Search Results (76)

We present a fresh perspective on the mixed halogen-ion effect (MHE) in lead silicate glasses containing a mixture of halogen ions with a correlative study of optical spectroscopy and halogen ion transport. PbO was partially substituted by either PbCl₂ or PbF₂ in the ternary glass system: (65 − x) − x(PbF₂ or PbCl₂)-35SiO₂ (where 0 ≤ x ≤ 20 mol%) and by a mixture of PbF₂ and PbCl₂ in the quaternary glass series: 45PbO − xPbF_{2 −} (20 − x)PbCl₂–35SiO₂ (where 0 ≤ x ≤ 20 mol%). A suite of improved characterization techniques, including 4-probe van der Pauw resistivity measurements, optical absorption spectroscopy, differential thermal analysis, etc., was employed to correlate composition with physical properties. Replacing PbO with small quantities of PbF₂ or PbCl₂ in binary 65PbO-35SiO₂ glass resulted in a dramatic increase in conductivity by 3–4 orders of magnitude, confirming a shift from Pb²⁺-mediated to halide ion-mediated conduction and, within the mixed-halogen series, a profound MHE was observed. Contrary to previously reported data, the activation energy for conduction and the resistivity both exhibited maxima at the mixed halogen-ion ratio, MHR = (F/(F + Cl), of 0.5. The glass transition temperature (T_g) exhibited a non-monotonic trend, peaking at 506 °C for the MHR = 0.5 composition. Optical absorption measurements have revealed that the MHR = 0.5 glass has the broadest absorption edge and also exhibits certain features in the near IR region of the Urbach tail, which are suggestive of maximum electronic disorder. Full article

This study presents a robust estimation approach for linear discrete-time systems subject to parametric uncertainties. To address model mismatch, the proposed method enhances the MHE framework, thereby improving estimation accuracy. Based on this framework, the estimator is derived by minimizing the expected estimation error. A detailed derivation is provided, along with a novel recursive formulation for the pseudo-covariance of the estimation error. The resulting estimator maintains structural similarity to the Kalman filter and supports recursive implementation. Theoretical analysis establishes convergence to a stable system, with guaranteed boundedness and asymptotic unbiasedness of the estimation error. Simulation results demonstrate that the proposed strategy maintains high effectiveness and robustness under different uncertain conditions. Full article

The aim of this study is to assess the impact of extensive husbandry on slaughter characteristics and turkey meat quality in two utility types. The experiment was divided into two stages: for the first 6 weeks, 200 medium-heavy (MH) and heavy (H) turkey females were kept in intensive rearing conditions and then divided into a control (MHC/HC) and an extensive group (MHE/HE), with five replications in each group (10 birds per replication) for 10 weeks. In E groups, the balanced mixtures were gradually replaced with wheat at 30, 50, and 70% in subsequent feeding periods. Additionally, birds received green fodder (nettles, clover, and alfalfa) and steamed potatoes. After 16 weeks of rearing, birds were slaughtered, their carcasses were dissected, and the meat was assessed for technological traits (pH, L*a*b*, WHC, and tenderness), chemical composition (protein, ash, and fat), fatty acid profile, and sensory analysis. A higher proportion of liver and gizzard and a lower proportion of abdominal fat were found in the E groups, which resulted from more intensive mobility. Meat from these birds was less tender than that from females in the C groups, but it also contained considerably more protein and less fat. Lower values of fatty acid indices such as PI and AI, as well as a higher content of MUFA and a narrower n-6:n-3 ratio in meat from MH birds, indicate a stronger response of these birds to the extensive rearing system and confirm the health-promoting properties of their meat. The sensory evaluation of the meat meets the expectations of modern consumers regarding both the origin and taste of poultry meat. Full article

Migrant populations drive Canada’s demographic expansion, making their successful integration a national priority. However, research has consistently shown that refugees and immigrants experience declining mental health and encounter significant barriers to accessing culturally appropriate mental health support. This scoping review examined the breadth of evidence on culturally adapted mental health education (MHE) programs for migrant populations, including those that integrate traditional and complementary healing practices, and their effectiveness. Systematic searches across six databases (Medline, EMBASE, PsycINFO, Global Health, CINAHL, and Scopus) identified 4075 peer-reviewed articles, with 28 studies meeting inclusion criteria. These included mental health education and awareness programs that integrate psychoeducation and skill-building. Inclusion criteria required cultural adaptation of programs through one or more approaches such as language modification, culturally adapted content, community-based delivery, or integration of traditional and complementary healing practices. Thematic analysis of the programs revealed seven key themes characterizing effective MHE programs: cultural adaptation and sensitivity, addressing unique migration-related stressors, integration of traditional and Western approaches, use of theoretical frameworks and evidence-based practices, rigorous evaluation methodologies, application of holistic frameworks, and community-based peer support models. Programs predominantly utilized psychoeducation and culturally adapted interventions, with common theoretical frameworks including cognitive–behavioral therapy and the PRECEDE–PROCEED model. Across the reviewed studies, program evaluations reported positive outcomes including increased mental health literacy, reduced stigma, enhanced coping skills, and decreased depression, anxiety, and PTSD symptoms, suggesting that culturally adapted MHE programs are acceptable and feasible interventions for migrant populations. Full article

