Search Results (61,227)

This paper presents a yarn tension sensor based on Surface Acoustic Waves (SAW). To enhance the detection accuracy of the sensor, an improved beam structure is designed for tension measurement, along with intelligent algorithms for temperature compensation. Firstly, regarding the sensor structure, a simply supported beam with a hyperbolic surface is designed to achieve stress concentration by reducing the section modulus at the beam’s midpoint. Secondly, by incorporating an unbalanced split-electrode Interdigital Transducer (IDT) design, the sensor effectively suppresses signal sidelobe interference and significantly improves the structure’s tension sensitivity. Finally, in terms of signal processing, to eliminate the influence of environmental temperature fluctuations on measurements, a temperature-compensation algorithm based on Bayesian Optimization Least Squares Support Vector Machine (BO-LSSVM) with Gaussian Process regression is proposed. Experimental results show that the tension sensitivity of the improved structure was 8.2% higher than that of the doubly clamped beam and 12.7% higher than that of the cantilever beam. For temperature compensation, the BO-LSSVM model reduced the Mean Relative Error (MRE) by 5.67 percentage points relative to raw data and by 2.04 percentage points relative to the fixed-parameter LSSVM model, lowering the temperature sensitivity coefficient from 4.09 $(\times {10}^{-3}/°\mathrm{C})$ to 0.41 $({10}^{-3}/°\mathrm{C})$. Full article

Early life represents a critical developmental programming window during which nutrition and microbial exposures shape long-term physiological function. Feeding mode is a major determinant of infant gut microbiota assembly and metabolic activity. This narrative review synthesizes current evidence comparing breastfeeding (BF) and formula feeding in relation to microbial composition, functional capacity, and immune programming during the preweaning and early postweaning periods. BF may support a relatively stable, bifidobacteria-dominated microbiota enriched in pathways involved in carbohydrate utilization, vitamin biosynthesis, and immune modulation. Human milk oligosaccharides, secretory IgA, lactoferrin, and milk-associated microbes collectively guide microbial succession, enhance barrier integrity, and support immune tolerance. In contrast, formula-fed infants typically exhibit greater microbial diversity, earlier transition toward adult-like profiles, and increased abundance of facultative anaerobes, alongside the enrichment of pathways related to bile acid and amino acid metabolism. Microbiota patterns in formula-fed infants are further influenced by formula composition, including protein load, lipid structure, and supplementation with prebiotics, probiotics, and human milk oligosaccharide analogues. Although advances in formula design have reduced compositional gaps, functional differences in microbial stability and immune programming persist. Recognizing early infancy as a sensitive programming window underscores the need for microbiome-informed nutritional strategies and longitudinal, multi-omics research to clarify causal mechanisms and optimize early-life interventions. Full article

Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE), characterized by immune system disorders and multiple organ damage. Current clinical treatment of LN requires a complex multi-drug combination, which is often associated with severe side effects and low patient compliance. The aim of this study was to design a self-nanoemulsifying drug delivery system (SNEDDS) co-loading total glucosides of Paeonia (TGP) and dihydroartemisinin (DHA) to increase the solubility of the drug as well as achieve synergistic anti-inflammatory and immunomodulatory effects for LN therapy. Network pharmacology, molecular docking and molecular dynamics simulations were employed to predict the core therapeutic targets and related signaling pathways. The SNEDDS co-loaded with TGP and DHA was optimized via central composite design response surface methodology (CCD-RSM). Its physicochemical properties, particle size and the polydispersity index (PDI) of the optimized formulation were characterized. In vivo therapeutic efficacy was evaluated in MRL/lpr mice by measuring disease-related indicators (urinary protein, serum ANA, and anti-ds-DNA) and inflammatory cytokines (TNF-α, IL-6, and IL-1β). Renal tissue pathology was also examined. All data were analyzed by one-way analysis of variance (ANOVA) with p < 0.05 considered statistically significant. The core therapeutic targets predicted with high relevance were AKT1, MAPK1, MAPK3, and RELA. The optimized SNEDDS achieved a high loading capacity of 16.11 ± 0.43 mg/g for TGP and 12.79 ± 1.33 mg/g for DHA, with a particle size of (25.84 ± 0.30) nm and PDI of (0.07 ± 0.02). In MRL/lpr mice, SNEDDS treatment significantly reduced urinary protein levels (p < 0.01), serum ANA (p < 0.01) and anti-ds-DNA titers (p < 0.01) compared with the model group. Additionally, the levels of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) were markedly decreased (p < 0.05), and renal tissue damage was alleviated. Conclusions: The SNEDDS co-loaded TGP and DHA is a promising oral nanotherapeutic strategy for LN, offering synergistic anti-inflammatory and immunomodulatory effects. Full article

As the exponential growth in advanced compute workloads drives intra-datacenter interconnects to ever increasing bitrates, optical networking equipment has risen to the challenge by shifting from NRZ signaling to bandwidth efficient modulation methods such as PAM4. As these modulation schemes introduce an inherent SNR penalty, maintaining low bit error rates (BER) forces optical links to operate at significantly higher optical powers. However, increasing the optical power leads to photodetectors reaching one of their fundamental bottlenecks caused by the space-charge effect, limiting their ability to provide a high-speed response under high-power illumination. This work presents the design, fabrication, and characterization of a waveguide-integrated photodiode with dual optical inputs (DIPD) designed to overcome this limitation. Specifically, we demonstrate that combining a dual-fed architecture with targeted cross-sectional geometric optimizations effectively distributes the photocurrent density to delay the onset of space-charge saturation. Experimental validation demonstrates a high responsivity of ≈0.91 [A/W] (for O-band wavelengths) and a large electro-optic bandwidth (EOBW) of ≈58 [GHz], all under high-power illumination and CMOS driving voltages. Full article

