Search Results (1,534)

Background/Objectives: Food insecurity affects college students at nearly twice the rate of US households, with documented impacts on student academic performance, physical and mental health, and socialization. While frameworks exist to conceptualize general food insecurity and food insecurity in specific contexts, researchers and practitioners lack resources to guide system-level responses to food insecurity on college and university campuses and assess those responses. In this study, we aimed to develop and validate a simple yet comprehensive framework for assessing food insecurity responses within the context of higher education. Methods: We adapted an eight-phase process for framework development: (1) map selected data sources within the multidisciplinary literature, (2) read and categorize selected sources, (3) identify and name concepts, (4) deconstruct and categorize concepts based on their features, (5) group similar concepts together, (6) synthesize concepts into a framework, (7) validate the framework using expert panel review, and (8) revise as necessary. Results: The developed Campus Food Aid Self-assessment (CFAS) framework consists of six dimensions: Student Services and Supports; Involvement; Advocacy; Awareness and Culture Efforts; Education and Training; and Research, Scholarship, and Creative Works. Expert panelists (n = 7) reviewed the proposed framework and confirmed the clarity, comprehensiveness, and representativeness of the proposed dimensions, conceptual definitions, and operational variables. Conclusions: With a comprehensive yet accessible structure, the CFAS framework supports the development, coordination, and improvement of campus-based strategies to address food insecurity and support positive student outcomes. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Background: The resurgence of Mpox (formerly known as monkeypox) since the 2022 global outbreak has exposed weaknesses in surveillance, diagnosis, and public risk communication systems. Despite increased clinical understanding, limitations in knowledge, attitudes, and practices (KAP) among both healthcare workers (HCWs) and the general population continue to challenge prevention and control measures. Numerous systematic reviews have been published on KAP toward Mpox, yet their findings remain fragmented. This review aimed to consolidate the existing evidence from published systematic reviews to provide a unified understanding of global KAP levels related to Mpox. Methods: We followed the PRISMA guidelines for this systematic review of systematic reviews. The article search was conducted in PubMed, Embase, and the Cochrane Library for systematic reviews published between January 2010 and October 2025. Data was extracted on study design, population, and reported quantitative outcomes. Results: Five studies met the inclusion criteria: three focused on HCWs, while two focused on the general population. Among HCWs, knowledge ranged from 26.0% to 46.7%, and attitudes from 28.2% to 62.2%. In the general population, knowledge ranged from 33.0% to 46.6%, attitudes from 40.0% to 71.9%, and perceptions averaged around 40.0%. Across both groups, Mpox knowledge was limited, attitudes were moderately positive, and preventive behaviors remained consistently low, revealing a persistent gap between awareness and practice. Conclusions: This review highlights persistent gaps in knowledge, attitudes, and practices among HCWs and the general population. Although global attention increased substantially following the 2022 outbreak, important weaknesses remain in translating knowledge into consistent preventive behaviors. Addressing these gaps requires structured and context-specific interventions. Integrating Mpox-focused modules into mandatory Continuing Medical Education credits for HCWs could ensure sustained competency in diagnosis, infection prevention, and outbreak response beyond peak epidemic periods. For the general population, strategic risk communication campaigns should leverage trusted community leaders and social media influencers in high-risk regions to counter misinformation, reduce stigma, and promote evidence-based preventive behaviors. Embedding these targeted strategies within broader pandemic preparedness and global health security frameworks will be essential to strengthening early detection, public trust, and coordinated outbreak response in future Mpox or other emerging infectious disease events. Full article

