Search Results (2,808)

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article

Background: Previous studies have shown a relationship between Visceral Adipose Tissue (VAT) and Hepatic Fat Content (HFC), and increases in HFC are linked to metabolic abnormalities similar to those associated with elevated VAT. Several short-term and long-term studies have supported these findings. Lifestyle interventions remain the cornerstone of treatment for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), although the ideal dietary regimen is still under debate. Methods: Data on 2040 patients were extracted from the Clinical Nutrition Unit database between 2017 and 2019. Of these, 474 subjects with MASLD and Body Mass Index (BMI) ≥ 35 kg/m² were treated with a four-month low-carbohydrate dietary intervention called the “Strong Diet” (StD). VAT and liver stiffness were measured at baseline and after four months of treatment using ultrasound. Results: Our study demonstrates the significant efficacy of StD in reducing VAT in MASLD patients with moderate hepatic steatosis. In subjects with severe steatosis, there is no statistically significant response to dietary intervention. This may be attributed to several irreversible molecular mechanisms that fundamentally alter the hepatic microenvironment and limit the liver’s capacity for regeneration and metabolic recovery. Conclusions: Improvements were largely confined to patients with moderate MASLD, with limited benefit in severe disease. Although dietary intervention remains the cornerstone of MASLD management, patients with severe steatosis should be informed about the potential limited resolution of steatosis, even with optimal metabolic control. Full article

Risk prediction models are increasingly used in the management of coronary artery disease (CAD), with applications ranging from diagnostic stratification to prognostic assessment and therapeutic guidance. In the context of CAD and percutaneous coronary intervention, clinical decision-making often relies on risk scores to estimate the likelihood of ischemic and bleeding events and to tailor antithrombotic strategies accordingly. Traditional scores are derived from clinical, anatomical, procedural, and laboratory variables, and their performance is evaluated based on discrimination and calibration metrics. While many established models are simple, interpretable, and externally validated, their predictive ability is often moderate and may be limited by outdated derivation cohorts, overfitting, or lack of generalizability. Recent advances have introduced artificial intelligence and machine learning models that can process large, high-dimensional datasets and identify patterns not apparent through conventional methods, with the aim to incorporate complex data; however, they are not exempt from limitations and struggle with integration into clinical practice. Notably, ethical issues, such as equity in model application, over-stratification, and real-world implementation, are of critical importance. The ideal predictive model should be accurate, generalizable, and clinically actionable. This review aims at providing an overview of the main predictive models used in the field of CAD and to discuss methodological challenges, with a focus on strengths, limitations and areas of applicability of predictive models. Full article

Climate change is elevating temperatures, shifting weather patterns, and increasing frequency and severity of extreme weather events. Despite the urgency with which solutions are needed, relatively few studies comprehensively investigate the effectiveness of alternative flood risk management options under different climate conditions. Specifically, we are interested in a comparison of the effectiveness of resistance, nature-based, and managed retreat strategies. Using an integrated 1D-2D PCSWMM model, this paper presents a comprehensive investigation into the effectiveness of alternative adaptation strategies in reducing flood risks in Eastwick, a community of Philadelphia, PA, subject to fluvial, pluvial, and coastal flood hazards. While addressing the urgent public need to develop local solutions to this community’s flood problems, the research also presents transferable insights into the limitations and opportunities of different flood risk reduction strategies, manifested here by a levee, watershed-scale green stormwater infrastructure (GSI) program, and a land swap. The effectiveness of these options is compared, respectively, under compound climate change conditions, with the spatiotemporal patterns of precipitation and Delaware river tidal conditions based on Tropical Storm Isaias (2020). The hypothesis was that the GSI and managed retreat approaches would be superior to the levee, due to their intrinsic ability to address the compound climate hazards faced by this community. Indeed, the findings illustrate significant differences in the predicted flood extents, depths, and duration of flooding of the various options under both current and future climate scenarios. However, the ideal remedy to flooding in Eastwick is more likely to require an integrated approach, based on more work to evaluate cost-effectiveness, stakeholder preferences, and various logistical factors. The paper concludes with a call for integrating multiple strategies into multifunctional flood risk management. Full article

