily make a clinical narrative non-identifiable. Rare diagnoses, social circumstances, locations, temporal patterns, and treatment trajectories may still permit re-identification. Methods: This PRISMA-ScR-informed scoping review combined PubMed/MEDLINE, OpenAlex, and targeted NLP searches with OpenAlex title-and-abstract, Web of Science, and Scopus sensitivity checks. Eligible publications addressed clinical or biomedical text de-identification from 1 January 2018 to 31 March 2026. Results: The searches yielded 275 records and 258 unique records after deduplication. Eligibility assessment produced 110 core records, 11 background or review records, and 22 exclusions. All 143 eligibility-stage records underwent independent double screening and reconciliation; structured extraction covered all 110 core records. Abstract screening of 47 additional Scopus candidates found no task function or implementation paradigm outside the proposed classification. The resulting two-axis framework separates a method’s role in the workflow from its technical implementation. Conclusions: Clinical text de-identification cannot be reduced to named-entity recognition. Local hybrid or transformer pipelines remain the most defensible baseline for routine PHI detection. LLMs are better suited to defined tasks in augmentation, transformation, and assurance, with local validation and explicit control of data exposure.
]]>Authors: Barbara Gordon Natalie Christensen Jenifer Reader Cynthia Blanton
Background: This study explored the observations and experiences of athletic trainers (ATs) regarding food security constraints and drivers among collegiate student-athletes through the framework of the social determinants of health (SDOH). Methods: Seventeen ATs from nine public Mountain West Division I schools participated in one of six 90 min, virtual focus groups conducted in July–December 2023. A phenomenological design and constructivist theoretical framework were employed to analyze transcripts, compile themes, infer food security constraints and drivers, and map them to SDOH domains. Results: Emerging constraints and drivers mapped to seven of the eight SDOH domains (education, employment, health systems and services, housing, income and wealth, physical environment, and social environment); no themes related to the public safety domain. Conclusions: ATs identified a considerable number of food security constraints and drivers; however, based on the findings of other studies, their observations were not comprehensive. Incorporating the findings of those other studies, food security constraints and drivers exist across all eight SDOH domains. Four concepts spanned multiple domains: student-athlete normative expectations, nutrition/food security prioritization, financial disparities, and residing off campus. To ensure food security among collegiate student-athletes, AT perceptions suggest that structural changes facilitating the implementation of necessary food drivers may be warranted.
]]>Authors: Rizos N. Krikkis
A numerical bifurcation analysis is presented for inductively coupled plasmas and wall-stabilized arcs for argon and hydrogen. Because of the non-linear transport and radiative properties, both problems admit multiple solutions, up to three for argon and up to four for hydrogen. The multiplicity structure primarily dependents on the non-linear, and, especially, the non-monotonic relationship between thermal conductivity and temperature. As a result of the non-monotonicity, a multipoint energy equilibrium between joule heating (heat generation) and heat dissipation by conduction and radiation exists, giving rise to the multiplicity that is a characteristic feature of both radiating and non-radiating arcs. Despite the relatively simple one-dimensional model employed, the agreement with the experimental data is good.
]]>Authors: M.J. Siahdashti Adolfo Foriero
Bentonite slurry is frequently used as a support fluid in the construction of drilled shafts. During the piling process, the slurry acts as a sealant and slightly penetrates the nearby soil. However, the degree to which bentonite slurry penetrates the soil affects the resulting frictional capacity of the bored piles. This experimental study examines the extent of this phenomenon, arising from the formation of what is typically known as the bentonite filter cake or mud cake. The frictional properties of the filter cake are examined through three groups of direct shear tests, employing three pre-cast concrete blocks positioned on a sand layer that has been subject to bentonite slurry for varying durations. To ensure comparison, a similar pre-cast concrete block was utilized in each test series, resulting in uniform surface roughness in the concrete. A handheld surface roughness device was utilized to measure the roughness profile of each concrete block, assessing the surface roughness of all concrete surfaces. The outcomes of the direct shear test performed were subsequently normalized based on the assessed roughness of the concrete surface. Experimental results showed that the friction capacity of the soil–concrete interface for granular materials (“sand–concrete interface”) decreases with longer exposure to bentonite slurry. Specifically, the shear strength is inversely proportional to the square root of the bentonite slurry exposure time. Tests on the internal friction angle of Québec Valcartier granitic sand and the friction angles at sand–concrete interfaces with and without bentonite slurry exposure revealed that the non-exposed sand–concrete interface achieves a peak friction angle equal to 77% of the peak internal friction angle of Québec Valcartier granitic sand. This value represents 69% and 60% of the peak friction angle of the sand tested for bentonite exposure durations of 2 and 4 h, respectively.
]]>Authors: Carmine P. Polito
Cyclic triaxial tests are often used to evaluate the behavior of soils under seismic loads. The stress conditions imposed on a soil specimen during a cyclic triaxial test, however, are very different than those acting on an element of soil during an earthquake. One major difference is that the element in the field is subjected to a change in total confining stress, whereas in a conventional cyclic triaxial test the total confining stress (as applied through the cell pressure) is held constant. This use of constant cell pressure is usually justified by the assumption that in a saturated specimen the change in total stress is offset by a change in pore pressure, thus resulting in no change in the effective confining stress or liquefaction susceptibility. A laboratory study using cyclic triaxial tests was conducted on several soils to assess the validity of this assumption. For each soil, two series of stress-controlled cyclic triaxial tests were run: one set with a constant cell pressure, and thus a constant total confining stress, and a second set with a variable total stress/cell pressure. These tests were then compared in terms of both the resulting cyclic resistance curves and the amount of energy dissipated to trigger liquefaction. It was found that the two conditions of confining stress yielded results that were not statistically different. Therefore, the assumption that the change in pore pressure caused by the variation in total stress is offset by the change in pore pressure and thus results in no change in effective stress or liquefaction susceptibility appears valid. Based on these findings, cyclic triaxial tests performed with constant cell pressure, and thus a constant total confining stress, provide valid results for liquefaction analyses.
]]>Authors: Ryuichiro Hosoya Takao Tobe Yoshimi Ichimaru Tetuzi Suzuki Takashi Saita Takahiro Otani Ayumi Ozeki Shigeki Yamada Masaaki Kurihara Hajime Kagaya
Frailty is a geriatric syndrome associated with adverse outcomes, particularly in super-aged societies. Hospitalization-related inactivity may contribute to frailty progression and worsen prognosis and post-discharge quality of life; however, frailty identification at admission is challenging. This study used diagnosis procedure combination (DPC) data to examine associations between patient and medication factors at admission and hospitalization-induced frailty. The cohort comprised patients aged ≥65 years hospitalized at Shonantobu General Hospital with records of oral medication prescribed or brought in at admission. Four PRISMA-7 frailty risk index items were evaluated. Patients meeting at least three criteria were defined as the frailty risk group. Bivariate and multivariate analyses assessed the influence of patient background and medication categories on frailty risk. The frailty risk group included 888/1948 cases. Risk and nonrisk groups differed significantly in sex, age, and body mass index (BMI). Logistic regression analysis showed that low BMI, antithrombotics, mineral supplements, and antiparkinsonian drugs were associated with frailty risk, although the findings for male sex and advanced age should be interpreted cautiously because these variables were included as components of the modified frailty-risk definition. Patient background and medication information from DPC data at admission may contribute to future frailty risk estimation. Future prospective studies using detailed clinical data and machine learning models are needed to clarify the relationship between medication use and frailty.
]]>Authors: Abdul-Muaizz Koray Hamid Rahnema Emmanuel Appiah Kubi Adewale Amosu Oshokoya Gbenga
Waterflooding in oilfields for oil displacement and reservoir pressure maintenance has led to the production of scale in several reservoirs. The formation of scale occurs both in the porous media of the reservoir and in the production equipment, leading to production disruptions that result in a decline in revenue. The aim of this paper is to investigate the effects of mixing samples of seawater and aquifer water. This is achieved by conducting turbidity, salinity, pH, and zeta potential measurements. The risk of self-precipitation of the prepared samples was assessed using the PHREEQC program. A PVT cell was used to assess the impact of temperature and pressure on the prepared seawater and aquifer samples. When 40% of the seawater sample was combined with 60% of the aquifer water sample, the turbidity findings indicated maximum precipitation. The amount of precipitation dropped as temperature and pressure increased. To assess the impact of scale formation on the permeability of a Berea sandstone core, a core flooding experiment was conducted employing liquid and gas as the flowing fluid. Additionally, SEM and EDS analyses were used to examine the shape and composition of scale. It was found that SO42− and Ca2+ ions predominated in scale precipitation.
]]>Authors: Cheska Jane A. Cudog Trisha Anne A. Arcilla Angel Mae D. Gregorio Samantha D. Ramos Eunice S. Salazar Jenny L. Sindingan Marianne Joy L. Tubalinal Huai-Ying Huang Po-Hua Wu Hoang Minh Kuo-Pin Chuang Brian Harvey Avanceña Villanueva
mRNA vaccines are a relevant approach in cancer immunotherapy, using messenger RNA to induce immune responses against tumor-associated antigens. In this review, BNT111 and BNT122 are compared as representative off-the-shelf and personalized models. BNT111 is a fixed mRNA vaccine that has demonstrated significant antitumor efficacy against shared melanoma antigens, particularly when combined with immune checkpoint inhibitors. It allows a standardized production via in vitro transcription (IVT) in a cell-free system. Conversely, BNT122 is a personalized vaccine designed to match an individual’s tumor mutations by targeting patient-specific neoantigens to elicit more robust immune responses. It has significant suitability in the adjuvant setting to target minimal residual disease. Despite favorable safety and immunogenicity, the effectiveness of these vaccines is influenced by various factors, including tumor heterogeneity, differences in antigen expression, off-target effects on mRNA-LNP distribution, molecular instability, and complex manufacturing constraints. Neither approach can be directly considered as the definitive optimal vaccine. A comprehensive analysis of their strengths and limitations is vital for a balanced and objective future research direction. Collectively, this emphasizes the need for further improvements in vaccine design and strategies, prioritizing high-quality, safe, and accessible treatments for every cancer-based patient and ensuring their successful integration into healthcare.
]]>Authors: Kazuyuki Matsumoto Minoru Yoshida Chikaho Karino
Lifestyle-related diseases such as diabetes are closely influenced by daily habits, yet the complex interactions between lifestyle factors and blood glucose variation remain insufficiently quantified. This study proposes a natural language processing (NLP) framework that analyzes long-form illness blogs to identify lifestyle factors associated with elevated blood glucose levels. Diabetes-related narratives were collected from a Japanese illness blog portal (TOBYO) and processed through GPT-4o-based automated labeling, BERT-series contextual embeddings, and LightGBM classification. For Type 2 Diabetes classification, the model achieved an F1-score of 0.73 using JMedRoBERTa embeddings, outperforming baseline models (BERT = 0.70; Twitter-RoBERTa = 0.65). Key factors contributing to glucose elevation were identified through feature importance analysis, with dietary behavior, lack of exercise, poor sleep, and stress emerging as major contributors. These findings demonstrate the potential of combining large language models with structured machine learning to extract health-relevant knowledge from patient narratives. The proposed approach contributes to preventive healthcare by offering interpretable, data-driven insights into lifestyle–glycemic relationships, and provides a foundation for personalized diabetes risk monitoring and AI-based health management applications.
]]>Authors: Fernando Mata
Precision Livestock Farming (PLF) has emerged as an approach in modern animal production, integrating advanced technologies such as sensors, automation, data analytics, and artificial intelligence to enable continuous, individualised monitoring of livestock and their environment. This review examines the impact of PLF technologies on three critical dimensions of livestock systems: productivity, animal welfare, and environmental sustainability. PLF applications, including wearable and environmental sensors, automated feeding and milking systems, and video-based monitoring, allow for early detection of health and behavioural deviations, optimisation of feed efficiency, and improved reproductive and disease management. These technologies support proactive, data-driven decision-making that enhances productivity while promoting animal welfare and reducing the environmental footprint of livestock production. Despite these benefits, the adoption of PLF faces significant challenges, including high initial investment costs, technical limitations, system integration issues, data ownership and privacy concerns, and ethical considerations related to automation. Future research and policy efforts should focus on developing cost-effective, scalable solutions, standardised data frameworks, and supportive regulatory measures to enable equitable and responsible implementation across diverse production systems. By addressing these challenges, PLF offers a pathway towards more efficient, welfare-oriented, and environmentally sustainable livestock production, contributing to global food security and resilient agricultural systems.
]]>Authors: Carlo Spampinato
All-inorganic cesium lead iodide (CsPbI3) has been investigated for more than a decade as an absorber for perovskite photovoltaics thanks to its attractive bandgap, thermal robustness compared with hybrid perovskites, and compatibility with tandem concepts. Yet, despite remarkable efficiency progress, CsPbI3 remains far from widespread commercialization. The core roadblock is the metastability of the photoactive black perovskite phases (α/γ/β) against transformation to the photoinactive yellow δ-phase under realistic conditions, amplified by defect chemistry, ion migration, and interfacial reactions. Additional barriers arise from scale-up constraints (film uniformity, throughput, solvent management), long-term operational stability (humidity, heat, UV, bias), and environmental/safety requirements, especially lead containment, sequestration, and end-of-life strategies. This review critically analyzes the intertwined physical, chemical, and engineering factors that still limit CsPbI3 deployment, with emphasis on how solutions in one domain can fail without co-design in others. This review summarizes state-of-the-art stabilization strategies (size/strain engineering, additive/doping routes, surface/interface passivation, and encapsulation), highlight scalable manufacturing pathways including solvent-minimized and vacuum-assisted approaches, and discuss lead-mitigation technologies such as Pb-adsorbing functional layers. Finally, I argue that artificial intelligence (AI)—from machine-learning stability models to process monitoring, robotic optimization, and digital twins—has become essential to navigate the enormous parameter space of CsPbI3 materials and manufacturing. It concludes with actionable recommendations and future directions toward bankable, scalable, and sustainable CsPbI3 photovoltaics.
