Search Results (42,939)

Higher education institutions face a critical methodological challenge in pursuing net-zero commitments: Within the amount ofhe emissions related to Scope 3, including indirect emissions from water consumption, waste disposal, business travel, and mobility, employees commuting represents 50–92% of campus carbon footprints, yet reliable quantification remains elusive due to fragmented data collection and governance silos. The present research investigates how purposeful integration of the Home-to-Work Commuting Plan (HtWCP)—mandatory under Italian Decree 179/2021—into the Climate Neutrality Plan (CNP) could constitute an innovative strategy to enhance emissions accounting rigor while strengthening institutional governance. Stemming from the University of Genoa case study, we show how leveraging mandatory HtWCP survey infrastructure to collect granular mobility behavioral data (transportation mode, commuting distance, and travel frequency) directly addresses the GHG Protocol-specified distance-based methodology for Scope 3 accounting. In turn, the CNP could support the HtWCP in framing mobility actions into a wider long-term perspective, as well as suggesting a compensation mechanism and paradigm for mobility actions that are currently not included. We therefore establish a replicable model that simultaneously advances three institutional dimensions, through the operationalization of the Avoid–Shift–Improve framework within an integrated workflow: (1) methodological rigor—replacing proxy methodologies with actual behavioral data to eliminate the notorious Scope 3 data gap; (2) governance coherence—aligning voluntary and regulatory instruments to reduce fragmentation and enhance cross-functional collaboration; and (3) adaptive management—embedding biennial feedback cycles that enable continuous validation and iterative refinement of emissions reduction strategies. This framework positions universities as institutional innovators capable of modeling integrated governance approaches with potential transferability to municipal, corporate, and public administration contexts. The findings contribute novel evidence to scholarly literature on institutional sustainability, policy integration, and climate governance, whilst establishing methodological standards relevant to international harmonization efforts in carbon accounting. Full article

Weed management has become a critical agricultural practice, as weeds compete with crops for nutrients, host pests and diseases, and cause major economic losses. The invasive weed Ambrosia artemisiifolia (common ragweed) is particularly problematic in Hungary, endangering crop productivity and public health through its fast proliferation and allergenic pollen. This review examines the current challenges and impacts of A. artemisiifolia while exploring sustainable approaches to its management through precision agriculture. Recent advancements in unmanned aerial vehicles (UAVs) equipped with advanced imaging systems, remote sensing, and artificial intelligence, particularly deep learning models such as convolutional neural networks (CNNs) and Support Vector Machines (SVMs), enable accurate detection, mapping, and classification of weed infestations. These technologies facilitate site-specific weed management (SSWM) by optimizing herbicide application, reducing chemical inputs, and minimizing environmental impacts. The results of recent studies demonstrate the high potential of UAV-based monitoring for real-time, data-driven weed management. The review concludes that integrating UAV and AI technologies into weed management offers a sustainable, cost-effective, mitigate the socioeconomic impacts and environmentally responsible solution, emphasizing the need for collaboration between agricultural researchers and technology developers to enhance precision agriculture practices in Hungary. Full article

The rapid advancement of fifth-generation (5G) communication technologies has increased the demand for high-frequency circuits that offer high signal transmission rates and low latency. Traditional epoxy resin materials, characterized by their high dielectric constant (εr) and dielectric loss (tanδ), lead to significant signal attenuation and reflection in high-frequency applications, thus limiting their suitability for modern communication devices. Accordingly, reducing the dielectric constant and dielectric loss of epoxy resins has become a prominent research focus in materials science. This paper reviews various methods for developing low-dielectric epoxy resin composites, emphasizing strategies to reduce polarization and material density. It subsequently provides a concise analysis of the advantages and current challenges associated with each technique and offers insights into potential future research directions. Full article

Pancreatic adenocarcinoma is one of the most aggressive malignancies, with a steadily increasing incidence rate. Due to the asymptomatic nature of early cancer and frequent late diagnosis, only 10–20% of patients are considered for radical treatment. In approximately 40% of patients, local advancement precludes primary surgical treatment, and in approximately half of patients, the cancer is diagnosed at the metastatic stage. Treatment of advanced pancreatic cancer is based on systemic therapy, while a growing number of studies are focusing on the potential use of molecularly targeted agents. The median survival time for metastatic patients treated with FOLFIRINOX chemotherapy is 11 months, compared to 8.5 months for patients treated with gemcitabine and nab-paclitaxel-based chemotherapy. Olaparib in the maintenance treatment of patients with advanced pancreatic cancer prolongs the time to progression compared to placebo but does not affect median overall survival. Immunotherapy and targeted therapy have so far been used in a narrow group of patients with a specific molecular profile, but further research on this cancer offers a real opportunity to develop new treatment approaches. This review article is based on the NCCN (National Comprehensive Cancer Network) guidelines and publications available in the PubMed database. Full article

