Search Results (7,885)

Direct copper-plated ceramic (DPC) substrates have emerged as a favored solution for power device packaging due to their unique technical advantages. AuSn, characterized by its high hermeticity and environmental adaptability, represents the optimal sealing technology for DPC substrates. Through the application of vacuum sintering techniques and adjustment of peak temperatures (325 °C, 340 °C, and 355 °C), the morphology and composition of interfacial compounds were systematically investigated, along with an analysis of their formation mechanisms. A gradient aging experiment was designed (125 °C/150 °C/175 °C × oxygen/argon dual atmosphere × 600 h) to elucidate the synergistic effects of environmental temperature and atmosphere on the growth of intermetallic compounds (IMCs). The results indicate that the primary reaction in the sealing weld seam involves Ni interacting with Au-Sn to form (Ni, Au)₃Sn₂ and Au₅Sn. However, upon completion of the sealing process, this reaction remains incomplete, leading to a coexistence state of (Ni, Au)₃Sn₂, Au₅Sn, and AuSn. Additionally, Ni diffuses into the weld seam center via dendritic fracture and locally forms secondary phases such as δ(Ni) and ζ’(Ni). These findings suggest that the weld seam interface exhibits a complex, irregular, and asymmetric microstructure comprising multiple coexisting compounds. It was determined that Tpeak = 325 °C to 340 °C represents the ideal welding temperature range, where the weld seam morphology, width, and Ni diffusion degree achieve optimal states, ensuring excellent device hermeticity. Aging studies further demonstrate that IMC growth remains within controllable limits. These findings address critical gaps in the understanding of the microstructural evolution and interface characteristics of asymmetric welded joints formed by multi-material systems. Full article

Complex wastewater matrices hinder the efficacy of conventional treatment methods due to the presence of various inorganic and organic pollutants, along with their intricate interactions. Leveraging the synergy between algae and bacteria, algal–bacterial symbiosis (ABS) systems offering an evolutionary and highly effective approach. The ABS system demonstrates 10–30% higher removal efficiency than conventional biological/physicochemical methods under identical conditions, especially at low C/N ratios. Recent advances in biology techniques and big data analytics have deepened our understanding of the synergistic mechanisms involved. Despite the system’s considerable promise, challenges persist concerning complex pollution scenarios and scaling it for industrial applications, particularly regarding system design, environmental adaptability, and stable operation. In this review, we explore the current forms and operational modes of ABS systems, discussing relevant mechanisms in various wastewater treatment contexts. Furthermore, we examine the advantages and limitations of ABS systems in treating complex wastewater matrices, highlighting challenges and proposing future directions. Full article

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

One hundred presumptive Pseudomonas isolates, recovered from 15 sites impacted by anthropogenic activity in the Foggia district (Italy), were screened for key adaptive and functional traits important for environmental applications. The isolates were phenotypically characterized for their ability to grow under combined pH (5.0–8.0) and temperature (15–37 °C) conditions, to produce proteolytic enzymes, pigments, and exopolysaccharides, and to tolerate SDS. Moreover, the resistance to six environmentally relevant heavy metals (Cd, Co, Cu, Ni, Zn, As) was qualitatively assessed. The results highlighted wide inter-strain variability, with distinct clusters of isolates showing unique combinations of stress tolerance, enzymatic potential, and resistance profile. PERMANOVA analysis revealed significant effects of both the isolation site and the metal type, as well as their interaction, on the observed resistance patterns. A subset of isolates showed co-tolerance to elevated temperatures and heavy metals. These findings offer an initial yet insightful overview of the adaptive diversity of soil-derived Pseudomonas, laying the groundwork for the rational selection of strains for bioaugmentation in contaminated soils. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

The repeated occurrence of SARS-CoV-2 variants, largely driven by virus–host interactions, was and will remain a public health concern. Spike protein mutations shaped viral infectivity, transmissibility, and immune escape. From February 2022 to April 2024, a local genomic surveillance program in Verona, Italy, was conducted on 1333 SARS-CoV-2-positive nasopharyngeal swabs via next generation full-length genome sequencing. Spike protein mutations were classified based on their prevalence over time. Mutations were grouped into five categories: fixed, emerging, fading, transient, and divergent. Notably, some divergent mutations displayed a “Lazarus effect,” disappearing and later reappearing in new lineages, indicating potential adaptive advantages in specific genomic contexts. This two-year surveillance study highlights the dynamic nature of spike protein mutations and their role in SARS-CoV-2 evolution. The findings underscore the need for ongoing mutation-focused genomic monitoring to detect early signals of variant emergence, especially among mutations previously considered disadvantageous. Such efforts are critical for driving public health responses and guiding future vaccine and therapeutic strategies. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

