Search Results (12,390)

A balance between support and independence in guardianship systems is of high concern, especially with those who need help in making decisions. The research presents a novel approach to evaluating voluntary models of guardianship, focusing on the preservation of individual autonomy and examining the underlying decision symmetry in assessing diverse guardianship options. The ultimate solution to the inherent uncertainty and lack of objectivity in expert evaluations is to apply the circular q-rung orthopair fuzzy (Cq-ROF) combined compromise for ideal solution (CoCoFISo) approach, an effective multi-criteria decision-making (MCDM) model that integrates ranking and sorting views using a Cq-ROF framework within a symmetry-oriented analytical perspective. These are five major assessment factors: how well autonomy is preserved, legal and ethical adherence, psychological health, social integration aid, and risk prevention. It explores ten alternative approaches to guardianship, ranging from complete legal guardianship to community-based self-management solutions, and the use of technology as an element of support. The suggested approach can facilitate more sophisticated modelling of expert opinions, rather than relying on simplistic and straightforward distinctions and diverse evaluations. The case study results indicate that the hybrid and supported forms of decision-making could offer opportunities to preserve a high degree of personal autonomy while ensuring safety and compliance. The research gives a coherent, adaptable, and explainable approach to managing ethical and policy-level judgment concerning voluntary guardianship systems. Full article

The growing demand for dermocosmetics with ingredients of natural origin reflects the pivotal role of cutaneous health and appearance in consumer self-esteem. Under this context, clays have attracted attention for their potential applications in dermatological care. Our research work aimed to increase knowledge on the short-term impact of cosmetic formulations containing a blend of red, green, and black clays, assessing their effects on sebum regulation and in cutaneous biomechanical behavior (firmness/elasticity). Unlike daily skincare products, clay masks are used infrequently and for short durations; thus, an in vivo assessment was conducted after a 2-h application to reflect typical consumer use. The mineralogical and physicochemical properties of the different clays were characterized. Mineralogical analysis revealed distinct compositions among the clays: black clay exhibited a simpler mineral profile, lower density, and smaller particle size; green clay contained expandable smectite and was the densest; and red clay displayed the largest average particle size and highest iron content. Thermal analysis identified two major transitions: dehydration and kaolinite dehydroxylation. In vivo studies conducted in participants showed a significant reduction in skin oiliness across all clay-based formulations compared to baseline, control, and placebo following a 2-h application, and the rebound sebum production was dependent on clay concentration. Cutometry measurements did not reveal statistically significant improvements in skin firmness or elasticity compared to the control and placebo. The findings suggested that while clay-based formulations effectively reduced skin oiliness in the short term, their impact on sebum regulation and on skin biomechanical properties was limited after such a short product application period. Additional studies are warranted to elucidate the distinct effects of each clay, assess their behavior in different formulation bases, and evaluate their efficacy after repeated use. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is associated with impaired functional capacity, persistent fatigue, and poor health-related quality of life despite advances in pharmacological therapy. Exercise training has been proposed as a non-pharmacological intervention, but its efficacy and safety remain incompletely defined. This systematic review aimed to evaluate the effects of exercise training on functional aerobic capacity and quality of life in adults with SLE. Methods: A comprehensive search of PubMed, EMBASE, Cochrane Library, and PEDro was conducted to identify randomized controlled trials published up to October 2022, in accordance with the PRISMA guidelines. Results: Twelve randomized controlled trials involving 619 participants were included. Exercise interventions were heterogeneous and comprised aerobics, resistance, combined programs, vibration training, home-based protocols, and counseling strategies, with durations ranging from 6 weeks to 12 months. Supervised aerobic and combined interventions consistently improved functional aerobic capacity, while quality of life benefits were reported across several domains, particularly physical health, vitality, and fatigue. Additional positive effects were observed on fatigue, depression, pain, sleep, insulin sensitivity, and self-care ability, without evidence of increased disease activity. Conclusions: Structured exercise is safe and can meaningfully enhance functional capacity and quality of life in patients with SLE, supporting its incorporation into multidisciplinary clinical management. Full article