Background and Purpose: Hepatic encephalopathy (HE) is a neuropsychiatric syndrome associated with liver cirrhosis (LC) that often results in cognitive impairment. Minimal HE (mHE), a subtle form of the condition, significantly affects patients’ quality of life. Advanced imaging techniques, such as quantitative susceptibility mapping (QSM), provide new insights into the brain changes associated with HE. Materials and Methods: The study included 28 patients (17 with mHE and 11 without) with alcohol-induced LC and 25 healthy controls. MR imaging, including QSM, was utilized to assess microstructural tissue changes and iron deposition in the brain. Cognitive function was assessed through a neuropsychological test battery. QSM quantified magnetic susceptibility in deep gray matter, while enlarged perivascular spaces (EPVS) were evaluated using T2-weighted images. Statistical analyses, including non-parametric tests and linear regression, assessed differences in susceptibility and their correlation with cognitive performance and EPVS. Results: Significant differences in cognitive performance and brain susceptibility were observed between patients and controls. Patients exhibited lower susceptibility in the caudate nucleus with the accumbens (CNA); mHE patients, in particular, had a significant reduction in CNA susceptibility. Additionally, EPVS grade correlated positively with cognitive decline, suggesting that EPVS may play an essential role in the pathophysiology of mHE. Conclusions: This study demonstrates that QSM can detect subtle brain changes in LC patients, with decreased susceptibility in the CN (caudate nucleus) linked to cognitive impairment in mHE. The role of EPVS in HE warrants further investigation, as it may affect the efficacy of current diagnostic and therapeutic approaches. These findings highlight the potential of QSM to improve HE assessment. Full article

Digital surface models (DSMs) derived from high-resolution satellite imagery often contain mismatches, voids, and coarse building geometry, limiting their suitability for accurate and standardized 3D reconstruction. The scarcity of finely annotated samples further constrains generalization to complex structures. To address these challenges, an automated building reconstruction method based on two-stage polygon decomposition and adaptive roof fitting is proposed. Building polygons are first extracted and standardized to preserve primary contours while improving geometric regularity. A two-stage decomposition is then applied. In the first stage, polygons are coarsely decomposed, and redundant rectangles are removed by analyzing containment relationships. In the second stage, non-flat regions are identified and further decomposed to accommodate complex building connections. For 3D model fitting, flat-roof buildings are reconstructed by integrating structural analysis of DSM elevation distributions with adaptive rooftop partitioning, which enables accurate modeling of complex flat structures with auxiliary components. For non-flat roofs, a representative parameter space is defined and explored through systematic search and optimization to obtain precise fits. Finally, intersecting primitives are normalized and optimally merged to ensure structural coherence and standardized representation. Experiments on the US3D, MVS3D, and Beijing-3 datasets demonstrate that the proposed method achieves higher geometric accuracy and more standardized models, with an average IOU3 of 91.26%, RMSE of 0.78 m, and MHE of 0.22 m. Full article

Background and Objectives: Neuroinflammation plays a significant role in liver and neurological disorders via its disruption of neurotransmission, which alters cerebral function, resulting in cognitive and motor impairment, fatigue, anxiety, and depression. A key interaction exists between GABAergic neurotransmission and neuroinflammation, whereby excessive GABA_A receptor activation exacerbates cognitive and behavioural impairment. Golexanolone, a novel GABA_A-receptor-modulating steroid antagonist (GAMSA), primarily attenuates GABAergic potentiation via GABA_A-positive steroid allosteric receptor modulators such as allopregnanolone. This review aims to summarize new evidence showing that golexanolone improves peripheral inflammation, neuroinflammation, and neurological alterations in animal models of different neurological pathologies. We provide an overview of the first clinical trial using this novel compound. Results: In rat models of hyperammonemia and minimal hepatic encephalopathy (MHE), peripheral inflammation induces microglia and astrocyte activation and neuroinflammation, altering GABAergic neurotransmission and resulting in cognitive and motor impairment. Golexanolone’s unique dual action reduces peripheral inflammation and glial activation, thus normalizing neurotransmission and cognitive and motor function. Furthermore, a phase II study in cirrhotic patients with MHE shows that golexanolone is well tolerated and improves cognition. Similarly, in a model of primary biliary cholangitis (PBC) involving bile-duct ligation, peripheral inflammation, neuroinflammation, and altered neurotransmission—associated with fatigue, impaired memory, and locomotor gait and motor incoordination—were reversed by the dual action of golexanolone. In the Parkinson’s disease (PD) rat model induced by neurotoxin 6-OHDA, rats exhibited fatigue, anhedonia, impaired memory, and locomotor gait and motor incoordination, which were associated with microglia and astrocyte activation in the substantia nigra and striatum, in addition to tyrosine hydroxylase (TH) loss. Golexanolone reduces microglia and astrocyte activation, partially reduces TH loss, and improves fatigue, anhedonia, memory, locomotor gait, and motor incoordination. Golexanolone also normalizes elevated levels of α-synuclein. Conclusions: These findings suggest that golexanolone has beneficial therapeutic effects for treating fatigue, depression, motor, and cognitive impairment across diverse neuroinflammatory conditions, including synucleinopathies. Full article

