Search Results (9,959)

Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition and how both polymer chemistry and salt identity, rather than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric assays, and immunoassays), and we contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA-approved IsoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates between high-grade prostate cancer and benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive values within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA. Full article

Meat quality and safety are critical aspects of global food security. However, traditional evaluation techniques, including sensory analysis and chemical and instrumental tests, are constrained by subjectivity, high time consumption, their destructive character, and susceptibility to bias. With rapid advances in sensor technologies and computational methods, there is a growing demand for the nondestructive, accurate, and fast measurement of meat quality and safety attributes. In recent years, artificial intelligence (AI) integrated with nondestructive sensing has emerged as a transformative paradigm, offering unparalleled capabilities for extracting quality information from complex datasets generated by various nondestructive sensing technologies. This review provides a comprehensive analysis of AI-driven nondestructive technologies for meat quality and safety assessment, focusing on the integration of machine learning and deep learning with various sensing techniques. Additionally, the review evaluates state-of-the-art algorithms and their performances and identifies deployment barriers, particularly calibration transfer, environmental sensitivity, reproducibility issues, sensor fouling, and generalization challenges across batches and processing plants. Furthermore, economic and regulatory constraints, including high sensor costs, small and medium enterprise (SME) adoption challenges, and alignment with HACCP/ISO frameworks that further limit commercial scalability, are discussed. Unlike previous reviews that primarily focus on individual sensing techniques, this review emphasizes the practical challenges associated with industrial implementation and the development of scalable solutions for real-world deployment. Finally, strategic research priorities and recommendations are highlighted to accelerate the industrial adoption of intelligent meat quality monitoring systems across the global meat industry. Full article

With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter performance limitations when extracting high-dimensional features and generally cannot dynamically focus on critical information within long-term sequences. To address these challenges, this study proposes a multi-head attention fusion model (MAFM) designed to enhance the predictive accuracy and modelling capability for high-dimensional and nonlinear data across diverse application scenarios. Experiments were conducted on two heterogeneous datasets from the environmental and financial domains. After the key hyperparameters of the MAFM were optimized through an orthogonal experimental design, the model achieved coefficients of determination exceeding 0.90 on both datasets. Furthermore, the results of four comparative experiments demonstrate that the MAFM consistently outperforms traditional machine learning models, including support vector regression and extreme gradient boosting, as well as state-of-the-art deep learning models such as long short-term memory, temporal convolutional networks, and transformers. Compared with the best-performing baseline model on each sub-dataset, the MAFM reduced the mean squared error by 44.4%, 8.3%, 29.4%, and 65.5%, respectively, highlighting its superior predictive performance and strong generalization capability. In summary, the proposed MAFM provides an efficient, robust, and interpretable solution for time series forecasting tasks across multiple domains. Its outstanding performance demonstrates significant potential for practical applications in environmental monitoring, financial forecasting, and other real-world scenarios. Full article

As online luxury fashion resale platforms continue to emerge and expand, persistent challenges related to trust and product authenticity have become increasingly pronounced. digital product passports (DPPs) offer a potential solution to these trust-related challenges by providing detailed information about a product, thereby enhancing product transparency and traceability throughout the product lifecycle. Drawing on signaling theory, this study investigates how DPP-enabled platforms influence consumers’ purchase intentions toward luxury fashion resale consumption. An online survey generated 307 valid responses. Structural equation modeling (SEM) was employed to test the proposed hypotheses. A multi-group chi-square difference test was also conducted to test the moderating role of DPP usage experience. The results demonstrate that trust and attitude are decisive predictors of consumers’ intention to purchase second-hand luxury fashion products from DPP-enabled platforms. Risk reduction alone does not directly drive intention, highlighting the distinction between eliminating uncertainty and fostering positive motivation. This study focuses on consumer perception and intention regarding DPPs within the luxury fashion resale market, an area that has received limited empirical attention. By integrating signaling theory with fashion resale and digital innovation, this study offers novel insights into the role of technological transparency in driving consumer engagement in circular fashion. Full article