]]>Authors: Brahim El Mathari Julia Kuzniar Ramin Tadayoni Aurélie Goyenvalle Alvaro Rendon Ophélie Vacca
The dystrophin gene encodes multiple dystrophin isoforms with tissue-specific functions, including several shorter isoforms expressed in the central nervous system and retina. While Duchenne muscular dystrophy (DMD) has historically been characterized as a primary myopathy resulting from loss of the full-length dystrophin Dp427, increasing clinical evidence indicates that dysfunction of shorter dystrophin isoforms contributes to significant extramuscular pathology, including retinal disease. In particular, loss of the Dp71 isoform has been implicated in retinal inflammation, blood–retinal barrier breakdown, and pathological angiogenesis. In this study, we investigated whether low-level residual expression of Dp71 is sufficient to mitigate retinal inflammation in the mdx3Cv mouse model, which displays reduced—but not absent—expression of multiple dystrophin isoforms. Western blot analysis revealed that mdx3Cv retinas express approximately 4% of wild-type Dp71 protein levels. Despite this marked reduction, mdx3Cv mice did not exhibit the inflammatory phenotype previously observed in Dp71-null mice. Retinal VEGF protein levels and VEGF receptor (FLT-1 and KDR) mRNA expression were preserved, while VEGF mRNA levels were modestly reduced. Furthermore, expression of inflammatory markers ICAM-1 and ALOX5AP, leukocyte adhesion to retinal vasculature, Aquaporin-4 expression, and BRB permeability to albumin were all comparable to wild-type littermates. Together, these findings demonstrate that minimal residual expression of Dp71 is sufficient to preserve retinal vascular homeostasis and prevent inflammatory and permeability defects in the mdx3Cv retina. These results further suggest that partial dystrophin restoration—at levels achievable with current exon-skipping or gene-based therapies—may be adequate to prevent or attenuate retinal pathology in DMD, providing a realistic and clinically relevant therapeutic target.
]]>Authors: Nazuku Kato Tetsuji Ohmura Takeshi Maruyama Yukitaka Hamada Toshihiko Shakouchi
Gas atomization is one method for producing fine metal powder. In close-coupled gas atomization, a high-speed gas jet is ejected near the molten metal, and the molten metal is further broken down in the shear layer at the outer edge of the jet, producing fine metal powder of several micrometers to several tens of micrometers. By the way, in close-coupled gas atomization, if the protrusion length of the molten metal nozzle is short, a backflow occurs that goes around the melt delivery nozzle tip and reaches the gas nozzle tip, and the small droplets of molten metal that are atomized at the exit of the melt delivery nozzle are carried by this backflow to the gas nozzle tip, causing it to erode. In this study, we experimentally clarified the existence of the backflow for the first time through measurements of velocity distribution, then the flow state of the gas flow inside the gas atomizer was visualized approximately using the atomized water flow, and the existence of a backflow was confirmed. It was shown that microdroplets of water are carried by the backflow and reach the gas nozzle tip. This was also clarified through numerical analysis results for the air flow. Furthermore, the protrusion length of the melt delivery nozzle at which backflow does not occur was determined, and this was verified in actual gas atomization experiments using molten copper. In addition, the length of the melt delivery nozzle at which backflow does not occur, i.e., the gas nozzle tip does not melt, was found. Furthermore, molten-copper experiments were conducted using this gas atomizer to evaluate its performance.
]]>Authors: Jose Luis Rubio Tamayo Mary-Anahí Serna-Bernal Valeria Levratto Hernando Gómez Gómez
Information and communication technologies have evolved exponentially in recent years, significantly expanding their diversification and applicability. Extended reality (XR) technologies—including virtual, augmented, and mixed reality—have solidified the conceptualization of space and function. XR represents the definitive medium due to its close analogy with physical reality, enabling an unprecedented degree of interaction compared to previous media. By leveraging spatial and temporal factors, XR allows for the emergence of suprainteractions—interactions that do not occur naturally in physical environments. The integration of AI into these workflows heralds a new era, reevaluating technological utility as the current landscape poses challenges for identifying use cases and dead zones within the XR field. This article proposes a model, derived from a narrative literature review, that identifies key features in technological applications and the evolution of XR. Based on concepts such as representativeness, realism, system performance, and spatial narrative, the model designs a framework for the development of diverse functions within the XR domain.
]]>Authors: Luis Ramalhete Paula Almeida Ruben Araújo Eduardo Espada
Background: Non-classical MHC class I molecules MICA and MICB are stress-inducible NKG2D ligands that contribute to immune surveillance, non-HLA antibody formation, and alloreactivity in solid organ and hematopoietic stem cell transplantation; population-level data for Southern Europe remain limited. Methods: High-resolution MICA and MICB genotyping was performed in 1364 unrelated individuals from southern Portugal using a hybrid-capture next-generation sequencing workflow, and allele calls were analyzed with standard population-genetic metrics (allele and genotype frequencies, heterozygosity, Hardy–Weinsberg equilibrium, and LD-like D, D′, r2) and multilocus allele presence/absence encodings explored by k-means clustering, spectral clustering, principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Results: Forty-two MICA and twenty-two MICB alleles were identified; MICA*002:01, MICA*004:01, MICA*008:01, MICA*008:04 and MICB*002:01, MICB*004:01, MICB*005:02, MICB*008:01 were most frequent, and most individuals carried at least two distinct MICA and two distinct MICB allotypes. Co-occurrence and LD-like analyses revealed conserved MICA–MICB combinations, including a strong association between MICA*009:02 and MICB*005:06, while unsupervised analyses identified partially overlapping multilocus genotype backgrounds and recurrent four-allele constellations. Conclusions: These findings provide a detailed non-classical MHC reference for southern Portugal and a multilocus framework to support interpretation of non-HLA antibodies and MICA/MICB-aware donor evaluation in selected clinical scenarios, as well as the development of machine learning-based immunologic risk models.
]]>Authors: John W. B. de Araújo Daniel F. C. Ferrando Fabricio Ferrari Pedro A. B. Kuroda Edson M. Kakuno
This work demonstrates that simple calibration procedures can reduce the errors in determining relative humidity with a psychrometer by up to 75 times. The psychrometer uses two temperature transducers to measure relative humidity. The calibration consists of comparing both transducers before assembling the psychrometer. A psychrometer assembly is presented, built from readily available parts, which provides temperature data for comparison with a theoretical model. The results are compared against a reference psychrometer to assess errors with and without calibration. A twenty-two-fold decrease in error was observed for a relative humidity of 62%.
]]>Authors: Cheng-fu Chen Mike Ophoff Nick Samuel
This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path. The mechanism uses a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal to ensure self-leveling and alignment in gravity on uneven terrain. Unlike approaches that rely on broadband digitization and require active control and a stable power supply, this housing–gimbal mechanism performs mechanical filtering for sub-Hz signal amplification and higher frequency attenuation without power. Response spectrum analysis shows that the transmissibility can be tuned to achieve peak sensitivities in the 0.5–0.8 Hz range. When tuned to 50–55 mm pendulum length and under assumed undamping, the pendulum-mounted mechanism improves detectability at best by 10–100× relative to a bare sensor for moderate magnitude (Ms = 3–6) in a 12 h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10−8 m/s2/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow Nmmin∝SNR1.2ASD1.2fs0.6, where fs is the quake wave frequency. The damping ratio, considering both structural damping and viscous drag, is estimated to be in the order of 10−3 to 10−2. A probabilistic sensitivity analysis is performed to account for the inherent uncertainty in the spectral mismatch between the narrowband sub-Hz resonance of the designed mechanical filter and the peak frequencies of seismic events; the derived probability model suggests strategies for improving the detection probability in the 0.01–1 Hz range.
]]>Authors: Jun Huang Yuheng Wang
Optical synaptic transistors employing polymer dielectrics have emerged as promising building blocks for neuromorphic computing due to their low power consumption and rich photo-induced memory behaviors. While extensive experimental studies have demonstrated various synaptic functions, a unified physical understanding of the coupled charge trapping and ionic polarization processes governing device dynamics remains incomplete. In this work, we develop a unified physical model to investigate optical synaptic behaviors in polymer-based transistors with oxide interlayers. The model explicitly describes the time-dependent evolution of photo-induced charge trapping at the semiconductor–dielectric interface and ionic polarization within the polymer dielectric, which jointly modulate the effective threshold voltage of the transistor channel. Based on this framework, key synaptic functions including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and pulse-dependent potentiation are quantitatively reproduced. The model further reveals how dielectric structure and trapping strength govern the transition between short-term and long-term plasticity. This study provides a physically intuitive and experimentally relevant modeling framework for understanding optical synaptic transistors, offering guidance for the rational design and optimization of polymer-based neuromorphic devices. Although simplified, the proposed model captures the essential physics governing optical synaptic behaviors and provides a general framework applicable to a wide class of ion–electronic neuromorphic devices. Experimental measurements are used as physically motivated proxies to validate the multi-timescale structure of the model rather than direct numerical fitting.
]]>Authors: Koichi Takahashi
Since Reynolds’ work, turbulence has been one of the most important subjects in fluid dynamics. Although its complete understanding seems still out of reach, there is at least one established physical basis that turbulence is a phenomenon of a random but non-trivially correlated assembly of vortices. The knowledge of vortices has thus become a prerequisite for promoting our understanding of the nature of turbulence. In this article, we first review the simple, compact vortex solutions to the Navier–Stokes equations for incompressible viscous fluids and a unified view of a certain type of vortices including Burgers, Sullivan and Bellamy-Knights solutions. The non-equivalence of the inviscid limit of the Navier–Stokes equations and the Euler equations is emphasized. Introducing the notion of observational non-uniqueness, which differs from the non-uniqueness in a certain class of differential equations, of solutions to the Navier–Stokes equations, the observation problem associated with the dense distribution of non-equivalent solutions is argued. The origin of the extreme sensitivity of the solutions to the boundary conditions is clarified. A few examples of vortex phenomena in the real world are also surveyed. We next review the works of constructing turbulence as a random assembly of simple, compact vortices. An attempt to combine the vortex model of turbulence with the Kármán–Howarth equation for the velocity correlation functions of anisotropic turbulence is presented. It is pointed out that the studies in this direction suggested that Kolmogorov’s 2/3 scaling law was generally compatible with anisotropy. A few quantities are proposed as candidates to measure anisotropy in turbulence experiments.
]]>Authors: Khawlah Harasheh Satinder Gill Kendra Brinkley Salah Garada Dindin Aro Roque Hayat MacHrouhi Janera Manning-Kuzmanovski Jesus Marin-Leal Melissa Isabelle Arganda-Villapando Sayed Ahmad Shah Sekandary
The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment.
]]>Authors: Ana Carolina Cambuí Pereira Thalya Fernanda Horsth Maltarollo Ana Carolina Brito Pereira Mary Caroline Skelton-Macedo Ericka Tavares Pinheiro
Due to the limitations of conventional antibiotics, antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives for the prevention and treatment of oral infections. This study systematically evaluated in vitro evidence regarding the antimicrobial and anti-biofilm activity of natural AMPs against oral pathogens. A systematic search using the PICOT strategy was conducted in PubMed, EMBASE, and Scopus, retrieving 7711 articles. After title and abstract screening, 109 studies were selected for full-text analysis, resulting in 26 articles that met the eligibility criteria. Among the AMPs evaluated, nisin (n = 15) and LL-37 (n = 5) were the most frequently investigated, while other peptides included lactoferrin, lactoferricin, melittin, lysozyme, histatin-5, cystatin C, chromogranin A, parasin-1, protamine, AmyI-1-18, and DCD-1L. Natural AMPs of human and animal origin demonstrated antimicrobial activity against bacteria associated with oral infections, particularly Streptococcus mutans and Enterococcus faecalis. These peptides were tested in different formulations, including solutions, incorporation into dental materials and polymers, and application in sonodynamic antimicrobial therapy. Overall, the findings indicate that natural AMPs represent a promising class of biomolecules for controlling oral biofilms; however, further clinical studies are required to validate their long-term efficacy and safety.
