Search Results (10,917)

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education. Full article

Residential buildings in Mediterranean regions remain major contributors to energy consumption and greenhouse gas emissions. Existing studies often assess renewable energy technologies or innovative building solutions in isolation, with limited attention to their combined performance across different residential typologies. This study evaluates the integrated impact of solar renewable energy systems and smart building technologies on the energy performance of residential buildings in Cyprus. A typology-based methodology is applied to three representative residential building types—detached, semi-detached, and apartment buildings—using dynamic energy simulation and scenario analysis. Results show that solar photovoltaic systems achieve higher standalone reductions than solar thermal systems, while smart building technologies significantly enhance operational efficiency and photovoltaic self-consumption. Integrated solar–smart scenarios achieve up to 58% reductions in primary energy demand and 55% reductions in CO₂ emissions, and 25–30 percentage-point increases in PV self-consumption, enabling detached and semi-detached houses to approach national nearly zero-energy building (nZEB) performance thresholds. The study provides climate-specific, quantitative evidence supporting integrated solar–smart strategies for Mediterranean residential buildings and offers actionable insights for policy-making, design, and sustainable residential development. Full article

Soybean, Glycine max (L.) Merrill, is usually grown on a large scale, with pest control based on chemical insecticides. However, the overuse of chemicals has led to several adverse effects requiring more sustainable approaches to pest control. Results from Integrated Pest Management (IPM) employed on Brazilian soybean farms indicate that adopters of the technology have reduced insecticide use by approximately 50% relative to non-adopters, with yields comparable to or slightly higher than those of non-adopters. This reduction can be explained not only by the widespread use of Bt soybean cultivars across the country but also by the adoption of economic thresholds (ETs) in a whole Soybean-IPM package, which has reduced insecticide use. However, low refuge compliance has led to the first cases of pest resistance to Cry1Ac, thereby leading to the return of overreliance on chemical control and posing additional challenges for IPM practitioners. The recent global agenda for decarbonized agriculture might help to support the adoption of IPM since less chemical insecticides sprayed over the crops reduces CO₂-equivalent emissions from its application. In addition, consumers’ demand for less pesticide use in food production has favored the increased use of bio-inputs in agriculture, helping mitigate overdependence of agriculture on chemical inputs to preserve yields. Despite the challenges of adopting IPM discussed in this review, the best way to protect soybean yield and preserve the environment remains as IPM, integrating plant resistance (including Bt cultivars), ETs, scouting procedures, selective insecticides, biological control, and other sustainable tools, which help sustain environmental quality in an ecological and economical manner. Soon, those tools will include RNAi, CRISPR-based control strategies, among other sustainable alternatives intensively researched around the world. Full article

The advent of generative AI (GenAI) and its growing use in education has sparked a renewed wave of school digital transformation. School principals are pivotal in advancing and shaping school digital transformation, yet little is known about how they understand and lead digital transformation in the age of GenAI, particularly within China’s complex educational system. This study employed Q methodology to identify the perceptions and leadership styles of Chinese K–12 school principals toward school digital transformation in the age of GenAI. An analysis of a 30-item Q set with a P sample of 23 principals revealed four leadership types: Cautious Observation–Technological Gatekeeping Leadership, Moderate Ambition–Culturally Transformative Leadership, Moderate Ambition–Emotionally Empowering Leadership, and High Aspiration–Strategy-Driven Leadership. Overall, principals’ stances on GenAI formed a continuum, ranging from cautious observation and skeptical optimism to active embrace. These perceptions and leadership styles were shaped by Confucian cultural values, a flexible central–local governance arrangement, and parents’ high expectations for students’ academic achievement. Furthermore, structural constraints in resource provision further heightened principals’ reliance on maintaining guanxi-based relationships. This study enhances the understanding of the diversity of principals’ leadership practices worldwide and offers actionable insights for governments and principals to more effectively advance AI-enabled school digital transformation. Full article