Rapid electronic waste (e-waste) accumulation poses a critical challenge for urban sustainability in emerging economies. However, few studies have examined what motivates households to actively participate in formal disposal systems, particularly in contexts where infrastructure remains limited. This study investigates the determinants of e-waste recycling intention and behavior in Surabaya, Indonesia. A total of 168 active recyclers are surveyed and analyzed using structural equation modeling and importance–performance mapping. The findings reveal that awareness of environmental consequences significantly influences both recycling intention and actual behavior. Interestingly, while the perceived cost of recycling significantly shapes residents’ intention to participate, it does not translate into a significant effect on their actual recycling behavior. Similarly, the convenience of recycling services shows no significant influence on either intention or behavior. Mediation analysis confirms that environmental awareness indirectly shapes recycling behavior through its effect on intention. These findings suggest that, among early adopters of formal e-waste recycling in a developing-country context, cognitive drivers such as awareness outweigh structural barriers like cost and convenience in shaping long-term recycling engagement. For policymakers, this underscores the importance of highlighting awareness of e-waste impacts and the benefits of proper recycling, alongside efforts to remove physical and financial barriers for broader segments of the population. Full article

This article investigates whether increasing economic status through adopting digital payment capabilities in Delhi fosters economic and financial inclusion among urban women street vendors in Mahila Haat. Digital freedom is a new step forward in technology for everyone. Still, a woman not only balances the social responsibilities of childbearing, caring for her children and family, and struggling with economic issues, health issues, and undernourishment, but can also balance the household job of street vending to increase self-esteem and financial independence. This research work conducted a sampling survey and applied the Kruskal–Wallis H-test with a p-value (0.05) significance level by evaluating 11 variables to investigate the relationship between the digital capabilities and economic independence of street vendors in Mahila Haat (a women’s market where the vendors are all women) in the Red Fort area of New Delhi. UPI systems were created using measurements based on a five-point Likert scale to analyze different levels of satisfaction in clusters of digital capabilities on digital platforms. Further, the ordinary least squares (OLS) method was used to estimate quality of life and social happiness in the context of digital empowerment. Digital payment systems positively influence women’s empowerment. Women vendors can adopt digital payment methods, making them economically independent. The positive relationship between women vendors and customer satisfaction before UPI use and after UPI use is also analyzed. This research will be helpful for both government and non-government organizations to provide financial assistance, informational awareness, skill development training, and advocacy for gender equality to increase women’s empowerment. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article

Food waste prevention and reduction are some of the important initiatives to improve the environmental sustainability of food systems. The global agenda of the United Nations provides a framework of targets and actions against food waste to which the European Union (EU), within the “Farm to Fork” strategy, aims to contribute. In this context, evaluating the impacts of food prevention measures is of great importance for supporting policies. This LCA analyzes the impact of classic lasagna from cradle to grave, through a generic food case study, prepared by food shops in Bologna (Northern Italy). Four scenarios are simulated, comparing the impacts of some end-of-life alternatives for the management of leftover lasagna (landfilling, composting, and redistribution with the digital application of the circular start-up “Squiseat”) versus the ideal scenario where no leftover lasagna is assumed. The results show that the preparation of classic lasagna generates non-negligible impacts on the analyzed LCA categories due to some of its ingredients, such as Bolognese sauce and Parmigiano Reggiano, and their associated production processes. For this reason, it is important to prevent classic lasagna leftovers from being wasted. The comparison of the four scenarios shows that redistribution is the scenario with the lowest impacts in all the investigated impact categories, including global warming (6.24 kg CO₂ eq./kg of lasagna). The impacts are also lower than the ideal scenario due to the assumption of more sustainable means of transport. Normalization of characterized results confirms that Global Warming (GW) is only one of the most relevant impact categories in the life cycle of classic lasagna. The results have practical implications for raising awareness concerning the impacts of food production throughout the whole life cycle and the need for preserving the value of food by avoiding waste. Moreover, this study also shows that a reduction in the impact is a shared outcome that could be achieved by the joint efforts of all the stakeholders involved in the life cycle of food. In this regard, urban centers are confirmed to be important hubs of circular and more sustainable innovation. Finally, the LCA enriches the current research by investigating redistribution through the relationship of the food shop–virtual intermediate–consumer. So far, the prevalent focus of the LCA research allows us to assess the redistribution of collected surplus food from retailers and its delivery to the consumers by means of physical intermediaries and related infrastructures (e.g., food hubs, food banks, and food emporiums). Full article