]]>Authors: Margaret J. Falknor Eric A. Martin Steven B. Kim
Change in direction ability (COD) is a fitness component that may be related to safe and effective participation in pickleball. The general aim of the research was to examine a COD test that may be specific to the movement demands of the sport. Therefore, we tested the inter-trial reliability of the modified spider test for pickleball, compared learning effects between younger and older adults, and examined the reliability and validity of hand timing compared to timing gates. In this cross-sectional study, 36 participants (ages 19–78) were grouped as adults (ages 18–49) or seniors (ages 50+) according to the USA Pickleball age groupings. Participants completed a standard warm-up, one practice trial, and five full-effort trials with 4–6 min of rest between trials. Intraclass correlation coefficient (ICC) was used to determine reliability across five trials. Inter-rater reliability and validity of hand timing were also examined with ICCs. Pairwise comparison t-tests of individual trials were performed using the Hochberg method to determine learning effect. Linear regression analyses were used to determine if any segment could predict total trial time. During participation, older players provided unsolicited feedback that they were concerned about the safety of the backpedaling in the spider test. We observed that one person fell while backpedaling, though suffered no injury. Results indicate that the spider test was reliable across all five trials (ICC = 0.977). A learning effect was detected between the first and second trial (p = 0.001), and the magnitude of the effect was significantly different between age groups (p = 0.009). Hand timing demonstrated excellent inter-rater reliability (ICC = 0.993) and validity (ICC = 0.990). Splits 2, 3, and 4 significantly predicted total test time (R2 = 0.973, 0.973, and 0.986, respectively). The test demonstrated reliability, but older players expressed concern about backpedaling. This raises questions about backpedaling safety in pickleball. Therefore, we do not recommend this test. Future research needs to determine appropriate tests to screen for fall risk in the dynamic movements relevant to pickleball.
]]>Authors: Edwar D. Montenegro Marcia S. Rizzo Heurison de Sousa e Silva Marcília Pinheiro da Costa
Quantitative analyses in drug-delivery research are frequently distributed across multiple tools, which increases manual handling and the risk of transcription errors. NanoEDW 1.0 is an open source Python application that integrates calibration-curve generation, encapsulation-efficiency (EE%) calculation, and release kinetics modeling in a single, streamlined workflow. This study aims to validate the performance of NanoEDW 1.0 by benchmarking it against spreadsheet/OriginLab® OriginPro 2025 analyses on experimental datasets from polymeric nanocarrier systems commonly used in drug encapsulation. The software performs linear regression to convert absorbance into concentration, computes EE% from raw experimental values, and fits drug-release profiles to classical models (including zero/first-order, Higuchi, Korsmeyer–Peppas, Weibull, and Modified Gompertz) using non-linear least squares with standard goodness-of-fit metrics (R2, RMSE). Results show close agreement with reference workflows for calibration parameters and EE%, as well as statistically comparable release-model fits, while reducing manual steps and analysis time. In conclusion, the validation confirms that NanoEDW 1.0 can streamline routine analyses and enhance reproducibility and accessibility in nanopharmaceutical research; source code and example datasets are provided to foster adoption.
]]>Authors: David A. Servellón Fabrizzio R. Pérez Enrique Posada-Granados Marlon Enrique López Marvin J. Núñez
Banana pseudostem is an abundant lignocellulosic residue with potential for value-added applications. This study evaluated five banana varieties to determine their suitability for bioplastic production, with Williams showing the highest cellulose yield (26.99% ± 0.23). Cellulose extracted from this variety was combined with corn-starch (1:1 w/w) to synthesize a bioplastic through gelatinization and lyophilization. FTIR confirmed effective removal of lignin and hemicellulose from the pseudostem and evidenced new hydrogen-bond interactions between cellulose and starch through O–H band shifts (3335 → 3282 cm−1). SEM revealed a porous laminar morphology with cellulose particles (40–52 µm) embedded within the starch matrix. DSC analysis showed that the bioplastic exhibits an intermediate thermal profile between its components, while mechanical compression increased the endothermic transition temperature (from 69 °C to 85 °C) and reduced molecular mobility. Tensile testing demonstrated that compression markedly improved mechanical performance, increasing tensile strength from 0.094 MPa to 0.69 MPa and density from 110 to 638.7 kg/m3. These findings indicate that cellulose–starch bioplastics derived from banana pseudostem possess favorable structural, thermal, and mechanical characteristics for short-use applications. The approach also contributes to the valorization of agricultural waste through biodegradable material development.
]]>Authors: Mario Santoro Christine Nardini
The medical discourse entails the analysis of the modalities, which are far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications. This gives different emphases on the background, relevance, robustness, and assumptions that the audience takes for granted. This concept is extensively studied in socio-anthropology. However, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities; to avoid overseeing promising but less walked paths; to grasp different types of representations of diseases, therapies, patients, and other stakeholders; to understand how these terms are conditioned by time and culture. While socio-anthropologists traditionally use manual curation methods–limited by the lengthy process–machine learning and AI may offer complementary tools to explore the vastness of an ever-growing body of medical literature. In this work, we propose a pipeline for the analysis of the medical discourse on the therapeutic approaches to rheumatoid arthritis using topic modeling and transformer-based emotion and sentiment analysis, overall offering complementary insights to previous curation.
]]>Authors: Malika Keldiyarova Umidjon Usmanov Sanjarbek Ruzimov Akmal Mukhitdinov
This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively adjusted according to driving conditions in adaptive ECMS. In REEVs, however, the battery efficiency exhibits nonlinear behavior in the low SOC range, which directly leads to variability in the equivalence factor within conventional ECMS. This study investigates the influence of the variable equivalence factor on the overall fuel economy. The equivalence factors are usually considered constant or vary adaptively depending on driving cycles. However, the variation in battery efficiency is often neglected. The present study compares the results obtained for both constant and variable battery efficiencies in deriving the equivalence factors. The simulation results show that an improvement of approximately 3% in fuel economy was obtained for UDDS, NEDC, and WLTC driving cycles as a result of applying the variable equivalence factor. Additionally, through an analysis of various penalty function designs, the study highlights their crucial role in optimizing fuel consumption across different driving cycles.
]]>Authors: Aveline Ventura Giulia Capizzani Gonçalves Cristina Pacheco Soares Luciana Barros Sant’anna Vitor Luca Moura Marmo Sônia Khouri Sibelino Leandro Raniero
Conventional cancer treatments have limited efficacy for aggressive subtypes such as triple-negative breast cancer (TNBC), which points to the importance of new therapeutic strategies. Functionalized nanoparticles in conjunction with photodynamic therapy (PDT) represent a promising alternative. Additionally, 3D cell culture emerges as a more effective model, as it better replicates the structural and functional characteristics of the tumor microenvironment. In this study, 3D microtumors of TNBC were cultivated and treated with PDT using gold nanoparticles functionalized with Chlorin e6 (AuNPs@Ce6). Cell viability was assessed using the MTT colorimetric assay, combined with histological analysis using hematoxylin-eosin staining. The MTT assay and histological evaluation of the 3D spheroids demonstrated that PDT with AuNPs@Ce6 effectively reduced cell viability and induced necrotic morphological changes, while maintaining biocompatibility with the non-irradiated control group. These findings reinforce the potential of this approach for further investigation in TNBC models and underscore the value of 3D cultures as physiologically relevant and ethical alternatives to animal testing.
]]>Authors: Sivakumar Jeyarajan Harini Priya Ramesh Atchyasri Anbarasu Jayasudha Jayachandran Anbarasu Kumarasamy
In this study, we successfully cloned the fluorescent proteins eGFP and DsRed in-frame with the antimicrobial peptide epinecidin-1 (FIFHIIKGLFHAGKMIHGLV) at the N-terminal. The cloning strategy involved inserting the fluorescent reporters into the expression vector, followed by screening for positive clones through visual fluorescence detection and molecular validation. The visually identified fluorescent colonies were confirmed as positive by PCR and plasmid migration assays, indicating successful cloning. This fusion of fluorescent reporters with a short antimicrobial peptide enables real-time visualization and monitoring of the peptide’s mechanism of action on membranes and within cells, both in vivo and in vitro. The fusion of eGFP and DsRed to epinecidin-1 did not impair the expression or fluorescence of the reporter protein.
]]>Authors: Megha Chaudhari Sunita Verma Sushanta Deb
The simultaneous occurrence of tuberculosis (TB) and COVID-19 posed a major public health challenge, particularly in regions heavily impacted by both diseases, due to their shared effects on the lungs, immune system dysfunction, and the possibility of more severe clinical outcomes. The role of immunopathogenesis is crucial in influencing the progression of co-infection, which is marked by heightened inflammation, immune exhaustion, weakened T-cell responses, and unregulated cytokine production. To better understand the intricate interactions between host and pathogen and the immune disruptions associated with this dual epidemic, multi-omics approaches such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics have proven to be effective methods. These comprehensive strategies provide detailed insights into the mechanisms of disease, help identify potential biomarkers, and aid in the identification of therapeutic targets. This review emphasizes the importance of immune responses and systems biology in comprehending the TB-COVID-19 syndemic and highlights the promise of multi-omics in advancing precision medicine and enhancing disease management.
]]>Authors: Hiroshi Ogura Yasutaka Hanada Keitaro Osakabe Masato Kondo
The occurrence patterns of important words found in six texts (one historical pamphlet and five renowned academic books) are analyzed using both univariate and multivariate Hawkes processes. By treating the occurrence patterns as binary time-series data along the texts, we investigate how effectively univariate and multivariate Hawkes processes capture the characteristics of these word occurrence signals. Through maximum likelihood estimation and subsequent simulations, we found that the multivariate Hawkes process clearly outperforms the univariate Hawkes process in modeling word occurrence signals. Moreover, we found that the multivariate Hawkes process can provide a Hawkes graph, which serves as an intuitive representation of the relationships between concepts appearing in the analyzed text. Furthermore, our study demonstrates that the importance of concepts within a given text can be quantitatively estimated based on the optimized parameter values of the multivariate Hawkes process.
]]>Authors: Camila Carlino-Costa Marco Antonio de Andrade Belo
Fish have become increasingly prominent as experimental models due to their unique capacity to bridge basic biological research with translational applications across diverse scientific disciplines. Their biological traits, such as external fertilization, high fecundity, rapid embryonic development, and optical transparency, facilitate in vivo experimentation and real-time observation, making them ideal for integrative research. Species like zebrafish (Danio rerio) and medaka (Oryzias latipes) have been extensively validated in genetics, toxicology, neuroscience, immunology, and pharmacology, offering robust platforms for modeling human diseases, screening therapeutic compounds, and evaluating environmental risks. This review explores the multidisciplinary utility of fish models, emphasizing their role in connecting molecular mechanisms to clinical and environmental outcomes. We address the main species used, highlight their methodological advantages, and discuss the regulatory and ethical frameworks guiding their use. Additionally, we examine current limitations and future directions, particularly the incorporation of high-throughput omics approaches and real-time imaging technologies. The growing scientific relevance of fish models reinforces their strategic value in advancing cross-disciplinary knowledge and fostering innovation in translational science.
]]>Authors: George Valakas Daphne Sideri Konstantinos Modis
In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece.
]]>Authors: Pietro Cipresso
In an era marked by powerful AI content generators and the rapid spread of information on social media, the role of a scientific journal as a bastion of trustworthy knowledge has never been more critical [...]
]]>Authors: Kimberly Fenech Byron Baron
Pesticides are used in agriculture to protect crops from disease. Among these, the herbicide glyphosate, the insecticide deltamethrin, and the fungicides propamocarb and tebuconazole are approved for use in Europe. These pesticides, along with their metabolites, have been detected in the environment including in food and water sources. Human biomonitoring studies have confirmed the presence of these compounds in biological samples, indicating persistent exposure even among the general population, unrelated to agricultural occupations. Consequently, numerous studies have investigated the health effects of these four pesticides and their metabolites. This review focuses on their impacts on gut health primarily the gut microbiota, inflammation, metabolism, cancer and gut–brain axis. Epidemiological studies were included to assess health risks among various groups including adults, children and pregnant women. Animal and in vitro models have been employed to explore in a more controlled and targeted way the physiological and biochemical effects observed in epidemiological studies. Despite some controversy, pesticides and their metabolites have been linked to gut dysbiosis, inflammatory bowel disease (IBD), metabolic disorders, cancer and neurodevelopmental disorders. Mechanistically, these pesticides influence gut microbiome composition, sugar and lipid metabolism, oxidative stress, inflammatory pathways, cell death, oncogenic signalling pathways, endocrine disruption, and epigenetics. However, further studies are needed to confirm these risks and health impacts, particularly concerning low-dose, long-term exposure as experienced by the general population. A comprehensive investigation of these effects is essential, incorporating dietary factors, age, sex, health status, and the cumulative impact of multiple pesticides, to develop a thorough risk assessment.
]]>Authors: Amanda Dias Assoni Scartezini Flavia Mori Sarti
During the last decades, there have been increasing concerns in public health debates regarding the production and consumption of red meat, considering connections between the occurrence of nutrition transition and an increase in the prevalence of chronic noncommunicable diseases. The consumption of red meat has been linked to adverse health outcomes; however, current evidence reveals controversies regarding the intake of diverse red meats. In addition, barriers to meat consumption include sanitary legislation linked to foodborne diseases connected to livestock, whilst governments of diverse countries provide incentives for its production and export worldwide. Thus, the objective of the present study was to investigate the evolution in the global trade of processed and unprocessed red meat from 1986 to 2023, using network analysis. Data on the trade of red meat between pairs of 216 countries were obtained from the Food and Agriculture Organization Database (FAOSTAT). The dataset, comprising the mean annual volume of processed and unprocessed red meat exchanged from reporting countries (origin) to partner countries (destination), was used to map global trade networks of red meats and identify global trends in red meat consumption according to country income level. The results indicate substantial intensification in the global trade of processed (0.202 in 1986 to 0.453 kg per capita in 2023) and unprocessed red meat (1.415 in 1986 to 3.315 Kg per capita in 2023). The volume of trade of unprocessed red meat remains greater than the volume processed red meat; yet, the findings indicate a threefold increase in the average weighted degree of processed red meat trade (0.002 to 0.006) from 1986 until 2023, whilst unprocessed red meat showed a twofold increase (0.009 to 0.019). The results raise public health concerns regarding the long-term consequences of consuming processed foods with high sodium and fat content. Additionally, the global trade of red meat showed fluctuations in periods of major foodborne outbreaks related to meat consumption, particularly during the 1990s. The findings of the study highlight strategies at the national level to advance food system transformations towards improvements in public health, nutrition, and sustainability.