E-Learning is an emerging dominant phenomenon in education, making the development of robust models that can accurately represent the dynamic behavior of learners in MOOCs even more critical. In this article, we propose the Temporal Sparse Attention-Gated Recurrent Unit (TSA-GRU), a novel deep learning framework that combines TSA with a sequential encoder based on the GRU. This hybrid model effectively reconstructs student response times and learning trajectories with high fidelity by leveraging tthe emporal embeddings of instructional and feedback activities. By dynamically filtering noise from student interactions, TSA-GRU generates context-aware representations that seamlessly integrate both short-term fluctuations and long-term learning patterns. Empirical evaluation on the 2009–2010 ASSISTments dataset demonstrates that TSA-GRU achieved a test accuracy of 95.60% and a test loss of 0.0209, outperforming Modular Sparse Attention-Gated Recurrent Unit (MSA-GRU), Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and TSA in the same experimental design. TSA-GRU converged in five training epochs; thus, while TSA-GRU is demonstrated to have strong predictive performance for knowledge tracing tasks, these findings are specific to the conducted dataset and should not be implicitly regarded as conclusive for all data. More statistical validation through five-fold cross-validation, confidence intervals, and paired t-tests have confirmed the robustness, consistency, and statistically significant superiority of TSA-GRU over the baseline model MSA-GRU. TSA-GRU’s scalability and capacity to incorporate a temporal dimension of knowledge can make it acceptably well-positioned to analyze complex learner behaviors and plan interventions for adaptive learning in computerized learning systems. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

The paradigm-shift idea of murburn concept is no hypothesis but developed directly from fundamental facts of cellular/ecological existence. Murburn involves spontaneous and stochastic interactions (mediated by murzymes) amongst the molecules and unbound ions of cells. It leads to effective charge separation (ECS) and formation/recruitment of diffusible reactive species (DRS, like radicals whose reactions enable ATP-synthesis and thermogenesis) and emission of radiations (UV/Vis to ELF). These processes also lead to a chemo-electromagnetic matrix (CEM), ascertaining that living cell/organism react/function as a coherent unit. Murburn concept propounds the true utility of oxygen: generating DRS (with catalytic and electrical properties) on the way to becoming water, the life solvent, and ultimately also leading to phase-based macroscopic homeostatic outcomes. Such a layout enables cells to become simple chemical engines (SCEs) with powering, coherence, homeostasis, electro-mechanical and sensing–response (PCHEMS; life’s short-term “intelligence”) abilities. In the current review, we discuss the coacervate nature of cells and dwell upon the ways and contexts in which various radiations (either incident or endogenously generated) could interact in the new scheme of cellular function. Presenting comparative evidence/arguments and listing of systems with murburn models, we argue that the new perceptions explain life processes better and urge the community to urgently adopt murburn bioenergetics and adapt to its views. Further, we touch upon some distinct scientific and sociological contexts with respect to the outreach of murburn concept. It is envisaged that greater awareness of murburn could enhance the longevity and quality of life and afford better approaches to therapies. Full article

Background/Objectives: Medulloblastoma is the most common malignant brain tumor in children and comprises four molecular subtypes—WNT, SHH, Group 3, and Group 4—each with distinct genetic, epigenetic, and metabolic features. Increasing evidence highlights the critical role of metabolic reprogramming and epigenetic alterations in driving tumor progression, therapy resistance, and clinical outcomes. This review aims to explore the interplay between metabolic and epigenetic mechanisms in medulloblastoma, with a focus on their functional roles and therapeutic implications. Methods: A comprehensive literature review was conducted using PubMed and relevant databases, focusing on recent studies examining metabolic pathways and epigenetic regulation in medulloblastoma subtypes. Particular attention was given to experimental findings from in vitro and in vivo models, as well as emerging preclinical therapeutic strategies targeting these pathways. Results: Medulloblastoma exhibits metabolic adaptations such as increased glycolysis, lipid biosynthesis, and altered amino acid metabolism. These changes support rapid cell proliferation and interact with the tumor microenvironment. Concurrently, epigenetic mechanisms—including DNA methylation, histone modification, chromatin remodeling, and non-coding RNA regulation—contribute to tumor aggressiveness and treatment resistance. Notably, metabolic intermediates often serve as cofactors for epigenetic enzymes, creating feedback loops that reinforce oncogenic states. Preclinical studies suggest that targeting metabolic vulnerabilities or epigenetic regulators—and particularly their combination—can suppress tumor growth and overcome resistance mechanisms. Conclusions: The metabolic–epigenetic crosstalk in medulloblastoma represents a promising area for therapeutic innovation. Understanding subtype-specific dependencies and integrating biomarkers for patient stratification could facilitate the development of precision medicine approaches that improve outcomes and reduce long-term treatment-related toxicity in pediatric patients. Full article