Red mud, a by-product of aluminum production, leads to significant environmental challenges due to its alkalinity and presence of soluble compounds. This study explores its valorization through agglomeration with blast furnace sludge as a reducing agent to form self-reducing briquettes. Five C/Fe₂O₃ ratios (0.131, 0.262, 0.523, 0.840 and 1.000) were tested to determine the most effective reducing condition, with 0.840 emerging as optimal based on thermal analysis (mass loss of 27.44 wt.% at 1200 °C and iron formation specific energy of 450 J g⁻¹). Briquettes prepared with three agglomeration methods varying in water content (water/starch ratios of 6:1, 12:1 and 18:1) were evaluated through drop, compression and abrasion tests. The agglomeration method with a 12:1 water/solid ratio, involving both starch gelatinization and red mud water absorption, produced the most mechanically resistant briquettes (19.210 MPa). The mechanical and metallurgical properties of the 0.840-2W briquettes after reduction at 700, 950, 1200 and 1450 °C (temperature maintenance for 15 min) were assessed to define the best compromise between the reduction degree and mechanical strength. While reduction at 950 °C led to the weakest structure (0.449 MPa) but poor metallization, 1450 °C ensured the highest degree of reduction (94%) with adequate brittleness to facilitate a possible subsequent magnetic separation. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

Hydrogels are crosslinked polymer chains that form a three-dimensional network, widely used for wound dressing due to their ability to absorb significant amounts of fluid. This study aimed to develop a hydrogel patch for wound dressing with self-healing properties, particularly for joints and stretchable body parts, providing a physical barrier while maintaining an optimal environment for wound healing. Polyvinyl alcohol (PVA) and sodium carboxymethyl cellulose (Na CMC) were crosslinked with borax, which reacts with the active hydroxyl groups in both polymers to form a hydrogel. The patches were loaded with ciprofloxacin HCl (CIP), a broad-spectrum antibiotic used to prevent and treat various types of wound infections. Hydrogels were subjected to rheological, morphological, antimicrobial, self-healing, ex vivo release, swelling, cytotoxicity, wound healing, and stability studies. The hydrogels exhibited shear-thinning, thixotropic, and viscoelastic properties. Microscopic images of the CIP hydrogel patch showed a porous, crosslinked matrix. The antimicrobial activity of the patch revealed antibacterial effectiveness against five types of Gram-positive and Gram-negative bacteria, demonstrating a minimum inhibitory concentration of 0.05 μg/mL against E. coli. The swelling percentage was found to be 337.4 ± 12.7%. The cumulative CIP release percentage reached 103.7 ± 3.7% after 3 h, followed by zero-order release kinetics. The stability studies revealed that the crossover point shifted toward higher frequencies after 3 months of storage at room temperature, suggesting a relaxation in the hydrogel bonds. The cytotoxicity study revealed that the CIP hydrogel patch is non-cytotoxic. Additionally, the in vivo study demonstrated that the CIP hydrogel patch possesses wound-healing ability. Therefore, the CIP PVA/Na CMC/Borax patch could be used in wound dressing. Full article

Autonomous commercial vehicles often mount multiple LiDARs to enlarge their field of view, but conventional calibration is labor-intensive and prone to drift during long-term operation. We present an online self-calibration method that combines a ground plane motion constraint with a virtual RGB–D projection, mapping 3D point clouds to 2D feature/depth images to reduce feature extraction cost while preserving 3D structure. Motion consistency across consecutive frames enables a reduced-dimension hand–eye formulation. Within this formulation, the estimation integrates geometric constraints on $SE\left(3\right)$ using Lagrange multiplier aggregation and quasi-Newton refinement. This approach highlights key aspects of identifiability, conditioning, and convergence. An online monitor evaluates plane alignment and LiDAR–INS odometry consistency to detect degradation and trigger recalibration. Tests on a commercial vehicle with six LiDARs and on nuScenes demonstrate accuracy comparable to offline, target-based methods while supporting practical online use. On the vehicle, maximum errors are 6.058 cm (translation) and 4.768° (rotation); on nuScenes, 2.916 cm and 5.386°. The approach streamlines calibration, enables online monitoring, and remains robust in real-world settings. Full article