]]>Authors: Elaheh TaghaviGhalehsari Hassan Kardgar Ali Hasanzadeh
Sustainable pavement design requires a balanced consideration of economic, environmental, and social impacts. In line with Federal Highway Administration (FHWA) guidelines for sustainable roadway infrastructure, incorporating recycled materials such as reclaimed asphalt pavement (RAP), recycled pavement material (RPM), recycled asphalt shingles (RASs), and warm-mix asphalt (WMA) has been shown to reduce natural resource depletion while promoting circular construction practices. This study investigates the structural performance of Portland cement concrete (PCC) pavements constructed on RAP and RPM base layers. A series of design scenarios was modeled using site-specific laboratory and field data—particularly subgrade soil properties and climatic conditions—from El Paso and San Antonio, Texas. The analysis incorporates unsaturated soil parameters and follows the performance thresholds set by the Mechanistic-Empirical Pavement Design Guide (MEPDG). Findings indicate that concrete mixture design, pavement structure, and local weather conditions are the primary drivers of distress in jointed plain concrete pavements (JPCPs). However, subsoil characteristics have a significant impact on joint faulting in JPCP and punchout occurrences in continuously reinforced concrete pavements (CRCPs), especially in thinner sections. Notably, the use of up to 50% recycled material in the base layer had minimal adverse effects on pavement performance, underscoring its viability as a sustainable design strategy for rigid pavements.
]]>Authors: Florent Osmani María Villar-Varela Carlos Lago-Fuentes
Background/Objectives: To analyze how the different phases of the menstrual cycle affect agility in female football players. Methods: A total of 11 female football players were selected from the third tier of the Spanish Football Federation (Third RFEF) and an agility test (t-test) was conducted to measure agility during the three phases of the menstrual cycle: the menstrual, late follicular, and mid-luteal phases. These phases were determined through self-reporting and the use of ovulation test strips for luteinizing hormone detection. Perceptual variables, such as sleep quality, stress, muscle pain, and fatigue, as well as the rating of perceived exertion, were measured. Results: There were no statistically significant differences in agility performance across menstrual cycle phases (F(2,20) = 1.86; p = 0.18). However, performance in the mid-luteal phase was slightly better compared to other phases. Similarly, no significant differences were found in perceptual variables such as fatigue, sleep quality, stress, and muscle soreness (p > 0.05), although slightly better perceptual responses were observed in the late follicular phase. Conclusions: No significant differences were found when analyzing the influence of menstrual cycle phases on agility, although performance appeared slightly better in the mid-luteal phase. No significant differences were observed in the perceptual variables. Both objective and perceptual variables should be considered in future studies or training programs based on the menstrual cycle.
]]>Authors: Dario Gentile Francesco Musolino Mine Dastan Michele Fiorentino
It has been shown that Augmented Reality improves the efficiency and well-being of order pickers; however, the adoption of AR Headsets in real contexts is hindered by comfort, safety, and battery duration issues. AR Glasses offer a lightweight alternative, yet they are seldom addressed in the current literature, and there is a lack of user studies exploring suitable visualization designs for these devices. Therefore, this research designs three AR visualizations of target position for order picking: Numeric Code, 2D Map, and 3D Map. They take into account the layout of the repository and the constraints of a small, low-resolution monocular display. These visualizations are tested in a within-subject user study with 30 participants employing AR Glasses in a simulated order-picking task. The Numeric Code visualization resulted in lower Task Completion Time (TCT) and error rates and was also rated as the least cognitively demanding and most preferred. This highlights that, for lightweight devices, simpler graphical interfaces tend to perform better. This study provides empirical insights for the design of innovative AR interfaces in logistics, using industry-relevant devices such as AR Glasses and conducting the evaluation in an extensive laboratory setup.
]]>Authors: Gabriel J. Williams
Although numerous observational and theoretical studies have examined the mean and turbulent structure of the tropical cyclone boundary layer (TCBL) over the open ocean, there have been comparatively fewer studies that have examined the kinematic and thermal structure of the TCBL across the land–ocean interface. This study examines the impact of different continental environments on the thermodynamic evolution of the TCBL during the landfall transition using high-resolution, full-physics numerical simulations. During landfall, the changes in the wind field within the TCBL due to the development of the internal boundary layer (IBL), combined with the formation of a surface cold pool, generates a pronounced thermal asymmetry in the boundary layer. As a result, the maximum thermodynamic boundary layer height occurs in the rear-right quadrant of the storm relative to its motion. In addition, azimuthal and vertical advection by the mean flow lead to enhanced turbulent kinetic energy (TKE) in front of the vortex (enhancing dissipative heating immediately onshore) and onshore precipitation to the left of the storm track (stabilizing the environment). The strength and depth of thermal asymmetry in the boundary layer depend on the contrast in temperature and moisture between the continental and storm environments. Dry air intrusion enhances cold pool formation and stabilizes the onshore boundary layer, reducing mechanical mixing and accelerating the decay of the vortex. The temperature contrast between the continental and storm environments establishes a coastal baroclinic zone, producing stronger baroclinicity and inflow on the left of the track and weaker baroclinicity on the right. The resulting gradient imbalance in the front-right quadrant triggers radial outflow through a gradient adjustment process that redistributes momentum and mass to restore dynamical balance. Therefore, the surface thermodynamic conditions over land play a critical role in shaping the evolution of the TCBL during landfall, with the strongest asymmetries in thermodynamic boundary layer height emerging when there are large thermal contrasts between the hurricane and the continental environment.
]]>Authors: Eric Martin Matthew Ritchey Steven Kim Margaret Falknor George Beckham
There were errors in the original published manuscript [...]
]]>Authors: Tetsunori Haraguchi Tetsuya Kaneko Ichiro Kageyama
In recent years, novel Personal Mobility Vehicles (PMVs) with a narrow width and an inward tilting mechanism, similar to motorcycles (MCs), have been proposed to prevent overturning during turns. Due to their compact size, these vehicles have inherent limitations in collision safety, making their dynamic safety and accident avoidance capabilities particularly crucial. In this study, a comparative analysis was conducted using a simulated single lane change course to evaluate obstacle avoidance performance. The results reveal that PMVs equipped with an active inward tilting mechanism exhibit superior obstacle avoidance capabilities. Based on the roll moment equilibrium conditions of these vehicles, an investigation of vehicle states during avoidance maneuvers revealed that both actual and virtual tilt angles coexist in PMVs, and their combined equivalent tilt angle effectively balances the roll moment during turning. This unique mechanism, which integrates the responsiveness of passenger cars with motorcycle-like tire lateral force characteristics, underpins the exceptional obstacle avoidance capabilities of actively inward tilting PMVs.
]]>Authors: Tanuj Namboodri Csaba Felhő István Sztankovics
Machining chromium–nickel alloy steel is challenging due to its material properties, such as high strength and toughness. These properties often lead to tool damage and degradation of tool life, which overall impacts the production time, cost, and quality of the product. Therefore, it is essential to investigate early signs of tool damage to determine the effective machining conditions for chromium–nickel alloy steel, thereby increasing tool life and improving product quality. In this study, the early signs of tool wear were observed in a physical vapor deposition (PVD) carbide-coated tool (Seco Tools, Björnbacksvägen, Sweden) during the machining of X5CrNi18-10 steel under both dry and wet conditions. A finish turning operation was performed on the outer diameter (OD) of the workpiece with a 0.4 mm nose radius tool. At the early stage, the tool was examined from the functional side (f–side) and the passive side (p–side). The results indicate that dry machining leads to increased coating removal, more heat generation, and visible damage, such as pits and surface scratches. By comparison, wet machining helps reduce heat and wear, thereby improving tool life and machining quality. These findings suggest that a coolant must be used when machining chromium–nickel alloy steel with a PVD carbide-coated tool.
]]>Authors: Carmine Polito
Cyclic triaxial tests are widely used in laboratory studies to assess the liquefaction susceptibility of soils. Although standardized procedures exist for conducting these tests, there is no universally accepted criterion for defining liquefaction. The choice of a liquefaction criterion significantly influences the interpretation of test results and subsequent engineering analyses. This study evaluates the impact of different liquefaction criteria by analyzing 42 cyclic triaxial tests performed on soil mixtures containing plastic fines. Both stress-based and strain-based liquefaction criteria were applied to assess their influence on test outcomes. The analyses focused on two key parameters: the number of loading cycles required to initiate liquefaction and the normalized dissipated energy per unit volume needed for liquefaction to occur. Results indicate that for soils susceptible to liquefaction failures, these parameters remain relatively consistent across different failure criteria. However, for soils prone to cyclic mobility failures, the number of loading cycles and the dissipated energy required for liquefaction vary significantly depending on the selected failure criterion. These findings highlight the importance of carefully selecting a liquefaction criterion, as it directly affects the assessment of soil behavior under cyclic loading. A better understanding of these variations can improve the accuracy of liquefaction susceptibility evaluations and inform geotechnical design and hazard mitigation strategies.
]]>Authors: Ebony Thompson Justin Hensley Renfang Song Taylor
Cardiovascular disease and hypertension are major global health challenges, and increasing dietary salt intake is a known contributor. Emerging evidence suggests that excessive salt exposure during pregnancy may impact fetal development, yet its effects on early embryogenesis remain poorly understood. In this study, we used zebrafish (Danio rerio) embryos as a model to investigate the developmental and molecular consequences of high-salt exposure during early vertebrate development. Embryos subjected to elevated salt levels exhibited delayed hatching, reduced heart rates, and significant alterations in gene expression profiles. Transcriptomic analysis revealed over 4000 differentially expressed genes, with key disruptions identified in calcium signaling, MAPK signaling, cardiac muscle development, and vascular smooth muscle contraction pathways. These findings indicate that early salt exposure can perturb crucial developmental processes and signaling networks, offering insights into how prenatal environmental factors may contribute to long-term cardiovascular risk.
]]>Authors: Carmine Polito
Cyclic triaxial tests are frequently used in the laboratory to assess the liquefaction susceptibility of soils. This paper will serve a two-fold purpose: First, it will serve to explain how the mechanics of the tests represent the stresses that occur in the field. Topics covered include the differences in the stress paths for the soil in the field and in the lab, the differences in the actual stresses applied in the lab and the field, the differences between stress-controlled and strain-controlled tests, and the effects of other aspects of the testing methodology. The development of adjustment factors for converting the laboratory test results to the field is also briefly discussed. The second purpose of the paper is to serve as a guide to interpreting cyclic triaxial test results. The topics covered will include an examination of the two main liquefaction modes and the impact that the failure criteria selected have on the analysis, the differences between stress-controlled and strain-controlled test results, energy dissipation, and pore pressure generation. The author has run more than 1500 cyclic triaxial tests over the course of his career. He has found that, while the test is fairly straightforward to perform, it requires a much deeper understanding of the test mechanics and data interpretation in order to maximize the information gained from performing the test. This paper is intended as a guide, helping engineers to gain further insights into the test and its results. It has a target audience encompassing both those who are running their first tests and those who are looking to increase their understanding of the tests they have performed.
]]>Authors: Ignacio Sánchez-Gendriz Ivanovitch Silva Luiz Affonso Guedes
Kolmogorov–Arnold networks (KANs) represent a promising modeling framework for applications requiring interpretability. In this study, we investigate the use of KANs to analyze time series of water quality parameters obtained from a publicly available dataset related to an aquaponic environment. Two water quality indices (WQIs) were computed—a linear case based on the weighted average WQI, and a non-linear case using the weighted quadratic mean (WQM) WQI, both derived from three water parameters: pH, total dissolved solids (TDS), and temperature. For each case, KAN models were trained to predict the respective WQI, yielding explicit algebraic expressions with low prediction errors and clear input–output mathematical relationships. Model performance was evaluated using standard regression metrics, with R2 values exceeding 0.96 on the hold-out test set across all cases. Specifically for the non-linear WQM case, we trained 15 classical regressors using the LazyPredict Python library. The top three models were selected based on validation performance. They were then compared against the KAN model and its symbolic expressions using a 5-fold cross-validation protocol on a temporally shifted test set (approximately one month after the training period), without retraining. Results show that KAN slightly outperforms the best tested baseline regressor (multilayer perceptron, MLP), with average R2 scores of 0.998±0.001 and 0.996±0.001, respectively. These findings highlight the potential of KAN in terms of predictive performance, comparable to well-established algorithms. Moreover, the ability of KAN to extract data-driven, interpretable, and lightweight symbolic models makes it a valuable tool for applications where accuracy, transparency, and model simplification are critical.
