Search Results (5,704)

Highly pathogenic avian influenza (HPAI) is a lethal disease of poultry that has recently spilled over into mammals, including dairy cattle and humans, heightening concerns for livestock health, food security, and pandemic emergence. While vaccines that induce neutralizing antibodies against hemagglutinin and neuraminidase provide strain-specific protection, durable cross-subtype immunity requires T-cell responses targeting conserved internal antigens such as nucleoprotein (NP). To leverage these conserved targets, we utilized a previously engineered mosaic nucleoprotein (MNP) incorporating T-cell epitopes from thousands of influenza A virus (IAV) strains, conferring broad protection against epidemic (H3N2) and pandemic (H1N1) IAV in mice. Here, we tested whether precision adjuvancy could differentially imprint adaptive immunity to MNP in cattle. Combination formulations paired the carbomer-based nano-emulsion Adjuplex (ADJ) with either a STING agonist (cyclic dinucleotides; CdN) or a TLR4 agonist (glucopyranosyl lipid A; GLA) to program distinct inflammatory milieus. Both formulations elicited circulating IFN-γ–producing T cell responses and NP-specific antibodies in serum and milk. However, STING activation via CdN generated more potent and consistent cellular and humoral immunity than TLR4 engagement. These data demonstrate that selective activation of innate sensing pathways functionally imprints adaptive immune magnitude and quality in a large animal host. By advancing a broadly protective, T-cell-focused vaccine strategy in cattle, this work supports a One Health framework to mitigate H5N1 transmission risk at the human–animal interface. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

The advancements made in the mass spectrometry imaging (MSI) field have allowed for the generation of very large-scale data sets. These data are often interrogated by machine learning (ML), although storing and handling data sets of this size can be difficult. To aid impacted researchers, we seek to evaluate feature reduction strategies that will minimize the amount of data stored while still maintaining the ability to correctly classify the data. Two different feature selection strategies are tested on six different data sets, leveraging XGBoost as the machine learning algorithm. The study provides evidence that selecting features based on the greatest average abundance across all samples is best suited to scale down the feature set at a more modest trimming level, while selecting features based on statistical analysis via a Student’s t-test is better suited for a more aggressive trimming level. These trends were present regardless of training set size or cross-validation strategy. The results from this work provide insight into when these feature filtering steps can be used effectively and when another data reduction strategy, including not restricting the data set, should be considered. Full article

Exosomes are small vesicles released by cells that have attracted growing interest as drug delivery vehicles, particularly for brain diseases, where getting therapeutics across the BBB remains a fundamental problem. While conventional platforms such as liposomes, polymeric nanoparticles, and viral vectors often suffer from immune clearance and poor brain accumulation, engineered exosomes leverage natural cellular transport mechanisms to cross the BBB, protect cargo from degradation, and enable biocompatible interactions with target cells. This review takes a mechanistic and translational look at how exosomes are being engineered for CNS disorders, with a particular focus on glioblastoma. We cover exosome biogenesis through ESCRT-dependent and ESCRT-independent pathways, and how the competition between Rab27-driven secretion and Rab7-driven lysosomal degradation determines how many exosomes a cell releases, which has direct consequences for therapeutic production. We then discuss cargo loading strategies, from genetic approaches where donor cells are engineered to package specific molecules during biogenesis to physical methods like electroporation and sonication applied to isolated vesicles, alongside surface modification techniques for directing exosomes toward specific cell types. In glioblastoma, engineered exosomes have shown real promise for delivering chemotherapeutics across the BBB, targeting glioma stem cells, enabling CRISPR-based gene editing, and functioning as combined treatment and imaging tools. Applications in stroke and neurodegenerative diseases, where engineered exosomes carrying microRNAs and neuroprotective cargo have produced encouraging preclinical results, are also discussed. Scalable manufacturing and consistent targeting remain the hardest unsolved problems, and we outline emerging approaches including bioreactor-based production, programmable cargo loading, and patient-specific exosome design that are beginning to address these gaps. Overall, the progress reviewed here suggests that engineered exosomes are moving from an interesting biological concept toward a practically viable platform for CNS drug delivery. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

