Search Results (39,372)

Human Activity Recognition (HAR) is widely used for healthcare, but few works focus on Manual Material Handling (MMH) activities, despite their diffusion and impact on the workers’ health. We propose four Deep Learning algorithms for HAR in MMH: Bidirectional Long Short-Term Memory (BiLSTM), Sparse Denoising Autoencoder (Sp-DAE), Recurrent Sp-DAE, and Recurrent Convolutional Neural Network (RCNN). We explored different hyperparameter combinations to maximize the classification performance (F1-score,) using wearable sensors’ data gathered from 14 subjects. We investigated the best three-parameter combinations for each network using the full dataset to select the two best-performing networks, which were then compared using 14 datasets with increasing subject numerosity, 70–30% split, and Leave-One-Subject-Out (LOSO) validation, to evaluate whether they may perform better with a larger dataset. The benchmarking network DeepConvLSTM was tested on the full dataset. BiLSTM performs best in classification and complexity (95.7% 70–30% split; 90.3% LOSO). RCNN performed similarly (95.9%; 89.2%) with a positive trend with subject numerosity. DeepConvLSTM achieves similar classification performance (95.2%; 90.3%) but requires $\times 57.1$ and $\times 31.3$ more Multiply and ACcumulate (MAC) and $\times 100.8$ and $\times 28.3$ more Multiplication and Addition (MA) operations, which measure the complexity of the network’s inference process, than BiLSTM and RCNN, respectively. The BILSTM and RCNN perform close to DeepConvLSTM while being computationally lighter, fostering their use in embedded systems. Such lighter algorithms can be readily used in the automatic ergonomic and biomechanical risk assessment systems, enabling personalization of risk assessment and easing the adoption of safety measures in industrial practices involving MMH. Full article

The need for new medications to treat diabetes mellitus (DM) is a global health concern due to the cost and impact on patients and their families, health systems, and society. Recent approaches in drug development have focused on multitarget therapy for DM, considering its multifactorial and complex pathophysiology. The present work contributes to the review of the plant species Schinus molle L. (pirul), a tropical tree native to South America but now widespread worldwide, which has demonstrated anticancer, analgesic, antibacterial, and insecticidal properties. According to traditional uses, pirul has been employed as a food condiment, in the preparation of beverages and chewing gums, and in the treatment of DM. The antidiabetic effects of pirul appear to act through several mechanisms involved in DM. The methanolic extract of S. molle fruits collected in Tunisia exhibited a dose-dependent inhibition on both α-amylase and α-glucosidase enzymes (77.49% and 86.45%, respectively). A dose-dependent anti-inflammatory effect was also observed at 1, 2, 3, 4, and 5 h, in the carrageenan-induced rats’ paw edema model. Furthermore, in both the H₂O₂ and the superoxide radical assays, the pirul extract demonstrated moderate antioxidant activity (IC₅₀ = 0.22 mg/mL). Isomasticadienonic acid and Masazino-flavanone, the major components of active fractions and extracts of S. molle represent promising antidiabetic agents. Although pirul appears to be safe in in vivo acute and subchronic administrations, toxicological studies and clinical trials in individuals with DM are still pending. Full article

Cas9 with specialized temperature adaptations are essential for broadening the application of CRISPR-based genome editing across diverse biological contexts. Although Cas9 orthologs from thermophilic and mesophilic organisms have been characterized for high- and moderate-temperature applications, cold-active variants remain largely unexplored, limiting genome engineering in low-temperature systems such as aquaculture species. Here, we report the functional characterization of Fp2Cas9, a cold-adapted Type II-C nuclease from Flavobacterium psychrophilum. In vitro assays showed that Fp2Cas9 efficiently cleaves double-stranded DNA with a refined PAM requirement of 5′-SNAAAG-3′, and that its engineered sgRNA scaffold (sgRNA-V2) supports programmable DNA targeting. Notably, Fp2Cas9 retains 75% cleavage efficiency at 5 °C, approximately 2.5-fold higher than SpCas9 under the same conditions, but shows a marked reduction in activity at 35 °C. In vivo, a nuclear-localized variant (2NLS-Fp2Cas9) mediated efficient mutagenesis of the zebrafish slc45a2 gene, yielding ~60% indel frequencies and pigmentation-deficient phenotypes in ~43% of injected embryos. Collectively, these findings establish Fp2Cas9 as a cold-adapted Cas9 with reliable activity at low temperatures. This work adds a valuable tool to the CRISPR-Cas9 toolkit and may facilitate genome editing in cold-water organisms and other low-temperature systems. Full article