Background: Skin cancer represents one of the most prevalent malignancies worldwide, with melanoma accounting for approximately 75% of skin cancer-related deaths despite comprising fewer than 5% of cases. Early detection dramatically improves survival rates from 14% to over 99%, highlighting the urgent need for accurate and accessible diagnostic tools. While deep learning has shown promise in dermatological diagnosis, existing approaches lack clinical explainability and deployable interfaces that bridge the gap between research innovation and practical healthcare applications. Methods: This study implemented a comprehensive multimodal deep learning framework using the HAM10000 dataset (10,015 dermatoscopic images across seven diagnostic categories). Three CNN architectures (DenseNet-121, EfficientNet-B3, ResNet-50) were systematically compared, integrating patient metadata, including age, sex, and anatomical location, with dermatoscopic image analysis. The first implementation of XRAI (eXplanation with Region-based Attribution for Images) explainability for skin lesion classification was developed, providing spatially coherent explanations aligned with clinical reasoning patterns. A deployable web-based clinical interface was created, featuring real-time inference, comprehensive safety protocols, risk stratification, and evidence-based cosmetic recommendations for benign conditions. Results: EfficientNet-B3 achieved superior performance with 89.09% test accuracy and 90.08% validation accuracy, significantly outperforming DenseNet-121 (82.83%) and ResNet-50 (78.78%). Test-time augmentation improved performance by 1.00 percentage point to 90.09%. The model demonstrated excellent performance for critical malignant conditions: melanoma (81.6% confidence), basal cell carcinoma (82.1% confidence), and actinic keratoses (88% confidence). XRAI analysis revealed clinically meaningful attention patterns focusing on irregular pigmentation for melanoma, ulcerated borders for basal cell carcinoma, and surface irregularities for precancerous lesions. Error analysis showed that misclassifications occurred primarily in visually ambiguous cases with high correlation (0.855–0.968) between model attention and ideal features. The web application successfully validated real-time diagnostic capabilities with appropriate emergency protocols for malignant conditions and comprehensive cosmetic guidance for benign lesions. Conclusions: This research successfully developed the first clinically deployable skin lesion classification system combining diagnostic accuracy with explainable AI and practical patient guidance. The integration of XRAI explainability provides essential transparency for clinical acceptance, while the web-based deployment democratizes access to advanced dermatological AI capabilities. Comprehensive validation establishes readiness for controlled clinical trials and potential integration into healthcare workflows, particularly benefiting underserved regions with limited specialist availability. This work bridges the critical gap between research-grade AI models and practical clinical utility, establishing a foundation for responsible AI integration in dermatological practice. Full article

Background: Class II is considered one of the most common malocclusions, influencing 37% of schoolchildren in Europe and 33% of orthodontic patients in the United States. When this type of malocclusion is combined with increased overjet with proclined teeth and maxillary excess, then moving maxillary molars distally is suggested. According to the recent literature, modern appliances that lack patient cooperation can be combined with temporary anchorage devices to provide absolute and skeletal anchorage while supporting the non-compliance appliances to eliminate their side effects, such as anterior and posterior anchorage loss along with maxillary molar inclination and rotation. To counteract these limitations, the Advanced Molar Distalization Appliance (amda^®), a non-compliance appliance for maxillary molar distalization supported by two mini-implants (MIs) with anterior abutments, was recently developed. Methods: In this preliminary prospective clinical trial, eight consecutive patients treated with the amda^® are evaluated through lateral cephalometric radiographs, while its application, construction, and anchorage is presented and discussed. The evaluation of dentoalveolar and skeletal changes has been made with 14 variables measured on the pre- and post-cephalometric radiographs before and immediately after maxillary molar distalization (T₀ and T₁, respectively), along with cephalometric superimpositions by the structural method. Results: In total, the mean distal molar movement was 4.2 ± 1.37 mm, the mean distal tipping was 1.7 ± 1.9 degrees, and the vertical movement was 1.6 ± 2.6 mm. Conclusions: The amda^® seems to provide an ideal option for treating patients with Class II malocclusion, achieving bodily movement of the maxillary molars with only minimal distal tipping and no anchorage loss. Full article