Bacterial chondronecrosis with osteomyelitis (BCO) is a major cause of lameness in modern broiler chickens. It remains a persistent challenge in the broiler industry, resulting in substantial economic losses and serious animal health concerns. The pathogenesis of BCO involves rapid muscle growth, resulting in disproportionate body weight relative to skeletal maturity, thereby increasing mechanical stress and leading to microfractures and osteochondrotic clefts in the proximal growth plates of the femora and tibiae. Infection progresses via hematogenous dissemination and colonization of the leg bones by opportunistic pathogens, primarily Staphylococcus spp., Enterococcus spp., and Escherichia coli, originating from the gastrointestinal or respiratory tract, leading to chronic inflammation, ischemia, biofilm formation, and progressive bone necrosis. Control strategies, including selective breeding, enhanced management and production practices, nutritional interventions, and the administration of antimicrobial agents, offer partial mitigation but have failed to provide a permanent solution to BCO incidence. Vaccination is one of the most promising and targeted immunological approaches to enhance the host immune responses against bacterial pathogens. Electron beam (eBeam) inactivated vaccines represent a significant advancement among available vaccination strategies. This review leverages current knowledge of the etiology, pathophysiology, control measures, and the use of eBeam technology (EBT) in vaccine development to provide a scientifically grounded and innovative approach to controlling BCO-associated lameness in broiler chickens. This article also provides insights into future directions for integrated, antibiotic-free strategies to mitigate BCO and revenue losses, enhance broiler welfare, ensure consumer safety, and support the long-term sustainability of the global poultry industry. Full article

Population ageing and the need to provide affordable, healthy, and energy-efficient housing represent important sustainability challenges in many European countries. Sustainable senior housing should not only reduce environmental impacts through low-carbon construction technologies but also ensure high indoor environmental quality and occupant well-being. This paper presents a preliminary assessment of predicted thermal comfort and local thermal discomfort in a prototype senior home constructed a prefabricated timber-based building system incorporating renewable and recycled materials and designed to support low operational energy demand. The research forms part of a broader development study of technology, in which indoor thermal conditions were monitored in a prototype building consisting of two 30 m² residential units intended for older adults. Predicted thermal comfort was evaluated using the PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied) indices together with local thermal discomfort criteria. The analysis was based on short-term winter and summer measurement campaigns conducted in the prototype building. The results indicated category B thermal environment conditions in both winter and summer according to ISO 7730. In winter, local discomfort associated with a cool floor corresponded to category C, while summer conditions met category B requirements without significant local discomfort. The findings provide preliminary evidence that timber-based low-carbon construction technologies can support acceptable indoor thermal conditions while addressing environmental and social sustainability objectives related to an ageing population. The study also identifies the need for longer monitoring campaigns and future investigations involving older occupants to validate actual thermal sensation and further optimize sustainable senior housing solutions. Since the building was unoccupied during the measurements, the results should be interpreted as a prediction of predicted thermal comfort conditions rather than an assessment of thermal sensations experienced by older adults. Full article

Immersive technologies offer new possibilities for chemistry higher education. The field benefits from secondary education study visits that enable decision making for future careers. However, access to authentic laboratory visits is often limited by geographical constraints. Synchronous 360° videos provide an immersive virtual alternative, but the educational value of this technology remains unclear. Using a design-based research approach, this study explored the opportunities and challenges of immersive 360° video in virtual chemistry study visits, developed a synchronous 360° learning environment through co-design, and generated a pedagogical model for its implementation. The resulting design solution was examined through a qualitative case study. The data (n = 7) were gathered via semi-structured interviews and observations and analyzed through theory-driven content analysis. This study suggests that 360° videos may enhance accessibility, support learning, and improve the visual representation and perceived relevance of chemistry. Participants perceived immersive visits as engaging and reported an increased sense of presence and improved focus. Challenges include technical constraints, limited authenticity of interaction, resource demands, and certain limitations of virtuality for experimental work. These findings extend previous research on immersive environments by suggesting that synchronous 360° video may promote focus and presence. Also, the study generated a theory-informed framework and practical guidelines for designing immersive chemistry learning environments. The framework offers a starting point for future research and supports the integration of modern technology into higher education. Full article

