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The EU power market system has successfully maintained a centralized governance structure ensuring stable electricity supply and affordable prices for over two decades. However, the ongoing energy transition towards carbon neutrality has exposed critical governance limitations, leading to challenges in community projects implementation. Given that Heating and Cooling (H&C) accounts for more than 50% of the EU’s energy consumption, community H&C initiatives can drive local energy transitions and support renewable integration. This study analyzes the best practices from European community energy initiatives, supplemented by insights from the Energy Leap project. By employing a comparative analysis approach, the study proposes a technically sound and regulatory feasible governance model, alongside a robust ecosystem support framework. The proposed framework introduces new roles and new forms of partnerships between communities—private entities and consumers—taking advantage of the benefits offered by the operation of Energy Communities (ECs), enhancing community engagement and regulatory adaptability. These insights offer practical guidance and contribute to effective policymaking in support of the EU’s energy transition objectives. Full article

This study examines the integration and sustainability of solar energy technologies as a tool for rural electrification in Ghana, using the Lofetsume community as a case study. Persistent electricity access deficits in rural areas, coupled with unreliable grid systems and high energy costs, underscore the need for alternative energy solutions. Through semi-structured interviews and surveys, the study explores community perspectives and expert views on the viability of solar energy in rural Ghana. Findings reveal strong grassroots support for solar energy due to its reliability and environmental benefits, despite barriers such as high upfront installation costs and maintenance challenges. The study recommends multi-stakeholder partnerships, innovative financing models, and capacity-building initiatives to enhance solar energy adoption. By prioritizing solar energy technologies, the government, private sector, and local communities can collaborate to develop sustainable and affordable electrification solutions, ultimately improving living standards in remote areas and contributing to Ghana’s broader energy sustainability goals. Full article

As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article

A synthesis of substituted 1,4-benzodiazepines has been developed via palladium-catalyzed cyclization of N-tosyl-disubstituted 2-aminobenzylamines with propargylic carbonates. The reaction proceeds through the formation of π-allylpalladium intermediates, which undergo intramolecular nucleophilic attack by the amide nitrogen to afford seven-membered benzodiazepine cores. In reactions involving unsymmetrical diaryl-substituted carbonates, regioselectivity was observed to favor nucleophilic attack at the alkyne terminus substituted with the more electron-rich aryl group, suggesting that electronic effects play a key role in determining product distribution. The versatility of this reaction was further demonstrated by constructing a benzodiazepine framework found in bioactive molecules, indicating its potential utility in medicinal chemistry. Mechanistic insights supported by stereochemical outcomes and X-ray crystallographic analysis of key intermediates reinforce the proposed reaction pathway. This palladium-catalyzed protocol thus offers an efficient and practical approach to access structurally diverse benzodiazepine derivatives. Full article

Data is the starting point for generating information and knowledge in the decision-making process. Open data, which is information disclosed free of charge through open licenses and reusable formats, has great potential for value creation. Therefore, the objective of this research is to evaluate Spanish autonomous communities’ open data initiatives in a category of information of vital importance: housing. The methodology employed was a population analysis of datasets labeled as housing, followed by a necessary data cleansing process due to the identification of various errors, which reduced the number of labeled datasets from 1000 to 599. Only 12 of the 17 autonomous communities provided this type of information. The analysis of the results reveals that autonomous communities’ approaches to open data initiatives are highly heterogeneous and that the supply is irregular, with the Basque Country accounting for 70% of the datasets considered in the research. The creation of an indicator that equally assesses the existence of information and file formats (breadth and reusability) continues to identify the Basque Country as the undisputed leader, with Catalonia and Cantabria in second and third place, the only autonomous communities to exceed 50 points out of a possible 100. The study concludes by highlighting that the lack of uniformity in the formulation and implementation of open data policies will limit the use of information and, consequently, its value. Therefore, a series of recommendations is issued in this regard. Full article