Three-dimensional measurement of translucent objects using structured light techniques remained fundamentally challenging due to severe degradation of fringe patterns caused by subsurface scattering, which inevitably introduced phase errors and compromised measurement accuracy. Although deep learning had emerged as a powerful tool for fringe analysis, its practical implementation was hindered by the impractical requirement for large-scale labeled datasets, particularly in scattering-dominant measurement scenarios. To overcome these limitations, we developed a self-learning-based fringe domain conversion method inspired by image style transfer principles, where degraded and ideal fringe patterns were treated as distinct domains for cyclic translation. The proposed framework employed dual generators and discriminators to establish cycle-consistency constraints while incorporating both numerical intensity-based and physical phase-derived optimization targets, effectively suppressing phase errors and improving fringe modulation without requiring paired training data. Experimental validation demonstrated superior performance in reconstructing high-fidelity 3D morphology of translucent objects, establishing this approach as a robust solution for precision metrology of complex scattering media. Full article

Lawns have evolved from medieval European grasslands into globally accepted urban green surfaces, serving recreational, aesthetic and cultural purposes. Today lawn surfaces are essential components of public urban green space (PUGS), fulfilling ecosystem services such as urban heat mitigation, carbon sequestration and social well-being. However, their ecological and resource-intensive disservices, particularly in dry climates, have prompted growing concerns among environmental scientists, urban planners and landscape designers. In water-scarce regions like Perth, Western Australia, traditional lawns face increasing scrutiny due to their high irrigation demands and limited ecological diversity. This study contributed to the transdisciplinary LAWN as Cultural and Ecological Phenomenon project, focusing on the perspectives of professionals, landscape architects, park managers, turf producers and researchers responsible for the planning, design and management of urban lawn in PUGS. Using qualitative methods (semi-structured in-depth interviews), the research explores expert insights on the values, challenges and future trajectories of lawn use in a warming, drying climate. The interviews included 21 participants. Findings indicate that while professionals acknowledge lawns’ continued relevance for sports and active recreation, water scarcity is a major concern influencing design and species selection. Alternatives such as drought-tolerant plants, hard landscaping and multifunctional green spaces are increasingly considered for non-sporting areas. Despite growing concerns, the ideal lawn is still envisioned as an expansive, green, soft surface, mirroring entrenched public preferences. This study underscores the need to balance environmental sustainability with public preference and cultural expectations of green lawns. Balancing expert insights with public attitudes is vital for developing adaptive, water-conscious landscape design strategies suited to future urban planning and environmental conditions in Mediterranean climates. Full article

As vision and mission statements embody the directions set forth by an organization, their connection to the Sustainable Development Goals (SDGs) must be made explicit to guide overall decision-making in taking strides toward the sustainability agenda. The semantic alignment of these strategic statements with the SDGs is investigated in a previous study, although several limitations need further exploration. Thus, this study aims to advance two contributions: (1) utilizing the capabilities of LLMs (Large Language Models) in text semantic analysis and (2) integrating fuzziness into the problem domain by using a novel intuitionistic fuzzy set extension of the PROBID (Preference Ranking On the Basis of Ideal-average Distance) method. First, a systematic approach evaluates the semantic alignment of organizational strategic statements with the SDGs by leveraging the use of LLMs in semantic similarity and relatedness tasks. Second, viewing it as a multi-criteria decision-making (MCDM) problem and recognizing the limitations of LLMs, the evaluations are represented as intuitionistic fuzzy sets (IFSs), which prompted the development of an IF extension of the PROBID method. The proposed IF-PROBID method was then deployed to evaluate the 47 top Philippine corporations. Utilizing ChatGPT 3.5, 7990 prompts with repetitions generated the membership, non-membership, and hesitance scores for each evaluation. Also, we developed a cohort-dependent SDG–vision–mission matrix that categorizes corporations into four distinct classifications. Findings suggest that “highly-aligned” corporations belong to the private and technology sectors, with some in the industrial and real estate sectors. Meanwhile, “weakly-aligned” corporations come from the manufacturing and private sectors. In addition, case-specific insights are presented in this work. The comparative analysis yields a high agreement between the results and those generated by other IF-MCDM extensions. This paper is the first to demonstrate two methodological advances: (1) the integration of LLMs in MCDM problems and (2) the development of the IF-PROBID method that handles the resulting inherently imprecise evaluations. Full article

