Search Results (1,890)

Background: Food insecurity among college students is a multidimensional challenge shaped by individual, interpersonal, institutional, community, and policy factors. Although many campuses require or provide meal plans, students may experience food insecurity when barriers related to agency (choice and autonomy), utilization (nutrition security), and availability persist. This study explored how undergraduate students at a private, urban U.S. university experience and navigate the multiple dimensions of food insecurity. Methods: We conducted in-depth, semi-structured interviews via Zoom between December 2024 and January 2025 with n = 22 undergraduate students recruited based on food security status, determined by a Fall 2024 longitudinal survey using the USDA Six-Item Short Form. Transcripts were double-coded by trained research assistants in ATLAS.ti using an inductive codebook. Thematic analyses followed a phronetic, iterative approach, organizing findings within a socio-ecological determinants framework and comparing themes by food security status. Results: We identified nine themes across four domains (individual, interpersonal, institutional and community, and political). At the individual level, constrained personal resources for groceries and cooking, time scarcity leading to skipped meals, and health impacts that detracted from academics emerged as key themes. Interpersonally, reliable family financial support was protective and informal support from peers/coaches filled gaps sporadically for some. At the institutional and community level, dining hall hours misaligned with student schedules, perceived limited variety and nutrition quality reduced food agency and utilization, and transportation impeded use of the sole grocery partner accepting university meal plan benefits. Notably, meal plans including unlimited meal swipes provided stable access but did not guarantee food security when food agency and utilization barriers persisted. Many students relied on campus events for free food; formal assistance (e.g., food pantry) was largely underused. At the policy level, Supplemental Nutrition Assistance Program (SNAP) awareness and enrollment was limited among our sample. Conclusions: Meal plan access alone is insufficient to ensure food security. Campus strategies should extend beyond access to prioritize flexibility, variety, and alignment with students’ schedules and preferences, while strengthening communication and eligibility support for external benefits. Future work should design and evaluate interventions that integrate all dimensions of food security and address institutional policies affecting students’ basic needs. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

This paper analyses the role of landscape as a fundamental dimension of post-earthquake recovery in the inland areas of Central Italy, arguing that reconstruction must be understood not only as the repair of damaged buildings but as a broader territorial process affecting identity, spatial organization, and long-term settlement trajectories. In this sense, post-earthquake recovery is also interpreted as a strategic opportunity to reinforce coast–inland relationships, acknowledging the structural interdependence between inland Apennine areas and coastal urban systems. Drawing on insights from applied research conducted in the L’Aquila 2009 crater and on the conceptual framework developed within the PRIN TRIALS project, the paper discusses how seismic events accelerate pre-existing territorial dynamics and produce enduring transformations, particularly in the proximity landscapes surrounding historic centres. Rather than presenting empirical findings, the contribution offers a theoretical and operational framework aimed at integrating landscape considerations into reconstruction processes. It outlines key concepts such as landscape quality, transformative resilience, and permanent temporariness; reviews critical normative aspects linked to emergency procedures; and proposes a set of landscape-oriented guidelines and criteria for the contextual integration of reconstruction projects. These include landscape quality objectives, multiscalar readings of identity values, and operational tools such as visual-impact assessment, Project Reference Context analysis, and principles for managing transformations in peri-urban and historic environments. Overall, the paper argues that adopting a landscape-based perspective can strengthen territorial cohesion, support the sustainable redevelopment of historic centres and their surroundings, and embed post-earthquake reconstruction within broader coast–inland territorial strategies aimed at long-term resilience and balanced regional development in Apennine communities. Full article

This study examines how Generation Z’s digital practices on TikTok and Instagram shape their music festival experiences, focusing on event perception, engagement, and the development of collective identity. The aim is to identify key factors connecting online and offline aspects of festival participation. The research adopts a quantitative approach based on an online survey of 248 respondents born between 1995 and 2010 from various regions of Serbia. Data were analyzed in SPSS 26.0 using Spearman correlation, quantile regression, and the Mann–Whitney test. Given the exploratory nature of the study, the findings should be interpreted accordingly. Findings show that frequent social media use has a positive but limited effect on how important these platforms are perceived for the festival experience. However, user-generated content created by attendees plays a more significant role in shaping engagement and attitudes than influencer content. Influencer credibility also influences how festivals are interpreted digitally. The interplay between online interaction and offline participation motivates content sharing and reinforces a sense of community. Overall, the study concludes that social media and digital narratives are central to Generation Z’s festival experience. Authentic, attendee-created content strongly contributes to collective identity, helping bridge digital and physical dimensions—insights valuable for festival organizers, influencers, and cultural tourism. Full article