Landslides occur frequently under complex and variable global geomorphological and meteorological conditions, posing serious threats to the ecological environment, human life and property. Traditional monitoring approaches are often inefficient and highly susceptible to external conditions, making them inadequate for large-scale rapid deformation monitoring. Owing to its advantages of high precision, wide-area coverage, and all-weather continuous observation, Interferometric Synthetic Aperture Radar (InSAR) technology has been widely applied in landslide monitoring. This paper systematically reviews the development and application of InSAR technologies in landslide monitoring. Classical methods, including Differential InSAR (D-InSAR), Permanent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), and Distributed Scatterer InSAR (DS-InSAR), as well as derivative techniques such as Quasi-Permanent Scatterer InSAR (QPS-InSAR), Temporarily Coherent Point InSAR (TCP-InSAR), and Multiple Aperture InSAR (MAI), are comprehensively summarized. In addition, recent advances in artificial intelligence (AI) and multi-source data fusion are highlighted. Through comparative analysis of related studies, this paper summarizes the applicability and potential of various methods in landslide monitoring, and reviews the main solutions to challenges, including geometric distortion, decorrelation noise, atmospheric delay, and difficulties in three-dimensional deformation monitoring. Future research directions are also discussed. Overall, InSAR technology has evolved from single-method approaches toward integrated and intelligent frameworks; however, challenges remain in terms of adaptability in complex terrain, data-processing efficiency, and model interpretability. This review provides a technical reference for future landslide-monitoring research. Full article

Hydrogen (H₂) has emerged as a promising sustainable energy vector due to its scalability, high energy density, and its ability to enable sector coupling across electricity, heating, transportation, and industry. There remains a huge technical challenge to overcome. The economic implications of low round-trip efficiency, high capital costs, the limited lifespan of fuel cells and electrolyzers, and infrastructure constraints on H₂’s relative competitiveness have not been comprehensively studied. In a comparative assessment against other storage options such as batteries, pumped hydro, and compressed air energy storage (CAES), we highlight the potential of H₂ as a grid-scale storage solution. The novelty of this paper is that it compares H₂ as a competing option with other storage technologies and highlights its unique suitability for seasonal and grid-scale applications where others fall short. The paper also discusses the technological opportunities for AI and other digital technologies in the H₂ grid. Unlike general reviews, this work emphasizes the engineering performance of H₂’s production cost and economic viability, electrolyzer technology maturity, infrastructure readiness, safety and lifecycle considerations, and provides critical synthesis and implications. With this, the paper extends beyond the theoretical capacity for H₂ and gives an engineering-focused analysis that informs the drive toward sustainable and resilient power grids. Full article

In this article, we present a constructive method for designing an information security system (ISS). The method is based on the IEEE 1471 and IEEE 42010 standards. They provide an architectural framework for describing the system through conceptual modeling from different perspectives. The perspectives reflect the requirements of stakeholders—regulatory, normative, technological or budgetary. As result of analysis of the problem area, conceptual models are constructed. The resulting models are combined into a generalized multilayer model. The transformation of the conceptual model into a technology-independent object-oriented (OO) design model follows. The next stage is selection of an appropriate technological platform and subsequent transformation of the design model into an implementation model. An essential part of the method is the creation of an agent-based simulation model. It allows the simulation of the ISS in different environments, changing the input conditions. The method ensures technological independence of the ISS, due to the fact that the resulting conceptual model reflects the requirements of the system and the methods for implementing the tasks of the ISS without using a specific technological solution. The method also ensures universal communication between the individual stakeholders and unification of the used terminology. Full article

The growing demand for environmentally sustainable materials has accelerated the development of bio-based coatings as alternatives to conventional petroleum-derived surface treatments. Among renewable biopolymers, lignin has emerged as a particularly attractive candidate owing to its abundance, renewable origin, aromatic structure, antioxidant activity, ultraviolet shielding capability, and diverse functional groups suitable for chemical modification. As a major by-product of the pulp, paper, and biorefinery industries, lignin represents an underutilized renewable resource with significant potential for value-added coating applications. This review provides an overview of recent advances in lignin-based coatings for sustainable and biodegradable materials. The chemical structure, physicochemical properties, industrial sources, extraction technologies, purification methods, and functionalization strategies of lignin are discussed. Particular attention is given to nanostructured lignin systems, including lignin nanoparticles (LNPs) and chemically modified derivatives, which have demonstrated improved compatibility and performance in coating formulations. Fabrication technologies such as solution casting, dip coating, spray coating, layer-by-layer (LbL) assembly, extrusion processing, and nanocomposite approaches are examined. Mechanical, barrier, thermal, UV-shielding, antioxidant, antimicrobial, hydrophobic, and environmental performance are comparatively assessed. Lignin nanoparticles and chemically modified lignins generally show improved functionality, while waterborne coatings for paper and fiber-based packaging appear closest to practical application. However, lignin heterogeneity, durability, scalability, and limited regulatory evaluation and end-of-life assessment remain major barriers to commercialization. Full article