Public opinion on online platforms now plays an important role in agricultural markets, which have always been unpredictable. Although sentiment analysis has been widely applied to agricultural texts, most existing studies typically focus only on classification accuracy without connecting results to actual market intelligence systems, especially in multilingual contexts. This paper introduces a reliability-aware transformer-based framework for analyzing sentiment in agricultural market intelligence across multiple languages. The framework leverages weakly supervised multilingual transformers to extract sentiment signals from large-scale unlabeled Thai and English texts about major agricultural commodities found online. To enhance robustness under weak supervision, the framework incorporates reliability-aware mechanisms, including confidence-based pseudo-label filtering, cross-source consistency refinement, and expert-guided calibration to reduce noise and account for bias between different data sources. Sentiment predictions are further aligned with market intelligence objectives through reliability-weighted aggregation, yielding interpretable sentiment indices that enable cross-lingual and cross-source comparability. We tested the framework extensively using a multilingual agricultural corpus derived from social media and news coverage of agriculture. The results show consistent improvements over both classical machine learning approaches and standard multilingual transformer baselines. Additional ablation studies and sensitivity analyses confirmed that reliability-aware mechanisms, particularly confidence thresholding, play a crucial role in getting the right balance between label quality and data coverage. Overall, the results indicate that reliability-aware multilingual sentiment analytics provide robust and actionable insights for agricultural market monitoring and policy analysis. Full article

Man-made climate change is empirically proven and places ethical and strategic responsibility on the current generation to mitigate risks for future generations. Within this context, the selection of future energy carriers is a central determinant of sustainable development. While electrification is widely promoted, particularly in the transport sector, it is associated with complex production chains, critical raw material dependencies, unresolved recycling challenges, and potential resource scarcity. Synthetic fuels therefore re-emerge as a potential complementary option, especially for applications that are difficult to electrify directly. However, their role remains controversial due to efficiency losses and cost challenges. This paper uses qualitative research based on expert interviews to investigate the role of synthetic fuels in the sustainable management of energy transition and responsible practices. A total of 11 experts, representing the energy sector, research institutions, engineering fields, environmental organizations, and political–regulatory contexts participated. The analysis focused on four dimensions—efficiency, awareness, knowledge, and acceptance. The findings have shown that synthetic fuels are not a universal substitute for fossil fuels but a highly conditional option for hard-to-electrify applications. Efficiency losses, limited renewable electricity availability, knowledge gaps, conceptual ambiguity, and acceptance challenges significantly constrain their systemic role. The paper concludes that synthetic fuels can only make a meaningful contribution under strict conditions, with clear prioritization, realistic expectations, and coherent long-term policy frameworks aligned with intergenerational responsibility and genuine sustainability. The findings should be interpreted primarily within the German and European policy and innovation context, as the expert sample is largely embedded in institutions operating in this environment. Nevertheless, the insights provide relevant indications for broader international debates on the role of synthetic fuels in energy transition. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

