Search Results (1,411)

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Managing complex and large-scale building facilities requires reliable, easily interpretable, and computationally efficient models. Considering the electrical-circuit analogy, lumped-parameter resistance–capacitance (RC) thermal models have emerged as both simulation surrogates and advanced tools for energy management. This review synthesizes recent uses of RC models for building energy management in large facilities and aggregates. A systematic review of the most recent international literature, based on the analysis of 70 peer-reviewed articles, led to the classification of three main areas: (i) the physics and modeling potential of RC models; (ii) the methods for automation, calibration, and scalability; and (iii) applications in model predictive control (MPC), energy flexibility, and digital twins (DTs). The results show that these models achieve an efficient balance between accuracy and simplicity, allowing for real-time deployment in embedded control systems and building-automation platforms. In complex and large-scale situations, a growing integration with machine learning (ML) techniques, semantic frameworks, and stochastic methods within virtual environments is evident. Nonetheless, challenges persist regarding the standardization of performance metrics, input data quality, and real-scale validation. This review provides essential and up-to-date guidance for developing interoperable solutions for complex building energy systems, supporting integrated management across district, urban, and community levels for the future. Full article

The growing demand for sustainable energy has driven innovation in wind and marine turbines, where the conventional airfoils, though reliable, perform poorly in unsteady flows. This review explores the transition of blade design from conventional to bio-inspired blade designs. Although several studies have explored the use of biomimetic principles for turbine blade designs, this review highlights the core biological strategies successfully translated into engineering designs to improve aerodynamic and hydrodynamic performance. In addition, it emphasizes the critical role of interdisciplinary integration, linking biology, material science, and engineering, in advancing and enabling the practical realization of biomimetics in energy systems. This narrative review consolidates the trends, gaps, and underexplored opportunities in the current literature on biomimetics. Theoretically, it elevates bio-inspired design from descriptive analogy into a predictive framework grounded in natural efficiency mechanisms; practically, it articulates a framework for transforming biological design into robust, highly efficient, and commercially viable turbine systems. Further, the review highlighted a persistent gap between experimental advances and commercial deployment, underscoring the lack of scalable manufacturability and techno-economic validation. Full article

This paper presents a precision, wide-dynamic-range leakage current sensor tailored for in-situ monitoring of aging mechanisms such as Time-Dependent Dielectric Breakdown (TDDB) in both active and passive components. The proposed architecture supports high-voltage stress and is fully monolithic, integrating a current-to-voltage front-end, tunable-gain amplifier, and a successive approximation register (SAR) analog-to-digital converter (ADC). To validate the concept, a discrete-component prototype was implemented and evaluated across a leakage current range of 1 nA to 1 $\mathsf{\mu }$A. The sensor achieves 12-bit resolution with measured integral non-linearity (INL) and differential non-linearity (DNL) within $\pm 1.5$ LSB and $\pm 0.3$ LSB, respectively. Compared to prior monitors, the design enables linear current digitization and supports high-voltage stress, features essential for accurate and scalable TDDB characterization. Applications include embedded reliability monitoring in power converters, analog building blocks, and large-scale aging test arrays. Full article

This paper introduces GECO, a real-time, computer vision-assisted gesture controller for IoT-based smart home systems. The platform uses a markerless MediaPipe interface that combines gesture-driven navigation and command execution, enabling intuitive control of multiple domestic devices. The system integrates binary and analog gestures, such as continuous light dimming based on thumb–index angles, while operating on-device through a private MQTT network. Technical evaluations across multiple Android devices have demonstrated ultra-low latency times (<50 ms), enabling real-time responsiveness. A user experience study with seventeen participants reported high intuitiveness (9.5/10), gesture accuracy (9.2/10), and perceived inclusivity, mainly for individuals with speech impairments and low technological literacy. These results position GECO as a lightweight, accessible, and privacy-preserving interaction framework, advancing the integration of artificial intelligence and IoT within smart home environments. Full article