Thailand is globally recognized for its tourism potential and rich diversity of cultural and natural heritage. Community-based tourism (CBT), in particular, holds significant promise for inclusive and sustainable development. However, CBT initiatives across the country remain fragile in the face of uncertainty, whether from pandemics, climate events, or market shifts, and are often constrained by fragmented practices and the absence of a shared service development framework that addresses sustainability tensions. This study addresses that gap by developing and validating a sustainability-oriented new service development (NSD) process comprising five phases and sixteen steps, tailored specifically for CBT under uncertainty. Through expert interviews and iterative action research in two contrasting Thai communities, the process was refined to include tools for place identity, customer analysis, service testing, and adaptive planning. The framework enables CBT communities to move from ad hoc efforts to structured, resilient, and market-aligned service practices. Expert validation confirmed its effectiveness and adaptability, while also recommending digital transformation and financial integration as future directions. This process offers a pathway for improving CBT outcomes in Thailand, and a potentially adaptable framework for CBT development across diverse contexts in uncertain tourism environments. Full article

Global water management faces a critical challenge: whilst scholarly consensus recognises that multiple, interacting drivers fundamentally shape water availability and management capacity, operational governance frameworks fail to systematically incorporate this understanding. This disconnect is particularly acute in public good contexts where incomplete knowledge, diverse stakeholder values, and statutory planning mandates create distinct challenges. Using Australia’s Murray–Darling Basin as a pilot case, this research develops and demonstrates a rapid, policy-relevant heuristic for identifying, prioritising, and incorporating drivers of change in complex socio-ecological water systems. Through structured participatory deliberation with 70 experts spanning research, policy, industry, and community sectors across three sequential workshops and 15 semi-structured interviews, we systematically identified key drivers across environmental, governance, economic, social, and legacy dimensions. A risk and sensitivity assessment framework enabled prioritisation based on impact, vulnerability, and urgency. Climate change, drought, water quality events, and cumulative impacts emerged as the highest-priority future drivers, with climate change acting as a threat multiplier, whilst governance drivers show declining relative significance. Using these methodological innovations, we synthesise the I-PLAN heuristic: five interdependent dimensions (Integrative Knowledge, Prioritisation for Management, Linkages between Drivers, Adaptive Agendas, and Normative Collaboration) that provide water planners with a transferable, operational tool for driver identification and bridging to planning and management in data-sparse contexts. Full article

This paper presents the design and optimisation of a low-power embedded sensor-node architecture for real-time environmental monitoring with on-board machine-learning inference. The proposed system integrates heterogeneous sensing elements for air quality and ambient parameters (temperature, humidity, gas concentration, and particulate matter) into a modular embedded platform based on a low-power microcontroller coupled with an energy-efficient neural inference accelerator. The design emphasises end-to-end energy optimisation through adaptive duty-cycling, hierarchical power domains, and edge-level data reduction. The embedded machine-learning layer performs lightweight event/anomaly detection via on-device multi-class classification (normal/anomalous/critical) using quantised neural models in fixed-point arithmetic. A comprehensive system-level analysis, performed via MATLAB Simulink simulations, evaluates inference accuracy, latency, and energy consumption under realistic environmental conditions. Results indicate that the proposed node achieves 94% inference accuracy, 0.87 ms latency, and an average power consumption of approximately 2.9 mWh, enabling energy-autonomous operation with hybrid solar–battery harvesting. The adaptive LoRaWAN communication strategy further reduces data transmissions by ≈88% relative to periodic reporting. The results indicate that on-device inference can reduce network traffic while maintaining reliable event detection under the evaluated operating conditions. The proposed architecture is intended to support energy-efficient environmental sensing deployments in smart-city and climate-monitoring contexts. Full article

Family-implemented interventions are evidence-based practices used to support a range of developmental outcomes, including social communication. Social communication is a broad construct that encompasses a variety of skills, from foundational abilities such as joint attention (i.e., two people attending to the same object or event) to more advanced behaviors like triadic interactions (i.e., responding to or initiating conversation that involves reciprocal interactions). In a previous study, we examined the effects of a brief, parent-implemented Naturalistic Developmental Behavioral Intervention (NDBI), delivered over telepractice with video feedback coaching. The intervention resulted in increased strategy use by all mothers and the frequency of communication for three young children. In the current study, we conducted a secondary analysis of those data to explore whether the communication-focused intervention produced a collateral effect on activity-engaged triadic interactions (i.e., mother–child–mother or child–mother–child exchanges while simultaneously engaging in a joint activity). Although a functional relation was not established, critical theoretical implications are posed. These findings highlight the need for future research to break apart complex skills into subskills to detect any subtle changes in child outcomes. Limitations and directions for future research are discussed. Full article

