Search Results (3,359)

Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing DP-FL approaches rely on fixed global clipping bounds for client updates, which substantially overestimate sensitivity when privacy loss is composed using Rényi Differential Privacy (RDP), zero-Concentrated DP (zCDP), or Moments Accountant (MA) frameworks, leading to excessive noise and degraded utility. This work proposes an adaptive clipping-based RDP accountant that incorporates empirical, round-wise update magnitudes into privacy accounting by rescaling each round’s RDP contribution according to the observed clipping ratio. The method is optimizer-agnostic and is evaluated with FedAvg, FedProx, and SCAFFOLD on the SGCC smart-meter theft dataset under IID and Dirichlet non-IID partitions. Experimental results show consistently tighter privacy bounds and improved model utility compared to classical DP accountants, demonstrating the effectiveness of sensitivity-aware privacy accounting for practical differentially private FL. Full article

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

Munronia pinnata (Meliaceae), a medicinal plant used in Zhuang traditional medicine, is recognized as a rich source of structurally diverse limonoids. In our continuing investigation of bioactive constituents from Guangxi medicinal plants, four new prieurianin-type limonoids, munropins G–J (1–4), were isolated from their aerial parts. Their structures were determined through comprehensive spectroscopic analysis, including nuclear magnetic resonance and high-resolution mass spectrometry, and further supported by quantum chemical calculations for electronic circular dichroism and statistical probability analysis. Munropins G (1) and H (2) feature an unprecedented C-12 β-D-glucosylated α-methyl-2′-hydroxypentanoate side chain and a C-17 β-substituted furan ring, with 1 being the 7-O-acetyl derivative of 2. Munropins I (3) and J (4) possess a formyl group at C-11, a 3-methyl-2-hydroxypentanoate ester at C-12, and a C-17 γ-hydroxy-α,β-unsaturated γ-lactone unit (21-hydroxy for 3, 23-hydroxy for 4), each existing as an equilibrating mixture of C-21 epimers—a phenomenon observed for the first time within a prieurianin-type framework. The absolute configurations of 1 and 2 were established by quantum chemical electronic circular dichroism calculations, while those of 3 and 4 remain to be assigned. All compounds were evaluated for cytotoxicity against human lung (A549), liver (HepG2), breast (MCF-7), and colon (HCT116) cancer cell lines and for anti-inflammatory activity in lipopolysaccharide-induced RAW 264.7 murine macrophages, but none exhibited significant effects at a concentration of 80 μM. This study expands the chemical diversity of Munronia limonoids and provides new molecular scaffolds for future structure–activity relationship investigations and chemotaxonomic markers for the Meliaceae family. Full article

In the context of the global dual-carbon goals and China’s DP strategy, strengthening the coupling between digital productivity (DP) and the high-quality development of the energy industry (HQDEI) is essential for resource-based regions. Doing so can help these regions overcome transition constraints and advance green, low-carbon development. Using panel data for nine prefecture-level cities in Guizhou Province from 2014 to 2023, we construct composite indices for DP and HQDEI with an improved entropy-weight TOPSIS approach. We then characterize their spatiotemporal evolution using a coupling coordination degree (CCD) model and kernel density estimation. Finally, we examine the determinants of coupling coordination through panel regression and threshold models. The results show that: (1) The CCD between DP and HQDEI efficiency continues to increase, with regional differences displaying a periodic convergence–divergence pattern and a spatial structure characterized by core agglomeration and outward diffusion. Gradient disparities in coordinated development are evident between central and peripheral areas. (2) Consumption upgrading and fiscal self-sufficiency significantly promote CC, whereas a traditional resource-dependent growth model significantly suppresses it. Constrained by short-term adaptation and integration costs, digital innovation currently exerts a negative effect, and its enabling potential has not yet been fully realized. (3) Nonlinear tests identify a single digital-infrastructure threshold: the enabling effect of digital innovation turns positive only once infrastructure surpasses a critical level, revealing pronounced interval heterogeneity. This study advances the theoretical understanding of the bidirectional coupling between DP and HQDEI, provides empirical guidance for energy digital transformation and high-quality development in resource-based regions of western China, and offers transferable insights for green, low-carbon transitions in traditional energy regions worldwide. Full article