]]>Authors: Achim Schlather Martin Schlather
This paper investigates how algebraic structures can encode epistemic limitations, with a focus on object properties and measurement. Drawing from philosophical concepts such as underdetermination, we argue that the weakening of algebraic laws can reflect foundational ambiguities in empirical access. Our approach supplies instruments that are necessary and sufficient towards practical falsifiability. Besides introducing this new concept, we consider, exemplarily and as a starting point, the following two fundamental algebraic laws in more detail: the associative law and the commutative law. We explore and analyze weakened forms of these laws. As a mathematical feature, we demonstrate that the existence of a weak neutral element leads to the emergence of several transversal algebraic laws. Most laws are individually weaker than the combination of associativity and commutativity, but many pairs of two laws are equivalent to this combination. We also show that associativity and commutativity can be combined to a simple, single law, which we call cyclicity. We illustrate our approach with many tables and practical examples.
]]>Authors: Bozhana Chuchulska Mariya Dimitrova Boyan Dochev Kliment Georgiev
Polymers are essential materials in the fabrication of partial and complete dentures, where their mechanical properties directly impact durability, comfort, and clinical performance. This study examines the influence of different manufacturing temperatures on the surface hardness of polymeric materials used in dental applications. A total of 60 experimental samples with a rectangular shape of Vertex ThermoSens polymer (Vertex Dental, 3D Systems, Soesterberg, The Netherlands) were fabricated through injection molding at 280 °C and 300 °C and analyzed over time to assess changes in their properties. Hardness measurements, conducted using the EQUOTIP Shore D hardness tester (Proceq SA, Schwerzenbach, Canton of Zürich, Switzerland), indicated increased hardness over time, with higher values observed in samples fabricated at 300 °C. A two-way ANOVA was performed to evaluate the statistical significance of temperature and time on hardness, revealing a significant effect (F = 14.73, p = 0.0185). These findings suggest that processing polymers at elevated temperatures improves surface hardness, significant for denture longevity and patient comfort. Increased hardness contributes to greater wear resistance. Optimizing polymer manufacturing conditions can thus lead to improved clinical outcomes, ensuring more durable and biocompatible dental prostheses.
]]>Authors: Ishtiaq Ahmad Mustajab Ali Hadiya Asghar Miyoko Okamoto Yoshihisa Shirayama Zoofa Talha Aida Uzakova Hafiz Sultan Ahmad Motoyuki Yuasa
Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national level in Pakistan. However, the current study applied the logistic regression analysis technique to determine such a relationship on a more detailed scale, that is, subnational levels in addition to the national level in Pakistan, using a long-term analysis of two years of COVID-19 data. At the subnational level, the logistic regression analysis technique was applied, with infection rate as the predictive variable. The results showed an increase in the infection rate of COVID-19 with increasing humidity levels. In contrast, an increase in temperature has slowed the spread of COVID-19 cases at both the national and subnational levels. The minimum temperature was statistically significant (p < 0.001) for provinces, KPK and Sindh. Also, two federal territories, AJK and Islamabad, showed statistically significant p-values. At the national level, both maximum temperature and humidity showed such values that is, p < 0.001. We believe that this is the first study conducted in Pakistan to explore the direct and indirect relationship between variables such as temperature (min and max), humidity, and rainfall as predictive parameters for COVID-19 infection rates at a detailed level. The pattern observed in this study can help us predict the future spread of COVID-19, subject to climatic parameters in Pakistan at both the national and subnational levels.
]]>Authors: Anton Venouil Matthieu Egels Philippe Pannier Mohammed Benwadih Christophe Serbutoviez Chaouki Hannachi
This paper presents a comparative analysis of performance for several antenna prototypes using a screen-printing process. This analysis was performed using various bow-tie antenna configurations, including single-band and multi-band antennas with linear or circular polarization over multiple operating frequency ranges. For antenna implementations, three different conductive inks and two resolutions of screen masks were tested. The performance of the fabricated prototypes has then been compared to the copper laser-etched antennas. This study revealed that with the proper selection of ink thinness, screen-printed bow-tie antennas achieve similar performances to copper laser-etched bow-tie antennas up to 6 GHz, even for linearly polarized and circularly polarized antennas. However, the printing resolution should be improved by reducing the ink thickness for bow-tie antennas at higher operating frequencies. The measurement results show a successful agreement after improving the printing resolution of the fabricated 5.8 GHz and 15 GHz bi-band bow-tie antennas.
]]>Authors: Issam Al-Nader Rand Raheem Aboubaker Lasebae
The Multi-Objective Optimization Problem (MOOP) in Wireless Sensor Networks (WSNs) is a challenging issue that requires balancing multiple conflicting objectives, such as maintaining coverage, connectivity, and network lifetime all together. These objectives are important for a functioning WSN safety-critical applications, whether in environmental monitoring, military surveillance, or smart cities. To address these challenges, we propose a novel bio-inspired Bird Flocking Node Scheduling algorithm, which takes inspiration from the natural flocking behavior of birds migrating over long distance to optimize sensor node activity in a distributed and energy-efficient manner. The proposed algorithm integrates the Lyapunov function to maintain connected coverage while optimizing energy efficiency, ensuring service availability and reliability. The effectiveness of the algorithm is evaluated through extensive simulations, namely MATLAB R2018b simulator coupled with a Pareto front, comparing its performance with our previously developed BAT node scheduling algorithm. The results demonstrate significant improvements across key performance metrics, specifically, enhancing network coverage by 8%, improving connectivity by 10%, and extending network lifetime by an impressive 80%. These findings highlight the potential of bio-inspired Bird Flocking optimization techniques in advancing WSN dependability, making them more sustainable and suitable for real-world WSN safety-critical systems.
]]>Authors: Faisal bin Nasser Sarbaland Masashi Kobayashi Daiki Tanaka Risa Fujita Masahiro Furuya
Quantum dots with sizes between 1 and 100 nm possess unique optical and electronic properties, making them valuable in energy, bioimaging, and optoelectronics fields. While traditional synthesis methods offer control over QD properties, they face challenges in scalability and reproducibility. Integrating microfluidics addresses these issues, providing precise control and high-throughput capabilities. This review highlights the transition from PDMS-based devices to additive-manufactured microfluidics, emphasizing their ability to overcome limitations in traditional methods. These advancements smooth the way for scalable, cost-effective, and sustainable QD production with enhanced application potential.
]]>Authors: Sotiris Lycourghiotis
The main goal of this study is to investigate the possibility of using residual materials (biomass derived from used cooking oils and lignocellulosic biomass from plant waste) on a large scale for producing renewable fuels and, in particular, the best way to collect them. The methodology of Geographic Information Systems (GIS) as well as spatial analysis (SA) techniques were used to investigate the Greek case for this. The data recorded in the geographic database were quantities of waste cooking and household oils as well as quantities of lignocellulosic biomass. The most common global and local indices of spatial autocorrelation were used. Concerning the biomass derived from used cooking oils, it was found that their quantities were important (163.17 million L/year), and these can be used to produce green diesel in the context of the circular economy. Although the dispersion of the used cooking oils was wide, there is no doubt that their concentration in large cities and tourist areas is higher. This finding suggests a collection process that could be carried out mainly in these areas through the development of small autonomous collection units in each neighborhood and central processing plants in small regional units. The investigation of the geographical–spatial distribution of residual lignocellulosic biomass showed the geographical fragmentation and heterogeneity of the distributions. The quantities recorded were significant (4.5 million tons/year) but widely dispersed, such that the cost of collecting and transporting the biomass to central processing plants could be prohibitive. The “geography” of the problem itself suggests solutions of small mobile collection units in every part of the country. The lignocellulosic biomass would be collected and converted in situ into bio-oil by rapid pyrolysis carried out in a tanker vehicle. This would transport the produced bio-oil to the nearest oil refineries for the conversion of bio-oil into biofuels through deoxygenation processes.
]]>Authors: Pearl Abereton Best Ordinioha Jacob Mensah-Attipoe Oluyemi Toyinbo
Crude oil spills create environmental hazards, leading to air pollution and respiratory health risks in under-five children due to their developing organs. This study compares ambient air quality (AAQ) and the respiratory health (RH) of under-five children in crude oil-impacted and less-impacted communities. The study involved 450 under-five children (mean age: 3 years) from three Niger Delta communities: Bodo, K-Dere, and Beeri. AAQ was measured using sensors, and RH was assessed through interviewer-administered questionnaires between July and October 2022. Mean concentrations of pollutants, including PM2.5, PM10, TVOCs, and HCHO, were consistently higher in Bodo and K-Dere (oil-impacted communities) compared to Beeri (less-impacted community), with levels frequently exceeding both WHO and national standards. These concentrations were highest near spill sites and during evening periods, highlighting localized and temporal factors influencing air pollution. Respiratory symptoms such as cough, difficulty breathing, and persistent nasal congestion were significantly more prevalent among children in oil-impacted communities. Logistic regression analysis indicated a higher likelihood of respiratory issues in these communities, with odds ratios ranging from 2.53 to 14.18 for various symptoms. Elevated air pollution from crude oil spills correlates with a higher prevalence of respiratory conditions in children from impacted communities, underscoring the need for public health interventions in these areas.
]]>Authors: Adriany A. F. Eduardo Gustavo A. S. Martinez Ted W. Grant Lucas B. S. Da Silva Wei-Liang Qian
The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of 120×90 pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems.
]]>Authors: Ana Carolina Ibarra Fabíola Branco Filippin-Monteiro
(1) Background: The literature indicates that pesticide use and exposure can lead to neurodegenerative and carcinogenic effects in living organisms. Additionally, pesticides have been reported to influence lipid metabolism. Based on this, the objective of this analysis was to identify the most relevant authors, countries, institutions, and journals addressing the relationship between pesticides and lipoproteins; (2) Methods: The analysis was conducted using the Web of Science database and bibliometric tools, including Bibliometrix/Biblioshiny and VOSViewer software; (3) Results: A total of 72 publications from 1977 to 2014 were identified, spanning 49 sources, 3453 references, and 390 authors. The journal Pesticide Biochemistry and Physiology stood out, with seven articles and an h-index of 5. The most relevant author was Samira Salihovic. China was the top country in terms of scientific output on this topic. The United Kingdom and Spain were notable for their international collaborations. Additionally, Duk Hee Lee and Monica P. Lind were found to have the highest co-citation relationship; (4) Conclusions: This analysis highlights the relatively small number of publications on pesticides and lipoproteins between 1977 and 2024, despite growing interest in the field due to its health implications. Expanding collaborations between developed and emerging countries is essential for advancing knowledge in this critical area.
]]>Authors: Yomna K. Abdallah Alberto T. Estevez
A typical Mashrabiya in Islamic architecture represents an integral climatic and sustainable solution, not only by offering recycling and the responsible use of small pieces of wood assembled in stunning geometrical and natural abstract lattice panels, but also because it offers air cooling, filtration, and flow from the exterior to the interior of a building. This leads to the air flow being cooled by the water spray offered by the interior patio fountains, in addition to protecting the sanctity and privacy of a building’s inhabitants, which complies with religious beliefs and social considerations. This integral sustainable solution acts on multiple scales: material recycling and responsible use, as well as climatic and socio-cultural considerations similar to Gaudi’s approach with Trencadís technology, not far from the Arabic and Islamic architectural influence revived in the Catalan Modernism contemporary to his time. In these footsteps, we explore the Mashrabiya of our time: an interactive and living architectural membrane, a soft interface that reacts by growing, giving shade, filtrating air, and transforming in time. Despite attempts to design a contemporary concept of the Mashrabiya, none of them have adopted the living organism to form an interactive living lattice architectural system. In this work, we propose the biodigital micro-cellular Mashrabiya as a novel idea and a proof of concept based on employing the authors’ previously published research findings to utilize eco-friendly biopolymers inoculated with useful native–domestic microbial strains to act as soft and living membranes, where these organisms grow and vary in their chemical and physical characteristics, sustainable function, and industrial value. This study implements an analytical–descriptive methodology to analyze the key characteristics of a traditional Mashrabiya as an integral sustainable solution and how the proposed micro-cellular biodigital Mashrabiya system can fulfill these criteria to be integrated into the built environment, forging future research trajectories on the bio-/micro-environmental compatibility of this biomaterial-based biodigital Mashrabiya system by understanding these materials’ physical, chemical, and physiological traits and their sustainable value. In this work, a biodigital Mashrabiya is proposed based on employing previous research findings on experimentally analyzed biomaterials from a biomineralized calcium-phosphate-based hydrogel and bio-welded seashell–mycelium biocomposite in forging the lattice system of a biodigital Mashrabiya, analyzing the feasibility and sustainability impact of these systems for integration into the architectural built environment.
]]>Authors: Nicolas Dietrich
Cinematic and pop culture narratives offer powerful tools for engaging with science by contextualizing complex principles within familiar, imaginative stories. This paper investigates the scientific feasibility of a plan depicted in Godzilla Minus One to neutralize the iconic kaiju through buoyancy reduction, exposure to deep-sea pressure, and rapid decompression. Employing principles from fluid mechanics, thermodynamics, and biomechanics, the study critically examines the use of freon bubbles and additional weight to counteract buoyant forces, the effects of 1500-m oceanic pressure on Godzilla’s physiology, and the potential for barotrauma during rapid ascent. While theoretically plausible, the proposed strategies face insurmountable challenges, including logistical impracticality and Godzilla’s presumed biological adaptations. This interdisciplinary critique highlights the intersection of film and science, encouraging critical analysis of cinematic representations and fostering a deeper appreciation for the scientific principles they attempt to portray.