The continuous growth of the population of Resident Space Objects (RSOs) poses increasing challenges for Space Situational Awareness (SSA), particularly in terms of detection capability and collision risk mitigation. Ground-based radar systems represent a primary class of remote sensing instruments for RSO observation; however, their deployment is often constrained by cost and infrastructure requirements. In this context, the reuse of existing large radio astronomy facilities as radar receivers offers an innovative and potentially cost-effective alternative. This paper presents a fully model-based feasibility study of S-band bi-static radar observations of RSOs using the Sardinia Radio Telescope (SRT) as a high-sensitivity ground-based receiver. The analysis is entirely analytical and is conducted in the absence of experimental radar measurements. A bi-static radar equation framework is adopted to evaluate received signal power and the resulting signal-to-noise ratio (SNR) as functions of target size, range, and observation geometry. The model explicitly incorporates thermal noise, integration time and target dynamics, radio-frequency interference (RFI), atmospheric and environmental clutter contributions, and the realistic antenna radiation pattern of the SRT through a Gaussian beam approximation. Detection thresholds, maximum observable ranges, and performance envelopes are derived for representative RSO dimensions, and the impact of off-boresight reception on detectability is quantified. The results define the operational conditions under which RSOs may be detected in an S-band bi-static configuration and demonstrate the potential of the SRT as a non-conventional ground-based instrument for space object observation, supporting future developments in SSA and space debris monitoring strategies. Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Intrusion Detection Systems (IDSs) operate under asymmetric misclassification costs: false alarms (FP) consume analysts’ time and erode trust, whereas missed attacks (FN) carry business risks. This paper presents a complete pipeline for network anomaly detection on the CIC-IDS2017 dataset using Histogram-Based Gradient Boosting (HGB), with a particular focus on cost-aware threshold selection on a validation split for representative operating regimes $w_{FP}:w_{FN}\in \{1:1, 1:2, 1:3, 1:4, 1:5, 1:10\}$—treated as scenario-based proxies for varying risk posture, attack severity, and analyst workload rather than as universally fixed costs—and on the role of isotonic calibration. The results indicate that (i) under 1:1, the cost-optimal operating point aligns with the F1/MCC optimum; (ii) for 1:k cost regimes, the optimum shifts to lower thresholds, reducing FN at the expense of FP and increasing the alert rate; and (iii) isotonic calibration improves PR/ROC (ranking separation), but in the reported 1:5 experiment it did not reduce the final TEST-set operational cost relative to the uncalibrated run, despite using a separately selected post-calibration threshold. The evaluation includes PR/ROC curves, Cost–Threshold and Alert–Threshold sweeps, per-class recall, and permutation importance. In addition, the proposed approach is compared with unsupervised baselines (Isolation Forest, LOF). The results provide practical guidance for SOC decisions on how to choose thresholds consistent with alert budgets and risk profiles. In deployment, these operating points can be indexed to context (e.g., user type, service class, or time of day), yielding a small library of adaptive thresholds rather than one immutable global threshold. Full article

The protection of personal information has become a defining challenge for higher education institutions, particularly in developing contexts where regulatory frameworks are often strong on paper but weak in practice. This study investigates student perceptions of privacy within Zimbabwe’s higher education system, focusing on three constructs: awareness, expectations, and confidence across nine core privacy components derived from international principles (FIPPs, OECD, GDPR) and the Zimbabwe Data Protection Act (ZDPA). Using survey data from 287 students across diverse programmes and modes of study, descriptive and comparative analyses reveal a striking pattern: students demonstrate high awareness and very strong expectations, yet their confidence in institutional compliance remains significantly lower. The largest deficits were found in privacy education, consent, and notice/openness, suggesting that institutions are perceived as technically competent in data handling but weak in transparency, accountability, and student engagement. The research extends privacy perception models by considering the discrepancy between the students’ expectations and the institutional trust. It also encourages universities to go beyond mere compliance by implementing concrete measures such as privacy training, clear consent, and frequent data audits. The findings contribute to global debates on privacy by offering evidence from the Global South, showing that the key challenge is not student ignorance but institutional trustworthiness. Bridging this awareness-confidence gap is essential for building a privacy-conscious academic environment. Full article

The accelerated expansion of artificial intelligence (AI) is reshaping economic systems, labour markets and democratic life, giving rise to hybrid human–AI societies. In this context, education becomes a strategic arena for enabling sustainable and socially just transitions within the Fourth Industrial Revolution. This article examines how digital competence can be reconceptualized to prepare future citizens and educators for these emerging societal configurations, with particular attention to the Ibero-American context. A conceptual framework is proposed that integrates algorithmic literacy, critical data awareness, AI ethics, human–AI collaboration skills, and civic and socio-emotional capacities as core dimensions of “next-decade” digital competence. Methodologically, the study combines three complementary approaches: (a) a structured review of interdisciplinary literature on AI, digital competence and sustainability; (b) an analysis of international and regional policy documents and competence frameworks relevant to Ibero-America; and (c) selected empirical insights drawn from the first author’s doctoral research on digital competence and AI use in teacher education. The findings reveal significant tensions between rapid AI adoption and persistent structural inequalities in the Global South, while identifying key leverage points for aligning teacher education, public policy and institutional strategies with the Sustainable Development Goals. The proposed framework aims to support policymakers, universities and international organizations in fostering inclusive and sustainable AI-driven social change while mitigating new forms of exclusion and dependency. Full article