With the high penetration of renewable energy, the demand of the power system for flexible regulation resources is gradually growing. Pumped storage and battery energy storage are the most common storage types in the system, and the former can be further categorized into weekly-regulated (multi-day-regulated) and daily-regulated pumped storage. To fully leverage the regulation characteristics of these resources, this paper proposes a coordinated scheduling strategy for diversified energy storage considering varied regulation time scales. First, the correspondence of the regulation time scale of energy storage and the optimization time scale of scheduling is discussed. A two-stage coordinated scheduling framework for diversified energy storage is proposed. Second, based on models for pumped storage, battery energy storage, and thermal power units, considering deep peak shaving, an optimization model is established. This model achieves the optimal scheduling of regulation resources across day-ahead and intraday horizons. Finally, simulations are conducted on a modified IEEE 30-bus system. The results show that the proposed scheduling strategy reduces the system operating costs by 0.5% in the day-ahead scheduling and 16.1% in the intraday scheduling compared to the traditional strategy. The results verify that the proposed scheduling strategy can fully exploit the regulation characteristics of different types of storage, promote renewable energy accommodation, and ensure power supply in the power system. Full article

In recent years, short video platforms have rapidly developed into important media for cultural dissemination. The interactions of netizens in short video comment sections not only reflect their focus on cultural content but also contain rich emotional attitudes. However, given the vast and fragmented nature of comment data, accurately extracting keywords, identifying cultural themes, and analyzing sentiment tendencies pose significant challenges in understanding netizens’ cultural perceptions. To address these challenges, this study proposes a text analysis framework that integrates keyword extraction, clustering analysis, and sentiment analysis to explore the core topics and emotional characteristics of cultural dissemination in short video comment sections. Firstly, to address the challenge of balancing statistical information and semantic understanding in short-text keyword extraction, this paper proposes the TF-IDF-KeyBERT Integrated Algorithm (TKIA) keyword extraction algorithm, which integrates Term Frequency–Inverse Document Frequency (TF-IDF) and Key Bidirectional Encoder Representations from Transformers (BERT). Experiments on the CSL dataset demonstrate improvement in the F1@5 metric, showing its potential to enhance keyword extraction performance for short texts. Secondly, to address the difficulty of simultaneously considering semantic representation capability and clustering flexibility in short-text clustering analysis, this paper designs the Self-Supervised Contrastive Enhanced Clustering (SCEC) algorithm by integrating self-supervised contrastive learning with a soft clustering strategy. Compared to baseline methods, SCEC improves clustering accuracy (ACC) by 17.5% on AGNews and 6.8% on THUCNews, suggesting a more effective way to reveal the underlying structure of cultural topics. Finally, to address the challenge of effectively leveraging both text structural information and global semantic features in short-text sentiment analysis, this paper develops the BERT-GCN Cross-Attention (BGC) Model, integrating BERT embeddings and Graph Convolutional Network (GCN)-based structural features via a Cross-Attention mechanism. On the My_weibo_senti_100k dataset, the BGC model achieves a 2.45% increase in Macro-F1 and a 2.41% improvement in accuracy over strong baselines, offering its ability for high-precision modeling of user sentiment. This study offers effective data support and technical pathways for applications such as cultural content understanding, personalized recommendation, and user emotion guidance. Full article

In a duopoly market comprising an e-tailer and a physical retailer, we develop analytical models to explore the e-tailer’s strategy of introducing a virtual showroom to alleviate consumers’ fit uncertainty. While a virtual showroom can increase online consumer traffic, consumers may instead purchase from physical stores (i.e., webrooming) or reduce offline browsing before buying online (i.e., showrooming). Our findings indicate that consumers with a moderate online hassle cost tend to showroom when offline travel cost is not high, whereas those with a high online hassle cost rely on the virtual showroom for webrooming. Therefore, we identify the conditions under which a virtual showroom should be introduced. Specifically, when the cost of travel to the store is relatively high, the e-tailer can profit from introducing a virtual showroom if its return-handling cost is not too low. Under moderate travel cost, the e-tailer can leverage a virtual showroom to weaken competition if the return-handling cost is not too high, enabling both retailers to benefit. Notably, the impact of product fit probability on virtual showroom strategy decisions reverses between high and moderate travel costs. Under specific conditions, the virtual showroom can achieve a “win-win-win” situation for the e-tailer, the physical retailer, and consumers. Full article