Migraine is a neurovascular paroxysmal disorder characterized by neurogenic inflammation and has a remarkable impact on the quality of life. The Food and Drug Administration (FDA) approved onabotulinumtoxin A in 2010 for the prophylactic treatment of chronic migraine. Today, in its 4th decade, it is approved in 100 countries for 15 main indications. Its mechanism of action, based on the inhibition of neurotransmitter release from primary sensory neurons, is very complex: it affords antinociception, but it also has an analgesic effect on neuropathic pain conditions and reduces the need for rescue medications. Genetic variants have been investigated for their potential role in the pathogenesis and clinical expression of migraine and of the response to treatments. These studies primarily involved genes associated with vascular regulation and cardiovascular pathology, including those encoding angiotensin-converting enzyme (ACE) and methylenetetrahydrofolate reductase (MTHFR). However, epigenetics and, particularly, genetic and epigenetic modifications are still poorly studied in terms of understanding the mechanisms implicated in susceptibility to migraine, aura, chronification and response to symptomatic and preventive treatments. In particular, the aim of the present study is to gather evidence on the genetic variants and epigenetic modifications affecting the pathway of the calcitonin gene-related peptide (CGRP), the target of onabotulinumtoxin A and of all the novel monoclonal antibodies. Full article

Beef fat injection technology, used to enhance the perceived quality of lower-grade meat, often results in artificially marbled beef that mimics the visual traits of Wagyu, characterized by dense fat distribution. This practice, driven by the high cost of Wagyu and the affordability of fat-injected beef, has led to the proliferation of mislabeled “Wagyu-grade” products sold at premium prices, posing potential food safety risks such as allergen exposure or consumption of unverified additives, which can adversely affect consumer health. Addressing this, this study introduces a smart sensing system integrated with handheld mobile devices, enabling consumers to capture beef images during purchase for real-time health-focused assessment. The system analyzes surface texture and color, transmitting data to a server for classification to determine if the beef is artificially marbled, thus supporting informed dietary choices and reducing health risks. Images are processed by applying a region of interest (ROI) mask to remove background noise, followed by partitioning into grid blocks. Local binary pattern (LBP) texture features and RGB color features are extracted from these blocks to characterize surface properties of three beef types (Wagyu, regular, and fat-injected). A support vector machine (SVM) model classifies the blocks, with the final image classification determined via majority voting. Experimental results reveal that the system achieves a recall rate of 95.00% for fat-injected beef, a misjudgment rate of 1.67% for non-fat-injected beef, a correct classification rate (CR) of 93.89%, and an F1-score of 95.80%, demonstrating its potential as a human-centered healthcare tool for ensuring food safety and transparency. Full article

Medical phantoms are essential to imaging calibration, clinician training, and the validation of therapeutic procedures. However, most ultrasound phantoms prioritize acoustic realism while neglecting the viscoelastic and anisotropic properties of fibrous soft tissues. This gap limits their effectiveness in modeling realistic biomechanical behavior, especially in wave-based diagnostics and therapeutic ultrasound. Current materials like gelatine and agarose fall short in reproducing the complex interplay between the solid and fluid components found in biological tissues. To address this, we developed a soft, anisotropic composite whose dynamic mechanical properties resemble fibrous biological tissues such as skin and skeletal muscle. This material enables wave propagation and vibration studies in controllably anisotropic media, which are rare and highly valuable. We demonstrate the tunability of damping and stiffness aligned with fiber orientation, providing a versatile platform for modeling soft-tissue dynamics and validating biomechanical simulations. The phantoms achieved Young’s moduli of 7.16–11.04 MPa for skin and 0.494–1.743 MPa for muscles, shear wave speeds of 1.51–5.93 m/s, longitudinal wave speeds of 1086–1127 m/s, and sound absorption coefficients of 0.13–0.76 dB/cm/MHz, with storage, loss, and complex moduli reaching 1.035–6.652 kPa, 0.1831–0.8546 kPa, and 2.138–10.82 kPa. These values reveal anisotropic response patterns analogous to native tissues. This novel natural fibrous composite system affords sustainable, low-cost ultrasound phantoms that support both mechanical fidelity and acoustic realism. Our approach offers a route to next-gen tissue-mimicking phantoms for elastography, wave propagation studies, and dynamic calibration across diverse clinical and research applications. Full article

