Search Results (694)

Background/Objectives: The ¹⁰³Pd/^103mRh in vivo generator represents a promising Auger electron-emitting system, in which both parent and daughter radionuclides emit predominantly Auger electrons with minimal accompanying radiation. This study investigates the release dynamics of daughter radionuclides from the ¹⁰³Pd/^103mRh in vivo generator and evaluates the underlying mechanisms governing bond rupture and daughter retention. Methods: Cyclotron irradiation of rhodium foils was performed in two separate batches, followed by radionuclide separation using conventional wet chemistry and a novel dry distillation technique. The purified ¹⁰³Pd radionuclide was used to radiolabel DOTA-TATE, phthalocyanine-TATE, and DOTA-TOC chelators. The resulting complexes were immobilized on Strata-X and Strata-C18 solid-phase extraction columns. Scheduled elution experiments were conducted to quantify the release of the ^103mRh daughter radionuclide. Results: The measured ^103mRh release rates were 9.8 ± 3.0% and 9.6 ± 2.7% from Strata-X columns with DOTA-TATE and phthalocyanine-TATE, respectively, and 10.5 ± 2.7% and 12.0 ± 0.5% from Strata-X and Strata-C18 columns, respectively, with DOTA-TOC. These values are significantly lower than the ~100% release predicted based on the reported Auger electron yield of 186%. One explanation for this difference could be potential inconsistencies in decay data that may require correction; this needs further investigation. The results further demonstrated that delocalized π-electrons, introduced via phthalocyanine-based chelation, did not mitigate daughter release. Conclusions: The low observed daughter nuclide release represents a favorable characteristic for the future clinical translation of the ¹⁰³Pd/^103mRh Auger emitter pair. The findings support the conclusion that Auger electron cascades, rather than nuclear recoil energy, dominate bond rupture processes. Full article

The circadian cycle regulates metabolism in response to external stimuli, such as light exposure, sleep schedules, and eating patterns. However, misalignment between internal biological rhythms and social demands can compromise food choices, potentially leading to overweight and obesity. This research aimed to assess how a person’s chronotype links to social jet lag (SJL), which in turn would relate to their nutritional status and food consumption patterns as a university student. 617 students from a State University located in the State of Paraná, Brazil, completed a cross-sectional research study that collected sociodemographic information/anthropometrics by means of an online survey. It included self-reported height/weight data and dietary habits. The Munich Chronotype Questionnaire (MCTQ) was utilized to determine each participant’s chronotype classification and SJL calculation. Researchers found that nearly half of the students (49.3%) displayed an Intermediate Chronotype, which is associated with a diet that contained elements of the “Mixed” Diet, meaning there are equal portions of healthy food (Fresh Fruits, Beans, etc.) and unhealthy foods (Sweetened Beverages). The multivariate logistic regression analyses identified age as a significant predictor of obesity risk (OR: 1.15, p < 0.001), while dietary habits such as fruit consumption played a protective role. Additionally, having a breakfast protected them from being classified as obese compared to those who did not eat breakfast (OR = 0.59). Contrary to expectations, late-night supper was not a statistically significant predictor in the adjusted model. Predictors of an Intermediate chronotype included being male and eating morning snacks regularly. The results of this study suggest that students with an intermediate chronotype will predictably have skewed eating patterns, such as skipping breakfast and eating late—both of which affect obesity risks. Nutritional strategies for university students should focus on promoting circadian regularity and optimizing meal timing. Full article

