Search Results (121)

Developmental exposure to polybrominated diphenyl ethers (PBDEs), which are commonly used as flame retardants, results in irreversible cognitive impairments. Postnatal hippocampal neurogenesis, which occurs in the subgranular zone (SGZ) of the dentate gyrus, is critical for neuronal circuits and plasticity. Wnt7a-Frizzled5 (FZD5) is essential for both neurogenesis and synapse formation; moreover, Wnt signaling participates in PBDE neurotoxicity and also contributes to the neuroprotective effects of melatonin. Therefore, we investigated the impacts of perinatal decabromodiphenyl ether (BDE-209) exposure on hippocampal neurogenesis and synaptogenesis in juvenile rats through BrdU injection and Golgi staining, as well as the alleviation of melatonin pretreatment. Additionally, we identified the structural basis of Wnt7a and two compounds via molecular docking. The hippocampal neural progenitor pool (Sox2+BrdU+ and Sox2+GFAP+cells), immature neurons (DCX+) differentiated from neuroblasts, and the survival of mature neurons (NeuN+) in the dentate gyrus were inhibited. Moreover, in BDE-209-exposed offspring rats, it was observed that dendritic branching and spine density were reduced, alongside the long-lasting suppression of the Wnt7a-FZD5/β-catenin pathway and targeted genes (Prox1, Neurod1, Neurogin2, Dlg4, and Netrin1) expression. Melatonin alleviated BDE-209-disrupted memory, along with hippocampal neurogenesis and dendritogenesis, for which the restoration of Wnt7a-FZD5 signaling may be beneficial. This study suggested that melatonin could represent a potential intervention for the cognitive deficits induced by PBDEs. Full article

Accurate gene expression quantification using reverse transcription quantitative PCR (RT-qPCR) requires stable reference genes (RGs) for reliable normalization. However, few studies have systematically identified RGs suitable for simultaneous high salt, alkaline, and high-temperature conditions. This study addresses this gap by evaluating the stability of eight candidate RGs in the anaerobic halophilic alkalithermophile Natranaerobius thermophilus JW/NM-WN-LF^T under combined salt, alkali, and thermal stresses. The stability of these candidate RGs was assessed using five statistical algorithms: Delta CT, geNorm, NormFinder, BestKeeper, and RefFinder. Results indicated that recA exhibited the highest expression stability across all tested conditions and proved adequate as a single RG for normalization in both RT-qPCR and droplet digital PCR (ddPCR) assays. Furthermore, recA alone or combined with other RGs (sigA, rsmH) effectively normalized the expression of seven stress-response genes (proX, opuAC, mnhE, nhaC, trkH, ducA, and pimT). This work represents the first systematic validation of RGs under polyextreme stress conditions, providing essential guidelines for future gene expression studies in extreme environments and aiding research on microbial adaptation mechanisms in halophilic, alkaliphilic, and thermophilic microorganisms. Full article

Federated learning (FL) struggles under non-IID client data when local models drift toward conflicting optima, impairing global convergence and performance. We introduce entropy-regularized federated optimization (ERFO), a lightweight client-side modification that augments each local objective with a Shannon entropy penalty on the per-parameter update distribution. ERFO requires no additional communication, adds a single-scalar hyperparameter $\lambda$, and integrates seamlessly into any FedAvg-style training loop. We derive a closed-form gradient for the entropy regularizer and provide convergence guarantees: under $\mu$-strong convexity and L-smoothness, ERFO achieves the same $O(1/T)$ (or linear) rates as FedAvg (with only $O\left(\lambda \right)$ bias for fixed $\lambda$ and exact convergence when ${\lambda }_t\to 0$); in the non-convex case, we prove stationary-point convergence at $O(1/T)$. Empirically, on five-client non-IID splits of the UNSW-NB15 intrusion-detection dataset, ERFO yields a +1.6 pp gain in accuracy and +0.008 in macro-F1 over FedAvg with markedly smoother dynamics. On a three-of-five split of PneumoniaMNIST, a fixed $\lambda$ matches or exceeds FedAvg, FedProx, and SCAFFOLD—achieving 90.3% accuracy and 0.878 macro-F1—while preserving rapid, stable learning. ERFO’s gradient-only design is model-agnostic, making it broadly applicable across tasks. Full article