]]>Authors: Farzaneh Mohammadi Jouzdani Vahid Javidroozi Hanifa Shah Monica Mateo Garcia
This paper presents a state-of-the-art review of digital transformation for developing smart net-zero cities, highlighting the significance of systems thinking and the key components of digital transformation including people, data, technology and process. Urban areas are experiencing increasing challenges from rapid growth and climate issues, making digital transformation a crucial strategy for enhancing sustainability and efficiency. In this context, systems thinking is essential, as it provides a holistic perspective that acknowledges the interdependence of urban sectors which can facilitate a more comprehensive, adaptable, and strategically integrated approach. This review examines findings from 22 sources and proposes a framework to investigate and represent the necessity of a digital transformation approach that effectively balances these elements and promotes a systems thinking approach. Also, by examining the findings from a systems thinking perspective, this research analyses the potential of effective digital transformation to support the complex needs of smart net-zero city developments. The findings indicate a widespread recognition of the digital transformation potential as a practical implementation strategy. It is imperative to formulate digital transformation strategies that are practical and comprehensively incorporate all elements: people, technology, processes, and data. Additionally, the review highlights the critical role of systems thinking in the development of these digital transformations as it facilitates the integration of interdependent urban sectors, including energy, transformation, and building, to achieve a holistic and integrated transformation.
]]>Authors: Olajide Ibrahim Oladipo Foad Faraji Hossein Habibi Mardin Abdalqadir Jagar A. Ali Perk Lin Chong
Drilling fluids are vital in oil and gas well operations, ensuring borehole stability, cutting removal, and pressure control. However, fluid loss into formations during drilling can compromise formation integrity, alter permeability, and risk groundwater contamination. Water-based drilling fluids (WBDFs) are favored for their environmental and cost-effective benefits but often require additives to address filtration and rheological limitations. This study explored the feasibility of using vegetable waste, including pumpkin peel (PP), courgette peel (CP), and butternut squash peel (BSP) in fine (75 μm) and very fine (10 μm) particle sizes as biodegradable WBDF additives. Waste vegetable peels were processed using ball milling and characterized via FTIR, TGA, and EDX. WBDFs, prepared per API SPEC 13A with 3 wt% of added additives, were tested for rheological and filtration properties. Results highlighted that very fine pumpkin peel powder (PP_10) was the most effective additive, reducing fluid loss and filter cake thickness by 43.5% and 50%, respectively. PP_10 WBDF maintained mud density, achieved a pH of 10.52 (preventing corrosion), and enhanced rheological properties, including a 50% rise in plastic viscosity and a 44.2% increase in gel strength. These findings demonstrate the remarkable potential of biodegradable vegetable peels as sustainable WBDF additives.
]]>Authors: Kazuhide Hirose Koki Nishino Hisamatsu Nakano
Using the method of moments, we analyze three array antennas for low cross-polarized radiation. Each antenna comprises two dual-loop elements connected to a feedline horizontal to the ground plane. First, a feedline end is excited with an unbalanced source as a reference antenna. Next, the feedline center is excited with a balanced source, after the transformation of a decoupling array configuration. It is found that the antenna exhibits a cross-polarized radiation lower by 12 dB than the reference antenna. Last, the decoupling antenna is modified to have an unbalanced source without a complicated balun circuit design. It is pointed out that the modified antenna is an array of four loop elements, sequentially rotated by 90º.
]]>Authors: Walace Breno da Silva Larissa Martins Rocha Marcio Santos Soares Pedro Ivo Vieira Good God Sabrina Alves da Silva Daniele Birck Moreira Geraldo Humberto Silva
Caffeic and ferulic acids are critical phenolic compounds in coffee beans, known for their antioxidant properties and influence on coffee’s acidity. This study developed and validated a rapid and simple analytical method to quantify these acids in raw and roasted coffee extracts using high-performance liquid chromatography with a violet detector (HPLC-UV). Parameters such as linearity, accuracy, precision, robustness, limit of quantification, and detection were analyzed. The two acids were quantified in five Coffea arabica cultivars (Red catucai, Siriema, IPR 98, Yellow catuai, and Yellow catucai), and the results were within the standards recommended by ANVISA and INMETRO. The concentrations of caffeic acid ranged from 1.43 to 1.93 mg/g in roasted grains and from 0.16 to 0.38 mg/g in raw grains; for ferulic acid, the concentrations ranged from 0.13 to 0.16 mg/g in raw grains and from 0.27 to 0.50 mg/g in roasted grains. According to the Tukey test, only cultivar IPR 98 showed a statistically significant difference in roasted grains. The proposed method offers a rapid and accurate analysis, contributing to the study of bioactive properties and the relationship between beverage quality and the presence of these compounds in coffee beans.
]]>Authors: Bruna Leticia Dias Talita Pereira de Souza Ferreira Mateus Sunti Dalcin Dalmarcia de Souza Carlos Mourão Paulo Ricardo de Sena Fernandes Taila Renata Neitzke João Victor de Almeida Oliveira Tiago Dias Luis Oswaldo Viteri Jumbo Eugênio Eduardo de Oliveira Gil Rodrigues dos Santos
Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food safety, and have low toxicity to non-target organisms. In this study, we examined the biochemical changes in corn plants treated with Lippia sidoides essential oil and its major compound, thymol. Both treatments serve as preventive measures for inoculated plants and induced resistance. We tested five concentrations of each product in in vivo experiments. After evaluating the area under the disease progress curve, we analyzed leaf samples for enzymatic activities, including superoxide dismutase, catalase, ascorbate peroxidase, and chitinase. Phytoalexin induction was assessed using soybean cotyledons and sorghum mesocotyls. Cytotoxicity tests revealed lower toxicity at concentrations below 50 µL/mL. Both essential oil and thymol stimulated the production of reactive oxygen species, with thymol primarily activating catalase and L. sidoides oil increasing ascorbate peroxidase levels. Both thymol and L. sidoides were also key activators of chitinase. These findings suggest that L. sidoides essential oil and thymol are promising candidates for developing biological control products to enhance plant defense against pathogens.
]]>Authors: Thomas Kletschkowski
The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can be based on a virtual model that contains all relevant sub-systems, transfer paths and coupling effects on the one hand. On the other hand, the complexity of such a model has to be limited to focus on principal findings such as convergence speed, power consumption, and noise reduction potential. The present paper proposes a fully coupled electro-vibro-acoustic model for the evaluation of self-adaptive control strategies. This model consists of discrete electrical and mechanical networks that are applied to model the electro-acoustic behavior of noise and anti-noise sources. The acoustic field inside a duct, terminated by these electro-acoustic sources, is described by finite elements. The resulting multi-physical model is capable of describing all relevant coupling effects and enables an efficient evaluation of different control strategies such as the local control of sound pressure or active control of acoustic absorption. It is designed as a benchmark model for the benefit of the scientific community.
]]>Authors: Bangyu Lan Kenan Niu
Understanding the relationship between muscle activation and deformation is essential for analyzing arm movement dynamics in both daily activities and clinical settings. Accurate characterization of this relationship impacts rehabilitation strategies, prosthetic development, and athletic training by providing deeper insights into muscle functions. However, direct analysis of raw neuromuscular and biomechanical signals remains limited due to their complex interplay. Traditional research implicitly applied this relationship without exploring the intricacies of the muscle behavior. In contrast, in this study, we explored the relationship between neuromuscular and biomechanical signals via a motion classification task based on a proposed deep learning approach, which was designed to classify arm motions separately using muscle activation patterns from surface electromyography (sEMG) and muscle thickness deformation measured by A-mode ultrasound. The classification results were directly compared through the chi-square analysis. In our experiment, six participants performed a specified arm lifting motion, creating a general motion dataset for the study. Our findings investigated the correlation between muscle activation and deformation patterns, offering special insights into muscle contraction dynamics, and potentially enhancing applications in rehabilitation and prosthetics in the future.
]]>Authors: Manjunatha Shettigere Krishna Pedro Machado Richard I. Otuka Salisu W. Yahaya Filipe Neves dos Santos Isibor Kennedy Ihianle
Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection.
]]>Authors: Kelly Karoline dos Santos Isabelle Watanabe Daniel Letícia Carani Delabio Manoella Abrão da Costa Júlia de Paula Dutra Bruna Estelita Ruginsk Jeanine Marie Nardin Louryana Padilha Campos Fabiane Gomes de Moraes Rego Geraldo Picheth Glaucio Valdameri Vivian Rotuno Moure
One of the major challenges of studying biomarkers in tumor samples is the low quantity and quality of isolated RNA, DNA, and proteins. Additionally, the extraction methods ideally should obtain macromolecules from the same tumor biopsy, allowing better-integrated data interpretation. In this work, an in-house, low-cost, mixed-type tissue crusher combining blade and beating principles was made and the simultaneous isolation of macromolecules from human cells and tissues was achieved using TRIzol. RT-qPCR, genotyping, SDS-PAGE, and Western blot analysis were used to validate the approach. For tissue samples, RNA, DNA, and proteins resulted in an average yield of 677 ng/mg, 225 ng/mg, and 1.4 µg/mg, respectively. The same approach was validated using cell lines. The isolated macromolecule validation included the detection of mRNA levels of ATP-binding cassette (ABC) transporters through RT-qPCR, genotyping of TNFR1 (rs767455), and protein visualization through SDS-PAGE following Coomassie blue staining and Western blot. This work contributed to filling a gap in knowledge about TRIzol efficiency for the simultaneous extraction of RNA, DNA, and proteins from a single human tissue sample. A low-cost, high yield, and quality method was validated using target biomarkers of multidrug resistance mechanisms. This approach might be advantageous for future biomarker studies using different tissue specimens.
]]>Authors: Mostafa Sayahkarajy Hartmut Witte
Anguilliform swimming is one of the most complex locomotion modes, involving various interacting phenomena, necessitating multidisciplinary studies. Eel robots are designed to incorporate biological principles and achieve efficient locomotion by replicating natural anguilliform swimming. These robots are simpler to engineer and study compared to their natural counterparts. Nevertheless, characterizing the robot–environment interaction is complex, demanding computationally expensive fluid dynamics simulations. In this study, we employ machine learning strategies to investigate the temporal evolution of the system and discover a data-driven model. Three methods were studied, including dynamic mode decomposition (DMD), sparse system identification (SINDy using PySINDy package), and autoencoder neural network (AE NN), as a general function approximator. The models were simulated using MATLAB® R2022 to obtain the prediction errors. The results show that the SINDy model presents less error within the regression range and performs better in extrapolation. Additionally, the SINDy model has a compact form and can explicitly formulate the coupling phenomena amongst the modes. Thus, instead of the standard DMD, we propose the SINDy-DMD approach to describe the anguilliform locomotion of the soft robot. The identified model was employed to recover the system state data matrix. It is concluded that the proposed model with quadratic terms provides a parsimonious representation of the system dynamics.
]]>Authors: Muhammad Aufaristama
The application of blockchain technology and Non-Fungible Tokens (NFTs) into geology offers potential for the preservation, management, and dissemination of geological data. This perspective paper explores the feasibility, benefits, and challenges of utilizing NFTs in managing geological data, particularly focusing on geology research materials. NFTs provide immutable, decentralized records that enhance data integrity, accessibility, and provenance, addressing long-standing issues in geological data management. This study outlines the key advantages of NFTs, including immutable record-keeping, enhanced accessibility, clear provenance and ownership, and interoperability across platforms. Specific use cases are highlighted, such as the creation of digital specimen collections, the development of interactive educational resources such as museums, and novel funding mechanisms for research. While the potential applications are promising, the discussion also addresses current limitations, including technical complexity, environmental concerns, and regulatory uncertainties. The opinion concludes with prospects, emphasizing the need for further research and technological advancements to fully realize the benefits of NFTs in geological data management, potentially revolutionizing the field of geology by making data more accessible, reliable, and secure.
]]>Authors: Sofia Magalhães Carla Luís Abel Duarte
This study explored a novel method using fructose-derived carbon dots (FCDs) for antitumor therapy in breast cancer (BC), marking a pioneering use of fructose as a carbon source for nanoparticle synthesis. BC, known for its complexity and heterogeneity, was chosen as a model due to its increasing mortality and incidence rates. The FCD synthesis involved the decomposition of fructose through microwave irradiation, followed by purification and characterization using techniques such as transmission electron microscopy, dynamic light scattering, fluorescence spectrophotometry, and Fourier-transform infrared spectroscopy. The FCDs, ranging in size from 2 to 6 nm, presented a hydrodynamic diameter below 2 nm, a spherical morphology, and a crystalline structure. As expected, FCDs were composed by carbon, oxygen, and hydrogen, and exhibited fluorescence with absorption and emission spectra at 405 nm and around 520 nm, respectively. Cell-based assays on breast epithelial and tumor cell lines demonstrated a dose-dependent response, with a decreased viability rate more pronounced in breast tumor cells. In conclusion, FCDs showed significant potential as selective antitumor agents for breast cancer therapy. The comprehensive characterization and cell-based assay evaluations provided valuable insights into the applications of these nanoparticles in breast cancer treatment, highlighting their selective toxicity and impact on tumor cells.