Although fast-paced ongoing industrial growth, on the one hand, enhances the lifestyle of the population, on the other hand, it affects human health and the environment as a result of the discharge of pollutants. To address this, designing a novel and effective photocatalyst is necessary to mitigate increasing environmental pollutants. In the present work, we aim to synthesize a single-phase high-entropy zirconate pyrochlore oxide (Ce_0.2Pr_0.2Zn_0.2Nd_0.2Tb_0.2)₂Zr₂O₇ using a modified Pechini method. The physicochemical properties of the prepared nanoparticles were investigated using X-ray diffraction, UV-visible spectroscopy, field emission scanning electron microscopy, and X-ray photoelectron spectroscopy. The photocatalytic properties were examined using cationic dye (methylene blue), anionic dye (Congo red), and Cr(VI). Photocatalytic degradation experiments demonstrate exceptional efficiency in the removal of persistent organic pollutants. The photocatalytic results indicate that the prepared high-entropy (Ce_0.2Pr_0.2Zn_0.2Nd_0.2Tb_0.2)₂Zr₂O₇ zirconate pyrochlore oxide could effectively degrade dyes and reduce Cr(VI). Radical trapping experiments indicate that the degradation of dyes was driven by the hydroxyl radicals, superoxide radicals, and holes. Furthermore, the position of the valence band and conduction band promoted efficient photocatalytic reaction kinetics. The prepared photocatalyst remains structurally stable and can be reused three times without losing activity. Full article

Construction sites present intrinsically hazardous conditions characterized by dynamic and complex operational environments which persistently contribute to elevated rates of occupational injuries. While conventional safety management emphasizes controls during the execution phase, addressing safety considerations during the earliest phases of a project is more effective. In this work, a Nonlinear Multifactor Safety Index (NMF-SI) is developed to quantify activity-level occupational risk for use in project planning. The index synthesizes multiple risk factors, including accident probability, task severity, exposure duration, workforce size, and contextual variables, such as weekday effects, and site conditions, like waste accumulation. Additionally, it incorporates a structured questionnaire to capture the implementation of safety measures on site. Its nonlinear formulation reflects interactions among factors, penalizes temporal clustering of high-risk tasks, and prioritizes temporal distribution and proactive planning. To produce risk-aware schedules, a hybrid optimization framework that couples a tree-based constructive heuristic with the marine predators algorithm is introduced, maximizing NMF-SI subject to project-duration, precedence, and resource constraints. In simulation, the optimized schedules achieve higher NMF-SI values and a smoother risk profile over time than baseline plans. These results translate into a quantitative, data-driven contribution to safety by design, offering a practical decision-support tool for intelligent, risk-aware construction scheduling. Full article

Photocatalytic CO₂ reduction holds great potential for sustainable solar fuel production, yet its practical application is often limited by inefficient charge separation and poor product selectivity. The photothermal effect presents a viable strategy to address these challenges by reducing activation energies and accelerating reaction kinetics. In this work, we report a rationally designed CN-B/Ti₃C₂ heterojunction that effectively leverages photothermal promotion for enhanced CO₂ reduction. The black carbon nitride (CN-B) framework, synthesized via a one-step calcination of urea and Phloxine B, exhibits outstanding photothermal conversion, reaching 131.4 °C under 300 mW cm⁻² illumination, which facilitates CO₂ adsorption and charge separation. Coupled with Ti₃C₂ MXene, the optimized composite (3:1) achieves remarkable CO and CH₄ production rates of 80.21 and 35.13 μmol g⁻¹ h⁻¹, respectively, without any cocatalyst—representing a 2.9-fold and 8.8-fold enhancement over CN-B and g-C₃N₄ in CO yield. Mechanistic studies reveal that the improved performance stems from synergistic effects: a built-in electric field prolongs charge carrier lifetime (3.15 ns) and reduces interfacial resistance, while localized heating under full-spectrum light further promotes CO₂ activation. In situ Fourier transform infrared (FTIR) spectroscopy confirms the accelerated formation of key intermediates (*COOH and *CO). The catalyst also maintains excellent stability over 24 h. This study demonstrates the promise of combining photothermal effects with heterojunction engineering for efficient and durable CO₂ photoreduction. Full article