Criminal justice system (CJS) involvement is common among individuals with opioid use disorder (OUD), yet limited research examines retention in medications for OUD (MOUD) within community settings. This study assessed whether CJS involvement predicted retention on buprenorphine/naloxone and explored related demographic and clinical factors. A retrospective cohort included adults (n = 367) enrolled in a low-barrier outpatient MOUD program in Texas (January 2022–April 2024). CJS involvement was identified from program records. Retention was measured as the number of continuous days with buprenorphine/naloxone prescriptions. Analyses used univariate tests, logistic regression, and nonparametric kernel regression. Nearly one-quarter (24.8%) were CJS-involved. Retention at 180 days was similar between CJS and non-CJS groups (38%). CJS participants initiated substance use earlier and reported higher heroin and injection drug use. Behavioral health sessions were associated with both CJS involvement (OR = 1.10, p ≤ 0.001) and longer retention (β = 10.81 days/session, p = 0.001). With comprehensive, low-barrier services, individuals involved with CJS achieved MOUD retention comparable to their peers. Early behavioral health engagement was a strong predictor of retention, suggesting a key intervention point to enhance outcomes and advance equity for justice-involved populations. Full article

Background: Ostomy care consultations are essential for promoting patient autonomy and quality-of-life. The integration of innovative technologies may enhance health education and support effective self-care among ostomized patients. Objective: To evaluate the impact of a nursing-led health education intervention supported by smart-glasses on the quality of life of ostomized patients. Methods: A pre–post quasi-experimental design was employed with 14 patients who had undergone digestive surgery resulting in an ostomy. The intervention consisted of a single 60-min session comprising three phases: (1) assessment of baseline knowledge on ostomy management, (2) personalized feedback, and (3) a hands-on workshop using Vuzix© smart-glasses to demonstrate ostomy care techniques. Quality of life was assessed using the SF-36 questionnaire before and after the intervention. Results: The intervention significantly improved overall SF-36 scores, with notable advancements in emotional role (78.57 ± 36.06 to 97.44 ± 9.25, d = 10.54), mental health (79.14 ± 20.10 to 87.38 ± 13.94, d = 6.27), and vitality (69.29 ± 20.56 to 71.15 ± 16.98, d = 4.19). Social function remained high throughout the study, while bodily pain showed a slight decline. A strong correlation (ρ = 0.923, p = 0.001) was observed between pre- and post-intervention quality of life scores. Conclusions: The findings suggest that integrating smart-glasses into nursing-led health education may enhance the quality of life and self-care capabilities of ostomized patients. However, the small sample size, lack of a control group, and exploratory nature of the study limit the generalizability of the results. Further research is needed to validate these findings in larger, controlled trials. Full article

The study systematizes the current state of knowledge on the morphological diversity of dispersed-phase particles in the most widely used lubricating greases, encompassing their shape, size, surface structure, and overall geometry. The extensive discussion of the diversity of grease thickener particles is supplemented with their microscopic images. Particular emphasis is placed on the influence of thickener particle morphology, the degree of their aggregation, and interparticle interactions on the rheological, mechanical, and tribological properties of grease formulations. The paper reviews recent advances in investigations of grease microstructure, with special emphasis on imaging techniques—ranging from dark-field imaging, through scanning electron microscopy, to atomic force microscopy—together with a discussion of their advantages and limitations in the assessment of particle morphology. A significant part of the work is devoted to rheological studies, which enable an indirect evaluation of the structural state of grease by analyzing its response to shear and deformation, thereby allowing inferences to be drawn about the micro- and mesostructure of lubricating greases. The historical development of rheological research on lubricating greases is also presented—from simple flow models, through the introduction of the concepts of viscoelasticity and structural rheology, to modern experimental and modeling approaches—highlighting the close relationships between rheological properties and thickener structure, manufacturing processes, composition, and in-service behavior of lubricating greases, particularly in tribological applications. It is indicated that contemporary studies confirm the feasibility of tailoring the microstructure of grease thickeners to specific lubrication conditions, as their characteristics fundamentally determine the rheological and tribological properties of the entire system. Full article