]]>Authors: Marius Engler Michael Stich Christoph Baumer Andreas Bund
All-iron redox-flow batteries (AIRFB) are capable of addressing the needs for cost-effective long-term storage of renewable energies. Currently, a major limitation of AIRFB performance is the half-cell reaction of the anolyte utilising the redox couple Fe/Fe2+. In this work, the performance of sulphate and chloride-based iron electrolytes was investigated by combining cyclic voltammetry (CV) and electrochemical quartz crystal microbalance (EQCM). The investigations demonstrate that complexing agents exert a detrimental influence on the kinetics of plating/stripping reactions, resulting in diffusivity reduction, while favouring hydrogen evolution reaction (HER). The coulombic (plating) efficiency was found to be 87.1% at −1.2 V vs. Ag/AgCl (sat’d) at pH 3.5, while the coulombic efficiency in oxidation sweep (stripping) was observed to be 100% in an electrolyte containing 0.8 M FeCl2 and 3 M NH4Cl. In the context of iron deposition, the most crucial factors are the suppression of HER, and the influence of diffusion limitations, as well as the role of additives in this process to achieve a high reversibility. It is evident that the investigated complexing agents of glycine, malic acid and malonic acid are inadequate for battery-compatible, efficient properties, given that the overvoltages for the charge transfer reaction are too high and parasitic HER reduces coulombic efficiencies. Ultimately, the choice of deposition parameters from EQCM and electrolyte composition reduced to 0.8 M FeCl2, and 3 M NH4Cl can optimise the battery efficiencies as such.
]]>Authors: Caroline Willich
Hydrogen is set to become an important energy carrier in Germany in the next decades in the country’s quest to reach the target of climate neutrality by 2045. To meet Germany’s potential green hydrogen demand of up to 587 to 1143 TWh by 2045, electrolyser capacities between 7 and 71 GW by 2030 and between 137 to 275 GW by 2050 are required. Presently, the capacities for electrolysis are small (around 153 MW), and even with an increase in electrolysis capacity of >1 GW per year, Germany will still need to import large quantities of hydrogen to meet its future demand. This work examines the expected green hydrogen demand in different sectors, describes the available technologies, and highlights the current situation and challenges that need to be addressed in the next years to reach Germany’s climate goals, with regard to scaling up production, infrastructure development, and transport as well as developing the demand for green hydrogen.
]]>Authors: Rebeca Klamerick Lima Felipe Sousa Quintino Melquisadec Oliveira Luan Carlos de Sena Monteiro Ozelim Tiago A. da Fonseca Pushpa Narayan Rathie
This paper focuses on the estimation of multicomponent stress–strength models, an important concept in reliability analyses used to determine the probability that a system will function successfully under varying stress conditions. Understanding and accurately estimating these probabilities is essential in fields such as engineering and risk management, where the reliability of components under extreme conditions can have significant consequences. This is the case in applications where one seeks to model extreme hydrological events. Specifically, this study examines cases where the random variables X (representing strength) and Y (representing stress) follow extreme value distributions. New analytical expressions are derived for multicomponent stress–strength reliability (MSSR) when different classes of extreme distributions are considered, using the extreme value H-function. These results are applied to three l-max stable laws and six p-max stable laws, providing a robust theoretical framework for multicomponent stress–strength analyses under extreme conditions. To demonstrate the practical relevance of the proposed models, a real dataset is analyzed, focusing on the monthly water capacity of the Shasta Reservoir in California (USA) during August and December from 1980 to 2015. This application showcases the effectiveness of the derived expressions in modeling real-world data.
]]>Authors: Themistoklis D. Kontos Yiannis G. Zevgolis
The process of siting municipal solid waste landfills in Greece faces significant challenges due to land resource limitations, the country’s mountainous and water-permeable terrain, and strong public opposition. This study introduces a novel methodology for optimizing landfill sites on Lemnos Island in the North Aegean Sea using a Fuzzy Spatial Multiple Criteria Analysis (FSMCA) approach. By combining fuzzy sets theory, Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP), spatial autocorrelation, spatial clustering and sensitivity analysis, this methodology addresses the uncertainties and complexities inherent in landfill siting. The decision problem is structured hierarchically into five levels to manage multiple criteria effectively. Criteria weights are determined using AHP, with discrete criteria graded according to Greek and EU guidelines, and continuous criteria evaluated through fuzzy sets theory. The region’s suitability is assessed using multiple criteria analysis, revealing that 9.7% of Lemnos Island is appropriate for landfill placement. Sensitivity analysis confirms the robustness of the methodology to changes in criteria weights. The case study demonstrates the practical application and benefits of FSMCA in a real-world scenario, underscoring its potential to improve sustainable waste management practices and inform policy making.
]]>Authors: Raquel Francés Yuanji Fu Christophe Desterke Jorge Mata-Garrido
Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a growing health challenge in Spain. This review examines the current understanding of IBD through the lens of genetics, epigenetics, and metabolism, offering insights into future directions for research and clinical management. Recent advancements in genetic studies have identified numerous susceptibility loci, highlighting the complex interplay between genetic predisposition and environmental triggers. Epigenetic modifications, including DNA methylation and histone modification, further elucidate the pathogenesis of IBD, underscoring the role of gene–environment interactions. Metabolic alterations, particularly in the gut microbiome, emerge as crucial factors influencing disease onset and progression. The integration of multi-omics approaches has enhanced our comprehension of the molecular mechanisms underlying IBD, paving the way for personalized medicine. Looking forward, this review emphasizes the need for longitudinal studies and advanced bioinformatics tools to decode the intricate networks involved in IBD. Additionally, we discuss the potential of novel therapeutic strategies, including epigenetic drugs and microbiome modulation, as promising avenues for improved patient outcomes. This comprehensive overview provides a foundation for future research aimed at unraveling the complexities of IBD and developing innovative treatments tailored to the Spanish population.
]]>Authors: Ilaria Ernesti Mikiko Watanabe Alfredo Genco
Weight regain (WR) after bariatric surgery, particularly sleeve gastrectomy, is a significant challenge, often driven by a combination of metabolic, behavioral, and lifestyle factors. Non-surgical interventions to manage WR are critical, given the increased risks and reduced efficacy of revisional surgeries. In this context, very low-calorie ketogenic diets (VLCKDs) have gained attention for their potential to promote weight loss and improve body composition in individuals struggling with WR. This study assessed the safety and efficacy of a VLCKD in 11 patients who experienced WR following sleeve gastrectomy. Over an 8-week period, patients demonstrated a significant average weight loss of 6.3% (p = 0.005), along with improvements in body composition, including reductions in body fat percentage (p = 0.003) and waist circumference (p = 0.003). Metabolic markers, such as insulin resistance (HOMA-IR), also improved significantly (p = 0.041). Although a decrease in the glomerular filtration rate was observed (p = 0.007), this finding is unlikely to be clinically relevant over the short term. Importantly, no major adverse events were reported, with only mild constipation observed. These results suggest that VLCKDs may be a promising non-surgical approach for managing WR post-bariatric surgery, though further studies are needed to assess long-term effects, especially on renal function.
]]>Authors: Domotimi James Jato Felix M. Onyije Osaro O. Mgbere Godwin Ovie Avwioro
Air pollution contributes significantly to morbidity and mortality globally. The Niger Delta Region of Nigeria flares the second largest amount of natural gas in the world, with residents of oil-producing communities bearing the burden of outdoor pollution that may have adverse effects on their health and well-being. Our study aimed to investigate the haematological indices of residents of a selected gas-flaring site. We conducted a cross-sectional study, wherein a total of eighty adults aged 24 to 73 years were recruited from communities located within a radius of approximately 5 to 10 km from the gas-flaring facility. Blood specimens were collected from consenting participants and analysed for various haematological parameters, including Red Blood Cell (RBC) count, Packed Cell Volume (PCV), Haemoglobin (HB), Mean Cell Haemoglobin (MCH), platelet count (PLT), White Blood Cell (WBC) count, neutrophil (NEU), lymphocytes (LYMs), and Monocyte + Basophil + Eosinophil (MXD). The analysis was performed using an automated Sysmex KX21N haematological analyser. Overall, there was a significant decrease in RBC counts (p < 0.001) and a significant elevation in WBCs (p < 0.001) among people residing within a 5 km radius compared to those residing within a 10 km radius. About 42.5% of males residing within a 5 Km radius exhibited low RBC counts in contrast to only 15% of males residing within a 10 km radius. The WBC levels were found to be significantly higher (p < 0.001) than the reference range among both males and females residing within a 5 km radius compared to those residing at a distance of 10 km. In the female population, 15% of individuals residing within a 5 km and 10 Km radius exhibited RBC levels below the reference category, while 7.5% showed RBC levels above the reference range. Exposure to gas flaring may alter haematological indices. It is, therefore, recommended that a comprehensive longitudinal study be conducted among residents of oil-producing communities and workers at gas-flaring facilities in the Niger Delta region of Nigeria to assess the potential environmental and health implications of their exposure to chemical pollutants.
]]>Authors: Anna Luise Grab Alexander Nesterov-Müller
Understanding natural killer (NK) cell functionality is essential in developing more effective immunotherapeutic strategies that can enhance patient outcomes, especially in the context of cancer treatment. This review provides a comprehensive overview of both traditional and novel techniques for evaluating NK cell functionality, focusing on multiparameter assays and spatial methods that illuminate NK cell interactions within their microenvironment. We discuss the significance of standardized assays for assessing NK cell function across various research and clinical settings, including cancer immunotherapy, infectious diseases, and transplantation. Key factors influencing NK cell functionality include the origin of the sample, target–effector ratios, the functional state of NK cells, and the impact of pre-treatment conditions and their natural aging effect on NK cell activity. By emphasizing the importance of selecting a suitable technique for reliable measurements, especially for longitudinal monitoring, this review aims to give an overview on techniques to measure NK cell functionality in vitro and show the interaction with their microenvironment cells by spatial imaging. Ultimately, our understanding of NK cell functionality could be critical to biomarker development, drug design, and understanding of disease progression in the field of oncology or infectious disease.
]]>Authors: Jörg Leicher Anne Giese Christoph Wieland
The decarbonization of industrial process heat is one of the bigger challenges of the global energy transition. Process heating accounts for about 20% of final energy demand in Germany, and the situation is similar in other industrialized nations around the globe. Process heating is indispensable in the manufacturing processes of products and materials encountered every day, ranging from food, beverages, paper and textiles, to metals, ceramics, glass and cement. At the same time, process heating is also responsible for significant greenhouse gas emissions, as it is heavily dependent on fossil fuels such as natural gas and coal. Thus, process heating needs to be decarbonized. This review article explores the challenges of decarbonizing industrial process heat and then discusses two of the most promising options, the use of electric heating technologies and the substitution of fossil fuels with low-carbon hydrogen, in more detail. Both energy carriers have their specific benefits and drawbacks that have to be considered in the context of industrial decarbonization, but also in terms of necessary energy infrastructures. The focus is on high-temperature process heat (>400 °C) in energy-intensive basic materials industries, with examples from the metal and glass industries. Given the heterogeneity of industrial process heating, both electricity and hydrogen will likely be the most prominent energy carriers for decarbonized high-temperature process heat, each with their respective advantages and disadvantages.
]]>Authors: Khanh Nguyen Kevin Koch Swati Chandna Binh Vu
Local energy networks, known as microgrids, can operate independently or in conjunction with the main grid, offering numerous benefits such as enhanced reliability, sustainability, and efficiency. This study focuses on analyzing the factors that influence energy performance in East-West microgrids, which have the unique advantage of capturing solar radiation from both directions, maximizing energy production throughout the day. A predictive pipeline was also developed to assess the performance of various machine learning models in forecasting energy output. Key input data for the models included solar radiation levels, photovoltaic (DC) energy, and the losses incurred during the conversion from DC to AC energy. One of the study’s significant findings was that the east side of the microgrid received higher radiation and experienced fewer losses compared to the west side, illustrating the importance of orientation for efficiency. Another noteworthy result was the predicted total energy supplied to the grid, valued at €15,423. This demonstrates that the optimized energy generation not only meets grid demand but also generates economic value by enabling the sale of excess energy back to the grid. The machine learning models—Random Forest, Extreme Gradient Boosting, and Recurrent Neural Networks—showed superior performance in energy prediction, with mean squared errors of 0.000318, 0.000104, and 0.000081, respectively. The research concludes that East-West microgrids have substantial potential to generate significant energy and economic benefits. The developed energy prediction pipeline can serve as a useful tool for optimizing microgrid operations and improving their integration with the main grid.
]]>Authors: Mostefa Kara Konstantinos Karampidis Giorgos Papadourakis Mohammad Hammoudeh Muath AlShaikh
Quantum-secure cryptography is a dynamic field due to its crucial role in various domains. This field aligns with the ongoing efforts in data security. Post-quantum encryption (PQE) aims to counter the threats posed by future quantum computers, highlighting the need for further improvement. Based on the learning with error (LWE) system, this paper introduces a novel asymmetric encryption technique that encrypts entire messages of n bits rather than just 1 bit. This technique offers several advantages including an additive homomorphic cryptosystem. The robustness of the proposed lightweight public key encryption method, which is based on a new version of LWE, ensures that private keys remain secure and that original data cannot be recovered by an attacker from the ciphertext. By improving encryption and decryption execution time—which achieve speeds of 0.0427 ms and 0.0320 ms, respectively—and decreasing ciphertext size to 708 bits for 128-bit security, the obtained results are very promising.
