Search Results (5,329)

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

This study investigates the impact of different duct designs on the energy-harvesting performance of oscillating-wing systems in both partially and fully confined environments. Numerical simulations were conducted to examine the effects of straight, convergent–straight, and convergent–divergent duct configurations on the aerodynamic forces and overall energy extraction efficiency. Under partial confinement, the convergent–divergent duct demonstrated a significant improvement of 67.5% in power output over the ductless baseline configuration. This enhancement is attributed to the increased incoming flow velocity and amplified pressure difference around the wing, which improve the effectiveness of energy generation. However, the straight and convergent–straight ducts reduced the harvester’s performance due to the diminished flow velocity within each duct. Under full confinement, all duct configurations substantially enhanced energy-harvesting performance, with the convergent–straight duct providing the highest efficiency gain (84.9%). This improvement is primarily due to the increased velocity and pressure differential across the wing surfaces, which maximise the heaving force and overall energy generation performance. These findings highlight the critical role of duct geometry in optimising energy-harvesting performance, both in partially confined and fully confined flow environments. Full article

As environmental conservation and community development gain importance, ecotourism has emerged as a significant segment of the global tourism industry. However, the cultural factors that drive tourist behavior in this domain remain underexplored. This research examined how power distance belief (PDB), interacts with the type of tourism content shared on social media (conspicuous versus experiential) to influence travelers’ ecotourism intentions. To test our hypotheses, we conducted two experimental studies using a 2 (PDB: high vs. low) × 2 (tourism content type: conspicuous vs. experiential) between-subjects design. Participants for both experiments (N = 480) were recruited through an online survey platform. In the experiments, participants’ PDB was situationally primed, and tourism content type was manipulated using specifically created fictitious posts adapted from a real social media platform. Other key variables were measured using validated multi-item scales. Data were analyzed using analysis of variance (ANOVA) and moderated mediation analysis (PROCESS Model 15). The findings reveal that travelers with high PDB show higher ecotourism intentions when exposed to conspicuous content, whereas travelers with low PDB exhibit higher intentions when exposed to experiential content. This interactive effect is mediated by travelers’ social comparison motives. These findings offer novel insights into the motivations underlying ecotourism behavior by identifying distinct pathways through which social media can promote sustainable tourism behaviors, and provide practical guidance for eco-destination managers to design targeted marketing strategies that encourage sustainable tourism practices across different consumer segments. Full article

Swiftly advancing low-code development platforms (LCDPs) have created a new branch in software development, allowing for the rapid creation of applications with minimal knowledge of coding. However, in spite of the great opportunities gained, problems related to choosing the most appropriate platform from a wide range of alternatives that differ in features, usage scenarios, and performance metrics make it difficult to determine the most suitable solution. The use of the MULTIMOORA method can greatly facilitate the selection process, along with a strong evaluation and weighting system, which has a positive impact on the results. The evaluation system provides ten global criteria with internal sub-criteria of different factors. The list of tested platforms includes the seven most popular ones: Kissflow, Salesforce App Cloud, Zoho Creator, OutSystems, MS Power App, Mendix, and Appian. The results show the genuine value of this method, by accentuating the strengths of the proven platforms and the method itself. This study offers a multifaceted and sustainable approach to platform validation that allows the use of LCDPs for various applications and helps to make rational decisions. Full article

This study addresses the critical need to enhance productivity in industrial automatic systems by optimizing the mass of moving components. The primary challenge is determining the complex, dynamic loads on structural elements, a prerequisite for effective redesign, without access to physical prototypes for experimental measurement. This paper presents a solution through a case study of a load-bearing pylon in a fine blanking plant, which is subject to inertial loads and shocks from pneumatic actuators and shock absorbers. To overcome this challenge, a high-fidelity multibody simulation model is developed to accurately estimate the dynamic loads on the pylon. This data is given as input to the topology optimization (TO) process, following the Design for Additive Manufacturing (DfAM) framework, to redesign the pylon for mass reduction using a Powder Bed Fusion-Laser Beam (PBF-LB). Two materials, EOS Aluminum Al2139 AM and EOS Maraging Steel MS1, are evaluated. The findings demonstrate that the integrated simulation and redesign approach is highly effective. The redesigned pylon’s performance is verified within the same simulation environment, confirming the productivity gains before manufacturing. A cost analysis revealed that the additively manufactured solution is more expensive than traditional methods, and the final choice depends on the overall productivity increase. This research validates a powerful methodology that integrates dynamic multibody analysis with topology optimization for AM. This approach is recommended in the design phase of complex industrial machinery to evaluate and quantify performance improvements and make informed decisions on the cost-effectiveness of introducing AM components without the need for physical prototyping. Full article

