Search Results (19,489)

Background/Objectives: The incorporation of peer support within mental health services has shown benefits for service users’ recovery and engagement, yet implementation is often hindered by role ambiguity and limited institutional recognition. The aim of this study is to explore the experiences of workers in a programme that provides peer support within a personal assistance model. The focus is on how they perceive the shaping of their professional role and their integration within care teams, rather than on evaluating service outcomes or effectiveness. Methods: An interpretive qualitative methodology with an exploratory approach was used. The study was conducted in a single organisational setting and focused on the self-reported experiences of personal assistants. Fieldwork was conducted in 2025 with ten personal assistants. Data were obtained through individual semi-structured interviews and one focus group with the same participants. A thematic content analysis combining inductive and deductive coding strategies was conducted using MAXQDA (version 24.11). Results: Findings indicate that the Personal Assistant role was perceived as reducing some of the ambiguity commonly associated with peer support, due to a clearer contractual framework and a more explicit delineation of functions. However, tensions persisted in relation to its hybrid professional identity, experiences of task overload, and ongoing gaps in coordination with traditional professional roles. Key facilitators included institutional support, accessible coordination, a supportive culture of care, and informal peer networks. Perceived benefits were reported for service users, including increased trust, hope, and autonomy, as well as for assistants, who described enhanced professional purpose and progress in their own recovery, alongside risks of emotional strain. Conclusions: Analysing the perspective of participants, the personal assistance model may represent a promising framework for the professionalisation of peer support through functional clarity, continuous supervision, and recognition of experiential knowledge. Further progress requires strengthening internal communication, expanding training opportunities, and enhancing the structural participation of personal assistants in decision-making. The study contributes an exploratory qualitative perspective to the growing literature on integrating lived-experience professionals into mental health services. Full article

Optical amplification and spatial multiplexing technologies have important applications in quantum communication, quantum networks, and optical information processing. In this paper, based on the non-reciprocal amplification of a pair of co-propagating conjugate four-wave mixing (FWM) signals induced by a one-way pump field in a double-Λ-type hot atomic system, we demonstrate spatially multiplexed multiple FWM processes by introducing a counter-propagating collinear pump field. This configuration enables simultaneous amplification of bidirectional four-channel FWM signals. Furthermore, when the injected signal and pump beams are modulated to Laguerre–Gaussian beams carrying different optical orbital angular momentum (OAM), the OAM of the pump beam is transferred to each amplified field. Through the tilted lens method, we experimentally demonstrate that the OAM of the amplified signal light remains identical to that of the original injected signal light. In contrast, the OAM of the other three newly generated FWM fields is governed by the angular momentum conservation law of their respective FWM processes, which enables the precise manipulation of the OAM for the other generated amplified fields. Theoretical analysis of the dynamical transport equation for the density operator in light–matter interaction processes fully corroborates the experimental results. These findings establish a robust framework for developing OAM-compatible optical non-reciprocal devices based on complex structured light. Full article

The identification of community structure is pivotal for understanding the functional characteristics of complex networks. To address the limitations of most existing community detection algorithms, which often require predefining the number of communities and lack robustness, this paper proposes a novel community detection algorithm named D-means (K-means community detection algorithm based on density peaks). This algorithm integrates the concept of density peak clustering with K-means spectral clustering, employing Chebyshev’s inequality to automatically determine the number of community centers, thereby enabling unsupervised identification of community quantities. By designing a multi-dimensional evaluation framework, the comparative experiments were conducted on LFR benchmark networks (Lancichinetti-Fortunato-Radicchi benchmark networks) and real-world social network datasets. The results demonstrate that the D-means algorithm outperforms traditional algorithms in terms of ACC (accuracy), ARI (adjusted rand index), and NMI (normalized mutual information) metrics, while also achieving improvements in runtime efficiency, showcasing strong robustness. Finally, the D-means algorithm was applied to the public transportation network of Urumqi. Empirical analysis identified 12 functionally significant transportation communities, providing theoretical support for urban rail transit optimization and commercial facility layout planning. Full article