This study provides an experimentally validated workflow for the development and model-in-the-loop (MIL) validation of flight control laws for a small, low-cost fixed-wing UAV within a model-based design (MBD) framework, addressing the limitation that previous workflow demonstrations largely remain conceptual or simulation-only and that systematic processes for low-cost UAVs are lacking. A key advantage is that control law methods or parameters can be determined prior to flight testing, avoiding on-site tuning, a major challenge in UAV deployment. The Skysurfer X8 UAV served as the experimental platform. Linearized dynamic models were derived to design rate and attitude controllers using frequency-domain techniques, where loop shaping was applied to meet U.S. military flight quality standards. The control algorithms were validated in an MIL environment, enabling early evaluation of control logic, dynamic response, and robustness under idealized and perturbed conditions. Following MIL verification, the control logic was generated via Simulink Coder and deployed on a Pixhawk 6C flight controller with the PX4 autopilot. Flight test results on the Skysurfer X8 showed good agreement with MIL simulations, confirming the reliability and consistency of the proposed methodology in both simulated and real domains, while also demonstrating a systematic workflow that fills a practical gap in low-cost UAV development. Full article

Diagnosis of clinical obesity has been highlighted by the recent publication from a Commission Report in The Lancet, suggesting the addition of a new diagnostic category, “Preclinical Obesity,” to the already existing ones. Diagnostic criteria for obesity began in the first half of the 20th century, when life insurance companies provided information tables of ideal body weight levels and/or desirable body weight levels based on actuarial associations with mortality. This was replaced by the body mass index or BMI in the third quarter of the 20th century. This tool documented the epidemic of obesity in the US in the last three decades of the 20th century. The recognition of the importance of fat distribution, pioneered by the work of Jean Vague in France, provided a new understanding of obesity. The limitations of BMI and the availability of effective new treatments have heightened the need for new diagnostic guidelines. Obesity represents an increase in body fat and an alteration in its distribution and function. But at the same time, obesity is a stigmatized word and a pejorative term. This communication discusses ways to better diagnose the increase in body fat and its abnormal distribution. We ask whether there is an alternative word to replace obesity and suggest that adiposity or healthy weight could be options. Full article

Eel populations are globally threatened by overfishing and illegal trade, making accurate species identification essential for resource conservation and regulatory enforcement. Conventional molecular identification methods are generally applied in the laboratory, with limited rapid on-site application. This study developed a field-deployable assay to identify the Japanese eel (Anguilla japonica), by incorporating multienzyme isothermal rapid amplification (MIRA) technology with a visually readable lateral flow assay (LFA). Species-specific primers targeting a 286 bp region within the mitochondrial genome of A. japonica were designed and labeled with fluorescein amidite and biotin, respectively. The performance of the MIRA-LFA was validated by assessing its specificity against four other major eel species and its analytical sensitivity, i.e., limit of detection (LoD), under optimized temperature and reaction-time conditions. The MIRA-LFA demonstrated 100% specificity, generating a positive signal only for A. japonica, with no cross-reactivity. A clear visual result was obtained within 10 min at the optimal reaction temperature of 39 °C. Under these optimal conditions, the assay showed a high sensitivity, with an LoD of 0.1 ng/μL of genomic DNA. The proposed assay is an effective tool for the rapid, specific, and sensitive identification of A. japonica. The ability to obtain fast, equipment-free visual results makes this assay an ideal point-of-care testing solution to combat seafood fraud and support the sustainable management of this economically important and vulnerable species. Full article