Restless legs syndrome (RLS) is a sensorimotor disorder with evening-predominant symptoms; convergent models implicate brain iron dysregulation and alter dopaminergic/glutamatergic signaling. Because EEG provides millisecond-scale access to cortical dynamics, we synthesized waking EEG/ERP findings in RLS (sleep EEG excluded). A structured search across major databases (1980–July 2025) identified clinical EEG studies meeting prespecified criteria. Across small, mostly mid- to late-adult cohorts, four reproducible signatures emerged: (i) cortical hyperarousal at rest (fronto-central beta elevation with a dissociated vigilance profile); (ii) attentional/working memory ERPs with attenuated and delayed P300 (and reduced frontal P2), pointing to fronto-parietal dysfunction; (iii) network inefficiency (reduced theta/gamma synchrony and lower clustering/longer path length) that scales with symptom burden; and (iv) motor system abnormalities with exaggerated post-movement beta rebound and peri-movement cortical–autonomic co-activation, together with evening-vulnerable early visual processing during cognitive control. Dopamine agonist therapy partially normalizes behavior and ERP amplitudes. These converging EEG features provide candidate biomarkers for disease burden and treatment response and are consistent with models linking brain iron deficiency to thalamo-cortical timing failures. This mechanistic review did not adhere to PRISMA or PICO frameworks and did not include a formal risk-of-bias or quantitative meta-analysis; samples were small, heterogeneous, and English-only. Full article

This study addresses the limited integration of place-based education (PBE) into primary science learning, particularly regarding its potential to cultivate both cognitive understanding and emotional attachment to place—key dimensions of education for sustainability. Implemented in a rural Thai primary school, a ten-week (20-h) PBE program engaged Grade 6 students in locally relevant, hands-on activities grounded in Experiential Learning Theory and Social Learning Theory. Mixed-method analysis combined rubric-based scoring of a Sense of Place Assessment with qualitative content analysis of student reflections and group work. Post-intervention results showed that 84.21% of students achieved a high level of sense of place, with the strongest gains in place knowledge, followed by place dependence and place identity. Qualitative findings revealed three developmental pathways: sustained attachment through long-term experiences, growth from surface awareness to active participation, and limited emotional connection despite involvement. These patterns illustrate diverse routes through which PBE can foster environmental stewardship by linking personal meaning-making with collaborative action. Embedding PBE in authentic school contexts can enhance students’ capacity to value, care for, and improve their immediate environments, offering a transferable model for integrating sustainability-oriented learning across curricula. Full article

Background/Objectives: Botrytis cinerea is a necrotrophic fungal plant pathogen responsible for the gray mold disease, affecting several crops of economic importance worldwide. The primary line of control for the disease in the field and post-harvest fruits includes the application of fungicides. However, the emergence of fungal populations resistant to one or more fungicides has increased their application and diminished their effectiveness. Looking at new control strategies, metallic nanoparticles have appeared as a promising alternative for disease treatment. Green-synthesized copper oxide nanoparticles (CuONPs) are considered a feasible alternative, aiming to reduce the generation of environmentally toxic waste through chemical methods. Methods: In this work, CuONPs biosynthesized using the supernatant of Trichoderma asperellum and Trichoderma ghanense were evaluated to determine their antifungal activity against B. cinerea. Results: Four different formulations of CuONPs were obtained: Ta1, Ta2, Tg1, and Tg2. All formulations displayed antifungal properties, with Tg2 being the most effective and having a high potential in controlling the phytopathogen. CuONPs in the Tg2 formulation were quasi-spherical, ranging in size from 1 to 2.7 nm. Conclusions: Furthermore, Tg2 demonstrated greater efficacy than the copper-based commercial fungicide NORDOX^® 75W, which showed no inhibitory effect on B. cinerea mycelial growth. In summary, the CuONPs reported in this work offer a sustainable and effective alternative for managing the gray mold disease. Full article