Sequential change detection is a fundamental problem in statistics and signal processing, with the CUSUM procedure widely used to achieve minimax detection delay under a prescribed false alarm rate when pre- and post-change distributions are fully known. However, in many practical settings, raw observations cannot be shared with a trusted central curator, and privacy must be enforced at the data source, which prevents the computation of exact CUSUM statistics. We therefore introduce a local differentially private (DP) variant called LDP-CUSUM, which first applies a local DP mechanism to transform the raw data into privatized observations and then applies a CUSUM procedure to detect the change. We derive closed-form bounds on the average run length to false alarm and on the worst-case average detection delay, explicitly characterizing the tradeoff among privacy level, false alarm rate, and detection efficiency. Numerical simulations and a real-data case study were conducted to demonstrate the detection efficiency of our proposed LDP-CUSUM across various scenarios. Full article

Background: Chronic kidney disease (CKD) represents an escalating global health burden, fundamentally altering morbidity and mortality trajectories across the world, particularly as it advances into end-stage renal disease (ESRD). Beyond the primary decline in renal filtration and excretion, a wide spectrum of endocrine and metabolic derangements frequently accompanies kidney failure, with thyroid dysfunction emerging as a critical complication. Methods: The current study was designed to rigorously evaluate the nuanced association between thyroid hormone dynamics—specifically thyrotropin (TSH), triiodothyronine (T3), and thyroxine (T4)—and renal status in three distinct cohorts: individuals with suspected thyroid issues but normal renal function (NPs), non-dialysis kidney disease patients (NDKPs), and patients undergoing maintenance hemodialysis (DPs). Data were collected from a clinical setting in Bangladesh, involving 161 subjects. Results: The results demonstrated that patients in the DP cohort exhibited slightly elevated thyroid hormone levels relative to those in the NDKP cohort. Specifically, within the subgroups of patients exhibiting normal or sub-reference hormonal levels, dialysis patients maintained higher concentrations than their non-dialysis counterparts. Demographic stratification further revealed that males, females, and individuals younger than 45 years were more likely to demonstrate restorative hormonal profiles in the DP group than in the NDKP group. Conclusions: These collective outcomes suggest that renal replacement therapy, specifically hemodialysis, may serve to stabilize or improve thyroid function in ESRD patients by potentially mitigating the suppressive effects of uremic toxins and normalizing homeostatic feedback loops. Full article

The dystrophin gene encodes multiple dystrophin isoforms with tissue-specific functions, including several shorter isoforms expressed in the central nervous system and retina. While Duchenne muscular dystrophy (DMD) has historically been characterized as a primary myopathy resulting from loss of the full-length dystrophin Dp427, increasing clinical evidence indicates that dysfunction of shorter dystrophin isoforms contributes to significant extramuscular pathology, including retinal disease. In particular, loss of the Dp71 isoform has been implicated in retinal inflammation, blood–retinal barrier breakdown, and pathological angiogenesis. In this study, we investigated whether low-level residual expression of Dp71 is sufficient to mitigate retinal inflammation in the mdx3Cv mouse model, which displays reduced—but not absent—expression of multiple dystrophin isoforms. Western blot analysis revealed that mdx3Cv retinas express approximately 4% of wild-type Dp71 protein levels. Despite this marked reduction, mdx3Cv mice did not exhibit the inflammatory phenotype previously observed in Dp71-null mice. Retinal VEGF protein levels and VEGF receptor (FLT-1 and KDR) mRNA expression were preserved, while VEGF mRNA levels were modestly reduced. Furthermore, expression of inflammatory markers ICAM-1 and ALOX5AP, leukocyte adhesion to retinal vasculature, Aquaporin-4 expression, and BRB permeability to albumin were all comparable to wild-type littermates. Together, these findings demonstrate that minimal residual expression of Dp71 is sufficient to preserve retinal vascular homeostasis and prevent inflammatory and permeability defects in the mdx3Cv retina. These results further suggest that partial dystrophin restoration—at levels achievable with current exon-skipping or gene-based therapies—may be adequate to prevent or attenuate retinal pathology in DMD, providing a realistic and clinically relevant therapeutic target. Full article