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity. Full article

Personalized Federated Learning (PFL) seeks to train client-specific models across distributed data silos with heterogeneous distributions. We introduce Sign-Entropy Regularization (SER), a novel entropy-based regularization technique that penalizes excessive directional variability in client-local optimization. Motivated by Descartes’ Rule of Signs, we hypothesize that frequent sign changes in gradient trajectories reflect complexity in the local loss landscape. By minimizing the entropy of gradient sign patterns during local updates, SER encourages smoother optimization paths, improves convergence stability, and enhances personalization. We formally define a differentiable sign-entropy objective over the gradient sign distribution and integrate it into standard federated optimization frameworks, including FedAvg and FedProx. The regularizer is computed efficiently and applied post hoc per local round. Extensive experiments on three benchmark datasets (FEMNIST, Shakespeare, and CIFAR-10) show that SER improves both average and worst-case client accuracy, reduces variance across clients, accelerates convergence, and smooths the local loss surface as measured by Hessian trace and spectral norm. We also present a sensitivity analysis of the regularization strength $\rho$ and discuss the potential for client-adaptive variants. Comparative evaluations against state-of-the-art methods (e.g., Ditto, pFedMe, momentum-based variants, Entropy-SGD) highlight that SER introduces an orthogonal and scalable mechanism for personalization. Theoretically, we frame SER as an information-theoretic and geometric regularizer that stabilizes learning dynamics without requiring dual-model structures or communication modifications. This work opens avenues for trajectory-based regularization and hybrid entropy-guided optimization in federated and resource-constrained learning settings. Full article

Challenges in implementing machine learning (ML) include expanding data resources within the finance sector. Banking data with significant financial implications are highly confidential. Diverse breaches and privacy violations can result from a combination of user information from different institutions for banking purposes. To address these issues, federated learning (FL) using a flower framework is utilized to protect the privacy of individual organizations while still collaborating through separate models to create a unified global model. However, joint training on datasets with diverse distributions can lead to suboptimal learning and additional privacy concerns. To mitigate this, solutions using federated averaging (FedAvg), federated proximal (FedProx), and federated optimization methods have been proposed. These methods work with data locality during training at local clients without exposing data, while maintaining global convergence to enhance the privacy of local models within the framework. In this analysis, the UNSW-NB15 and credit datasets were employed, utilizing precision, recall, accuracy, F1-score, ROC, and AUC as performance indicators to demonstrate the effectiveness of the proposed strategy using FedAvg, FedProx, and FedOpt. The proposed algorithms were subjected to an empirical study, which revealed significant performance benefits when using the flower framework. Consequently experiments were conducted over 50 rounds using the UNSW-NB15 dataset, which achieved accuracies of 99.87% for both FedAvg and FedProx and 99.94% for FedOpt. Similarly, with the credit dataset under the same conditions, FedAvg and FedProx achieved accuracies of 99.95% and 99.94%, respectively. These results indicate that the proposed framework is highly effective and can be applied in real-world applications across various domains for secure and privacy-preserving collaborative machine learning. Full article

Federated learning (FL) offers a privacy-preserving framework for distributed machine learning, enabling collaborative model training across diverse clients without centralizing sensitive data. However, statistical heterogeneity, characterized by non-independent and identically distributed (non-IID) client data, poses significant challenges, leading to model drift and poor generalization. This paper proposes a novel algorithm, pFedKD-WCL (Personalized Federated Knowledge Distillation with Weighted Combination Loss), which integrates knowledge distillation with bi-level optimization to address non-IID challenges. pFedKD-WCL leverages the current global model as a teacher to guide local models, optimizing both global convergence and local personalization efficiently. We evaluate pFedKD-WCL on the MNIST dataset and a synthetic dataset with non-IID partitioning, using multinomial logistic regression (MLR) and multilayer perceptron models (MLP). Experimental results demonstrate that pFedKD-WCL outperforms state-of-the-art algorithms, including FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD in terms of accuracy and convergence speed. For example, on MNIST data with an extreme non-IID setting, pFedKD-WCL achieves accuracy improvements of $3.1\%$, $3.2\%$, $3.9\%$, $3.3\%$, and $0.3\%$ for an MLP model with 50 clients compared to FedAvg, FedProx, PerFedAvg, pFedMe, and FedGKD, respectively, while gains reach $24.1\%$, $22.6\%$, $2.8\%$, $3.4\%$, and $25.3\%$ for an MLR model with 50 clients. Full article

Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning methods, fail to generalize well due to varying sensor configurations and inconsistent data availability. To overcome these challenges, this study proposes a Personalized Federated Learning (pFL) framework that integrates multi-level feature masking, model ensemble techniques, and knowledge transfer to enhance predictive performance across diverse buildings. The proposed feature masking strategy extracts the most relevant time-series features, while model ensemble learning improves generalization, and knowledge transfer enables adaptive fine-tuning for each building. These techniques allow pFL to retain global knowledge while personalizing to local energy consumption patterns, making it more effective than traditional FL methods. Experiments conducted on a campus energy dataset demonstrate that pFL consistently outperforms FedAvg, FedProx, and standalone models in energy prediction accuracy. The most significant improvements are observed in buildings with highly fluctuating consumption patterns, validating the effectiveness of the proposed approach in handling heterogeneous sensing environments. This study highlights the potential of Federated Learning for scalable and adaptive energy prediction. Future work will focus on refining multi-horizon forecasting and developing strategies to enhance knowledge sharing among buildings for improved long-term performance. Full article

Federated Learning (FL) is a transformative decentralized approach in machine learning and deep learning, offering enhanced privacy, scalability, and data security. This review paper explores the foundational concepts, and architectural variations of FL, prominent aggregation algorithms like FedAvg, FedProx, and FedMA, and diverse innovative applications in thermal comfort optimization, energy prediction, healthcare, and anomaly detection within smart buildings. By enabling collaborative model training without centralizing sensitive data, FL ensures privacy and robust performance across heterogeneous environments. We further discuss the integration of FL with advanced technologies, including digital twins and 5G/6G networks, and demonstrate its potential to revolutionize real-time monitoring, and optimize resources. Despite these advances, FL still faces challenges, such as communication overhead, security issues, and non-IID data handling. Future research directions highlight the development of adaptive learning methods, robust privacy measures, and hybrid architectures to fully leverage FL’s potential in driving innovative, secure, and efficient intelligence for the next generation of smart buildings. Full article

The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardization is proposed, which can meet such challenges comprehensively. On the one hand, it can guarantee semantic consistency across different kinds of IoT devices using unified ontology, so that data from multiple sources could be seamlessly integrated; on the other hand, it solves the non-IID issues of data and limited resources in edge servers by FedProx. Experimental findings indicate that FedProx outperforms FedAvg, with a remarkable accuracy level of 89.4%, having higher convergence rates, and attaining a 30% saving on communication overhead through gradient compression. In addition, the ontology alignment procedure yielded a 95% success rate, thereby ensuring uniform data preprocessing across domains, including traffic monitoring and parking management. The model demonstrates outstanding scalability and flexibility to new devices, while maintaining high performance during ontology evolution. These findings highlight its great potential for deployment in smart cities, environmental monitoring, and other IoT-based ecosystems, thereby enabling the creation of more efficient and integrated solutions in these areas. Full article

On-year and off-year phenomena are common in Moso bamboo forests and significantly affect economic value and ecological functions. However, observational evidence regarding the impact of these cycles on surface soil moisture (SSM) remains scarce, and little is known about the implications of their landscape patterns for regional water conservation. Here, we first quantified the spatial distribution and temperature vegetation drought index (TVDI) of on-year and off-year Moso bamboo forests based on remote sensing images and landscape metrics. We then analyzed the role of on-year and off-year phenomena and their landscape patterns on SSM. Results showed that: (1) the proposed index derived from remote sensing imagery extracted on-year and off-year Moso bamboo forests with satisfactory accuracy, and the areas were 161.4 km² and 173.5 km², respectively; (2) a significant disparity was observed in the TVDI between on-year and off-year Moso bamboo forests, and mismatched growth stages and phenological characteristics were identified as primary influencing factors; and the (3) landscape metrics of the perimeter–area ratio (PAR), proximity index (PROX), perimeter–area fractal dimension index (PAFRAC), connectance index (CONNECT), and aggregation index (AI) exhibited negative correlations with the TDVI, indicating that the high spatial connectivity of Moso bamboo forests enhances soil water conservation. Our findings suggested that on-year and off-year phenomena and their spatial distribution intensified the heterogeneity in SSM. Therefore, considerations regarding the connectivity and edge complexity within Moso bamboo forests should be prioritized in future management strategies to achieve a balance between economic benefits and ecological functions in water-deficient mountainous areas. Full article