This paper proposes a cascaded state estimation framework based on proprioception for robots. A generalized-momentum-based Kalman filter (GMKF) estimates the ground reaction forces at the feet through joint torques, which are then input into an error-state Kalman filter (ESKF) to obtain the robot’s prior state estimate. The system’s dynamic equations on the Lie group are parameterized using canonical coordinates of the first kind, and variations in the tangent space are mapped to the Lie algebra via the inverse of the right trivialization. The resulting parameterized system state equations, combined with the prior estimates and a sliding window, are formulated as a moving horizon estimation (MHE) problem, which is ultimately solved using a parallel real-time iteration (Para-RTI) technique. The proposed framework operates on manifolds, providing a tightly coupled estimation with higher accuracy and real-time performance, and is better suited to handle the impact noise during foot–ground contact in legged robots. Experiments were conducted on the BQR3 robot, and comparisons with measurements from a Vicon motion capture system validate the superiority and effectiveness of the proposed method. Full article

This study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-based models within MHE towards data-driven techniques. Specifically, a Long Short-Term Memory (LSTM)-based DNN is trained using synthetic data derived from a high-fidelity thermal model of a Permanent Magnet Synchronous Machine (PMSM), applied within a thermal derating torque control strategy for battery electric vehicles. The trained DNN is directly embedded within an MHE formulation, forming a discrete-time nonlinear optimal control problem (OCP) solved via the acados optimization framework. Model-in-the-Loop simulations demonstrate accurate temperature estimation even under noisy sensor conditions and simulated sensor failures. Real-time implementation on embedded hardware confirms practical feasibility, achieving computational performance exceeding real-time requirements threefold. By integrating the learned LSTM-based dynamics directly into MHE, this work achieves state estimation accuracy, robustness, and adaptability while reducing modeling efforts and complexity. Overall, the results highlight the effectiveness of combining model-based and data-driven methods in safety-critical automotive control systems. Full article

To address the issues of path tracking accuracy and control stability in autonomous ship berthing, an improved algorithm combining nonlinear model predictive control (NMPC) and convolutional neural networks (CNNs) is proposed in this paper. A CNN is employed to train on a large dataset of ship berthing trajectories, combined with the rolling optimization mechanism of NMPC. A high-precision path tracking control method is designed, which accounts for ship motion constraints and environmental disturbances. Simulation results show an 88.24% improvement in tracking precision over traditional MPC. This paper proposes an improved nonlinear model predictive control (NMPC) strategy for autonomous ship berthing. By integrating convolutional neural networks (CNNs) and moving horizon estimation (MHE), the method enhances robustness and path-tracking accuracy under environmental disturbances. The amount of system overshoot is reduced, and the anti-interference capability is notably improved. The effectiveness, generalization, and applicability of the proposed algorithm are verified. Full article

Fast urbanization and economic development lead to a prosperous high-rise building industry with high material handling efficiency (MHE). However, the integrated material handling (IMH) framework optimization and empirical studies on Chinese high-rise buildings are not in-depth. Here, the IMH practice in Chinese Chongqing high-rise building projects (CHBPs) was researched, and the effect factors of MHE were discussed to propose improvement strategies. A questionnaire survey (191 participants), qualitative topic analysis, quantitative descriptive statistics, reliability/correlation analysis, an independent sample t-test, analysis of variance (ANOVA), and regression analysis were performed. As a result, the understanding of the IMH concept, effectiveness of training projects, and positive effect of regulations were found to favor an improved MHE. Moreover, a weak positive correlation between work experience and MHE was found. Through the proposed model development framework, the combination of theoretical analysis and empirical research can provide comprehensive tools and knowledge resources for IMH practices in CHBP to improve MHE. Through quantitative indicators such as the material handling efficiency index (MHEI), the training project impact score (TPIS) and the regulation perception index (RPI), this framework offers an objective basis for continuous monitoring and improvement. Full article