Vascularised composite allotransplantation (VCA) has an evolving role in the reconstruction of complex functional and aesthetic deficits non-amenable to autologous or implant-based reconstructive modalities. International applications of VCA span upper extremity, face, abdominal wall, uterus, and penile transplantation, with more than 300 procedures performed worldwide. Among these, abdominal wall transplantation has uniquely contributed to the development of the sentinel skin flap (SSF) concept, in which solid organ transplant patients undergo simultaneous transplantation of a solid organ and a donor-derived vascularised skin flap, with the skin component of the SSF being trialled internationally as a means of monitoring for rejection within the solid organ allograft. Despite growing clinical success, VCA continues to face substantial barriers to wider adoption. Acute rejection remains highly prevalent, affecting up to 89% of recipients, with significant morbidity linked to intensive systemic immunosuppression. Challenges are further amplified by the unique immunological heterogeneity of composite grafts, ethical concerns surrounding identity-linked tissues, and the lack of standardised outcomes reporting across VCA subtypes. Advances in machine perfusion technologies and emerging cellular and biomaterial-based immunomodulation strategies show promise in reducing immunosuppression burden and improving graft longevity. This review outlines the current state of VCA, including clinical applications, outcomes, and mechanistic insights from pre-clinical studies, while highlighting key ethical considerations and evolving regulatory frameworks. Future progress will depend on standardised reporting systems, improved donor–recipient matching, better understanding of ischemia–reperfusion injury, and the development of next-generation immunosuppressive/immuno-modulatory therapies. Collectively, these innovations position VCA as a rapidly advancing field with significant potential to redefine reconstructive and transplant surgery. Full article

The past decade has witnessed an unprecedented transformation in breast cancer management, driven by parallel advances in targeted therapies, immunomodulation, drug-delivery technologies, and molecular diagnostic tools. This review summarizes the key achievements of 2015–2025, encompassing all major biological subtypes of breast cancer as well as technological innovations with substantial clinical relevance. In hormone receptor-positive (HR+)/HER2− disease, the integration of CDK4/6 inhibitors, modulators of the PI3K/AKT/mTOR pathway, oral Selective Estrogen Receptor Degraders (SERDs), and real-time monitoring of Estrogen Receptor 1 (ESR1) mutations has enabled clinicians to overcome endocrine resistance and dynamically tailor treatment based on evolving molecular alterations detected in circulating biomarkers. In HER2-positive breast cancer, treatment paradigms have been revolutionized by next-generation antibody–drug conjugates, advanced antibody formats, and technologies facilitating drug penetration across the blood–brain barrier, collectively improving systemic and central nervous system disease control. The most rapid progress has occurred in triple-negative breast cancer (TNBC), where synergistic strategies combining selective cytotoxicity via Antibody-Drug Conjugates (ADCs), DNA damage response inhibitors, immunotherapy, epigenetic modulation, and therapies targeting immunometabolic pathways have markedly expanded therapeutic opportunities for this historically challenging subtype. In parallel, photodynamic therapy has emerged as an investigational and predominantly local phototheranostic approach, incorporating nanocarriers, next-generation photosensitizers, and photoimmunotherapy capable of inducing immunogenic cell death and modulating antitumor immune responses. A defining feature of the past decade has been the surge in patent-driven innovation, encompassing multispecific antibodies, optimized ADC architectures, novel linker–payload designs, and advanced nanotechnological and photoactive delivery systems. By integrating data from clinical trials, molecular analyses, and patent landscapes, this review illustrates how multimechanistic, biomarker-guided therapies supported by advanced drug-delivery technologies are redefining contemporary precision oncology in breast cancer. The emerging therapeutic paradigm underscores the convergence of targeted therapy, immunomodulation, synthetic lethality, and localized immune-activating approaches, charting a path toward further personalization of treatment in the years ahead. Full article

Automatic sleep staging technology based on wearable photoplethysmography can provide a non-invasive and continuous solution for large-scale sleep health monitoring. This study accordingly developed a novel cross-scale dynamically coupled extended long short-term memory network (SleepXLSTM) to realize automatic sleep staging based on heart rate signals collected by wearable devices. SleepXLSTM models the relationship between heart rate fluctuations and sleep stage labels by correlating physiological features with clinical semantics using a knowledge graph neural network. Furthermore, an excitation–inhibition dual-effect regulator is applied in an improved multiplicative long short-term memory network along with memory mixing in a scalar long short-term memory network to extract and strengthen the key heart rate timing features while filtering out noise produced by motion artifacts, thereby facilitating subsequent high-precision sleep staging. The benefits and functions of this comprehensive heart rate feature extraction were demonstrated using sleep staging prediction and ablation experiments. The proposed model exhibited a superior accuracy of 91.25% and Cohen’s kappa coefficient of 0.876 compared to an extant state-of-the-art neural network sleep staging model with an accuracy of 69.80% and kappa coefficient of 0.040. On the ISRUC-Sleep dataset, the model achieved an accuracy of 87.51% and F1 score of 0.8760. The dynamic coupling strategy employed by SleepXLSTM for automatic sleep staging using the heterogeneous temporal representation of heart rate data can promote the development of smart wearable devices to provide early warning of sleep disorders and realize cost-effective technical support for sleep health management. Full article