The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk. Full article

Food spoilage from microbial contamination and oxidation drives the search for natural preservatives. Phenolic acids (PAs) and phenolamides are plant-sourced metabolites with broad-spectrum antimicrobial and antioxidant activities. This review comprehensively examines their sources, classification, structure–activity relationships, and multi-target mechanisms. PA antimicrobial action involves membrane disruption, intracellular acidification, and oxygen species generation, while antioxidant effects rely on hydrogen donation and metal chelation. For phenolamides, antimicrobial evidence is largely indirect, based on computational docking and one non-food nucleotide biosynthesis study, and direct validation of these mechanisms in food matrices against common foodborne pathogens is lacking. Delivery strategies (direct incorporation, encapsulation, edible coatings, active packaging) are critically evaluated, with emphasis on PA-grafted chitosan systems. Applications of PAs in fruits, vegetables, meat, aquatic products, and lipid-rich emulsions are summarized. Phenolamide applications are limited by low natural abundance, high purification costs, poor aqueous solubility, and a historical bias toward pharmacology. Safety assessments confirm favorable profiles for many PAs and select phenolamides, though chronic toxicity data for phenolamides remain limited. This review provides a theoretical framework for leveraging PAs and emerging phenolamides as natural preservatives and identifies critical knowledge gaps requiring future investigation. Full article

Aggressive technology scaling has led to a significant increase in manufacturing process variations and transistor aging effects, which critically degrade the performance of radio frequency (RF) circuits. These reliability challenges are particularly pronounced in voltage-controlled oscillators (VCOs), where phase noise and operating frequency stability are compromised. While design strategies incorporating micro-electromechanical systems (MEMS) actuators enhance VCO performance by leveraging MEMS varactors or inductors with substantially higher quality factors (Q), this benefit is progressively undermined over time by process variations and aging-induced shifts in the threshold voltage and carrier mobility of the VCO’s transistors. This work presents an ultra-low-power current-reuse voltage-controlled oscillator (VCO) designed to maintain stable performance under process variability and reliability-induced parameter shifts. Robust operation is achieved using a self-detecting–correcting (SDC) bias scheme that senses performance drift and applies corrective feedback through body-bias control in the VCO core. Analytical relations are derived to describe the impact of threshold voltage and mobility variations, and the approach is validated via post-layout simulations in a 130 nm complementary metal-oxide semiconductor (CMOS). Under 18% variations in threshold voltage and carrier mobility, the proposed SDC scheme preserves oscillation frequency, phase noise, and figure of merit (FoM) while also mitigating the intrinsic output amplitude imbalance of conventional current-reuse VCOs. Monte Carlo analysis (500 runs) demonstrates low sensitivity to fabrication uncertainty, with a standard deviation below 0.14 dBc/Hz for phase noise, 210 kHz for oscillation frequency, and 0.4 dBc/Hz for FoM. The VCO operates from a 0.9 V supply, consumes 175 μW, and achieves −124 dBc/Hz phase noise at 1 MHz offset near 2.4 GHz (FoM ≈ −199 dBc/Hz). Full article

Traditional signal processing methods for rotating machinery fault diagnosis rely heavily on human experience, while deep learning models often suffer from unstable classification boundaries and poor generalization under complex operating conditions. To address these issues, this paper proposes a hybrid fault diagnosis method based on CWT and CNN-DOA-LSSVM. Firstly, CWT is employed to convert one-dimensional vibration signals into high-resolution time-frequency maps, fully highlighting the transient impact features of faults. Secondly, CNNs automatically extract deep discriminative features, avoiding the cumbersome process of manual feature engineering. Thirdly, LSSVM replaces the Softmax classification layer in traditional CNNs to overcome the deficiency of the Softmax classifier in nonlinear classification. Finally, by leveraging the two-stage separation mechanism of exploration and exploitation in DOA, along with its unique forgetting-supplement and dream-sharing strategies, an adaptive optimal configuration of the key parameters of LSSVM is achieved. Validation results on the Southeast University gearbox dataset and the Huazhong University of Science and Technology bearing dataset show that the proposed method achieves average classification accuracies of 99.59% and 99.50%, respectively, demonstrating good performance in both classification accuracy and stability. Full article