Solid oxide fuel cells (SOFCs) are attracting attention as an eco-friendly power source because they show high power density. However, SOFC requires a high-temperature environment of 800 °C or higher, and accordingly, the problem of thermal stability of the material constituting SOFC has been raised. On the other hand, low-temperature solid oxide fuel cells (LT-SOFCs) research is steadily progressing to improve the electrochemical performance at low temperatures by improving the oxygen reduction reaction of the cathode by applying a cathode interlayer of various materials. In this study, LT-SOFCs were manufactured and electrochemically evaluated using praseodymium oxide (PrO_x) as a cathode interlayer. Scandium Stabilized Zirconia (ScSZ) pellets were used as electrolyte support for LT-SOFC, and PrO_x was deposited by various thicknesses as a cathode interlayer on ScSZ pellets by a sputtering process. Pt and Ni were deposited under the same process conditions for the cathode and anode, respectively. To analyze the thin-film characteristics of the PrO_x cathode interlayer, SEM (Scanning Electron Microscopy), X-ray Diffraction (XRD), and XPS (X-ray Photoelectron Spectroscopy) were analyzed. The electrochemical characteristics of LT-SOFCs were evaluated by electrochemical impedance spectroscopy (EIS). Hydrogen was supplied to the anode at the flow rate of 50 sccm, and the performance of LT-SOFC was evaluated at 500 °C by exposing the cathode to the atmosphere. Full article

Background: Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions. Full article

Background: When performing the Upper Limb Neurodynamic Test 1 (ULNT1), the order of joint movement can be varied to place more stress onto certain nerve segments. However, the mechanisms underlying this phenomenon are still unclear. This study aimed to analyze the differences in the stiffness of the median nerve (MN) and the brachial plexus (BP) using ultrasound shear wave elastography during three sequences of the ULNT1: standard (ULNT1-STD), distal-to-proximal (ULNT1-DIST), and proximal-to-distal (ULNT1-PROX). Methods: Shear wave velocity (SWV) was measured at the initial and final position of each sequence at the MN (wrist) and at the C5 and C6 nerve roots (interscalene level) in 31 healthy subjects. Results: A significant interaction was found between ULNT1 sequence and location (p < 0.001). The ULNT1-STD and ULNT1-DIST induced a greater stiffness increase in the MN (5.67 ± 0.91 m/s, +113.94%; 5.65 ± 0.98 m/s, +115.95%) compared to C5 and C6 (p < 0.001). The ULNT1-PROX resulted in a significantly smaller increase in stiffness at the MN (4.13 ± 0.86 m/s, +54.17%, p < 0.001), but a greater increase at C5 (4.88 ± 1.23 m/s, +53.39%, p < 0.001) and at C6 (4.87 ± 0.81 m/s, +31.55%). The differences for the ULNT1-PROX at C6 were only significant compared to the ULNT1-STD (p < 0.001), but not the ULNT1-DIST (p = 0.066). Conclusions: BP and MN stiffness vary depending on the joint movement sequence during neurodynamic testing. However, the influence of the surrounding tissues may have affected SWV measurements; consequently, these results should be interpreted with caution. Full article

This study presents a novel category of nonconvex functions in Banach spaces, referred to as quasi-lower $C^2$ functions on nonempty closed sets. We establish the existence of solutions for nonconvex variational problems involving quasi-lower $C^2$ functions defined in Banach spaces. To illustrate the applicability of our findings, an example is provided in $L^p$ spaces. Full article