Caking and powder adhesion are widespread challenges in dry powder processes. The influence of process parameters such as humidity and temperature on the adhesion behavior of dry powders has been extensively studied in numerous studies. Besides that, the impact of other process characteristics, such as additional process parameters or wall materials, has received little attention so far. In addition, existing methods to characterize caking behavior do not account for powders in a fluidized state. To address phenomena based on process and material behavior, a test rig was specifically designed to investigate the adhesion of dry particles to different metal walls at varying speeds at a 90° angle, representing the main novelty of this study. The deposition area, deposition mass, and maximum deposition thickness were evaluated, and the correlations were discussed. The investigations revealed that at low velocities (<12 m/s) and for smooth surfaces (Sq < 0.3–0.4 µm), wall materials with a high ratio of dispersive to polar surface energy components (D/P: 13–15.8) exhibit minimal powder adhesion. The test rig has demonstrated its effectiveness as a straightforward method for measuring adhesion across various powder–wall material pairs and could serve as a valuable preliminary test for industrial applications. Full article

Glucosamine (GlcN) is an essential amino monosaccharide widely used in pharmaceuticals, nutraceuticals, and cosmetics. Microbial fermentation presents a sustainable alternative to its traditional chemical production. However, in Saccharomyces cerevisiae, competitive carbon flux towards ethanol significantly limits GlcN yields. In this study, an S. cerevisiae strain for GlcN biosynthesis was engineered by integrating heterologous GlmD (glucosamine-6-phosphate deaminase) and GlmP (glucosamine-6-phosphate phosphatase) genes. To redirect carbon flux, the pyruvate decarboxylase genes pdc1, pdc5, and pdc6 were sequentially knocked out using the Clustered Regularly Interspaced Short Palindromic Repeats Cas9 (CRISPR-Cas9) approach, generating strains S. cerevisiaeGlmDP/pdc1Δ, GlmDP/pdc1Δpdc5Δ, and GlmDP/pdc1Δpdc5Δpdc6Δ. S. cerevisiae GlmDP/pdc1Δpdc5Δpdc6Δ achieved a GlcN titer of 2.20 ± 0.11 g/L, a 1.54-fold increase over the parental S. cerevisia GlmDP strain, while its ethanol yield decreased by 26%. This enhancement was achieved without significantly affecting cell growth or glucose consumption. Comparative transcriptomics between the triple-knockout and parental yeasts revealed 892 differentially expressed genes. Pathways related to glycolysis and ethanol formation were predominantly downregulated, whereas pathways potentially supporting GlcN synthesis were upregulated. The engineered strain demonstrated high genetic stability over 50 generations. Our findings demonstrate that disrupting ethanol formation is an effective strategy to enhance GlcN production in S. cerevisiae, providing valuable insights for carbon flux redistribution. Full article