A maximum entropy (ME) methodology was used to infer the Hubble constant from the temperature anisotropies in cosmic microwave background (CMB) measurements, as measured by the Planck satellite. A simple cosmological model provided physical insight and afforded robust statistical sampling of a parameter space. The parameter space included the spectral tilt and amplitude of adiabatic density fluctuations of the early universe and the present-day ratios of dark energy, matter, and baryonic matter density. A statistical temperature was estimated by applying the equipartition theorem, which uniquely specifies a posterior probability distribution. The ME analysis inferred the mean value of the Hubble constant to be about 67 km/sec/Mpc with a conservative standard deviation of approximately 4.4 km/sec/Mpc. Unlike standard Bayesian analyses that incorporate specific noise models, the ME approach treats the model error generically, thereby producing broader, but less assumption-dependent, uncertainty bounds. The inferred ME value lies within 1σ of both early-universe estimates (Planck, Dark Energy Signal Instrument (DESI)) and late-universe measurements (e.g., the Chicago Carnegie Hubble Program (CCHP)) using redshift data collected from the James Webb Space Telescope (JWST). Thus, the ME analysis does not appear to support the existence of the Hubble tension. Full article

This study investigates the evaluation of housing policies for migrants in China, focusing on the interplay between rental and purchase decisions under the rent-and-purchase policy (RPP) framework. Employing a system dynamics model, we simulate migrant housing choices from 2001 to 2023 and forecast market trends from 2024 to 2030. The results indicate that RPPs significantly improve housing quality and reduce costs for migrants by mitigating institutional disparities and market distortions. Scenario analyses demonstrate that a coordinated approach combining supply-side interventions (e.g., affordable housing expansion) with rights-based policies (e.g., equalizing renter and buyer rights) effectively balances affordability and demand stability. The findings emphasize the critical role of addressing rights inequalities and advocate for a holistic policy framework to tackle migrant housing challenges, offering actionable insights for policymakers in system science and urban planning. Full article

As many companies adopted hybrid work arrangements during and after the COVID-19 outbreak, interest in Metaverse applications for virtual offices grew considerably. Along with this growing interest, the risk of data breaches has also increased, as virtual offices often handle confidential documents for businesses. For this reason, existing studies have explored Metaverse user authentication methods; however, their methods suffer from several limitations, such as the need for additional sensors and one-time authentication. Therefore, this paper proposes a novel behavioral authentication framework for secure Metaverse workspaces. The proposed framework adopts keyboard typing behavior that is common in the office and does not cause fatigue to users as an authentication factor to afford active and continuous user authentication. Based on our evaluation, the user identification accuracy achieved an average of approximately 95% among 11 of 15 participants, with the highest-performing user reaching an accuracy of 99.77%. In addition, the proposed framework achieved an average false acceptance rate of 0.41% and a false rejection rate of 4.02%. It was also evaluated with existing studies using requirements for user authentication in the Metaverse to demonstrate its strengths. Therefore, this framework can fully ensure a secure Metaverse office by preventing unauthenticated users. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