Ultra-wideband (UWB) technology is a crucial facilitator for high-data-rate wireless communication due to its extensive frequency spectrum and low power consumption. Simultaneously, multiple-input multiple-output (MIMO) systems have garnered considerable attention owing to their capability to enhance channel capacity and link dependability. This article discusses the development of small, high-performance MIMO UWB antennas with mutual suppression capabilities to fully use the benefits of both technologies. Additionally, the suggested antenna features a straightforward design and dual-band-notched characteristics. The antenna structure includes two radiating elements measuring 85 × 45 mm². These elements use a rectangular patch provided by a coplanar waveguide (CPW). Double U-shaped slots are incorporated into the rectangular patch to introduce dual-band-notched properties, which help mitigate interference from WiMAX and WLAN communication systems. The antenna is fabricated on a Mylar^® polyester film substrate of 0.3 mm in thickness, with a dielectric constant of 3.2. According to the measurement results, the suggested antenna functions efficiently across the frequency spectrum of 2.29 to 20 GHz, with excellent impedance matching throughout the bandwidth. Furthermore, it provides dual-band-notched coverage at 3.08–3.8 GHz for WiMAX and 4.98–5.89 GHz for WLAN. The antenna exhibits impressive performance, including favorable radiation attributes, consistent gain, and little mutual coupling (less than −20 dB). Additionally, the envelope correlation coefficient (ECC) is extremely low (ECC < 0.01) across the working bandwidth, which indicates excellent UWB MIMO performance. This paper offers an appropriate design methodology for future flexible and compact UWB MIMO systems that can serve as interference-resilient antennas for next-generation wireless applications. Full article

Accurate genomic prediction of complex phenotypes is crucial for accelerating genetic progress in swine breeding. However, conventional methods like Genomic Best Linear Unbiased Prediction (GBLUP) face limitations in capturing complex non-additive effects that contribute significantly to phenotypic variation, restricting the potential accuracy of phenotype prediction. To address this challenge, we introduce a novel framework based on a self-supervised, pre-trained encoder-only Transformer model. Its core novelty lies in tokenizing SNP sequences into non-overlapping 6-mers (sequences of 6 SNPs), enabling the model to directly learn local haplotype patterns instead of treating SNPs as independent markers. The model first undergoes self-supervised pre-training on the unlabeled version of the same SNP dataset used for subsequent fine-tuning, learning intrinsic genomic representations through a masked 6-mer prediction task. Subsequently, the pre-trained model is fine-tuned on labeled data to predict phenotypic values for specific economic traits. Experimental validation demonstrates that our proposed model consistently outperforms baseline methods, including GBLUP and a Transformer of the same architecture trained from scratch (without pre-training), in prediction accuracy across key economic traits. This outperformance suggests the model’s capacity to capture non-linear genetic signals missed by linear models. This research contributes not only a new, more accurate methodology for genomic phenotype prediction but also validates the potential of self-supervised learning to decipher complex genomic patterns for direct application in breeding programs. Ultimately, this approach offers a powerful new tool to enhance the rate of genetic gain in swine production by enabling more precise selection based on predicted phenotypes. Full article

