Search Results (21,912)

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Thoracoabdominal aortic aneurysms (TAAAs) are rare and often remain asymptomatic until rupture, leading to high morbidity and mortality. Elastin degradation, largely mediated by matrix metalloproteinases (MMPs), plays a central role in their pathogenesis. This study aimed to evaluate plasma desmosine (pDES), a specific biomarker of elastin breakdown, as a non-invasive tool for TAAA detection and risk stratification. In a prospective single-centre case–control study, 30 patients with TAAA and 30 age- and sex-matched controls were enrolled. Plasma pDES levels were quantified using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Aortic wall samples from 12 patients were analysed for elastic fibre content and MMP expression by histology and western blotting. Statistical analyses included correlation testing, propensity score matching, and receiver operating characteristic (ROC) analysis. TAAA patients exhibited significantly higher pDES levels compared with controls (0.40 ± 0.31 vs. 0.22 ± 0.15 ng/mL; p < 0.001). pDES correlated positively with MMP-2 (ρ = 0.68, p = 0.02), TIMP-1 (ρ = 0.72, p = 0.01), and the proportion of elastic fibres in the aortic media (ρ = 0.61, p = 0.03). ROC analysis showed good diagnostic performance (AUC = 0.82), with a threshold of 0.27 ng/mL yielding 78.6% sensitivity and 76.7% specificity. Elevated pDES levels reflect aortic elastolytic activity and may serve as a promising biomarker for TAAA detection and risk assessment. Full article

In recognising both the emerging industrial applications of multi-link robotic manipulators and the inherent challenges of modelling and controlling their highly complex nonlinear dynamics, this work proposes a completely model-free terminal sliding mode control (MFTSMC) design approach to reduce the sensitivity and complexity often associated with model-based routines. Consequently, the proposed design achieves strong robustness, simplicity, and good operation tuning by eliminating the need for system modelling and enabling direct operator–machine interaction. Simulink simulations on a 3-link case subjected to different disturbance conditions (free, low-frequency, high-frequency, and mixed) show rapid dynamic convergence, good tracking precision, and strong disturbance rejection. The system reaches the sliding surface within 0.07 s, maintains steady-state errors around ${10}^{-2}$, and achieves a smooth torque response with low energy costs. The benchmark results confirm the finite-time convergence and demonstrate that the proposed framework is practical and scalable for multi-DOF systems and has potential for underactuated manipulators. It should be noted that a generalised dynamic model for a planar n-link manipulator is presented in the study for (1) the ground truth of the manipulator in simulation (not for the MFTSMC design), (2) the model-based controller designs in comparison to the MFTSMC, and (3) understanding the dynamic characteristics. Full article

Background/Objectives: Spondylocostal dysostosis (SCDO) is a rare disorder characterized by congenital malformations of the spine and ribs. SCDO affects 1 in 40,000 human births, with rare cases also reported in dogs. Mutations in DLL3, encoding a critical Notch signaling pathway ligand, account for a majority of human SCDO cases. The remaining cases have variants in HES7, LFNG, MESP2, RIPPLY2, TBX6, and DLL1, which code for proteins in the Notch pathway. A mixed-breed litter of three dogs presented with varying degrees of spinal malformations and underwent comprehensive phenotyping including radiographic and neurologic examination. Two littermates demonstrated classic SCDO features including shortened torsos, vertebral malformations, and rib abnormalities, while a third showed only caudal vertebral truncation. Methods: Short-read whole-genome sequencing was performed on all three animals, followed by variant filtering and analysis using the two severely affected dogs as cases and 173 control dogs of various breeds. Variants were prioritized based on segregation patterns, population frequency, and predicted functional impact using established bioinformatics tools. Results: Variant analysis identified a novel splice acceptor variant in DLL3 (c.650-2A>C). This mutation, located at the splice acceptor site preceding exon 5, is predicted to disrupt critical EGF-like domains and O-fucosylation sites essential for DLL3 protein function. Conclusions: This study identifies a DLL3 splice variant causing SCDO in dogs, demonstrating phenotypic conservation with humans. These findings refine our understanding of genotype–phenotype correlations and demonstrate the value of comparative genomics for rare developmental disorders. Full article