Accurate inversion of the T₂ spectrum of shale oil reservoir fluids is crucial for reservoir evaluation. However, traditional nuclear magnetic resonance inversion methods face challenges in extracting features from multi-exponential decay signals. This study proposed an inversion method that combines autoencoder (AE) and Fourier transform, aiming to enhance the accuracy and stability of T₂ spectrum estimation for shale oil reservoirs. The autoencoder is employed to automatically extract deep features from the echo train, while the Fourier transform is used to enhance frequency domain features of multi-exponential decay information. Furthermore, this paper designs a customized weighted loss function based on a self-attention mechanism to focus the model’s learning capability on peak regions, thereby mitigating the negative impact of zero-value regions on model training. Experimental results demonstrate significant improvements in inversion accuracy, noise resistance, and computational efficiency compared to traditional inversion methods. This research provides an efficient and reliable new approach for precise evaluation of the T₂ spectrum in shale oil reservoirs. Full article

The Sporulation-Inspired Optimization Algorithm (SIOA) is an innovative metaheuristic optimization method inspired by the biological mechanisms of microbial sporulation and dispersal. SIOA operates on a dynamic population of solutions (“microorganisms”) and alternates between two main phases: sporulation, where new “spores” are generated through adaptive random perturbations combined with guided search towards the global best, and germination, in which these spores are evaluated and may replace the most similar and less effective individuals in the population. A distinctive feature of SIOA is its fully self-adaptive parameter control, where the dispersal radius and the probabilities of sporulation and germination are dynamically adjusted according to the progress of the search (e.g., convergence trends of the average fitness). The algorithm also integrates a special “zero-reset” mechanism, enhancing its ability to detect global optima located near the origin. SIOA further incorporates a stochastic local search phase to refine solutions and accelerate convergence. Experimental results demonstrate that SIOA achieves high-quality solutions with a reduced number of function evaluations, especially in complex, multimodal, or high-dimensional problems. Overall, SIOA provides a robust and flexible optimization framework, suitable for a wide range of challenging optimization tasks. Full article

Large Language Models (LLMs) exhibit remarkable generative capabilities but remain vulnerable to hallucinations—outputs that are fluent yet inaccurate, ungrounded, or inconsistent with source material. To address the lack of methodologically grounded surveys, this paper introduces a novel method-oriented taxonomy of hallucination mitigation strategies in text-based LLMs. The taxonomy organizes over 300 studies into six principled categories: Training and Learning Approaches, Architectural Modifications, Input/Prompt Optimization, Post-Generation Quality Control, Interpretability and Diagnostic Methods, and Agent-Based Orchestration. Beyond mapping the field, we identify persistent challenges such as the absence of standardized evaluation benchmarks, attribution difficulties in multi-method systems, and the fragility of retrieval-based methods when sources are noisy or outdated. We also highlight emerging directions, including knowledge-grounded fine-tuning and hybrid retrieval–generation pipelines integrated with self-reflective reasoning agents. This taxonomy provides a methodological framework for advancing reliable, context-sensitive LLM deployment in high-stakes domains such as healthcare, law, and defense. Full article