Machine learning (ML) is becoming a key enabler in building energy management systems (BEMS), yet most existing reviews focus on simulations and fail to reflect the realities of real-world deployment. In response to this limitation, the present work aims to present a systematic review dedicated entirely to experimental, field-tested applications of ML in BEMS, covering systems such as Heating, Ventilation & Air-conditioning (HVAC), Renewable Energy Systems (RES), Energy Storage Systems (ESS), Ground Heat Pumps (GHP), Domestic Hot Water (DHW), Electric Vehicle Charging (EVCS), and Lighting Systems (LS). A total of 73 real-world deployments are analyzed, featuring techniques like Model Predictive Control (MPC), Artificial Neural Networks (ANNs), Reinforcement Learning (RL), Fuzzy Logic Control (FLC), metaheuristics, and hybrid approaches. In order to cover both methodological and practical aspects, and properly identify trends and potential challenges in the field, current review uses a unified framework: On the methodological side, it examines key-attributes such as algorithm design, agent architectures, data requirements, baselines, and performance metrics. From a practical standpoint, the study focuses on building typologies, deployment architectures, zones scalability, climate, location, and experimental duration. In this context, the current effort offers a holistic overview of the scientific landscape, outlining key trends and challenges in real-world machine learning applications for BEMS research. By focusing exclusively on real-world implementations, this study offers an evidence-based understanding of the strengths, limitations, and future potential of ML in building energy control—providing actionable insights for researchers, practitioners, and policymakers working toward smarter, grid-responsive buildings. Findings reveal a maturing field with clear trends: MPC remains the most deployment-ready, ANNs provide efficient forecasting capabilities, RL is gaining traction through safer offline–online learning strategies, FLC offers simplicity and interpretability, and hybrid methods show strong performance in multi-energy setups. Full article

With continued transistor scaling and increasing interconnect density in very large-scale integration (VLSI) circuits, the parasitic capacitance of interconnect has become a major contributor to circuit delay and signal integrity degradation. Fast and accurate parasitic capacitance extraction is therefore essential in the back-end-of-line (BEOL) stage. Currently, 2.5D parasitic capacitance extraction flow based on the pattern matching method is widely used by commercial tools, which still suffer from lengthy pattern library construction, cross-section preprocessing, pattern mismatch, and poor accuracy for small capacitance extraction. To overcome these limitations, this work proposes an end-to-end parasitic capacitance extraction workflow, named residual multilayer perceptron interconnect parasitic capacitance extraction (RMLP-Cap), which leverages a residual multilayer perceptron (ResMLP) to enhance traditional workflow. RMLP-Cap integrates parasitic extraction (PEX) window acquisition, pattern definition, feature extraction, dataset generation, ResMLP model training, and capacitance aggregation into a unified flow. Experimental results show that RMLP-Cap can automatically define and model complex 2D patterns with 100% matching accuracy. Compared with a field solver based on the boundary element method (BEM), the ResMLP model achieves an average relative error below 0.9%, a standard deviation under 0.2%, and less than 0.5% error for small capacitances, while providing a 900% speed improvement for extraction speed. Full article

The present work focuses on wave scattering generated by an inverse T-type compound breakwater in the presence of the ocean current. The boundary value problem (BVP) is investigated using two distinct strategies: an exact formulation derived from the eigenfunction expansion method (EEM) and a computational framework developed with the boundary element method (BEM). A comparison of outcomes from both techniques with established studies confirms the consistency and accuracy of the present formulations. Reflection and transmission coefficients, along with the time-domain simulations of the free surface, are evaluated under different wave conditions and structural configurations. In the long-wave region, the reflection coefficient exhibits strong dependence on the wavenumber, with higher values observed as the height and width of the porous section increase. Increasing the friction coefficient within the porous layer considerably reduces wave transmission to the leeside, demonstrating the important role of friction in energy dissipation. Furthermore, greater ocean current velocity leads to an increase in the reflection curve, highlighting the significant effect of hydrodynamic conditions on wave–structure interaction. The time-domain simulations of the free surface are also presented to provide a clear visualization of the wave behavior on the surface, both with and without the presence of an ocean current. The findings shed light on the combined influence of breakwaters and ocean currents, enabling the development of coastal protection measures that enhance resilience, sustainability, and safety from erosion and damage. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