Under the “dual carbon” strategy and green energy transition, traditional static accounting models have coarse temporal granularity. These models cannot meet the needs of fine-grained management and dynamic control for industrial parks. Therefore, it is urgent to develop high-resolution dynamic accounting systems and analyze model uncertainty. This study first defines the carbon source structure and establishes the accounting boundary for industrial parks. Second, it proposes dynamic accounting methods for both direct and indirect carbon emissions. Third, the study develops an uncertainty analysis model that considers parameter variability and error propagation. Finally, the feasibility and effectiveness of the proposed method are validated through a case study of a typical industrial park in Sichuan Province, China. The results indicate that the overall uncertainty of carbon emissions in the park is 28.9%, with electricity consumption identified as the primary driver of uncertainty (Spearman correlation coefficient of 0.986). The proposed framework effectively captures real-time emission fluctuations, providing a scientific basis for fine-grained carbon management. Full article

Phosphatidylinositol-binding clathrin assembly protein (PICALM) is a clathrin adaptor essential for clathrin-mediated endocytosis. Genome-wide association studies (GWAS) have consistently identified PICALM as one of the most significant genetic susceptibility loci for late-onset sporadic Alzheimer’s disease (AD). However, the functional impact of the most validated AD-associated variant, rs3851179, remains unclear. Here, we examined PICALM mRNA and protein expression in post-mortem AD brains with reference to rs3851179 genotype. We found that PICALM mRNA levels were significantly increased in AD brains compared with controls, and that the protective rs3851179T allele was associated with reduced PICALM mRNA levels relative to the non-protective rs3851179C allele. In contrast, PICALM levels were significantly reduced in AD brain lysates compared with controls. PICALM expression did not significantly differ between carriers of the protective and non-protective alleles. Analysis of the mRNA-to-protein ratio revealed a significant dissociation between transcript and protein levels, suggesting relatively reduced protein expression efficiency in cases carrying the non-protective CC genotype. To assess whether reduced PICALM levels influence tau pathology, we used Picalm heterozygous knockout (Picalm+/−) mice, which express approximately 50% of the wild-type Picalm protein. Following stereotaxic injection of pathological tau extracted from AD brains, both wild-type and Picalm+/− mice developed tau pathology; however, the extent of tau accumulation did not significantly differ between genotypes. Together, these findings indicate that although PICALM protein level is reduced in AD, this reduction does not appear to affect tau propagation in this model. Therefore, the AD susceptibility associated with PICALM variant likely arises from mechanisms other than tau spread, possibly involving other aspects of autophagy, endocytic or vascular function. Full article

Background: Brain MRI abnormalities in anti-NMDA receptor encephalitis (ANMDARE) are classically described in limbic structures, particularly the medial temporal lobe. Paralimbic, neocortical, and meningeal abnormalities have been less consistently reported. Objective: The objective was to evaluate the diagnostic value of brain MRI abnormalities in patients with definite ANMDARE. Methods: We conducted a case–control study including 115 patients with ANMDARE and 115 controls with primary psychotic disorders or antibody-negative autoimmune encephalitis. Structural MRI studies were systematically reviewed by an expert neuroradiologist blinded to clinical diagnosis. Results: ANMDARE patients were younger and more frequently presented with seizures, dyskinesia, severe neuropsychiatric disturbances, abnormal cerebrospinal fluid and EEG findings, and worse outcomes, including mortality. T2-T2-FLAIR abnormalities commonly involved medial temporal limbic structures, paralimbic regions (anterior cingulate and insular cortices), and neocortical areas (parieto-occipital cortices). Pachymeningeal enhancement was observed in 26.1% of patients. MRI findings clearly differentiated ANMDARE from primary psychotic disorders but largely overlapped with antibody-negative autoimmune encephalitis, except for limited parietal and occipital differences. Conclusions: T2-FLAIR MRI abnormalities involving medial temporal, paralimbic, and posterior neocortical regions are common in ANMDARE. Pachymeningeal enhancement is not rare. While useful for distinguishing ANMDARE from primary psychotic disorders, a substantial overlap with antibody-negative autoimmune encephalitis was observed. Full article

Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The assistant was developed and iteratively refined across four Action Research cycles. Also, the system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and unit price conformity in material (MTR), labor (LBR), and machinery (MCR), given the provincial unit price books (UPBs). Deterministic equality is evaluated either on raw numerical values or on values transformed through explicitly declared, rule-governed operations, preserving auditability without introducing tolerance-based or inferential reasoning. A dedicated exact-match mechanism, which is activated only when a code is invalid, enables the recovery of typographical errors only when a project item’s full price vector well matches a normative entry. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding ambiguity and maintaining full auditability. Deterministic extraction and normalization routines facilitate robust handling of displaced headers, merged cells, and non-standard labeling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves evaluation consistency, and supports documentation compliance with human judgment. This research contributes a framework for large language model (LLM)-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical and regulation-driven environments. Limitations—including narrow diagnostic scope, unlisted quotation exclusion, single-province UPB compliance, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost verification practices in public-sector construction. Full article

The application of forest byproducts to cropland provides significant benefits, mitigating soil degradation, supplying essential nutrients, and increasing yields. Their impact is well known in the first years, but few studies have examined the effects several years after an application. A field study was initiated in Québec, QC, Canada, to assess the effects of wood ash (10 and 20 Mg dry wt. ha⁻¹), pine biochar (10 Mg dry wt. ha⁻¹), paper mill sludge (PS) (12 Mg dry wt. ha⁻¹), and a combination of wood ash and PS, relative to an untreated control and a mineral treatment, on crop yield and soil properties three to seven years after application in a temperate circumneutral loamy soil. The site was cropped to a maize (Zea mays L.)–soybean [Glycine max (L.) Merr.]–spring wheat (Triticum aestivum L.) rotation. Each crop received supplemental N and P from mineral fertilizers, when needed, according to local agronomic recommendations. Applying wood ash increased wheat yield by 0.25–0.44 Mg ha⁻¹ three years after the addition, but no effect was detected in other cases and for the other amendments. Wood ash also resulted in the largest increases (p < 0.05) in soil pH and Mehlich-3 P, K, Ca, Mg, Zn, and Cd, alone or in combination with PS. Pine biochar promoted soil C sequestration after seven years, but did not affect other soil properties owing to its high stability and low nutrient content. This study revealed that wood ash was more advantageous than pine biochar for improving soil quality and crop productivity. Full article

This study performs a comprehensive experimental analysis of the dynamic response of geocell-reinforced sandy subgrades exposed to traffic-induced loading. A series of laboratory tests were performed using a custom-manufactured loading apparatus capable of creating monitored dynamic waveforms representative of vehicular traffic. A steel strip footing was assigned on both unreinforced and geocell-reinforced sandy beds to evaluate the implementation of the reinforcement in attenuating transmitted vertical stresses and surface settlements. The influence of key parameters, among which were load amplitude (0.5 and 1.0 tons), loading frequency (0.5, 1.0, and 2.0 Hz), and relative density of sand (30% loose and 60% medium), was systematically examined. The applied dynamic loading was based on a force-controlled sinusoidal waveform with constant amplitudes and frequencies, which corresponded to low-frequency harmonic cyclic loading in the case of traffic-induced quasi-static effects. Therefore, the experimental results indicate that geocell reinforcement reduces the transmitted vertical dynamic stress by up to 45% and reduces surface settlement by about 60% compared to unreinforced sand. However, the heightening efficiency decreases with loading frequency, the amplitude of the load, and the relative sand density. Thus, the findings are important in highlighting the capacity of geocell systems to enhance the longevity and efficiency of sand substrates when the systems are subjected to low-frequency harmonic cyclical loading conditions pertaining to traffic-induced quasi-static influences. Full article