Antibiotics have dramatically reduced the burden of infectious diseases since their discovery, but the accelerating rise in antimicrobial resistance (AMR) now threatens these gains. AMR was responsible for nearly 5 million deaths in 2023 and continues to undermine the efficacy of existing treatments, particularly in low- and middle-income countries. While efforts to address AMR have focused heavily on antibiotic stewardship and new drug development, vaccines represent a powerful yet underutilized tool for prevention. By reducing the incidence of bacterial infections, vaccines lower antibiotic consumption, interrupt transmission of resistant strains, and minimize the selective pressures that drive resistance. Unlike antibiotics, vaccines offer long-lasting protection, rarely induce resistance, and confer indirect protection through herd immunity. This review examines the global burden and drivers of AMR, highlights the unique advantages of vaccines over antibiotics in mitigating AMR, and surveys the current development pipeline of vaccines targeting key multidrug-resistant bacterial pathogens. Full article

Photobiomodulation (PBM) consists of applying low-level laser light to biological tissues, leading to modulation of cellular functions. PBM has recently gained much attention in the field of regenerative dentistry thanks to its powerful effect on tissue repair and regeneration. Dental pulp mesenchymal stem/stromal cells (DP-MSCs) represent the ideal targets in regenerative dentistry due to their ability to stimulate the regeneration of mineralized and soft tissues and the paracrine factors that they produce. Although there have been several studies evaluating the influence of PBM on DP-MSCs’ regenerative capacity, the results are conflicting, and there are few studies on the influence of PBM on the paracrine factors released by DP-MSCs. Therefore, the aim of this study was to investigate the effect of PBM, using different energy doses of laser irradiation, on the osteogenic capacity of DP-MSCs, focusing on changes in gene expression, mineralizing ability, and release of pro-osteogenic factors. DP-MSCs were irradiated in vitro and differentiated into an osteogenic phenotype. A cell viability assay, alizarin red staining, and TEM analysis were carried out to evaluate the effect of PBM on cell activity, morphology, and mineralization ability. The expression of the main osteogenesis-related markers Runx2, Col1A1, ALP, and BMP was measured to evaluate the influence of PBM on the ability of DP-MSCs to differentiate toward an osteogenic phenotype. The release of IL-6 and IL-8, which are mainly involved in bone remodeling processes, was investigated in the cell medium following PBM irradiation. The results showed a high level of cell viability, suggesting a lack of phototoxicity under the tested conditions. Furthermore, PBM had a significant effect on mineral deposition, IL-6 and IL-8 release, and expression of osteogenic markers. TEM analysis showed intracellular modifications linked mainly to mitochondria, the endoplasmic reticulum, and autophagic vesicles after PBM treatment. These findings demonstrated that the impact of PBM on the osteogenic potential of DP-MSCs is energy dose-dependent, supporting its potential as an effective strategy in regenerative dentistry, particularly for enhancing bone remodeling. Full article

Three-dimensional integrated circuit (3D IC) technology is an innovative approach in the semiconductor industry aimed at enhancing performance and reducing power consumption. However, thermal management issues arising from high-density stacking pose significant challenges. Carbon nanotubes (CNTs) have gained attention as a promising material for addressing the thermal management problems of through-silicon vias (TSVs) owing to their unique properties, such as high thermal conductivity, electrical conductivity, excellent mechanical strength, and low coefficient of thermal expansion (CTE). This paper reviews various applications and the latest research results on CNT-based TSVs. Furthermore, it proposes a novel TSV design using CNT–copper–tin composites to optimize the performance and assess the feasibility of CNT-based TSVs. Full article

Optical fiber-based biosensors have proven to be a powerful platform for chemical and biological analysis due to their compact size, fast response, high sensitivity, and immunity to electromagnetic interference. Among the various fiber designs, tapered optical fibers have gained prominence due to the increased evanescent fields that significantly improve light–analyte interactions, making them well-suited for advanced sensing applications. At the same time, advances in microfluidics have allowed for the precise control of small-volume fluids, supporting integration with optical fiber sensors to create compact and multifunctional optofluidic systems. This review explores recent developments in optical fiber optofluidic sensing, with a focus on two primary architectures: in-fiber and outside-fiber platforms. The advantages, limitations, and fabrication strategies for each are discussed, along with their compatibility with various sensing mechanisms. Special emphasis is placed on tapered optical fibers, focusing on design strategies, fabrication, and integration with microfluidics. While in-fiber systems offer compactness and extended interaction lengths, outside-fiber platforms offer greater mechanical stability, modularity, and ease of functionalization. The review highlights the growing interest in tapered fiber-based optofluidic biosensors and their potential to serve as the foundation for autonomous lab-on-a-fiber technologies. Future pathways for achieving self-contained, multiplexed, and reconfigurable sensing platforms are also discussed. Full article