Hyperparathyroidism is a serious complication of chronic kidney disease (CKD) and can occur in patients not on renal replacement therapy, during dialysis therapy, or after kidney transplantation. The disease leads to an increased risk of cardiovascular events, bone loss, and fractures. Cinacalcet is a widely used drug, but its effectiveness in treating hyperparathyroidism in selected stages of chronic kidney disease remains unclear. This critical review aims to integrate findings from meta-analyses and clinical trials to assess optimal therapeutic strategies in patients suffering from CKD, who are non-dialysis-dependent, dialysis-dependent, and after kidney transplantation. The authors reviewed eligible studies, including meta-analyses, randomized controlled trials, and observational studies assessing biochemical outcomes, cardiovascular, bone, and survival outcomes with cinacalcet. Cinacalcet effectively reduced serum parathyroid hormone (PTH), calcium, and phosphorus across all CKD stages, particularly in hemodialysis patients. Combination therapy with vitamin D analogs enhanced biochemical control without increasing adverse events, although mild, transient hypocalcemia and gastrointestinal symptoms were common. In kidney transplant recipients, parathyroidectomy achieved greater normalization of PTH and calcium. Cinacalcet has been shown to reduce mortality in patients on hemodialysis and peritoneal dialysis. Full article

Deep coalbed methane (CBM) development faces significant challenges due to extreme geological conditions (high stress, elevated pressure, high temperature) that differ fundamentally from shallow reservoirs. Traditional productivity models developed for shallow CBM often fail to accurately predict deep reservoir performance. The complex “stress-desorption-flow” multi-field coupling mechanism, intensified under deep conditions, critically controls production dynamics but remains poorly understood. This study develops a multi-layer, commingled, coupled geomechanical-flow model for the Hujiertai deep CBM block (2140~2170 m) in Xinjiang, China. The model, integrating gas-water two-phase flow, Langmuir adsorption, and transient geostress evolution, was validated against field production data, achieving a low relative error of 1.2% in the simulated average daily gas rate. Results indicate that: (1) Geomechanical coupling is critical. The dynamic competition between effective stress compaction and matrix shrinkage limits fracture porosity reduction to ~2%, enabling a characteristic “rapid incline, 1–2-year plateau, gradual decline” production profile and significantly enhancing cumulative gas production. (2) Porosity (10~30%) is positively correlated with productivity: a 10-percentage-point increase raises the peak gas rate by 2.1% and cumulative production by 2.8%. Conversely, high initial cleat permeability boosts early rates but accelerates geomechanical damage (cleat closure), lowering long-term productivity. (3) Stimulation parameters show a trade-off. SRV only dictates short-term, near-wellbore production. Higher fracture permeability (peak rate +17% per 500 mD) boosts early output but accelerates depletion and stress-induced closure. The multi-field coupling mechanisms revealed and the robust model developed provide a theoretical basis for optimizing fracturing design and production strategies for analogous deep CBM plays. Full article

The proteasome β5 subunit plays a central role in protein degradation and is an established therapeutic target in glioblastoma. Marizomib (MZB), a natural β5 inhibitor, has shown promising anticancer activity, yet suboptimal pharmacological properties limit its clinical translation. Using a comprehensive computational approach, this study aimed to identify and characterize novel MZB analogs with improved binding affinity, stability, and drug-like profiles. An integrative in silico study was performed, including molecular docking, frontier molecular orbital (FMO) analysis, pharmacophore modeling, molecular dynamics (MD) simulations over 200 ns, MM/PBSA binding free energy calculations, and per-residue energy decomposition. ADMET profiling evaluated the pharmacokinetic and safety properties of MZB and top-performing analogs. Docking and pharmacophore modeling revealed strong complementarity between MZB analogs and the β5 catalytic pocket. MD simulations showed that MZBMOD-77 and MZBMOD-79 exhibited exceptional structural stability with low RMSD values (0.40–0.42 nm), persistent binding within the active site cavity, and significant disruption of hydrogen-bond networks in the active loop regions Ala19–Lys33 and Val87–Gly98. MM/PBSA analysis confirmed their superior binding free energies (−19.99 and −18.79 kcal/mol, respectively), surpassing native MZB (−6.26 kcal/mol). Per-residue decomposition highlighted strong contributions from Arg19, Ala20, Lys33, and Ala50. ADMET predictions indicated improved oral absorption, reduced toxicity, and favorable pharmacokinetics compared to native MZB. This integrative computational study identifies MZBMOD-77 and MZBMOD-79 as promising next-generation proteasome β5 inhibitors. These analogs mimic and enhance the inhibitory mechanism of native MZB, offering potential candidates for further optimization and preclinical development in glioblastoma therapy. Full article