We present the first aero-structural evaluation of a 3 m-diameter hybrid wind-PV rotor employing flat-plate blades stiffened by a peripheral ring. Owing to the lack of prior data, we combine low-Reynolds BEM, elastic FEM sizing, and steady-state CFD (k-ω SST) to build a coherent preliminary load and performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm) and spokes (Ø 40 mm), von Mises stresses stay below 25% of the 6061-T6 yield limit and tip deflection remains within 0.5% R across Cut-in (3 m/s), Nominal (5 m/s) and Extreme (25 m/s) wind cases. CFD confirms a flat efficiency plateau at λ = 2.4–2.8 (β = 10°) and zero braking torque at β = 90°, validating a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). The study addresses only the rotor; off-the-shelf generator, brake, screw-pitch and azimuth/tilt drives will be integrated later. These findings set a solid baseline for full-scale testing and future transient CFD/FEM iterations. Full article

The transition toward energy-efficient and smart buildings requires Digital Twins (DTs) that can couple real-time data with physics-based Building Energy Models (BEMs) for predictive and adaptive operation. Yet, despite rapid digitalisation, there remains a lack of practical guidance and real-world implementations demonstrating how calibrated BEMs can be effectively integrated into Building Management Systems (BMSs). This study addresses that gap by presenting a complete and reproducible end-to-end framework for embedding physics-based BEMs into operational DTs using two setups: (i) encapsulation as Functional Mock-up Units (FMUs) and (ii) containerisation via Docker. Both approaches were deployed and tested in a real educational building in Cáceres (Spain), equipped with a LoRaWAN-based sensing and actuation infrastructure. A systematic comparison highlights their respective trade-offs: FMUs offer faster execution but limited weather inputs and higher implementation effort, whereas Docker-based workflows provide full portability, scalability, and native interoperability with Internet of Things (IoT) and BMS architectures. To enable real-time operation, a surrogate modelling framework was embedded within the Docker architecture to replicate the optimisation logic of the calibrated BEM and generate predictive blind control schedules in milliseconds—bypassing simulation overhead and enabling continuous actuation. The combined Docker + surrogate setup achieved 10–15% heating energy savings during winter operation without any HVAC retrofit. Beyond the case study, this work provides a step-by-step, in-depth guideline for practitioners to integrate calibrated BEMs into real-time control loops using existing toolchains. The proposed approach demonstrates how hybrid physics- and data-driven DTs can transform building management into a scalable, energy-efficient, and operationally deployable reality. Full article

The concept of carbon-neutral buildings encompasses not only carbon emission reductions but also sustainability. Building sustainability includes the physical durability of the structure, the health and safety of its tenants, and harmony with the surrounding environment. The achievement of these goals requires alignment among diverse stakeholders associated with buildings; however, such alignment is limited by economic (cost), environmental (global warming), and social (institutions and policies) factors. This study proposes an operation model that integrates buildings, the carbon permit market, and deep reinforcement learning (DRL) to address these limitations. The DRL model reduces energy consumption while maintaining indoor comfort, generates carbon permits equivalent to the amount of energy saved, and creates a new revenue stream by selling them. To achieve more precise comfort management, the model incorporates a policy that combines predicted mean vote (PMV) and Humidex. In the context of a privately owned commercial office building, the DRL model achieved indoor comfort levels of 98.51% for PMV and 97.22% for Humidex, while reducing energy consumption by 34,376 kWh, lowering carbon emissions by 26,607 kgCO₂eq, and generating USD 176 in carbon permit revenue. These results translated into a total reduction in operating costs of 7.5%, amounting to USD 2951. Consequently, the proposed approach provides cost reductions for building owners, comfort for tenants, efficiency for managers, and carbon emission reductions that contribute to carbon neutrality. Full article