]]>Authors: Yuehan Yang Mara Grace C. Kessler M. Raquel Marchán-Rivadeneira Yuxi Zhou Yong Han
The COVID-19 pandemic is caused by a novel and rapidly mutating coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although several drugs are already in clinical use or under emergency authorization, there is still an urgent need to develop new drugs. Through the mining and analysis of 2776 genomes of the SARS-CoV-2 virus, we identified papain-like protease (PLpro), which is a critical enzyme required for coronavirus to generate a functional replicase complex and manipulate post-translational modifications on host proteins for evasion against host antiviral immune responses, as a conserved molecular target for the development of anti-SARS-CoV-2 therapy. We then made an infection model using the NCI-H1299 cell line stably expressing SARS-CoV-2 PLpro protein (NCI-H1299/PLpro). To investigate the effect of targeting and degrading PLpro mRNA, a compact CRISPR-Cas13 system targeting PLpro mRNA was developed and validated, which was then delivered to the aforementioned NCI-H1299/PLpro cells. The results showed that CRISPR-Cas13 mediated mRNA degradation successfully reduced the expression of viral PLpro protein. By combining the power of AAV and CRISPR-Cas13 technologies, we aim to explore the potential of attenuating viral infection by targeted degradation of important viral mRNAs via safe and efficient delivery of AAV carrying the CRISPR-Cas13 system. This study demonstrated a virus-against-virus gene therapy strategy for COVID-19 and provided evidence for the future development of therapies against SARS-CoV-2 and other RNA viral infections.
]]>Authors: Thomas Mourgues María José González-Olmo Matthieu Martel-Lambert Carolina Nieto-Moraleda Martín Romero
Background: The objective of this study was to compare the facial pattern according to Steiner’s cephalometric analysis with other facial measurement methods (Ricketts, Björk-Jarabak, and McNamara). Methods: 200 patients from a university orthodontic clinic were studied. Measurements were taken using Ricketts, Steiner, Björk-Jarabak, and McNamara methods. Results were compared using standard deviation proportions. Results: Significant differences were found between Steiner’s method and the gold standard. No differences were observed between mixed and permanent dentition groups. Errors were noted in facial type classification: 54.8% in the brachyfacial group, 80% in the mesofacial group and 14.5% in the dolichofacial group. Conclusion: The mandibular angle of Steiner tends to make a diagnosis more towards the dolichofacial type compared to other methods. A protocol is proposed to adjust the value of the mandibular angle of Steiner to the other three methods in a Spanish population.
]]>Authors: Alexander Robitzsch
Haebara and Stocking–Lord linking methods are frequently used to compare the distributions of two groups. Previous research has demonstrated that Haebara and Stocking–Lord linking can produce bias in estimated standard deviations and, to a smaller extent, in estimated means in the presence of differential item functioning (DIF). This article determines the asymptotic bias of the two linking methods for the 2PL model. A bias-reduced Haebara and bias-reduced Stocking–Lord linking method is proposed to reduce the bias due to uniform DIF effects. The performance of the new linking method is evaluated in a simulation study. In general, it turned out that Stocking–Lord linking had substantial advantages over Haebara linking in the presence of DIF effects. Moreover, bias-reduced Haebara and Stocking–Lord linking substantially reduced the bias in the estimated standard deviation.
]]>Authors: Christine Tara Peterson
The discovery of natural products has been pivotal in drug development, providing a vast reservoir of bioactive compounds from various biological sources. This narrative review addresses a critical research gap: the largely underexplored role of gut microbiota in the mediation and biotransformation of medicinal herb-derived natural products for therapeutic use. By examining the interplay between gut microbiota and natural products, this review highlights the potential of microbiota-mediated biotransformation to unveil novel therapeutic agents. It delves into the mechanisms by which gut microbes modify and enhance the efficacy of natural products, with a focus on herbal medicines from Ayurveda and traditional Chinese medicine, known for their applications in treating metabolic and inflammatory diseases. The review also discusses recent advances in microbiota-derived natural product research, including innovative methodologies such as culturomics, metagenomics, and metabolomics. By exploring the intricate interactions between gut microorganisms and their substrates, this review uncovers new strategies for leveraging gut microbiota-mediated processes in the development of groundbreaking therapeutics.
]]>Authors: Lea Zimmermann Thomas Stegmaier Cigdem Kaya Götz T. Gresser
Due to climate change, population increase, and the urban heat island effect (UHI), the demand for cooling energy, especially in urban areas, has increased and will further increase in the future. Technologies such as radiative cooling offer a sustainable and energy-free solution by using the wavelength ranges of the atmosphere that are transparent to electromagnetic radiation, the so-called atmospheric window (8–13 µm), to emit thermal radiation into the colder (3 K) outer space. Previous publications in the field of textile building cooling have focused on specific fiber structures and textile substrate materials as well as complex multi-layer constructions, which restrict the use for highly scaled outdoor applications. This paper describes the development of a novel substrate-independent coating with spectrally selective radiative properties. By adapting the coating parameters and combining low-emitting and solar-reflective particles, along with a matrix material emitting strongly in the mid-infrared range (MIR), substrate-independent cooling below ambient temperature is achieved. Moreover, the coating is designed to be easily applicable, with a low thickness, to ensure high flexibility and scalability, making it suitable for various applications such as membrane architecture, textile roofs, or tent construction. The results show a median daytime temperature reduction (7 a.m.–7 p.m.) of 2 °C below ambient temperature on a hot summer day.
]]>Authors: Shreejit Poudyal Alex Lindquist Nate Smullen Victoria York Ali Lotfi James Greene Mohammad Meysami
Recently, the United States has experienced, on average, costs of USD 20 billion due to natural and climate disasters, such as hurricanes and wildfires. In this study, we focus on wildfires, which have occurred more frequently in the past few years. This paper examines how various factors, such as the PM10 levels, elevation, precipitation, SOX, population, and temperature, can influence the intensity of wildfires differently across counties in California. More specifically, we use Bayesian analysis to classify all counties of California into two groups: those with more wildfires and those with fewer wildfires. The Bayesian model incorporates prior knowledge and uncertainty for a more robust understanding of how these environmental factors impact wildfires differently among county groups. The findings show a similar effect of the SOX, population, and temperature, while the PM10, elevation, and precipitation have different implications for wildfires across various groups.
]]>Authors: Theodora Sanida Maria Vasiliki Sanida Argyrios Sideris Minas Dasygenis
Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.
]]>Authors: Shirin Kazemzadeh Pournaki Ricardo Santos Aleman Mehrdad Hasani-Azhdari Jhunior Marcia Ajitesh Yadav Marvin Moncada
Due to global development and increased public awareness of food’s effects on health, demands for innovative and healthy products have risen. Biodegradable and environmentally friendly polymer usage in modern food products is a promising approach to reduce the negative health and environmental effects of synthetic chemicals. Also, desirable features such as flavor, texture, shelf-life, storage condition, water holding capacity, a decrease in water activity, and an oil absorption of fried food have been improved by many polysaccharides. One of the important polymers, which is applied in the food industry, is alginate. Alginates are a safe and widely used compound in various industries, especially the food industry, which has led to innovative methods for for the improvement of this industry. Currently, different applications of alginate in stable emulsions and nano-capsules in food applications are due to the crosslinking properties of alginate with divalent cations, such as calcium ions, which have been studied recently. The main aim of this review is to take a closer look at alginate properties and applications in the food industry.
]]>Authors: Eric Martin Matthew Ritchey Steven Kim Margaret Falknor George Beckham
Background: The lack of knowledge about physical responses to pickleball creates a clear gap about performance in this sport. The purpose of this study was to investigate neuromuscular fatigue caused by playing doubles pickleball. Methods: Recreational pickleball players (n = 32, mean age = 60.0 years) were recruited to perform sets of four countermovement jumps (CMJs) on a force plate before and after doubles pickleball matches. Results: For players who had not played a match prior to testing, there was a significant learning effect across trials within the baseline set of jumps for five outcomes from the CMJ test, including propulsive peak force (p = 0.005); however, there was no significant learning effect for jump height. There were significant improvements in the large effect size for all except one dependent variable (propulsive phase time) between the first and second set of jumps (i.e., after one match). Neither further increases nor decreases were seen after the second set of jumps. Conclusions: Participants saw significant increases in CMJ performance across trials after one pickleball match, indicating learning and potentiation effects. After three matches of doubles pickleball, no fatigue effect was detected.
]]>Authors: Ilona Buchem Stefano Sostak Lewe Christiansen
Collaborative learning has been widely studied in higher education and beyond, suggesting that collaboration in small groups can be effective for promoting deeper learning, enhancing engagement and motivation, and improving a range of cognitive and social outcomes. The study presented in this paper compared different forms of human and robot facilitation in the game of planning poker, designed as a collaborative activity in the undergraduate course on agile project management. Planning poker is a consensus-based game for relative estimation in teams. Team members collaboratively estimate effort for a set of project tasks. In our study, student teams played the game of planning poker to estimate the effort required for project tasks by comparing task effort relative to one another. In this within- and between-subjects study, forty-nine students in eight teams participated in two out of four conditions. The four conditions differed in respect to the form of human and/or robot facilitation. Teams 1–4 participated in conditions C1 human online and C3 unsupervised robot, while teams 5–8 participated in conditions C2 human face to face and C4 supervised robot co-facilitation. While planning poker was facilitated by a human teacher in conditions C1 and C2, the NAO robot facilitated the game-play in conditions C3 and C4. In C4, the robot facilitation was supervised by a human teacher. The study compared these four forms of facilitation and explored the effects of the type of facilitation on the facilitator’s competence (FC), learning experience (LX), and learning outcomes (LO). The results based on the data from an online survey indicated a number of significant differences across conditions. While the facilitator’s competence and learning outcomes were rated higher in human (C1, C2) compared to robot (C3, C4) conditions, participants in the supervised robot condition (C4) experienced higher levels of focus, motivation, and relevance and a greater sense of control and sense of success, and rated their cognitive learning outcomes and the willingness to apply what was learned higher than in other conditions. These results indicate that human supervision during robot-led facilitation in collaborative learning (e.g., providing hints and situational information on demand) can be beneficial for learning experience and outcomes as it allows synergies to be created between human expertise and flexibility and the consistency of the robotic assistance.
]]>Authors: Mohammed Abuhussein Iyad Almadani Aaron L. Robinson Mohammed Younis
This research paper presents a novel approach for occlusion inpainting in thermal images to efficiently segment and enhance obscured regions within these images. The increasing reliance on thermal imaging in fields like surveillance, security, and defense necessitates the accurate detection of obscurants such as smoke and fog. Traditional methods often struggle with these complexities, leading to the need for more advanced solutions. Our proposed methodology uses a Generative Adversarial Network (GAN) to fill occluded areas in thermal images. This process begins with an obscured region segmentation, followed by a GAN-based pixel replacement in these areas. The methodology encompasses building, training, evaluating, and optimizing the model to ensure swift real-time performance. One of the key challenges in thermal imaging is identifying effective strategies to mitigate critical information loss due to atmospheric interference. Our approach addresses this by employing sophisticated deep-learning techniques. These techniques segment, classify and inpaint these obscured regions in a patch-wise manner, allowing for more precise and accurate image restoration. We propose utilizing architectures similar to Pix2Pix and UNet networks for generative and segmentation tasks. These networks are known for their effectiveness in image-to-image translation and segmentation tasks. Our method enhances the segmentation and inpainting process by leveraging their architectural similarities. To validate our approach, we provide a quantitative analysis and performance comparison. We include a quantitative comparison between (Pix2Pix and UNet) and our combined architecture. The comparison focuses on how well each model performs in terms of accuracy and speed, highlighting the advantages of our integrated approach. This research contributes to advancing thermal imaging techniques, offering a more robust solution for dealing with obscured regions. The integration of advanced deep learning models holds the potential to significantly improve image analysis in critical applications like surveillance and security.
]]>Authors: Rajesh Shah Vikram Mittal Angelina Mae Precilla
Recent advances in all-solid-state battery (ASSB) research have significantly addressed key obstacles hindering their widespread adoption in electric vehicles (EVs). This review highlights major innovations, including ultrathin electrolyte membranes, nanomaterials for enhanced conductivity, and novel manufacturing techniques, all contributing to improved ASSB performance, safety, and scalability. These developments effectively tackle the limitations of traditional lithium-ion batteries, such as safety issues, limited energy density, and a reduced cycle life. Noteworthy achievements include freestanding ceramic electrolyte films like the 25 μm thick Li0.34La0.56TiO3 film, which enhance energy density and power output, and solid polymer electrolytes like the polyvinyl nitrile boroxane electrolyte, which offer improved mechanical robustness and electrochemical performance. Hybrid solid electrolytes combine the best properties of inorganic and polymer materials, providing superior ionic conductivity and mechanical flexibility. The scalable production of ultrathin composite polymer electrolytes shows promise for high-performance, cost-effective ASSBs. However, challenges remain in optimizing manufacturing processes, enhancing electrode-electrolyte interfaces, exploring sustainable materials, and standardizing testing protocols. Continued collaboration among academia, industry, and government is essential for driving innovation, accelerating commercialization, and achieving a sustainable energy future, fully realizing the transformative potential of ASSB technology for EVs and beyond.
]]>Authors: Mohammad Al-Shaar Pierre-Charles Gerard Ghaleb Faour Walid Al-Shaar Jocelyne Adjizian-Gérard
Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls.
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