Search Results (3,207)

The increasing regulatory pressure on the maritime sector to decarbonize, driven in part by market-based mechanisms at the European level, is accelerating the development of onboard carbon management and energy-efficiency solutions. In this context, this study evaluates an integrated architecture that combines a CO₂ liquefaction system with organic Rankine cycles. The system captures 66% of the total CO₂ emitted by ship engines and is capable of recovering up to 2600.8 kW of energy from onboard hot and cold sources. To identify the most suitable working fluids, an extensive screening of 208 low-GWP zeotropic mixtures is conducted, assessing their thermophysical behavior and energy recovery performance. A detailed thermo-economic assessment is undertaken, including the calculation of CO₂-equivalent savings, GHG abatement cost, and payback periods. To account for fuel price variability, probabilistic modelling based on Monte Carlo sampling is applied to estimate the distribution of discounted payback outcomes. The results demonstrate that Novec 649-based zeotropic mixtures combined with the proposed architecture reduce fuel consumption and enhance onboard CO₂ management while remaining safe and economically viable across a wide range of operating scenarios. Full article

During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. Full article

As the core pressure-regulating component of the Electronic Controlled Pneumatic Braking System (ECPBS) for commercial vehicles, the Automatic Pressure Regulating Valve (APRV) directly determines the accuracy and responsiveness of brake pressure adjustment, which is crucial for ensuring braking safety, stability, and ride comfort—especially in the context of autonomous driving. The pressure change rate is a key indicator reflecting braking smoothness and dynamic response performance, and its accurate calculation is the foundation for optimizing braking control strategies. To address the complexity and computational inefficiency in calculating the pressure change rate of multi-component, nonlinear APRV systems, this study proposes an equivalent calculation method based on throttle orifice flow characteristics. By equating the openings and chambers of an APRV to throttling orifices (fixed and variable) and variable-volume cavities, we simplified the complex pneumatic system while preserving its core dynamic characteristics. Theoretical derivation was conducted by integrating the first law of thermodynamics, ideal gas law, and flow equations for fixed/variable throttle orifices to establish a pressure change rate calculation model. The validity of the proposed method was verified through theoretical analysis, numerical simulation, and experimental testing. Compared with existing models, the proposed method achieved a balance between calculation accuracy and efficiency, with the simulation error within 2% (pressure) and 10% (pressure change rate), and it significantly improved computational efficiency compared to conventional models. This research provides a concise and accurate theoretical tool for the rapid prediction and precise control of pressure change rate in ECPBS, which is of great significance for optimizing autonomous driving braking planning, enhancing braking ride comfort by reducing vehicle jerk, and promoting the development of active safety technologies. The proposed equivalent modeling approach also offers a reference for the performance analysis of similar complex pneumatic components or systems. Full article

Background/Objectives: Nutritional management is central to the care of patients with end-stage renal disease (ESRD), yet malnutrition often remains under-recognized due to gaps in nursing knowledge and competencies. This study aimed to develop and validate the Nursing Education and Competencies in Nutrition for Patients with CKD in ESRD (NECN-ESRD) questionnaire, designed to assess nephrology nurses’ competencies, attitudes, and practices in nutritional care. Methods: A methodological and cross-sectional design was adopted, following the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) recommendations for instrument development. The process comprised five phases: construct definition and item generation, expert consultation and revision, quantitative content validity analysis, pilot testing, and psychometric testing. Data were collected between August and September 2025 from 405 nephrology nurses across Italy. Exploratory Factor Analyses (EFAs) and Confirmatory Factor Analyses (CFAs) were conducted on split samples (60/40), and key psychometric properties were evaluated. Results: EFA identified a four-factor structure—Recommendations, Attitudes, Practice, and Advanced Competencies—which was confirmed through CFA with good fit indices [Comparative Fit Index (CFI) = 0.995, Tucker–Lewis Index (TLI) = 0.994, Root Mean Square Error of Approximation (RMSEA) = 0.07]. A higher-order model further improved fit (CFI = 0.994, RMSEA = 0.029), explaining 68.2% of variance. Internal consistency was excellent (ω = 0.89–0.96), test–retest reliability showed perfect agreement [Intraclass Correlation Coefficient (ICC) = 1.00], and invariance testing supported equivalence across educational and experience levels. Conclusions: The NECN-ESRD demonstrated strong validity, reliability, and stability, providing a robust and context-specific tool to assess and enhance nurses’ competencies in nutritional care for ESRD patients. Its application can support targeted educational interventions, improve clinical practice, and contribute to enhancing the quality of nutritional care for patients with ESRD within healthcare systems. Full article