Persistent discrepancies remain in the perceived far-field noise of automotive permanent-magnet synchronous motors (PMSMs) and the predictions of conventional NVH simulations. To bridge this gap, a Tri-source Electromagnetic Coupling NVH Integrated Framework (Tri-ECNVH) is developed, in which air-gap electromagnetic force harmonics, torque ripple, and cogging torque are treated as a coupled excitation system rather than as independent sources. Traditional workflows usually superpose their responses in the power domain, which tends to underestimate the radiating contribution of torque-related excitations and neglect their phase and order coupling with radial electromagnetic forces. In the proposed Tri-ECNVH framework, the three sources are mapped into the order domain, aligned by spatial order, and applied to the stator with phase consistency, so that inter-source coupling and cross terms are explicitly retained along a unified electromagnetic–structural–acoustic chain. Acoustic radiation is evaluated by prescribing the normal velocity on the stator outer surface as a Neumann boundary condition and computing the far-field A-weighted sound pressure level (SPL) using a boundary element method (BEM) model. Numerical results reveal pronounced cooperative amplification of the three sources at critical orders and within perceptually sensitive frequency bands; relative to independent-source modeling with power-domain summation, Tri-ECNVH predicts peak levels that are typically 5–10 dB higher and reproduces the spectral envelope and peak–valley evolution more faithfully. The framework therefore offers a practical, radiation-oriented basis for multi-source noise mitigation in traction PMSMs and helps narrow the gap between simulation and perceived sound quality in automotive applications. Full article

In this study, bio-based polyurethanes (PUs) were synthesized using renewable polyols derived from sunflower seed oil, aiming to develop flexible yet robust polymeric films and scaffolds. Given their composition and favorable physico-chemical properties, these materials may represent promising candidates for the design and development of advanced biomedical systems. Two distinct oil polyols were prepared via glycerol transesterification (GM) and epoxidation (EPO) with hydrogen peroxide/glacial acetic acid, respectively. These polyols, in combination with poly(tetramethylene ether) glycol (PTMEG) and/or poly(ethylene glycol) (PEG), served as diol components in a one-step reaction with 1,6-hexamethylene diisocyanate (HDI). The structure of the polyol precursors was thoroughly characterized by MALDI-TOF MS and NMR spectroscopy, confirming successful functionalization. The resulting PU films exhibited excellent flexibility (885%) and mechanical properties (23 MPa), as evaluated by ATR-FTIR, Tensile test, DSC, DMA and SEM methods. The crosslink density of the order of 10⁻³ also contributes to the development of outstanding mechanical properties. Stress relaxation experiments were described using a stretched exponential (Kohlrausch–Williams–Watts) model to capture the viscoelastic behavior of the materials. In addition, stress vs. relative elongation curves revealing strain-hardening behavior were also analyzed and modeled mathematically to better describe the mechanical response under deformation. Furthermore, salt leaching techniques were employed to fabricate porous scaffolds. This work highlights the versatility of vegetable oil-based feedstocks in producing functional polyurethanes with tunable mechanical properties for applied polymer systems. Full article

The wake characteristics of tidal turbines are significantly influenced by turbulence intensity (TI) and flow velocity in the marine environment. This study employs the Blade Element Momentum (BEM)–CFD method to model two-bladed horizontal tidal turbine wakes, simplifying the turbine geometry while ensuring computational efficiency. The numerical model, validated against experimental data, demonstrates reliable accuracy. Simulations were conducted for background TI levels of 2%, 6%, 10%, 14%, and 18%. Results indicate that wake regions initially expand and then contract, with the contraction point moving closer to the turbine as TI increases. At 2% TI, the wake influence region extends to an axial distance/diameter (X/D) ratio of 20, while at 18% TI, contraction begins at X/D = 4. Low TI results in more extensive low-speed regions, whereas high TI accelerates wake recovery. As TI increases, the wake’s turbulence rapidly blends with the background, leading to a reduction in turbulence increments within the wake. Additionally, an analytical wake model for tidal turbines was developed, incorporating turbulence intensity into the formula. The predicted curve exhibited good agreement with the CFD data. This model enables a quick and efficient prediction of wake velocity changes under varying turbulence intensities. Full article