Modulating enzymatic activity through physical strategies is increasingly recognized as a powerful approach to optimizing biocatalytic processes in food and biotechnology applications. Cellobiose 2-epimerase (C2E), a key enzyme for synthesizing epilactose, a non-digestible disaccharide with established prebiotic effects, is gaining relevance in functional foods. Emerging strategies, such as the application of moderate electric fields (MEFs), have attracted attention due to their non-thermal, non-invasive nature and their capacity to influence the structural and functional properties of proteins. This review assesses the potential of MEFs to modulate C2E activity and provides an overview of the physicochemical principles governing MEF–protein interactions and summarizes findings from various enzymatic systems, highlighting changes in activity, stability, and substrate affinity under electric field conditions. Particular attention is given to the mechanistic plausibility and processing implications of applying MEFs to C2E-catalyzed reactions. The integration of biochemical, structural, and engineering perspectives suggests that MEF-assisted modulation could overcome current bottlenecks in epilactose production. This approach may enable the sustainable valorization of lactose-rich byproducts and support the development of non-thermal, clean-label technologies for producing functional ingredients. Full article

Hybrid electric vehicles (HEVs) have gained significant attention for their superior energy efficiency and are becoming a predominant mode of urban transportation. The DC/DC converter plays a critical role in HEV energy management systems, especially in matching the voltage levels between the battery and DC bus. This paper proposes a novel high-gain DC/DC converter with a wide input voltage range based on coupled inductors. The innovation lies in the integration of a resonant cavity and the simultaneous realization of zero-voltage switching (ZVS) and zero-current switching (ZCS), effectively reducing both voltage/current stresses on the power switches and switching losses. Compared with conventional topologies, the proposed design achieves higher voltage gain without extreme duty cycles, improved conversion efficiency, and enhanced reliability. Detailed operating principles are analyzed, and design conditions for voltage stress reduction, gain extension, and soft switching are derived. The simulation model has been conducted in a PSIM environment, and a 300 W experimental prototype, implemented using a dsPIC33FJ64GS606 digital controller, has been established and demonstrates 93% peak efficiency at a 10 times voltage gain. The performance and practical feasibility of the proposed topology have been evaluated by both simulation and experiments. Full article

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood and adolescence, characterized by symptoms of inattention, hyperactivity, and impulsivity. These symptoms often interfere with academic, social, and family functioning. In recent years, the use of digital tools and video games has garnered attention as an innovative and engaging approach for neurocognitive rehabilitation. The primary objective of this randomized controlled study was to investigate the comparative effects of two cognitive intervention approaches—one based on new technologies and one using traditional methods—on attention, inhibitory control, and processing speed in children and adolescents diagnosed with ADHD. Thirty-three participants aged 6–17 years were randomly assigned to either an experimental group (n = 17), which received Nintendo Switch-based therapy, or a control group (n = 16), which received traditional board game therapy. Both interventions lasted 8 weeks and included 16 sessions. Outcomes were assessed using the WISC-V, STROOP, and CARAS-R tests. Results showed significant within-group improvements in both groups. The control group exhibited gains in sustained attention and inhibitory control (CARAS-R and STROOP tests, p < 0.05), while the experimental group improved significantly in processing speed, as measured by the WISC-V (p = 0.001). However, no significant differences were found between groups. These findings suggest that both interventions may be effective for enhancing different cognitive processes in children with ADHD. Importantly, the use of familiar digital technologies like the Nintendo Switch may promote greater motivation and adherence to treatment. Further research with larger samples and long-term follow-up is warranted to validate and extend these preliminary findings, as the current sample size was not powered to detect medium or small effects. Full article

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers. Full article