This paper proposes a source-level electromagnetic interference suppression strategy for high-voltage inverters that uses a series-connected IGBT topology and discrete staircase voltage shaping. From an information-theoretic perspective, the staircase shaping transforms chaotic wideband switching noise into a deterministic harmonic structure, thereby reducing the spectral entropy of the EMI source. This information optimization is achieved using a CPLD-based sequential gate drive circuit, which eliminates the need for complex active gate profiling algorithms. Experimental results obtained using a 1140 V explosion-proof motor drive platform demonstrate harmonic attenuation of 4–16 dB μV within a 2 MHz band. Importantly, this targeted entropy reduction occurs alongside a 68.7% reduction in active-region switching losses, suggesting a concurrent decrease in local thermodynamic entropy production during switching transients. Increasing spectral determinism and relaxing requirements for subsequent physical filters effectively lower the conditional entropy of the overall electromagnetic environment. Leveraging the structural flexibility of series IGBTs, this method provides a practical, low-complexity solution and establishes a novel framework between power electronics and information theory for electromagnetic compatibility. Full article

Kubernetes Horizontal Pod Autoscaler (HPA) primarily relies on resource-based metrics, such as CPU utilization, which are poorly suited to capturing the latency variability of AI inference workloads. In this paper, we propose a custom-metric-driven autoscaling approach that leverages inference latency histograms as first-class scaling signals for Kubernetes HPA. The proposed framework integrates a Prometheus Operator (PO)-based observability stack with the Prometheus Adapter to expose and aggregate per-pod inference latency metrics, enabling workload-aware scaling decisions. We evaluate the approach using four mid-scale transformer-based inference services, comprising two reasoning-like and two latency-stable workloads, under high-concurrency conditions. The experiments analyze latency variation, tail behavior, and replica dynamics across multiple autoscaling policies, including variations in scale-up aggressiveness (3 pods/30 s, 3 pods/60 s, 6 pods/60 s), inference-time thresholds, and stabilization windows. Compared to CPU-based autoscaling, inference-driven policies reduce mean response time by 18–27% for reasoning-like workloads and 12–20% for stable workloads. The results show that latency-variable workloads exhibit wider tails and higher variance, indicating the need for moderately aggressive scale-up strategies to avoid long-lasting latency spikes. Overall, the findings show that inference-latency-driven custom metrics significantly improve autoscaling efficiency and stability for transformer-based inference workloads in cloud-native environments. Full article

Sustainable dietary transitions are increasingly recognised as essential for addressing the interconnected challenges of public health, environmental degradation and food system sustainability. Although the Mediterranean Diet (MD) is widely acknowledged as a healthy and sustainable dietary model, adherence has progressively declined across diverse populations. This study develops an integrated conceptual framework to explain the sustainable adoption of the Mediterranean Diet, with particular emphasis on the conceptual mediating role of plant-based foods. A structured conceptual narrative review was conducted using interdisciplinary evidence from nutrition science, sustainability research, behavioural sciences and food policy. The proposed framework integrates individual capacities, socio-cultural contexts, structural environments and ecological awareness within a systems-oriented perspective. The findings suggest that dietary behaviour is shaped by dynamic and context-dependent interactions influenced by feedback mechanisms, life-course transitions and structural constraints. Within this framework, plant-based foods function as integrative leverage points linking health, environmental and cultural dimensions. The study highlights the limitations of individual-centred approaches and emphasises the importance of coordinated multi-level strategies to support sustainable dietary transitions. Overall, the framework provides a theoretically grounded basis for future research, policy development and integrated interventions aimed at promoting resilient and sustainable food systems. Full article