With the increasing prevalence of microplastics (MPs) and nanoplastics (NPs) in global aquatic environments, their potential ecotoxicological and health impacts have become a major concern in environmental science. Slow sand filtration (SSF) is widely recognized for its low energy demand, ecological compatibility, and operational stability; however, its efficiency in removing small or neutrally buoyant MPs remains limited. In recent years, integrating modified activated carbon (MAC) into SSF systems has emerged as a promising approach to enhance MP removal. This review comprehensively summarizes the design principles, adsorption and bio-synergistic mechanisms, influencing factors, and recent advancements in MAC-SSF systems. The results indicate that surface modification of activated carbon—through controlled pore distribution, functional group regulation, and hydrophilic–hydrophobic balance—significantly enhances the adsorption and interfacial binding of MPs. Furthermore, the coupling between MAC and biofilm facilitates a multi-mechanistic removal process involving electrostatic attraction, hydrophobic interaction, physical entrapment, and biodegradation. In addition, this review discusses the operational stability, regeneration performance, and environmental sustainability of MAC-SSF systems, emphasizing the need for future research on green and low-cost modification strategies, interfacial mechanism elucidation, microbial community regulation, and life-cycle assessment. Overall, MAC-SSF technology provides an efficient, economical, and sustainable pathway for microplastic control, offering valuable implications for a safe water supply and aquatic ecosystem protection in the future. Full article

With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered strategies including machine learning, deep learning, and large language models for identifying cybersecurity vulnerabilities and supporting automated patching. In this review, we conducted a synthesis and appraisal of 29 peer-reviewed studies published between 2019 and 2024. Our results indicate that AI methods substantially improve the precision of detection, scalability, and response speed compared with human-driven and rule-based approaches. We detail the transition from conventional ML categorization to using deep learning for source code analysis and dynamic network detection. Moreover, we identify advanced mitigation strategies such as AI-powered prioritization, neuro-symbolic AI, deep reinforcement learning and the generative abilities of LLMs which are used for automated patch suggestions. To strengthen methodological rigor, this review followed a registered protocol and PRISMA-based study selection, and it reports reproducible database searches (exact queries and search dates) and transparent screening decisions. We additionally assessed the quality and risk of bias of included studies using criteria tailored to AI-driven vulnerability research (dataset transparency, leakage control, evaluation rigor, reproducibility, and external validation), and we used these quality results to contextualize the synthesis. Our critical evaluation indicates that this area remains at an early stage and is characterized by significant gaps. The absence of standard benchmarks, limited generalizability of the models to various domains, and lack of adversarial testing are the obstacles that prevent adoption of these methods in real-world scenarios. Furthermore, the research suggests that the black-box nature of most models poses a serious problem in terms of trust. Thus, XAI is quite pertinent in this context. This paper serves as a thorough guide for the evolution of AI-driven vulnerability management and indicates that next-generation AI systems should not only be more accurate but also transparent, robust, and generalizable. Full article

This work presents a combined numerical and experimental investigation into the laser machining of aluminum alloy Al 1050 H14 using a high-power Continuous Wave (CW) fiber laser. Advanced three-dimensional, coupled thermal–structural Finite Element Method (FEM) simulations are developed to model key laser–material interaction processes, including laser-induced plastic deformation, laser etching, and engraving. Cases for both static single-shot and dynamic linear scanning laser beams are investigated. The developed numerical models incorporate a Gaussian heat source and the Johnson–Cook constitutive model to capture elastoplastic, damage, and thermal effects. The simulation results, which provide detailed insights into temperature gradients, displacement fields, and stress–strain evolution, are rigorously validated against experimental data. The experiments are conducted on an integrated setup comprising a 2 kW TRUMPF CW fiber laser hosted on a 3-axis CNC milling machine, with diagnostics including thermal imaging, thermocouples, white-light interferometry, and strain gauges. The strong agreement between simulations and measurements confirms the predictive capability of the developed FEM framework. Overall, this research establishes a reliable computational approach for optimizing laser parameters, such as power, dwell time, and scanning speed, to achieve precise control in metal surface treatment and modification applications. Full article