The Mediterranean ecosystem is characterized by marked seasonality; it is composed of species such as shrublands that are subjected to high levels of water and thermal stress, making these species an important source of secondary metabolites of significant chemical and ecological interest. In this work, 21 plants were selected from the Mediterranean scrub. These abundant and characteristic representations of the ecosystem produce a total of 197 terpenes. The majority of these are monoterpenes (46.70%), followed by sesquiterpenes (38.07%), with a minority of diterpenes (5.53%) and triterpenes (10.15%). Tetraterpenes accounted for only 0.5% of the total compounds in the species studied, corresponding to only 1%. The major terpenes include 1,8-cineole, terpinen-4-ol, α-terpineol, borneol, camphor, γ-terpinene, limonene, linalool, o-cymene, α-tujene, α-pinene, β-pinene, sabinene, myrcene, β-phellandrene, and β-caryopylene. Species such as Pistacea terebinthus, Rosmarinus officinalis, Cistus ladanifer, Myrtus communis, Lavandula stoecha, and Thymus mastichina contain the most terpenic compounds in their chemical composition. Furthermore, these metabolites are involved in various biological functions, including antimicrobial, antioxidant, neuroprotective, antibacterial, cardiovascular, analgesic, antitumor, and insecticidal activities, among others. Various terpenes present in Mediterranean scrub species, such as 1,8-cineole, α-pinene, limonene, borneol, and terpinen-4-ol, have demonstrated synergistic effects that enhance their antimicrobial, insecticidal, and neuroprotective properties. These interactions between compounds make the natural extracts more effective than they would be individually, increasing their therapeutic and biotechnological value. The synergism among terpenes suggests a promising approach for developing more effective and sustainable phytotherapeutic products. Full article

Decoding visual content from neural activity remains a central challenge at the intersections of engineering, neuroscience, and computational modeling. Prior work has primarily leveraged electroencephalography (EEG) with generative models to recover static images. In this study, we advance EEG-based decoding by introducing a temporal encoding framework that approximates dynamic object transformations across time. EEG recordings from healthy participants (n = 20) were used to model neural representations of objects presented in “initial” and “later” states. Individualized classifiers trained on time-specific EEG signatures achieved high discriminability, with Random Forest models reaching a mean accuracy and standard deviation of 92 ± 2% and a mean AUC-ROC and standard deviation of 0.87 ± 0.10, driven largely by gamma- and beta-band activity at the frontal electrodes. These results confirm and extend evidence of strong interindividual variability, showing that subject-specific models outperform intersubject approaches in decoding temporally varying object representations. Beyond classification, we demonstrate that pairwise temporal encodings can be integrated into a generative pipeline to produce approximated reconstructions of short video sequences and 3D object renderings. Our findings establish that temporal EEG features, captured using low-cost open-source hardware, are sufficient to support the decoding of visual content across discrete time points, providing a versatile platform for potential applications in neural decoding, immersive media, and human–computer interaction. Full article