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

This paper discusses a multimodal AI system applied to legal reasoning for tax law. The results given here are very general and apply to systems developed for other areas besides tax law. A central goal of this work is to gain a better understanding of the relationships between LLMs (Large Language Models) and automated theorem-proving methodologies. To do this, we suppose (1) two cases for the theorem-proving system: one where it has a countable number of total meanings for its countable number of atoms and the other is where it has an uncountable number of total meanings for its countable number of atoms, and (2) LLMs can have an uncountable number of token meanings. With this in mind, the results given in this paper use the downward and upward Löwenheim–Skolem theorems and logical model theory to contrast these two AI modalities. One modality focuses on syntactic proofs and the other focuses on logical semantics based on LLMs. Particularly, one modality uses a rule-based first-order logic theorem-proving system to perform legal reasoning. The objective of this theorem-proving system is to provide proofs as evidence of valid legal reasoning when enacted laws are applied to particular situations. These proofs are syntactic structures that can be presented in the form of narrative explanations of how the answer to the legal question was determined. The second modality uses LLMs to analyze and transform a user’s tax query so this query can be sent to a first-order logic theorem-proving system to perform its legal reasoning function. The main goal of our application of LLMs is to enhance and simplify user input and output for the theorem-proving system. Using logical model theory, we show how there can exist an equivalence between laws represented in logic of the theorem-proving system, fixed in time when the theorem-proving system was set up, and new semantics given by LLMs. These results are based on logical model theory and Löwenheim–Skolem theorems. Full article

Ecuador’s energy system, heavily reliant on hydropower, is increasingly exposed to climate-related disruptions. The 2023–2024 crisis triggered by a historic drought revealed critical structural weaknesses. During this period, the government implemented scheduled electricity rationing of up to 14 h per day in major cities and industrial zones. The blackouts led to cascading economic and social impacts, with an estimated economic toll of USD 2 billion from the energy crisis, equivalent to 2% of Ecuadorian GDP. Hence, this study aims to apply the LEAP model to quantitatively simulate demand, supply, and policy outcomes under two long-term scenarios through 2050. The findings underscore the urgent need for energy diversification, efficiency improvements, and decarbonization of the transport sector to enhance system resilience. The results offer actionable insights for building a more resilient and low-carbon energy future in Ecuador and in similar hydropower-dependent economies. Additionally, the analysis highlights that institutional reforms, technological modernization, and energy integration are essential to mitigating long-term climate risks. By incorporating scenario-based projections, this study provides evidence to guide public policy and investment decisions. These findings contribute to the broader discourse on sustainable energy transitions in vulnerable economies under climate stress. Full article

The use of a corrugated steel plate shear wall (CSPSW) lateral load-bearing system in a steel moment frame (SMF) significantly increases the system’s energy absorption and stiffness. However, the design of CSPSWs involves many parameters and details that greatly increase the complexity of the structure’s response. This study aims to evaluate the effectiveness of the geometric parameters of this system using modern optimization algorithms and an alternative mathematical technique, Response Surface Methodology (RSM). Five geometric parameters, namely crest width (a), diagonal section width (b), corrugation depth (c), sheet thickness (t), and aspect ratio of plate dimension (d), were analyzed to improve the performance of CSPSWs. Design of experiments (DOE) was performed using Design-Expert software, and the required response surface methodology models were designed based on the dimensions of the five variables. Structure weight per meter reduction was set as the optimization goal of the problem. The problem constraints were also defined based on an increase in load-bearing capacity and a reduction in the equivalent plastic strain (PEEQ) percentage in three safety levels 80%, 85% and 90%. Subsequently, the alternative equations developed by RSM to define the objective function and nonlinear constraints were also optimized using modern algorithms in MATLAB 2015. Results revealed a coefficient of determination (R²) of 0.9995 between the experimental and numerical findings and a 1% error between the values obtained from the optimization and reanalysis of the finite elements. Also, they showed an increase in the frame’s lateral load-bearing capacity with the CSPSW, along with a reduction in weight. Full article

Land readjustment (LR) constitutes the foundation of orderly and sustainable urbanization, serving as the primary implementation tool for development plans. LR implementations are generally addressed within the framework of development implementation models—namely area-based, value-based, and hybrid models—based on the principle of redistribution. The present study aims to implement an equivalence-based LR model in the Davultepe Neighborhood of Mezitli, Mersin. In addition, it compares an equivalence-based LR implementation with an area-based LR implementation. The area-based LR implementation was conducted according to Article 18 of Law No. 3194 within the scope of Turkish Zoning Legislation. The equivalence-based implementation was performed using the hybridized multi-criteria decision analysis methods—specifically, SWARA and WASPAS. Cadastral and zoning criteria were determined separately. For data related to spatial criteria, walking distances were calculated using network analysis in Geographic Information Systems software. The weighting of the criteria was performed using the SWARA method. Cadastral and zoning parcels were treated as alternatives, and the WASPAS weight for each parcel was determined. The results indicate that, although allocated zoning parcel areas were generally smaller than the original cadastral parcel areas, in some cases, they exceeded the cadastral parcel areas due to the allocation of zoning parcels designated for agricultural use. Full article