With the rapid development of the automotive industry, research on the internet of vehicles (IoV) has become a hot topic in the field of automobiles. Considering the privacy of data collected from vehicles, this paper proposes a novel multiparty homomorphic encryption scheme (MHE) for secure multiparty computation without the need for a trusted third party. The scheme ensures efficient computation of data while preserving the privacy of each party’s data. It consists of four phases: construction, computation, recombination, and refreshing. In the recombination phase, the key is reconstructed using a span program, enabling secure computation among participating parties under a semi-honest model. Finally, we compare the proposed scheme with mainstream approaches and conduct experiments within the framework of federated learning. Through both experimental and theoretical analyses, the performance of the proposed scheme is comprehensively evaluated, demonstrating its efficiency and correctness. Full article

This paper examines the impact of two storage policies—dedicated storage (D-SLAP) and randomized storage (R-SLAP)—on warehouse operational efficiency. It integrates the Storage Location Assignment Problem (SLAP) with the unrelated parallel machine scheduling problem (UPMSP), which represents the scheduling of the material handling equipment (MHE). This integration is intended to elucidate the interplay between storage strategies and scheduling performance. The considered evaluation metrics include transportation cost, average waiting time, and total tardiness, while accounting for product arrival and demand schedules, precedence constraints, and transportation expenses. Additionally, considerations such as MHE eligibility, resource requirements, and available storage locations are incorporated into the analysis. Given the complexity of the combined problem, a tailored Non-dominated Sorting Genetic Algorithm (NSGA-II) was developed to assess the performance of the two storage policies across various randomly generated test instances of differing sizes. Parameter tuning for the NSGA-II was conducted using the Taguchi method to identify optimal settings. Experimental and statistical analyses reveal that, for small-size instances, both policies exhibit comparable performance in terms of transportation cost and total tardiness, with R-SLAP demonstrating superior performance in reducing average waiting time. Conversely, results from large-size instances indicate that D-SLAP surpasses R-SLAP in optimizing waiting time and tardiness objectives, while R-SLAP achieves lower transportation cost. Full article

The objective of this study was to evaluate the effect of fecal bacterial community and metabolomics in goats when consuming a lower-protein diet with different energy levels. Eight healthy Leizhou goats, with 11 ± 0.78 kg of body weight, were selected and housed individually in cages. The animals were randomly allocated to a lower-protein diet that varied with four metabolites energy levels (7.01, 8.33, 9.66, and 10.98 MJ/kg DM) in a replicated 4 × 4 Latin square design. Notably, energy-dependent microbial restructuring was observed at both phylum and genus levels. At the phylum level, the relative abundances of Firmicutes and Spirochaetote increased linearly, whereas the Bacteroidota and Patescibacteria decreased linearly with increasing dietary energy levels (p < 0.05). The relative abundances of Verrucomicrobiota increased quadratically, whereas others decreased quadratically with increasing dietary energy levels (p < 0.05). At the genus level, a total of 316 bacteria were identified in the 32 fecal samples. The relative abundances of Christensenellaceae_R-7_group, unclassified_f__Lachnospiraceae, Ruminococcus, norank_o__Clostridia_UCG-014, Treponema, [Eubacterium]_siraeum_group, and [Eubacterium]_ruminantium_group increased linearly, whereas the Oscillospiraceae_UCG-005, norank_f__[Eubacterium]_coprostanoligenes_group, Prevotellaceae_UCG-004, unclassified_c__Clostridia, norank_f__Ruminococcaceae, unclassified_f__ Oscillospiraceae, and others decreased linearly with an increasing dietary energy levels (p < 0.05). In addition, the metabolomic analysis of feces showed that there are many differential metabolites in goats when consuming a lower-protein diet with different energy levels; for example, lipid metabolism and amino acid metabolic pathways were increased in MLE, MHE, and HE groups compared to the LE group. In conclusion, this study provides further information regarding the effects on fecal bacterial community composition and metabolites in goats when consuming a lower-protein diet with different energy levels. Full article

This paper presents a moving horizon estimator-based cooperative model predictive control strategy for a low-voltage distribution area equipped with symmetric distributed generators (DGs). First, DGs have their symmetries in the control structures that can be utilized for the control design. Then, a simplified model using feedback linearization theory for the symmetric DGs with hierarchical control reduces the high-order detailed models to low-order ones. To supplement the loss of accuracy and reliability in the proposed model, the controller introduces a moving horizon estimator to observe the unmeasured state variables under the poor communication condition of a low-voltage distribution network. Compared to the conventional method, the moving horizon estimator has advantages in handling uncertain disturbances, communication delays, constraints, etc. Furthermore, with all measured and observed state information, a cooperative distributed model predictive controller can be executed, and the stability and feasibility of controller are given. Finally, the effectiveness of the proposed control technique is verified through simulation based on Matlab/Simulink. Full article