The hypothalamus–pituitary–ovarian (HPO) axis orchestrates reproductive functions through intricate neuroendocrine crosstalk. Here, we integrated single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics (ST) to decode the cellular heterogeneity and intercellular communication networks in the reproductive systems of pregnant Mongolian cattle. We retained a total of 6161 high-quality nuclei from the hypothalamus, 14,715 nuclei from the pituitary, and 26,072 nuclei from the ovary, providing a comprehensive cellular atlas across the HPO axis. In the hypothalamus, neurons exhibited synaptic and neuroendocrine specialization, with glutamatergic subtype Glut4 serving as a TGFβ signaling hub to regulate pituitary feedback, while GABAergic GABA1 dominated PRL signaling, likely adapting maternal behavior. Pituitary stem cells dynamically replenished endocrine populations via TGFβ, and lactotrophs formed a PRL–PRLR paracrine network with stem cells, synergizing mammary development. Ovarian luteal cells exhibited steroidogenic specialization and microenvironmental synergy: endothelial cells coregulated TGFβ-driven angiogenesis and immune tolerance, while luteal–stromal PRL–PRLR interactions amplified progesterone synthesis and nutrient support. Granulosa cells (GCs) displayed spatial-functional stratification, with steroidogenic GCs persisting across pseudotime as luteinization precursors, while atretic GCs underwent apoptosis. Spatial mapping revealed GCs’ annular follicular distribution, mediating oocyte–somatic crosstalk, and luteal–endothelial colocalization supporting vascularization. This study unveils pregnancy-specific HPO axis regulation, emphasizing multi-organ crosstalk through TGFβ/PRL pathways and stem cell-driven plasticity, offering insights into reproductive homeostasis and pathologies. Full article

This research addresses urban inequality by focusing on the rehabilitation of communal rooftops in Ciutat Vella, Barcelona, the city’s historic district, where residential vulnerability is concentrated in a particularly dense heritage urban environment with a shortage of outdoor spaces. Using participatory methodologies, this research develops low-cost, removable, and recyclable prototypes aimed at improving social interaction, technical performance, and environmental conditions. The focus is on vulnerable populations, particularly the elderly. The approach integrates a bottom–up process and scalable solutions presented as a Toolkit of micro-projects. These micro-projects are designed to improve issues related to health, safety, durability, accessibility, energy savings, and acoustics. In addition, several possible material solutions for micro-projects are examined in terms of sustainability and cost. These plug-in interventions are designed for adaptability and replication throughout similar urban contexts and can significantly improve the quality of life for people, especially the elderly, in dense historic environments. Full article

Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practical engineering scenarios. To tackle these issues, this study presents a novel slope stability classification model grounded in the Optuna-TPE-CatBoost framework. By leveraging the Tree-structured Parzen Estimator (TPE) within the Optuna framework, the model adaptively optimizes CatBoost hyperparameters, thus enhancing prediction accuracy and robustness. It incorporates six key features—slope height, slope angle, unit weight, cohesion, internal friction angle, and the pore pressure ratio—to establish a comprehensive and intelligent assessment system. Utilizing a dataset of 272 slope cases, the model was trained with k-fold cross-validation and dynamic class imbalance strategies to ensure its generalizability. The optimized model achieved impressive performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.926, an accuracy of 0.901, a recall of 0.874, and an F1-score of 0.881, outperforming benchmark algorithms such as XGBoost, LightGBM, and the unoptimized CatBoost. Validation via engineering case studies confirms that the model accurately evaluates slope stability across diverse scenarios and effectively captures the complex interactions between key parameters. This model offers a reliable and interpretable solution for slope stability assessment under complex failure mechanisms. Full article