Transdisciplinarity and the co-production of knowledge have become fundamental approaches to addressing complex social problems. However, the sustainability of collaborative partnerships remains underexplored from an empirical perspective. This article examines the mechanisms that shape the continuity of collaborative networks in social innovation projects in the field of social services, particularly those linked to community-based welfare systems in Andalusia (Spain). Drawing on a thematic qualitative analysis of 15 social innovation projects and 14 semi-structured interviews with project coordinators, the study explores how diverse actors (universities, public administrations, third-sector organisations, and citizens) mobilise different types of social capital within local social services. The findings reveal that collaboration success depends on a balance between relational enablers (trust and shared experiences) and structural barriers (bureaucracy, work overload, and lack of time). The analysis also shows that participatory methodologies and connections with pre-existing networks are essential for sustaining collaboration after project completion. The article concludes that the sustainability of transdisciplinary social innovation in social services requires moving beyond project management logics and investing in the care of invisible relational structures, with implications for public policies aimed at consolidating trust ecosystems and long-term collective learning. Full article

The renovation of dilapidated housing has become a focal point of social concern. However, traditional approaches—such as repair and reinforcement or unified demolition and relocation—face bottlenecks that hinder sustainability. There is an urgent need to explore new models for addressing the risks posed by dilapidated residential buildings. In recent years, multiple regions have explored the “original demolition and original reconstruction” approach for dilapidated housing. For instance, Zhejiang Province introduced the “Resident-led Renewal” model, sparking widespread attention and discussion. This model is characterized by residents serving as the primary investors. However, the manner in which stakeholders—particularly residents—collaborate in governance and interact during the renovation process under this model remains unclear. Using the Zhegong New Village original demolition and reconstruction project as a case study, this paper employs social network analysis to construct relational networks encompassing information, trust, consultation, and support. It quantitatively reveals the characteristics of social networks among stakeholders and their interactive practices within the Resident-led Renewal model. Findings reveal that in this case, “Resident-led Renewal” primarily manifested through residents serving as principal investors and establishing a Self-Driven Renewal Committee to submit the original demolition and reconstruction application on behalf of residents to local authorities. In stakeholder interactions, the government and community neighborhood committees play a coordinating role in the renovation process. However, resident organizations and residents themselves ranked lower in metrics such as reciprocity and degree centrality, indicating their limited influence during the renovation process. To alleviate the pressure of the government’s excessive involvement and enhance resident participation in the “original demolition and original reconstruction” process, efforts should focus on: raising residents’ awareness and capacity for participation; ensuring accessible channels for resident involvement; clarifying the rights and responsibilities of all stakeholders; and establishing a standardized approval process for “original demolition and original reconstruction” projects. This approach would realize a “Resident-led Renewal” model characterized by government guidance and resident participation. Full article

Various sensors are increasingly being adopted to support intelligent healthcare systems, which address the growing problem of staff shortages in assisted-living communities. In this context, detecting and assessing pain remain critical yet challenging tasks in both clinical and non-clinical settings. Traditional approaches such as self-reporting, physiological signal monitoring, and facial expression analysis often face limitations related to accessibility, equipment costs, and the need for professional support. To overcome these challenges in this work, we investigate a sensor-assisted system for pain detection and propose a lightweight framework that enables real-time classification of pain levels using acoustic sensors. Our system exploits the spectral features of voice signals that strongly correlate with pain to train Convolutional Neural Network (CNN) models. Our system has been validated through simulations in Jupiter Notebook and a Raspberry Pi-based hardware prototype. The experimental results demonstrate that the proposed three-level pain classification approach obtains an average accuracy of 72.74%, outperforming existing methods with the same pain-level granularity by 18.94–26.74% and achieving performance comparable to that of binary pain detection methods. Our hardware prototype, built from commercial off-the-shelf components for under 100 USD, achieves real-time processing speeds ranging from approximately 6 to 22 s. In addition to CNN models, our experiments demonstrate that other machine learning algorithms, such as Artificial Neural Networks, XGBoost, Random Forests, and Decision Trees, also prove to be applicable within our pain level classification framework. Full article