Computerized adaptive testing (CAT) systems face major challenges at the beginning of test administration, when limited response data produces unstable ability estimates and poor item selection. This cold-start problem reduces measurement precision and testing efficiency, especially for students whose abilities diverge from population norms. This study introduces a hybrid ability-estimation model that dynamically integrates neural network predictions with classical item response theory (IRT) estimation throughout the adaptive testing process. The neural component uses auxiliary student information-including demographics, prior performance, and early response patterns-to generate accurate initial ability estimates, while the IRT component preserves psychometric validity as response data accumulate. A dynamic fusion mechanism gradually shifts estimation weight from the neural model to the IRT model as more items are administered. Experimental validation on 2847 students across four subject domains shows that the hybrid approach reduces RMSE in ability estimation by 34.2% during the first five items compared with traditional CAT methods, while maintaining equivalent precision in later stages. The system also decreases the number of items required to reach target precision (SE < 0.3) by 28.7% on average, with the largest gains observed for students at ability extremes. Full article

Advances in multimodal therapy for high-risk neuroblastomas (NBs) have plateaued, prompting therapeutic initiatives to harness the immune system. NBs, however, are immunologically “cold” and a significant challenge to immunotherapy. Here, in a Jurkat lymphocyte cytotoxicity model, we describe an antigen-independent, cell-mediated mechanism for eliminating NB cells, first detected in PMA-activated low pcDNA-SH-SY5Y and high TrkAIII-SH-SY5Y TrkAIII-expressing cells, which are resistant to Jurkat elimination under normal conditions. Characterization of this mechanism through live cell imaging, adhesion assays, RT-PCR, Western blotting and indirect IF, employing a variety of inhibitors, indicates that it initiates with PMA-induced NB cell CCL2 expression. This results in CCL2 promotion of Jurkat CCR2b expression, CCL2/CCR2b-mediated Jurkat LFA-1 activation and the formation of cytotoxic lipid-raft LFA1/ICAM-1 immune synapses, through which Jurkat m-TRAIL combines with PMA-enhanced NB cell DR5/TRAIL-R2 expression to induce NB cell apoptosis. This mechanism is enhanced by the NB-associated oncoprotein TrkAIII through Shp/Src-regulated c-FLIP sequester and is PD-L1/PD-1-independent and resistant to osteoprotegerin. It eliminates both non-MYCN-amplified (SH-SY5Y and SK-N-SH) and MYCN-amplified (SMS-KCNR) NB cells that exhibit PMA-inducible CCL2 expression but not MYCN-amplified NB cells (IMR-32 and NB-1) that exhibit CCL2 repression, and is offset by reciprocal NB cell-induced Fas-mediated Jurkat cell apoptosis. These findings form a solid foundation for further pre-clinical development aimed at identifying clinically relevant physiological immune cell equivalents and alternative PKC activators, with the ultimate goal of translating this mechanism into an effective immune-therapeutic approach for the treatment of high-risk non-immunogenic NBs, especially NBs that exhibit CCL2 and TrkAIII expression. Full article

An increased interest in decreasing the environmental impact of the construction sector and in vertical urbanization has renewed attention to timber as a primary structural material in multi-story buildings. This study investigated whether an existing 10-story reinforced concrete (RC) residential building can be redesigned as an equivalent mass-timber structure while satisfying the same structural performance requirements. It addressed the lack of like-for-like building-scale comparisons that redesigned an existing multi-story RC residential building into a functionally equivalent mass-timber scheme. A real RC building in Gävle, Sweden, was modeled, analyzed, and designed using StruSoft FEM-Design software in accordance with the Eurocodes and the Swedish National Annex, after which all main structural elements were systematically replaced with timber. Through iterative adjustments of member sizes, support conditions, and added reinforcing elements, both the RC and timber schemes were verified with respect to load-bearing capacity, serviceability, and global stability under identical load combinations. The RC and timber buildings reached maximum utilization ratios of 99% and 98%, respectively; displacements were higher in the timber building but remained within serviceability limits, and both systems were classified as globally stable. The timber alternative reduced the total structural weight to about 19% of the RC building and roughly halved the maximum vertical reaction forces, at the expense of additional beams, columns, and basement wall segments. Moreover, this article developed an equivalent-design methodology for material substitution, a bottom-up reinforcing elements logic that resolved serviceability and stability constraints in tall timber, and a performance trade-off map based on structural performance, offering guidance for future mass-timber design. Full article

Parallel–series systems are fundamental in many industrial and engineering applications, yet their reliability assessment and improvement remain challenging, particularly when components exhibit non-constant failure rates. This study addresses this challenge by modeling a hybrid parallel–series system whose components follow the Akshaya lifetime distribution, a flexible model that can capture various hazard-rate shapes. For this system, we derive closed-form analytical expressions for key reliability indices, including the system reliability function, mean time to failure (MTTF), reliability equivalence factors (REFs), and $\delta$-fractiles. To enhance system performance, four improvement strategies are formulated and analytically compared: failure-rate reduction, hot duplication, cold duplication with a perfect switch, and cold duplication with an imperfect switch. A comprehensive numerical case study validates the theoretical derivations and demonstrates the effectiveness of each strategy. The results show that cold duplication with a perfect switch yields the highest reliability gain, and the computed REFs provide a quantitative tool for balancing redundancy against component-level improvements. This work provides reliability engineers with a comprehensive analytical framework for the design and enhancement of complex parallel-series systems. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article