A novel methodology is proposed for accelerating the calculation efficiency of the boundary element modeling of rolling contact. This methodology involves the implementation of an initial solution estimate. The method is to provide the initial estimate value by means of simplifying the method, which is used for the iterative calculation of the boundary element method to solve the normal and tangential contact problems. In the normal contact problem, the normal pressure and contact patch are provided as the initial values for the iterative calculation of the boundary element method. In the tangential contact problem, the initial values for tangential stress and adhesion-slip distribution are provided. A first novel aspect is breaking the initial iteration setting of the traditional BEM, which can significantly reduce the iterations. A second novelty is that a method for determining the potential contact area is proposed to ensure the correctness of boundary element modeling without increasing the computational cost. In the following section, an analysis is conducted to ascertain the impact of the initial solution estimate method on the efficacy of boundary element modeling. The result is that efficiency increased by 69.1% of normal contact calculations, with the initial contact patch exerting the most significant influence. The efficiency of the process under investigation increased by 56.9%, and the most pronounced effect is the distribution of adhesion-slip. Full article

This study reports the preparation of alginate (Alg) beads incorporating different amounts of halloysite nanotubes (HNTs) and kaolin (K) in the presence of Ca²⁺ ions to compare their drug loading and release behaviors. The resulting composites, HNTs@Alg and K@Alg, were characterized using FTIR, SEM–EDS, XRD, and XPS analyses. Chlortetracycline hydrochloride (CTC) was employed as a model antibiotic to evaluate their drug delivery performance. The concentration of Alg and the incorporation of HNTs or K markedly influenced the adsorption capacity and release profile. The maximum drug loading capacities were 48.12 ± 1.4 mg/g for HNTs, 40.1 ± 1.2 mg/g for K, 59.85 ± 2.3 mg/g for HNTs@Alg-1 (1 g HNTs and 1% Alg), and 68.74 ± 2.1 mg/g for K@Alg-1 (1 g K and 1% Alg). The inclusion of Alg enhanced sustained release, extending beyond 100 h. Among the composite beads, HNTs@Alg-1 showed superior CTC release behavior compared to K@Alg-1. Furthermore, antibacterial assays confirmed that the CTC-loaded beads effectively inhibited E. coli and S. aureus, demonstrating maintained drug activity after encapsulation. Both systems effectively prolonged CTC release and exhibited antibacterial efficacy, highlighting their potential as controlled drug delivery matrices for wound treatment applications. Full article

Intersex people continue to face barriers in healthcare. Despite notable ethical and legal advances, the role of socio-cognitive factors influencing clinical decision-making remains insufficiently understood. Critical perspectives call for revising the epistemological and normative foundations of medical practice, as clinical judgments may still be shaped by professionals’ beliefs and limited access to accurate information. Objective: This study examined how levels of knowledge, beliefs about gender determinism, and adherence to gender roles influence healthcare professionals’ attitudes toward intersex people. Methods: A total of 210 healthcare professionals from Spain participated in a cross-sectional survey. Participants completed the Intersex Knowledge Questionnaire, the short version of the Bem Sex-Role Inventory, and the Gender Determinism Scale. Data were analyzed using χ² tests, one-way ANOVA, and t-tests. Results: Higher levels of knowledge (conceptual, procedural, and legislative) were associated with more affirmative and non-normative attitudes toward intersex healthcare. Neither gender determinism nor adherence to traditional gender roles was associated with professionals’ attitudes. Participants with prior contact with intersex people demonstrated higher conceptual knowledge and lower support for corrective surgeries. Significant disciplinary differences were also found: physicians tended to display more corrective and ambivalent attitudes, whereas psychologists and social workers were more frequently aligned with affirmative and diversity-respectful perspectives. Conclusions: Intersex healthcare attitudes may be influenced by limited training opportunities and the low visibility of intersex topics in medical education. Knowledge appears to be an important factor associated with more affirmative professional attitudes. Future studies using larger samples are needed to confirm these associations and explore underlying causal mechanisms. Full article