Background: Understanding the determinants of children’s outdoor play is an important element for child development and broader public health outcomes. There is growing evidence that children’s opportunities for play, particularly outdoor risky play, are diminishing. Parents are concerned with keeping their child safe while affording them independence to play. This study explored parents’ attitudes to risky play and practices around children’s independent mobility in Ireland with the aim of informing public health strategies promoting healthy childhood environments. Methods: An online survey comprising validated scales and standardised questions was completed by a nationally represented sample of 376 parents of children up to 16 years. Data was analysed via descriptive statistics, chi-square tests, and regression analysis. Results: A total of 376 participants accessed the survey, of which 349 completed it. A total of 84% of participants were female. A total of 74% agreed that children need regular exposure to actual risk to develop risk management skills, and 71% trusted their children to play safely. Chi-square tests reveal significant associations between outdoor play in the rain and school travel (p < 0.01), and appropriate age to begin activities at home and in educational settings (p < 0.05). A moderate association was found between the method of school travel and children’s permission to play in the rain (Cramer’s V = 0.51). Respondents considered supervision to be a necessity to ensure their children’s safety. Overall, the results indicate that parents were risk-averse in three of the six categories of risky play, namely, play near dangerous elements, play with adult tools, and out-of-sight play. Conclusions: This study presents a descriptive analysis of findings from the Ireland State of Play Survey. Findings indicate that although parents recognise the benefits of risky play, there is some contradiction between parental attitudes and actual practices, with a lack of willingness or confidence in permitting their children to participate in all such activities. Full article

Side-scan sonar (SSS) image classification is crucial for underwater applications, but the trade-off between the accuracy afforded by high-resolution images and the associated computational cost challenges deployment, particularly on resource-constrained platforms like AUVs. This study systematically investigates and quantifies this accuracy–efficiency trade-off in SSS image classification by varying input resolution. Using two distinct SSS datasets and a resolution-adaptive deep learning strategy employing MobileNetV2 and ResNet variants across six resolutions, we evaluated classification accuracy and computational metrics. Results demonstrate a clear inverse relationship: decreasing resolution significantly reduces computational load and processing times but lowers classification accuracy, with the degradation being more pronounced for the more complex four-class dataset. Notably, model test accuracy did not necessarily increase monotonically with resolution. Importantly, acceptable accuracy levels above 90% or 80% could be maintained at significantly lower resolutions, offering substantial efficiency gains. In conclusion, strategically reducing SSS image resolution based on application-specific accuracy requirements is a viable approach for optimizing computational resources. This work provides a quantitative framework for navigating this trade-off and underscores the need for developing SSS-specific architectures for future advancements. Full article

Perovskite solar cells (PSCs) have already been reported as a promising alternative to traditional energy sources due to their excellent power conversion efficiency, affordability, and versatility, which is particularly relevant considering the growing worldwide demand for energy and increasing scarcity of natural resources. However, operational concerns under environmental stresses hinder its economic feasibility. Through the addition of cesium (Cs), this study investigates how to optimize perovskite solar cells (PSCs) based on methylammonium lead-iodide (MAPbI₃) by creating mixed-cation compositions of MA_1−xCs_xPbI₃ (x = 0, 0.25, 0.5, 0.75, 1) for devices A to E, respectively. The impact of cesium content on the following factors, such as open-circuit voltage (V_oc), short-circuit current density (J_sc), fill factor (FF), and power conversion efficiency (PCE), was investigated using simulation software, with ITO/TiO₂/MA_1−xCs_xPbI₃/Spiro-OMeTAD/Au as a device architecture. Due to diminished defect density, the device with x = 0.5 (MA_0.5Cs_0.5PbI₃) attains a maximum power conversion efficiency of 18.53%, with a V_oc of 0.9238 V, J_sc of 24.22 mA/cm², and a fill factor of 82.81%. The optimal doping density of TiO₂ is approximately 10²⁰ cm⁻³, while the optimal thicknesses of the electron transport layer (TiO₂, 10–30 nm), the hole-transport layer (Spiro-OMeTAD, about 10–20 nm), and the perovskite absorber (750 nm) were identified to maximize efficiency. The inclusion of a small amount of Cs may improve photovoltaic responses; however, at elevated concentrations (x > 0.5), power conversion efficiency (PCE) diminished due to the presence of trap states. The results show that mixed-cation perovskite solar cells can be a great commercially viable option because they strike a good balance between efficiency and performance. Full article