Background/Objectives: Hemiplegic shoulder pain (HSP) is a frequent post-stroke complication affecting 30–65% of survivors, contributing to motor dysfunction and reduced quality of life. Balance impairment is another major concern that increases fall risk. This study aimed to examine the associations between HSP, shoulder range-of-motion (ROM) limitations and balance performance using both clinical and sensor-based evaluations. Methods: In this cross-sectional study, 108 stroke survivors (54 with HSP, 54 without) were assessed. Pain intensity was evaluated using the Visual Analog Scale (VAS), balance with the Berg Balance Scale (BBS), and shoulder mobility and postural sway with the validated Euleria Lab IMU-based system integrated with a force platform. Between-group differences were analyzed using the Mann–Whitney U test, and correlations between pain, ROM, balance, and fall-risk indices were determined via Spearman coefficients. Results: Participants with HSP had significantly lower BBS scores (20.96 ± 8.71) than those without HSP (34.58 ± 11.71; p < 0.001). VAS activity scores were negatively correlated with BBS (r = −0.196, p = 0.043) and positively correlated with postural sway and fall-risk parameters, particularly under eyes-closed conditions. Shoulder ROM limitations were linked to poorer balance, and both static and dynamic fall-risk indices were strongly correlated with pain severity (r = 0.676 and r = 0.657; p < 0.001). Conclusions: HSP was associated with impaired balance and elevated fall risk in stroke survivors. The combination of clinical scales and wearable sensor-based measurements provides a comprehensive understanding of postural control deficits. These findings emphasize the need for rehabilitation strategies targeting pain reduction, shoulder mobility, and balance to support functional recovery. Full article

Platinum-based drugs play a pivotal role in contemporary cancer treatment, but their therapeutic utility is often limited by acquired resistance. The diiodido analog, cis-[PtI₂(NH₃)₂] is a promising derivative that has demonstrated the ability to overcome cisplatin resistance in vitro. To establish the molecular basis for this superior activity, we integrated experimental (NMR) spectroscopy with computational density functional theory (DFT) methods to precisely and comparatively understand the drug activation mechanisms. Comparative ¹⁴N NMR experiments elucidated the initial ligand substitution step, confirming halide displacement and a markedly higher tendency for ammonia release from cis-[PtI₂(NH₃)₂], particularly when reacting with sulfur-containing amino acids. Complementary DFT calculations determined the substitution energy values, revealing that the superior leaving-group ability of iodide results in a thermodynamically more favorable activation. Conceptual DFT parameters (softness, hardness, and Fukui indices) further demonstrated that initial substitution induces a strong trans effect, leading to the electronic sensitization of the remaining iodide ligand. This strong agreement between computational predictions and experimental data establishes a coherent molecular activation mechanism for cis-[PtI₂(NH₃)₂], demonstrating that iodide substitution promotes both thermodynamic and electronic activation of the platinum center, which is the key to its distinct pharmacological profile and ability to circumvent resistance. Full article

This work presents a complete multi-serial adaptive bus interface system compatible with the most widely used industrial serial communications standards: RS-232, RS-485, RS-422, and CAN. The proposed system automatically detects the connected serial interface type through analog line sensors and dynamically redirects the bus to the appropriate transceiver using a logical multiplexer. This approach aims to simplify the configuration of heterogeneous serial devices in complex and modular integration scenarios, such as body builders in industrial or vehicular systems. The hardware is designed as a scalable PCIe card-based device, allowing multiple adaptive bus interfaces to be integrated within a rack-based modular architecture. In addition, a single 5-pin plug-and-play connector is proposed by unifying the different bus signals of the transceivers, thereby simplifying cabling and deployment. Complementary implemented capabilities include baud rate auto-detection and supervision, as well as automatic direction-control functionality for RS-485 communication. Experimental validation demonstrated that the proposed system successfully detected and redirected all supported interfaces, achieving reliable connection and disconnection within an average time of 2.5 s. Furthermore, the integrated baud rate auto-detection algorithm accurately identified transmission speeds up to 1 Mbps in under 80 ms, while the automatic direction-control capability operated reliably at speeds up to 576,000 bps. Full article