Potato leaf diseases are recognized as a major threat to agricultural productivity and global food security, emphasizing the need for rapid and accurate detection methods. Conventional manual diagnosis is limited by inefficiency and susceptibility to bias, whereas existing automated approaches are often constrained by insufficient feature extraction, inadequate integration of multiple leaves, and poor generalization under complex field conditions. To overcome these challenges, a ResNet18-SAWF model was developed, integrating a self-attention mechanism with a multi-scale feature-fusion strategy within the ResNet18 framework. The self-attention module was designed to enhance the extraction of key features, including leaf color, texture, and disease spots, while the feature-fusion module was implemented to improve the holistic representation of multi-leaf structures under complex backgrounds. Experimental evaluation was conducted using a comprehensive dataset comprising both simple and complex background conditions. The proposed model was demonstrated to achieve an accuracy of 98.36% on multi-leaf images with complex backgrounds, outperforming baseline ResNet18 (91.80%), EfficientNet-B0 (86.89%), and MobileNet_V2 (88.53%) by 6.56, 11.47, and 9.83 percentage points, respectively. Compared with existing methods, superior performance was observed, with an 11.55 percentage point improvement over the average accuracy of complex background studies (86.81%) and a 0.7 percentage point increase relative to simple background studies (97.66%). These results indicate that the proposed approach provides a robust, accurate, and practical solution for potato leaf disease detection in real field environments, thereby advancing precision agriculture technologies. Full article

Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R² of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms. Full article

Hybrid marine energy platforms that integrate wave energy converters (WECs) and hydrokinetic turbines (HKTs) offer potential to improve energy yield and system stability in marine environments. This study identifies a compatible WEC–HKT integrated system concept through a structured concept selection framework based on multi-criteria decision analysis (MCDA). The framework follows a two-stage process: individual technology assessment using eight criteria (efficiency, TRL, self-starting capability, structural simplicity, integration feasibility, environmental adaptability, installation complexity, and indicative cost) and pairing evaluation using five integration-focused criteria (structural compatibility, PTO feasibility, mooring synergy, co-location feasibility, and control compatibility). Criterion weights were assigned through a four-level importance framework based on expert judgment from 11 specialists, with unequal weights for the individual evaluation and equal weights for the integration stage. Four WEC types (oscillating water column, point absorber, overtopping wave energy converter, and oscillating wave surge converter) and four HKT types (Darrieus, Gorlov, Savonius, and hybrid Savonius–Darrieus rotor) are assessed using literature-derived scoring and weighted ranking. The results show that the oscillating water column achieved the highest weighted score among the WECs with 4.05, slightly ahead of the point absorber, which scored 3.85. For the HKTs, the Savonius rotor led with a score of 4.05, surpassing the hybrid Savonius–Darrieus rotor, which obtained 3.50, by 0.55 points. In the pairing stage, the OWC–Savonius configuration achieved the highest integration score of 4.2, surpassing the PA–Savonius combination, which scored 3.4, by 0.8 points. This combination demonstrates favorable structural layout, PTO independence, and mooring simplicity, making it the most promising option for early-stage hybrid platform development. Full article

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design. Full article

Background: Patient satisfaction with diabetes technology is increasingly recognized as a key factor in therapeutic success. Patient-reported outcomes (PROs) are gaining importance in diabetes care and in the evaluation of advanced insulin delivery systems. Objectives: This study aimed to design and validate a new questionnaire, the Insulin Pump Infusion Sets Satisfaction Scale (IPISS), to assess satisfaction with insulin infusion sets among individuals with type 1 diabetes. Methods: The questionnaire was developed by our Diabetology Unit in two versions: one for patient self-reporting and one for caregivers when the patient is too young to complete it autonomously. Content validity was assessed by six healthcare professionals (three diabetologists and three nurses) based on Polit and Beck’s methodology. The Item Content Validity Index (I-CVI) was calculated for both relevance and comprehensibility and was considered satisfactory if expert agreement reached ≥83%. The Scale Content Validity Index (S-CVI) was computed as the average of I-CVIs, with a cut-off value > 90% deemed acceptable. Results: Almost all items achieved 100% positive agreement for both relevance and comprehensibility, except one item in the caregiver version, for which one rater did not provide a rating for comprehensibility (I-CVI = 83.3%). The S-CVI was 100% for relevance in both versions, 99.24% for comprehensibility in the caregiver version, and 100% in the patient version. Conclusions: The IPISS is a content-validated, self-reported tool, suitable for evaluating satisfaction with infusion sets in individuals using insulin pumps, with versions adapted for both patients and caregivers. Full article