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

The increasing demand for rapid identification of bacteria capable of degrading environmentally relevant organic compounds highlights the need for scalable and selective analytical tools. Cupriavidus necator catabolizes several hydroxybenzoic acids, including 2-hydroxybenzoate (salicylate, 2-HBA), 4-hydroxybenzoate (4-HBA), and 3-hydroxybenzoate (3-HBA), funneling them into central aromatic catabolism via monooxygenation to 2,5-dihydroxybenzoate (gentisate, 2,5-dHBA) and 3,4-dihydroxybenzoate (protocatechuate, 3,4-dHBA) followed by the oxidative cleavage reaction, enabling complete conversion to tricarboxylic acid (TCA) cycle intermediates. To quantify how readily C. necator is able to activate catabolic genes in response to hydroxybenzoic acid, an extracellular ligand, we applied an approach centered on a transcription-factor (TF)-based biosensor that combines ligand-bound regulator activity with a fluorescent reporter. This approach allowed to evaluate the ligand sensitivity by determining gene activation threshold AC_min and half-maximal effective concentration EC₅₀. Amongst studied hydroxybenzoic acids, 2-HBA and 4-HBA sensors from C. necator showed very low thresholds 4.8 and 2.4 μM and EC₅₀ values of 19.91 and 13.06 μM, indicating high sensitivity to these compounds and implicating a scavenging characteristic of associated catabolism. This study shows that the TF-based-biosensor approach applied for mapping functional sensing ranges of hydroxybenzoates combined with the research and informatics of catabolism can advance our understanding of how gene expression regulation systems have evolved to respond differentially to the availability and concentration of carbon sources. Furthermore, it can inform metabolic engineering strategies in the prevention of premature pathway activation or in predicting competitive substrate hierarchies in complex mixed environments. Full article

Learner engagement is a pivotal factor affecting the effectiveness of Massive Open Online Courses (MOOCs), as it promotes collaborative learning environments. However, measuring the extent of learners’ contributions in MOOC learning forums presents challenges due to the complex nature of engagement and its variability. Given the limited research in this domain, further investigation is necessary. This study aims to address this gap by utilizing the Hidden Markov Model (HMM) to identify latent states of MOOC learners and improve their participation in learning forums. The study constructs a multidimensional observable signal sequence based on learner-generated post data from MOOC forums, with a particular focus on the widely attended course on a MOOC platform. To evaluate the predictive accuracy of HMM in forecasting learner contributions, the study employs several prominent prediction models for comparative analysis, including k-nearest neighbor, logistic regression, random forest, extreme gradient boosting tree, and the long short-term memory network. The results demonstrate that HMM provides superior accuracy in predicting learner contributions compared to other models. These findings not only validate the effectiveness of HMM but also offer significant insights and recommendations for enhancing forum management practices. This research represents a substantial advancement in addressing the challenges related to learner engagement in MOOC learning forums and underscores the potential benefits of employing the HMM approach in this context. Full article

In the context of environmental challenges and digital transformation, artificial intelligence (AI) plays a key role in promoting sustainable development within Industry 4.0 and the emerging paradigm of Industry 5.0. This study systematically reviewed the literature (2015–2025) from Scopus and Web of Science on the connections between AI, circular economy, industrial paradigms, and the Sustainable Development Goals (SDGs), with a particular focus on supply chains and SDG 12—responsible consumption and production. The majority of research emphasizes managerial aspects, the application of machine learning and robotics, as well as waste reduction, resource optimization, and circular economy practices within supply chain and production–consumption systems. Geographical analysis shows that larger economies serve as central research hubs, while some countries that are not among the most populous often achieve the highest average citations per document. Temporal keyword trends indicate a shift in research focus from operational efficiency in traditional supply chains (optimization) toward supply chain digitalization (artificial intelligence) and sustainability (circular economy). Keyword trends reveal four thematic clusters: supply chain digitalization, agritech, smart industry, and sustainability. The study highlights future research directions, including integrating circular economy with managerial and technical approaches, linking Industry 5.0 with SDG 12, and applying advanced AI in sustainable industrial practices. The increasing attention to ethical and social dimensions underscores the need for AI solutions that are both technologically advanced and sustainability oriented. Full article