Large-scale composite equipment manufacturing imposes stringent requirements on the lean management of multi-specification fiber prepreg sheet storage, while existing optimization methods suffer from poor process adaptability, insufficient multi-objective collaborative optimization capability, and low space utilization of static layouts. This study constructs a symmetric optimization framework for multi-objective composite sheet storage to address these critical bottlenecks. Specifically, the multi-dimensional process value of fiber sheets is quantified, and the layered storage optimization problem is transformed into a 0–1 knapsack problem with symmetric constraints. An improved Dynamic Programming–Backtracking (DP-BT) material selection algorithm and an adaptive dynamic programming iterative space optimization algorithm are proposed to achieve a symmetric balance of inter-layer space utilization and global optimization. Experimental validation with actual production data of 17 fiber sheet types verifies that the proposed method enables space optimization for specified layer counts to maximize average space utilization, with the rate rising from 79.4% (initial 4-layer layout) to 95.7% (3-layer) and 99.9% (2-layer), and a peak single-layer utilization of 100%. This framework achieves favorable optimization performance in the target production scenario and provides a referenceable symmetric optimization approach for the lean storage management of similar fiber sheet storage scenarios in composite manufacturing. Full article

Background/Objectives: Point-of-care (PoC) manufactured fresh chimeric antigen receptor (CAR)-T cells are typically formulated in hypothermic preservation formulations (HPFs) and stored under hypothermic conditions (2–8 °C) until administered to the patient. However, in current HPFs the shelf life of fresh CAR-T cells is short (~24–36 h) due to limited CAR-T cell stability, which poses significant time constraints on manufacturing procedures and logistics. The objective of this study was to improve the stability and extend the shelf life of fresh clinical-grade CAR-T cell drug products (DPs). Methods: A novel HPF was developed by supplementing a base HPF with the novel excipient SUL-138, which stabilizes mitochondria during hypothermic storage and subsequent rewarming, alone or in combination with endogenous mitochondrial substrates. This panel of HPFs was first screened for their stability-improving characteristics in the model cell line Jurkat cells. Subsequently, HPFs were assessed for their stability-improving characteristics of clinical-grade CD19 CAR-T cell DPs. Critical quality attributes, including CAR-T cell viability, T-cell differentiation state, exhaustion markers, and functional potency were evaluated in a good manufacturing practice (GMP)-compliant stability study up to 72 h. Results: For Jurkat cells, HPFs supplemented with SUL-138 and a combination of glucose, glutamine, and succinate demonstrated the greatest stability improvement at 2–8 °C, improving cell viability from ~1% to >85% after 72 h. For CAR-T cells, supplementation of HPFs with SUL-138 alone demonstrated the greatest improvement, resulting in a CAR-T cell viability from ~40% to >85% after 72 h of storage at 2–8 °C, while no additional benefits from mitochondrial substrates were observed. The novel HPF did not significantly impact CAR-T cell potency test results, T cell subset distribution, or exhaustion markers compared to control. Conclusions: A novel clinical-grade HPF that significantly improved fresh CAR-T cell stability during hypothermic storage was developed. This novel HPF can aid in the establishment of GMP-compliant and PoC CAR-T cell manufacturing platforms. Full article

Cellulose paper, a natural polymeric dielectric, determines the lifetime of oil–paper insulation systems in transformers, yet its molecular degradation behavior in ester-based insulating media remains insufficiently clarified. This study investigates the electro–thermal aging of cellulose polymer immersed in soybean-based natural ester (SBNE) and palm fatty acid ester (PFAE), with emphasis on depolymerization and its relationship with partial discharge (PD) activity. Accelerated aging experiments were conducted under combined electrical and thermal stress, and the evolution of the degree of polymerization (DP) was measured to quantify polymer chain scission. Phase-resolved PD (PRPD) patterns were recorded during aging, and multi-dimensional statistical features were extracted and reduced using principal component analysis to characterize degradation-sensitive electrical responses. The results show a progressive decrease in DP with aging time in both ester media, accompanied by distinct PD evolution characteristics, indicating different influences of the two esters on cellulose polymer stability. An ensemble learning model integrating multiple classifiers was further employed to identify aging stages based on PD features, achieving reliable discrimination performance. These findings establish a correlation between cellulose depolymerization and dielectric discharge behavior, providing a polymer-centered interpretation of aging mechanisms in ester-based oil–paper insulation systems. Full article