Recent reports have revealed that the number of lower limb amputees worldwide has increased as a result of war, accidents, and vascular diseases and that transfemoral amputation accounts for 39% of cases, highlighting the need to develop an improved functional prosthetic knee joint that improves the amputee’s ability to resume activities of daily living. To enable transfemoral prosthesis users to walk on level ground, accurate prediction of the intended knee joint angle is critical for transfemoral prosthesis control. Therefore, the purpose of this research was to develop a technique for estimating knee joint angle utilizing a long short-term memory (LSTM) network and kinematic data collected from inertial measurement units (IMUs). The proposed LSTM network was trained and tested to estimate the contralateral knee angle using data collected from twenty able-bodied subjects using a lab-developed sensory gadget, which included four IMUs. Accordingly, the present work represents a feasibility investigation conducted on able-bodied individuals rather than a clinical validation for amputee gait. This study contributes to the field of bionics by mimicking the natural biomechanical behavior of the human knee joint during gait cycle to improve the control of artificial prosthetic knees. The proposed LSTM model learns the contralateral knee’s motion patterns in able-bodied gait and demonstrates the potential for future application in prosthesis control, although direct generalization to amputee users is outside the scope of this preliminary study. The contralateral LSTM models exhibited a real-time RMSE range of 2.48–2.78° and a correlation coefficient range of 0.9937–0.9991. This study proves the effectiveness of LSTM networks in estimating contralateral knee joint angles and shows their real-time performance and robustness, supporting its feasibility while acknowledging that further testing with amputee participants is required. Full article

This study investigated the fault ride through capability of inverter-based resources in weak distribution networks and proposes a fault-oriented reactive power compensation strategy using only point of common coupling voltage measurements. The proposed strategy determines the reactive power command based on the minimum phase voltage, which represents the most severely depressed phase during unbalanced faults, without fault type detection or sequence component analysis. As a result, the same control framework can be applied to single-line-to-ground, double-line-to-ground, and three-phase faults. A detailed MATLAB/Simulink model of a Korean distribution feeder was developed using actual system parameters. The proposed strategy was compared with a no control case and a conservative fixed capacity reactive power injection scheme derived from commonly adopted power factor limits. Simulation results show that the no control case provides no voltage support, while the fixed capacity approach yields limited improvement in weak grids. In contrast, the proposed strategy maintains stable inverter operation and improves voltage recovery. At locations with an extremely low weighted short circuit ratio of 0.303, the proposed strategy prevents inverter tripping during temporary faults and satisfies low voltage ride through requirements, demonstrating its practical effectiveness. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Biological, biomimetic, and engineering systems make extensive use of hydraulic asymmetries to control flow inside tubular structures. Examples span physiological valves, the guided transport observed in shark intestines, and passive devices such as Tesla valves. Here we investigate the mechanisms that generate these asymmetries using the notion of diodicity, defined as the ratio between pressure drops required to drive the same flow in opposite directions. We first focus on 2D geometries, which allow us to identify and study the main contributions to hydraulic asymmetry: channel geometry and internal obstacles embedded within a channel with rigid walls. By considering both rigid and deformable obstacles, we model channels that always remain open in both directions and channels that can be completely blocked by valve-like structures. We then extend the analysis to 3D geometries, again considering rigid and elastic cases. As a general trend, we find that geometry alone establishes a baseline diodicity, while higher dimensionality and structural reconfiguration consistently amplify the effect. Full article

Background: Hashimoto’s thyroiditis (HT) is a prevalent autoimmune disorder, often diagnosed late due to its asymptomatic or nonspecific presentation. Emerging evidence suggests that gut-derived lipopolysaccharides (LPS) may contribute to autoimmune activation. Objective: The primary objective of this study was to assess circulating LPS concentrations and dietary patterns in patients with Hashimoto’s thyroiditis compared to healthy controls. Methods: A hospital-based case–control study was conducted involving 105 HT patients and 25 healthy controls. Serum LPS concentrations, thyroid hormone profiles, and autoantibody levels were assessed. Dietary patterns were evaluated using the validated KomPAN questionnaire. Results: HT patients exhibited significantly higher serum LPS levels, particularly those with elevated anti-TPO and TRAB antibodies. A positive correlation was found between LPS and the fT3/fT4 ratio (r = 0.247, p = 0.006), and a negative correlation with fT4 (r = −0.314, p < 0.001). Dietary analysis revealed lower Pro-Healthy Diet Index scores in HT patients (3.94 vs. 5.34, p = 0.001), with increased consumption of processed foods and reduced intake of whole grains and oats. Conclusions: Elevated levels of lipopolysaccharides (LPS) and unhealthy dietary patterns may play a role in the development of thyroid autoimmunity. Taken together, these observations are consistent with a multifactorial model that potentially involves gut barrier dysfunction, endotoxemia, and nutritional factors in HT pathogenesis. Full article