Modeling ion transport through membrane channels is crucial for understanding cellular processes, and Poisson–Nernst–Planck (PNP) equations provide a fundamental continuum framework for such ionic fluxes. We investigate a quasi-one-dimensional steady-state PNP system for two oppositely charged ion species, focusing on how large permanent charges within the channel and realistic boundary conditions impact ion transport. In contrast to classical models that impose ideal electroneutrality at the channel ends (a simplification that eliminates boundary layers near the membrane interfaces), we adopt relaxed neutral boundary conditions that allow small charge imbalances at the boundaries. Using asymptotic analysis treating the large permanent charge as a singular perturbation, we derive explicit first-order expansions for each ionic flux, incorporating boundary layer parameters $(\sigma ,\rho )$ to quantify slight deviations from electroneutrality. This analysis enables a qualitative characterization of individual cation and anion flux behaviors. Notably, we identify two critical transmembrane potentials, $V_{1c}$ and $V_{2c}$, at which the cation and anion fluxes, respectively, vanish, signifying flux-reversal thresholds that delineate distinct monotonic regimes in the flux-voltage response; these critical values depend on the permanent charge magnitude and the boundary layer parameters. We further show that both ionic fluxes exhibit saturation: as the applied voltage becomes extreme, each flux approaches a finite limiting value, with the saturation level modulated by the degree of boundary charge imbalance. Moreover, allowing even small boundary charge deviations reveals non-intuitive discrepancies in flux behavior relative to the ideal electroneutral case. For example, in certain parameter regimes, a large permanent charge that enhances an ionic current under strict electroneutral conditions will instead suppress that current under relaxed-neutral conditions (and vice versa). This new analytical framework exposes subtle yet essential nonlinear dynamics that classical electroneutral assumptions would otherwise obscure. It provides deeper insight into the interplay between large fixed charges and boundary-layer effects, emphasizing the importance of incorporating such realistic boundary conditions to ensure accurate modeling of ion transport through membrane channels. Numerical simulations are performed to provide more intuitive illustrations of our analytical results. Full article

Water resources allow us to trace the history of many of our towns. In settings with limited surface water, a very interesting case study is represented by the presence/preservation of water in the rupestrian towns located along the rocky walls of canyons (locally named “gravine”) southward, cutting the Murge karst area (Puglia, Southern Italy). In some sections of their valleys, soft rocks, easy to dig, are exposed, and, along the canyon flanks, favored the development of rupestrian towns (cities where dwellings are carved in these soft rocks). Here, before the construction of aqueducts that now bring water from the “distant” Apennines (at least 30 km away), the building of historical fountains, in addition to the collection of rainwater in cisterns, testifies to the presence of an aquifer now undervalued as a local water resource useful for human settlements in a predominantly karst territory. Our study regards an aquifer feeding the Mascheroni Fountain (Great Masks Fountain) through a short qanat that allowed for the development of the old town of Laterza, in Puglia (Southern Italy). Starting from the attractiveness of the ancient fountain, the connection between geological features of the area and the ancestral origin of the city could be proposed to a large audience, representing an intriguing opportunity to develop themes useful for geotouristic purposes and disseminating concepts about sustainability and the importance of preserving local renewable resources. This topic is of paramount importance since the town of Laterza is located at the boundary between the UNESCO MurGEopark and the “Terra delle Gravine” Regional Park, making it the ideal starting point for both parks. Full article

Phytoplankton plays a pivotal role in marine ecosystems and global biogeochemical cycles. Accurate identification and monitoring of phytoplankton are essential for understanding environmental dynamics and climate variations. Despite the significant progress made in automatic phytoplankton identification, current datasets predominantly consist of idealized laboratory images, leading to models that demonstrate persistent limitations in the fine-grained differentiation of phytoplankton species. To achieve high accuracy and transferability for morphologically similar species and diverse ecosystems, we introduce a hybrid dataset by integrating laboratory-based observations with in situ marine environmental data. We evaluate the performance of our dataset on contemporary deep learning models, revealing that CNN-based architectures offer superior stability (85.27% mAcc., 93.76% oAcc.). Multimodal learning facilitates refined phytoplankton recognition through the integration of visual and textual representations, thereby enhancing the model’s semantic comprehension capabilities. We present a fine-tuned visual language model leveraging enhanced textual prompts augmented with expert-annotated morphological descriptions, significantly enhancing visual-semantic alignment and allowing for more accurate and interpretable recognition of closely related species (84.11% mAcc., 94.48% oAcc.). Our research establishes a benchmark dataset that facilitates real-time ecological monitoring and aquatic biodiversity research. Furthermore, it also contributes to the field by enhancing model robustness and transferability to diverse environmental contexts and taxonomically similar species. Full article