In the automotive industry, the most critical factor affecting dimensional stability during the forming of Advanced High-Strength Steels (AHSSs) is the springback phenomenon. This study systematically investigates the springback behavior of four distinct dual-phase steel grades (DP450, DP600, DP800, and DP1000) in U-shaped body-in-white (BIW) structures across 180 distinct scenarios. The experimental design varied sheet thicknesses (1.2, 1.6, 2 mm), die clearance angles (5°, 10°, 15°), and bending radii (R6, R8, R10, R12, R14). Numerical simulations using Autoform R8 were validated against Atos 3D optical scanning data, achieving values exceeding 0.90 for all grades. Quantitative validation metrics showed exceptional fidelity for lower-strength grades with error margins below 1.1%, while the maximum deviation was limited to 3.1% for the ultra-high-strength DP1000 grade. The findings demonstrate that while increasing material strength substantially intensifies springback, the strategic augmentation of sheet thickness and optimization of die radius effectively mitigate these deviations, thereby enhancing process stability. Full article

The blending process in raw material yards is essential for maintaining precise material proportions in downstream production, directly influencing product quality and energy efficiency in industries such as steel and coal processing. However, stringent operational constraints, including silo capacity limits, discharge rates, equipment movement delays, and a strict no-empty-silo requirement, result in a strongly coupled, high-dimensional combinatorial scheduling problem. In this paper, we develop a mixed-integer nonlinear programming (MINLP) model to capture the complex dynamics of silo weight and equipment operations. The primary scientific contribution of this work lies in the theoretical discovery of a structural decoupling property within the complex MINLP. We analytically prove that by fixing the replenishment sequence, the intractable global problem can be rigorously decomposed into two subproblems: a linear programming (LP) problem for silo-filling cart scheduling and a shortest-path problem solvable via dynamic programming (DP) for reclaimer scheduling. Leveraging this decomposition, a two-stage metaheuristic algorithm is proposed, combining greedy initialization with multi-round simulated annealing enhanced by local search. Experimental validation using real industrial data demonstrates that the proposed method consistently outperforms the greedy algorithm. Crucially, while the commercial solver Gurobi struggles to converge within a practical 1800 s time limit, our approach yields comparable solution quality in mere seconds. Furthermore, robustness analysis under a 20% demand surge confirms the algorithm’s adaptive capability, maintaining the silo weight stability through re-optimization. This research provides a robust, computationally efficient solution for the blending process in raw material yards. Full article

Brain stroke (BS) and heart disease (HD) are leading causes of global mortality and long-term disability, underscoring the critical need for early and accurate diagnostic tools. This research addresses the dual challenge of developing high-performance predictive models while ensuring the privacy of sensitive patient data. We propose a framework that integrates ensemble machine learning (ML) models with a formal differential privacy (DP) mechanism. Using a dataset of 5110 samples with clinical features, we evaluate Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Categorical Boosting (CAT) for BS and HD prediction. To protect individual privacy, we apply the Gaussian mechanism of DP with two probabilities of failure (POF) parameters (10–5 and 10–6) and a privacy budget ranging from 0.5 to 5.0. A key novelty of this work is the application of Pareto frontier multi-objective optimization (PFMOO) to systematically identify the optimal trade-off between model accuracy and privacy constraints. Our approach successfully identifies optimal, privacy-preserving models: XGB achieves top performance for BS prediction (92.3% accuracy, 92.29% F1 score), with a POF of 10–6, while RF excels for HD detection (95.61% accuracy, 97.8% precision), with a POF of 10–5. Furthermore, we employ explainable AI (XAI) techniques, SHAP and LIME, to provide interpretability of the model decisions, enhancing clinical trust. This research delivers a robust, interpretable, and privacy-conscious framework for early disease detection, offering a significant advancement over existing methods by holistically balancing accuracy, data security, and transparency. Full article