Contemporary sustainability practices in the built environment often focus narrowly on reducing short-term impacts within the boundaries of individual buildings. This Special Issue aims to challenge that paradigm by proposing a broader, resource-centric approach grounded in long-term system balance and post-fossil logic. It argues that sustainability should not merely mitigate harm but actively support resource regeneration. Key issues include the flawed concept of non-renewable resources, the obsolescence of primary energy metrics, insufficient system boundaries, and the undervaluation of residual material impact. Drawing on historical analogies and real-world observations, the paper outlines a framework for a regenerative built environment—where buildings take responsibility for their energy and material footprints and contribute positively over time. It concludes that truly sustainable design must be based on predictable, annual resource budgets and a holistic integration of material, ecological, and human systems. It requires re-inventing the way we evaluate and organize our built environment. Full article

Industrial environments still rely heavily on analog instruments for process supervision, as their robustness and low cost make them suitable for harsh conditions. However, these devices require manual readings, which limit automation and digital integration within Industry 4.0 frameworks. To address this gap, this study proposes an intelligent and cost-effective system for non-invasive acquisition of measurement data from analog industrial instruments, leveraging machine vision and Artificial Neural Networks (ANNs). The proposed framework exploits the geometric symmetry inherent in circular and linear scales to interpret pointer positions under varying lighting and perspective conditions. A dedicated image-processing pipeline is combined with lightweight ANN architectures optimized for embedded platforms, ensuring real-time inference without the need for high-end hardware. The processed data are wirelessly transmitted to a Human–Machine Interface (HMI) and web-based dashboard for real-time visualization. Experimental validation on pressure and flow instruments demonstrated an average Mean Absolute Error (MAE) of 0.589 PSI and 0.085 GPM, Root Mean Square Error (RMSE) values of 0.731 PSI and 0.097 GPM, and coefficients of determination (R²) of 0.985 and 0.978, respectively. The system achieved an average processing time of 3.74 ms per cycle on a Raspberry Pi 3 platform, outperforming Optical Character Recognition (OCR) and Convolutional Neural Network (CNN)-based methods in terms of computational efficiency and latency. The results confirm the feasibility of a symmetry-driven vision framework for real-time industrial supervision, providing a practical pathway to digitalize legacy analog instruments and promote low-cost, intelligent Industry 4.0 implementations. Full article

Meat provides high-quality protein and essential micronutrients such as vitamin B12, heme iron, zinc, and selenium, along with conditionally essential compounds including creatine, carnitine, and taurine. Growing concerns over environmental sustainability, animal welfare, and potential health risks associated with excessive meat consumption have spurred the development of plant-based and cultured alternatives intended to replicate the nutritional and sensory attributes of meat. This review critically examines the extent to which these emerging products achieve nutrient equivalence with conventional meat, focusing on essential and conditionally essential nutrients, their bioavailability, and implications for human health. After outlining the physiological importance of nutrients characteristically supplied by meat, the review compares the composition of plant-based meat analogs (PBMAs) and cultured meat prototypes. Differences in fortification strategies, ingredient formulation, and the presence of anti-nutritional factors are discussed in relation to nutrient absorption and utilization. Current PBMAs can approximate protein content but generally provide lower levels and reduced bioavailability of vitamin B12, heme iron, creatine, taurine, and long-chain omega-3 fatty acids unless fortified. Cultured meat offers theoretical potential for compositional optimization through cellular engineering but remains limited by scarce empirical data. Achieving nutrient equivalence with conventional meat thus represents a major scientific, technological, and regulatory challenge. Future progress will depend on integrating nutritional design into product development, validating bioavailability in human studies, and implementing transparent labeling to ensure that next-generation meat alternatives meet both health and sustainability goals. Full article