As unicellular systems, protoplasts derived from medicinal plant cells exhibit high totipotency and hold significant value in applications such as gene function analysis, genetic improvement, and cell engineering optimization. This review focuses on how protoplast technology addresses longstanding bottlenecks in medicinal plant research by systematically collating recent advances in the study of medicinal plant protoplasts. It explores the multifaceted factors influencing protoplast preparation, including the intrinsic and extrinsic properties of medicinal plant materials, pretreatment methods prior to enzymatic hydrolysis, the composition of enzymatic solutions, enzymatic hydrolysis parameters, external environmental conditions, and protoplast purification techniques. Additionally, the review summarizes the significance and value of medicinal plant protoplasts in gene function verification, gene editing, genetic transformation, single-cell sequencing, and cell fusion regulation. By comprehensively synthesizing the optimization of the preparation of medicinal plant protoplasts and their application in transient expression, gene function research, and plant regeneration, this work aims to provide critical guidance for subsequent research in genetic modification, germplasm resource breeding, spatiotemporal programming of active substances, and regulatory network analysis. Ultimately, it serves as a valuable reference for advancing research in the plant sciences. Full article

Busbars are key components in electric vehicle (EV) battery packs, providing electrical connections between individual cells to form modules and between modules to form the full battery pack while operating under demanding environmental conditions. In this context, busbar-to-busbar and busbar-to-cell terminal interconnections are critical to overall system performance, making their design and reliability of paramount importance. Any failure occurring in these interconnections, including thermal fatigue, vibration-induced cracking, corrosion, and interfacial degradation, can compromise joint integrity and result in a progressive increase in electrical resistance. In this review paper, to support the selection and development of suitable joining solutions for busbar interconnections, a detailed analysis of joining technologies is presented, including mechanical fastening, welding, and joining by forming techniques. Their performance is compared in terms of electrical resistance, mechanical strength, fatigue behavior, and suitability for busbar interconnections. This work outlines current limitations in the understanding and characterization of mechanical, electrical, and fatigue behavior and identifies key research gaps, particularly the lack of fatigue data under coupled electro-thermo-mechanical loading that is representative of real EV operation. The review thus provides a comprehensive foundation for the design and optimization of reliable interconnections, supporting improved durability, safety, and sustainability in next-generation EV battery systems. Full article

The intelligent transportation systems (ITS), including autonomous driving technologies, have increased the need for a capable communication system. The optical domain offers a promising spectrum for supporting multi-connection and high-data-rate applications. This paper proposes a dynamic resource allocation algorithm for vehicle-to-vehicle (V2V) 6G visible light communication (VLC) systems. A wavelength division multiple access (WDMA) method is utilized as a technique for supporting multiple connections. Two optimization objectives of the resource allocation are evaluated, which are referred to as Max SNR and Max spectral efficiency (SE) objectives. The best resource assignment for each vehicle is obtained by using the optimized resource allocation model. Five scenarios are examined where vehicles are moved in this work. The Max SE objective shows a fair allocation of resources based on the SNR compared to the Max SNR objective. In addition, a dynamic algorithm is developed for real-time solutions. The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min. Thus, the proposed dynamic scheme can be used in real-time applications. Full article

This article describes the process of designing and developing an Android application to support students in a university environment by automating study schedule management, facilitating access to study materials, providing navigation to educational buildings, and sending notifications about upcoming classes. The main problem that current development addresses is the lack of a centralized system for timely notifications and difficulties in navigating university campuses and obtaining summaries of study material files. Using two multilingual machine learning (ML) models, the solution integrates an automated notification system using Firebase Cloud Messaging (FCM), which operates in real time and provides geospatial navigation to educational buildings. Two ML models for natural language processing are used to automatically generate short and meaningful text summaries, and they accept long articles or documents in different languages and create abstract summaries, which makes them suitable for easy absorption of academic/educational materials. The technology stack includes the Django REST Framework 3.10 for the server part, PostgreSQL 18 for database management, and Java SE 21 for the mobile application, with security guaranteed through JWT (JSON Web Token) authentication and TLS encryption 1.2. The result is a comprehensive application that provides students with personalized access to weekly schedules, information about classes and assigned classroom numbers, and access to learning materials that are trained with a model optimized to create short, informative summaries. This contributes to better organization, reducing absences and increasing the efficiency of the educational process. Full article