The continuous detection of emerging pollutants (EPs) in water poses potential threats to aquatic environmental safety and human health, and their efficient removal is a frontier in environmental engineering research. This review systematically summarizes research progress from 2005 to 2025 on the application of activated carbon (AC) and its composites for removing EPs from water and analyzes the development trends in this field using bibliometric methods. The results indicate that research has evolved from the traditional use of AC for adsorption to the design of novel materials through physical and chemical modifications, as well as composites with metal oxides, carbon-based nanomaterials, and other functional components, achieving high adsorption capacity, selective recognition, and catalytic degradation capabilities. Although AC-based materials demonstrate considerable potential, their large-scale application still faces challenges such as cost control, adaptability to complex water matrices, material regeneration, and potential environmental risks. Future research should focus on precise material design, process integration, and comprehensive life-cycle sustainability assessment to advance this technology toward highly efficient, economical, and safe solutions, thereby providing practical strategies for safeguarding water resources. Full article

Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes. Full article

Pathological scars are fibrotic lesions that result from aberrant wound healing following tissue injury, such as burns. They are frequently associated with disfigurement and dysfunction, thereby severely impairing the quality of life of affected patients. Current clinical treatments, including surgery, laser therapy, and corticosteroid injections, are often characterized by limited efficacy, high recurrence rates, and undesirable side effects, including skin atrophy. Furthermore, the dense structure and excessive extracellular matrix (ECM) deposition in scar tissue present a significant barrier to effective drug penetration, thereby further limiting therapeutic efficacy. In recent years, biomaterial-based drug delivery systems, which integrate sustained drug release with minimally invasive transdermal technologies, have emerged as a promising strategy to overcome the limitations of traditional therapies. This review systematically outlines the pathogenesis and molecular mechanisms of pathological scars, summarizes established and emerging treatments, and highlights the application strategies and future prospects of novel biomaterial-based drug delivery systems for managing this condition. Full article

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study ($N=30$), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance. Full article

Elastomeric ablative coatings are essential for protecting solid rocket motor (SRM) combustion chambers from extreme thermal and erosive environments, and their performance is governed by both material composition and processing strategy. This review examines the main elastomer systems used for SRM insulation, including ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), hydroxyl-terminated polybutadiene (HTPB), polyurethane (PU), silicone-based compounds, and related hybrids, and discusses how their rheological behavior, cure kinetics, thermal stability, and ablation mechanisms affect manufacturability and in-service performance. A comprehensive assessment of coating technologies is presented, covering casting, molding, centrifugal forming, spraying, automated deposition, and emerging additive-manufacturing approaches for complex geometries. Emphasis is placed on processing parameters that control adhesion to metallic substrates, layer uniformity, defect formation, and thermomechanical integrity under high-heat-flux exposure. The review integrates current knowledge on how material choice, surface preparation, and application sequence collectively determine insulation efficiency under operational SRM conditions. Practical aspects such as scalability, compatibility with complex chamber architectures, and integration with quality-control tools are highlighted. By comparing the capabilities and limitations of different materials and technologies, the study identifies key development trends and outlines remaining challenges for improving the durability, structural robustness, and ablation resistance of next-generation elastomeric coatings for SRMs. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

Background: Most older adults prefer to age in place. Technology-assisted monitoring can enhance safety while maintaining independence. However, there is limited understanding of older adult end users’ preferences and experiences. Methods: In this interpretive phenomenological study, we interviewed eight older adults, with and without dementia, to understand their lived experiences with monitoring technology and its impact on self-identity, independence, and aging-in-place. Results: We found that older adults use pragmatic strategies to process the meaning of life as “monitored” individuals, reflected in four themes: (1) freedom to age in place, (2) the need for active and integrated intervention, (3) individualized approaches to technology based on temperament, usefulness, and worldview, and (4) a sense of changing situations while remaining unchanged. Adaptive techniques for older adults with dementia successfully elicited complex thoughts and desires when participants were given sufficient time and space. Conclusions: As technology-assisted monitoring becomes more common, it is imperative to understand the perspectives of older adult end users. Focusing on lived experiences offers valuable insights to ensure technology-assisted monitoring interventions are effective and accepted as older adults navigate changes in their capabilities and endeavor to age in place. Full article