The eutrophication of aquatic ecosystems often triggers the excessive growth of cyanobacteria, many of which release toxic metabolites such as microcystins (MCs). When irrigation water is contaminated by these compounds, adverse consequences may arise for plants as well as for animal and human health. In contrast, certain non-toxic cyanobacterial species like Limnospira platensis are increasingly regarded as valuable tools for sustainable agriculture, given their ability to enhance plant nutrition, growth, yield, and stress tolerance while also mitigating the detrimental impacts of MCs. The present work aimed to investigate the potential of L. platensis extract to enhance growth, physiological responses, and tolerance of radish (Raphanus sativus) plants stressed with Microcystis aeruginosa extract containing microcystins. Experiments were conducted in a hydroponic system under controlled environmental conditions, where radish seedlings were cultivated in perlite and exposed for 45 days to M. aeruginosa extract (10 and 40 µg/L of MCs) and L. platensis extract (0.1 and 1 g/L), applied either separately or in combination. The results showed that the application of L. platensis extract, especially at 1 g/L in combination with 40 µg/L of MCs, decreased the bioaccumulation of MCs from 8.81 to 5.35 µg/kg FW in the leaves and from 14.64 to 10.15 µg/kg FW in the taproots. In addition, it significantly stimulated radish growth and improved several biochemical parameters. In contrast, exposure to MCs at 10 and 40 µg/L negatively affected growth, chlorophyll pigments and protein contents while promoting the accumulation of malondialdehyde (MDA), polyphenols and sugars. The activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) were also increased under MCs stress, suggesting activation of the antioxidant defense system in response to oxidative damage. Combinations of MCs with L. platensis extract, especially at 1 g/L, improved antioxidant enzyme activities by significantly reducing MDA levels, biometric parameters, chlorophyll pigment, and protein and sugar contents. These results indicate that the application of L. platensis extract as a biostimulant can improve radish development, growth, and tolerance to MC-induced stress. Full article

The Mediterranean region is regarded as a hot spot on Earth because of its placement at the junction of many aerosols. Numerous studies have demonstrated that the North Atlantic Oscillation (NAO), which is closely related to the El Niño–Southern Oscillation (ENSO) phenomenon, influences the weather in the area. However, a recent study by the same author examined the ENSO effect on atmospheric processes in this area and discovered a slight but notable influence. This study builds on that earlier work, but it divides the Mediterranean region into four smaller regions during the same time span as the previous study, which is extended by two years, from 1980 to 2024. The division is based on geographical, climatological, and atmospheric process features. The findings demonstrate that volcanic eruptions significantly affect the total amount of aerosols. Additionally, the current study reveals that the Granger-causality test of the physical phenomena of solar activity, ENSO, and NAO indicates that all have a significant impact, either separately or in combination, on the atmospheric process over the four Mediterranean regions, and this effect can last up to six months. Moreover, a taxonomy of the different forms of aerosols across the four subregions is given. Full article

Large Language Models (LLMs) offer new opportunities to devise automated implementation generation methods that can tackle problem solving beyond traditional methods, which usually require algorithmic specifications and use only static domain knowledge. LLMs can support devising new methods to support activities in tackling open-ended problems, like problem framing, exploring possible solving approaches, feature elaboration and combination, advanced implementation assessment, and handling unexpected situations. This paper presents a detailed overview of the current work on LLMs, including model prompting, retrieval-augmented generation (RAG), and reinforcement learning. It then proposes a novel, LLM-based Cognitive Architecture (CA) to generate programming code starting from verbal discussions in natural language, a particular kind of problem-solving activity. The CA uses four strategies, three top-down and one bottom-up, to elaborate, adaptively process, memorize, and learn. Experiments are devised to study the CA performance, e.g., convergence rate, semantic fidelity, and code correctness. Full article

Effective management of multidrug-resistant cancers depends on effective, localized drug release and accumulation within the tumor microenvironment. In our work, Pluronic P105 and F127 mixed nanogels (PM) were fabricated through self-assembly to combat multidrug-resistant cancer. The approximate diameter of our prepared PM is 115.7 nm, an optimal size for tumor accumulation through the enhanced permeability and retention (EPR) effect. An in vitro drug release assay indicated that ultrasound could accelerate the drug release rate in doxorubicin-loaded Pluronic nanogels (PM/D). Additionally, the resistance reversion index (RRI) in the ultrasound-treated PM/D group was 4.55 and was two times higher than that in the free PM/D group, which represented better MDR reverse performance. Cell experiments demonstrated that, after 3 min of ultrasound, a greater amount of chemo-drug was released and absorbed by the MDR human breast cell line (MCF-7/ADR), resulting in significant cytotoxicity. Such enhanced therapeutic efficiency could be attributed to the combined effects of the two independent mechanisms: (i) ultrasound-controllable drug release realized effective release within resistant tumors with spatial and temporal precision and (ii) the contained Pluronic in the PM/D inhibited P-gp-mediated efflux activity to overcome MDR in tumors. Collectively, our findings support the feasibility of ultrasound-responsive PM as a drug-delivery platform for resistant cancers. Full article