Techno-economic analysis (TEA) plays a vital role in assessing the feasibility and scalability of emerging technologies, especially in the context of innovation and development. Central to any effective TEA is a reliable and detailed model of capital and operational costs. This paper reports the development of such a model for optical networks in the framework of the SEASON project, aimed at supporting a broad spectrum of techno-economic evaluations. The model is constructed using publicly available data and expert insights from project participants. Its generalizable design allows it to be used both within the SEASON project and as a reference for other studies. By harmonizing assumptions and cost parameters, the model fosters consistency across different analyses. It includes cost and power consumption data for a wide range of commercially available optical network components (including transceivers for point-to-multipoint communications), introduces a statistical framework for estimating values for emerging technologies, and provides a cost model for multiband-doped fiber amplifiers. To demonstrate its practical relevance, the paper applies the model to two case studies: an evaluation of how the cost of various multiband node architectures scales with network traffic in meshed topologies and a comparison of different transport solutions to carry fronthaul flows in the radio access network. Full article

In recent years, flying ad hoc networks (FANETs) have attracted attention as aerial communication platforms for large-scale disasters. In wide, city-scale disaster zones, survivors’ devices often form multiple isolated clusters, while battery-powered unmanned aerial vehicles (UAVs) must periodically return to a ground station (GS). Under such conditions, conventional delay/disruption-tolerant networking (DTN) routing (e.g., PRoPHET) often traps bundles in clusters or UAVs, degrading the bundle delivery ratio (BDR) to the GS. This study proposes a DTN-based FANET architecture that integrates (i) a mobility model assigning UAVs to information–exploration UAVs that randomly patrol the disaster area and GS–relay UAVs that follow spoke-like routes to periodically visit the GS, and (ii) an extended PRoPHET-based routing protocol that exploits exogenous information on GS visits to bias delivery predictabilities toward GS–relay UAVs and UAVs returning for recharging. Simulations with The ONE in a 10 km × 10 km scenario with multiple clusters show that the proposed method suppresses BDR degradation by up to 41% relative to PRoPHET, raising the BDR from 0.27 to 0.39 in the five-cluster case and increasing the proportion of bundles delivered with lower delay. These results indicate that the proposed method is well-suited for relaying critical disaster-related information. Full article

Accurate trajectory tracking is a key requirement in unmanned ground vehicles (UGVs) operating in autonomous driving, mobile robotics, and industrial automation. In wireless Time-Sensitive Networking (WTSN) scenarios, trajectory accuracy strongly depends on deterministic packet delivery, precise traffic scheduling, and time synchronization among distributed devices. This paper quantifies the impact of IEEE 802.1Qbv time-aware traffic scheduling on trajectory tracking accuracy in UGVs operating over Wi-Fi-enabled TSN networks. The analysis focuses on how misconfigured real-time (RT) and best-effort (BE) transmission windows, as well as clock misalignment between devices, affect packet reception and control performance. A mathematical framework is introduced to predict the number of correctly received RT packets based on cycle time, packet periodicity, scheduling window lengths, and synchronization offsets, enabling the a priori dimensioning of RT and BE windows. The proposed model is validated through extensive simulations conducted in an ROS–Gazebo environment, utilising Linux-based traffic shaping and scheduling tools. Results show that improper traffic scheduling and synchronization offsets can significantly degrade trajectory tracking accuracy, while correctly dimensioned scheduling windows ensure reliable packet delivery and stable control, even under imperfect synchronization. The proposed approach provides practical design guidelines for configuring wireless TSN networks supporting real-time trajectory tracking in mobile robotic systems. Full article

This paper proposes the development of an IEC 61850-compliant platform that is readily programmable and deployable for future digital twin applications. Given the compatibility between IEC-61850 and digital twin concepts, a focused case study was conducted involving the robust development of a Raspberry Pi platform with protection relay functionality using the open-source libIEC61850 library. Leveraging IEC-61850’s object-oriented data modelling, the relay can be represented by fully consistent virtual and physical models, providing an essential foundation for accurate digital twin instantiation. The relay implementation supports high-speed Sampled Value (SV) subscription, real-time RMS calculations, IEC Standard Inverse overcurrent trip behaviour according to IEC-60255, and Generic Object-Oriented Substation Event (GOOSE) publishing. Further integration includes setting group functionality for dynamic parameter switching, report control blocks for MMS client–server monitoring, and GOOSE subscription to simulate backup relay protection behaviour with peer trip messages. A staged development methodology was used to iteratively develop features from simple to complex. At the end of each stage, the functionality of the added features was verified before proceeding to the next stage. The integration of the Raspberry Pi into Curtin’s IEC = 61,850 digital substation was undertaken to verify interoperability between IEDs, a key outcome relevant to large-scale digital twin systems. The experimental results confirm GOOSE transmission times below 4 ms, tight adherence to trip-time curves, and performance under higher network traffic. Such measured RMS and trip-time errors fall well within industry and IEC limits, confirming the reliability of the relay logic. The takeaways from this case study establish a high-performing, standardised foundation for a digital twin system that requires fast, bidirectional communication between a virtual and a physical system. Full article

Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO₂ uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles. Full article

This study analyzes the ICT infrastructure in teaching practice centers from the perspective of students enrolled in early childhood and primary education degree programs at a Spanish university during the 2024–2025 academic year. A quantitative, cross-sectional design was employed. A questionnaire was distributed to all first- to fourth-year students via the university platform, with a sample of 556 participants. The data collection instrument consisted of an ad hoc adaptation and extension of the validated EdSocEval_V2 questionnaire, ensuring factorial validity. It was used to examine the availability of technological resources for communication and digital management, together with personal and contextual variables to support data classification. Results indicate high availability of basic digital resources, including projectors, Wi-Fi, interactive whiteboards, printers, alongside limited access to robotics, digital tablets, and classrooms of the future. High homogeneity was observed in communication and digital management resources, such as websites, virtual learning environments and corporate email. MANOVA analyses revealed that students perceive ICT infrastructure to be more integrated at higher levels of primary education, with no significant differences based on school ownership. Binary logistic regressions showed that school ownership predicts the availability of certain ICT resources, with private schools exhibiting lower network presence. Full article

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO₂ emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments. Full article

This study aims to optimize the voltage profile of the grid by obtaining an optimum level of reactive power support from photovoltaic (PV) plants, thereby enhancing the efficiency of PV systems in power distribution networks and ensuring grid stability. Initially, voltage profiles in the sector, together with the structure and operating principles of PV plants, were considered in detail. Subsequently, the limits of reactive power support that can be provided by PV plants were determined. Then, the optimum levels of reactive power from the plants were determined using particle swarm optimization, genetic algorithm, Jaya algorithm, and firefly algorithm separately. The algorithms were tested through simulations conducted on a power distribution system operator in Türkiye. Additionally, a Modbus-based communication application was developed and tested, as a feasibility demonstration, to verify PV inverter accessibility and the capability of remotely writing reactive power reference setpoints. The quantitative optimization results reported in this manuscript are obtained from DIgSILENT PowerFactory simulations using the actual feeder model and time-series profiles. The results have revealed that PV plants can be effectively utilized as reactive power compensators to contribute to the operation of the grid under more ideal voltage profile conditions. In Türkiye, there is no regulatory or market mechanism to support reactive power provision from PV plants. Therefore, this study is novel in the Turkish market. The experimental results confirm that power generation from renewable energy can provide reactive support effectively when needed, which reveals that this approach is both technically feasible and practically relevant. Full article

This qualitative, longitudinal study explores how student members’ sense of belonging changed over time in a Networked Improvement Community (NIC). Co-authored by researchers and student participants, this study uses the “students as partners” framework and four constructs of belonging (motivation, competencies, opportunities, and perceptions) to examine how sense of belonging manifested and developed for five student NIC members across their first and final semesters of participation in the NIC. Retrospective analysis of journal entries and interview data collected over two years revealed: student motivations shifted from individual goals to a collective investment in the NIC’s systemic improvement goals; students developed competencies in leadership, data-informed decision-making, and equity based on their lived experiences; and intentional NIC structures supported a shift in the student members’ opportunities for belonging from relationship-building and anonymous decision-making to taking on more responsibilities in NIC initiatives. As a result, student perceptions evolved from uncertainty about their roles to having a strong sense of agency, influence, and community within the department. The findings from this study offer evidence that intentionally structured NICs can function as spaces where motivations, competencies, opportunities, and perceptions grow together and position student members as contributors to inclusive systemic change in undergraduate STEM education. Full article