The subject of this study is gastronomic tourism as a broader concept, specifically focusing on gastronomic festivals and events centered around food, with a focus on the study area, Corfu island. Using a combination of quantitative research and open-ended questions, a questionnaire was developed targeting the permanent residents of the island. The questionnaire was completed by 89 individuals who provided their personal opinions on the gastronomic festivals in the study area and offered ideas for the future planning of similar events. Based on the findings of this research, it is deemed beneficial to implement and promote gastronomic festivals that consider the needs of gastronomic tourists/visitors and local residents. Further research is recommended to identify strategic plans to assist local communities and improve event content, aiming to promote and develop gastronomic tourism. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

To address the issues of insufficient privacy protection, lack of confidentiality, and absence of traceability mechanisms in resource-constrained application scenarios such as IoT nodes or mobile network group communications, this paper proposes a traceable ring signcryption privacy protection scheme based on the SM9 algorithm. In detail, the ring signcryption structure is designed based on the SM9 identity-based cryptography algorithm framework. Additionally, the scheme introduces a dynamic accumulator to compress ciphertext length and optimizes the algorithm to improve computational efficiency. Under the random oracle model, it is proved that the scheme has unforgeability, confidentiality, and conditional anonymity, and it is also demonstrated that conditional anonymity can be used to trace the identity of the actual signcryptor in the event of a dispute. Performance analysis shows that, compared with related schemes, this scheme improves the efficiency of signcryption, and the size of the signcryption ciphertext remains at a constant level. Full article

Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description and event-triggering mechanism. Firstly, a formation description method based on a geometric transformation parameter set is established to uniformly describe the translation, rotation, and scaling movements of the formation, providing a foundation for time-varying formation control. Secondly, a cooperative architecture for adaptive target enclosing tasks is designed. This architecture achieves an organic combination of formation control and target enclosing in a unified framework, thereby meeting flexible transitions between multiple formation patterns such as equidistant surrounding and variable-distance enclosing. Thirdly, a distributed dynamic event-triggered cooperative enclosing controller is developed. This strategy achieves online adjustment of communication thresholds through internal dynamic variables, significantly reducing communication while strictly ensuring system performance. By constructing a Lyapunov function, the stability and Zeno free behavior of the closed-loop system are proven. The simulation results verify this strategy, showing that this strategy can significantly reduce communication frequency while ensuring enclosing accuracy and formation consistency and effectively adapt to uniform and maneuvering target scenarios. Full article

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

This work studies the formation control problem of general linear multi-agent systems (MASs) under hybrid attacks that include man-in-the-middle attacks (MITM) and denial-of-service attacks (DoS). First, an improved Diffie–Hellman key exchange (DHKE)-based encryption–decryption mechanism is proposed. This mechanism combines the challenge–response mechanism and hash function, which can achieve identity authentication, detect MITM attacks and ensure the confidentiality and integrity of information. Second, considering that DoS attacks on different channels are independent, a division model for distributed DoS attacks is established, which can classify attacks into different patterns. Third, an edge-based event-triggered (ET) formation control scheme is proposed. This control method only relies on the information of neighbor agents, which not only saves communication resources but also resists distributed DoS attacks. Finally, sufficient conditions for the implementation of formation control for MASs under hybrid attacks are provided, and the effectiveness and advantages of the proposed strategy are verified by simulation. Full article

This article presents an integrated framework for robust control and cybersecurity of an industrial robot, combining Quantitative Feedback Theory (QFT), digital twin (DT) technology, and a programmable logic controller–based architecture aligned with the requirements of the NIS2 Directive. The study considers a five-axis industrial manipulator modeled as a set of decoupled linear single-input single-output systems subject to parametric uncertainty and external disturbances. For position control of each axis, closed-loop robust systems with QFT-based controllers and prefilters are designed, and the dynamic behavior of the system is evaluated using predefined key performance indicators (KPIs), including tracking errors in joint space and tool space, maximum error, root-mean-square error, and three-dimensional positional deviation. The proposed architecture executes robust control algorithms in the MATLAB/Simulink environment, while a programmable logic controller provides deterministic communication, time synchronization, and secure data exchange. The synchronized digital twin, implemented in the FANUC ROBOGUIDE environment, reproduces the robot’s kinematics and dynamics in real time, enabling realistic hardware-in-the-loop validation with a real programmable logic controller. This work represents one of the first architectures that simultaneously integrates robust control, real programmable logic controller-based execution, a synchronized digital twin, and NIS2-oriented mechanisms for observability and traceability. The conducted simulation and digital twin-based experimental studies under nominal and worst-case dynamic models, as well as scenarios with externally applied single-axis disturbances, demonstrate that the system maintains robustness and tracking accuracy within the prescribed performance criteria. In addition, the study analyzes how the proposed architecture supports the implementation of key NIS2 principles, including command traceability, disturbance resilience, access control, and capabilities for incident analysis and event traceability in robotic manufacturing systems. Full article