Transformer-based detectors have demonstrated exceptional accuracy in visible-object detection tasks. However, adversarial patches, specific types of adversarial examples, can disrupt these detectors by introducing unrestricted perturbations into specific image regions. Traditional methodologies focus on placing patches directly on objects and increasing attention scores between the patch and all areas of the image to impair detector performance. Nevertheless, these approaches are suboptimal due to significant discrepancies between background and object features, which contradict optimization objectives. Moreover, they overlook the impact of cross-attention mechanisms on detection results. To address these limitations, we introduce a novel approach named Localized Query Attack (LQA), designed to interfere with both self-attention within the encoder and cross-attention in the decoder. Unlike conventional global interference methods, LQA targets object features specifically, enhancing self-attention interactions between the adversarial patch and foreground regions to redirect model focus toward the patch. In the context of decoder cross-attention, we compute the joint attention matrix connecting encoder outputs with object queries. By diminishing the influence of encoder outputs and residual components in this matrix, we amplify the relative importance of the adversarial patch, thereby intensifying the attack’s effectiveness. Our experiments show that LQA achieves an approximately 20% improvement in transfer attack performance compared to the second-best method across various transformer-based detectors. The practical efficacy of LQA is further substantiated through real-world scenario validations, underscoring its applicability. Full article

To meet the growing demand for autonomous decision-making and real-time optimization in industrial manufacturing, integrating Artificial Intelligence-Generated Content (AIGC) services with Industry 5.0 can enable real-time industrial intelligence. The effectiveness of a generative model is closely related to the current state of the production environment. However, existing studies often ignore the dynamic temporal relationship between generative models and production environments, especially in industrial scenarios with large model transmission delays and random AIGC task arrivals. Therefore, we define a novel metric, namely the Age of Model (AoM), to measure the freshness of generative models with respect to current industrial tasks. We then formulate an average-AoM-minimization problem that jointly considers LoRA-based fine-tuning, wireless transmission and resource allocation. To solve this problem, we propose a Hybrid-Action Multi-Agent Proximal Policy Optimization (HA-MAPPO) algorithm. The proposed algorithm follows the centralized training and decentralized execution (CTDE) paradigm and introduces a Main-Agent Priority State Strategy to support coordinated training and independent execution. In addition, a multi-head output structure is designed to handle the hybrid-action space, which includes discrete fine-tuning association decisions and continuous transmission resource allocation. Simulation results show that the proposed scheme outperforms all benchmark methods. Specifically, the cumulative rewards are improved by approximately $11.13\%$, $20.32\%$, $36.61\%$, and $38.78\%$ compared with the four benchmark algorithms, respectively. These results demonstrate that the proposed scheme can significantly reduce the average AoM while providing high-quality and timely industrial AIGC services. Full article

The paper proposes an intelligent energy management system designed for a university laboratory room, centered on the user and based on the integration of smart sensors and fuzzy logic for the simultaneous optimization of thermal comfort and energy efficiency. The system architecture integrates three control methods, On/Off controller, Proportional Integral Derivative (PID) controller, and Fuzzy Logic, within a hybrid structure capable of managing multiple factors such as thermal comfort, energy consumption, and the availability of renewable energy sources. The system is implemented and tested using Zigbee 3.0 sensors, smart relays, and photovoltaic panels, while variables such as temperature, humidity, energy consumption, and user feedback are monitored. The simulation results, obtained in the MATLAB/Simulink development environment, demonstrate that the fuzzy algorithm reduces thermal oscillations, optimizes energy costs, and maintains perceived comfort within an optimal range. The main contribution of the study lies in the development of a user-centered, interpretable, and scalable architecture, along with a PowerApps application that records occupants’ feedback in real time, which can be implemented in smart buildings with limited computational resources. Two operating scenarios with different time periods were developed for the proposed system. The fuzzy controller maintained a mean temperature deviation below ±0.2 °C, reduced oscillatory behavior compared to PID controller, and enabled photovoltaic coverage of up to 29.97% during peak intervals, with an average daily contribution of 8.77%. The total simulated energy cost was 8.49 RON for the one-day scenario and 48.12 RON for the five-day interval. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article

Hydrogels constructed from natural biomacromolecules with multifunctional properties, such as improved mechanical strength, ionic stability, biocompatibility, and ionic conductivity, are highly desirable for advanced food and biomedical applications, yet remain challenging to design. Although alginate is one of the most widely used hydrogel-forming polysaccharides due to its biocompatibility and gelation ability, its intrinsic limitations often hinder the development of hydrogels with fully optimized performance. This review provides a systematic comparison of alginate-based composite hydrogels formed with complementary biopolymers, including whey proteins, gelatin, pectin, starch, and chitosan, focusing on their synergistic effects on structural, mechanical, and functional properties. Recent studies are critically analyzed to elucidate how polymer–polymer interactions influence gel network formation, environmental ionic stability, and encapsulation performance. Particular attention is given to fabrication strategies and formulation parameters that enhance the immobilization and controlled release of probiotics, vitamins, polyphenols, and other bioactive compounds. By integrating current knowledge on structure–function relationships and processing approaches, this review offers practical design guidelines for the development of multifunctional alginate-based hydrogel systems for applications in functional foods and nutraceutical delivery. Full article

The long-term accumulation of phosphogypsum (PG) stacks has caused combined pollution of total phosphorus (TP), fluoride (F⁻), metals and metalloids (MMs), posing a severe threat to regional ecological security. To clarify the migration characteristics of pollutants in PG stacks, water leaching experiments and environmental risk assessment were conducted in 21 typical PG stacks in Southwest China. The spatial differentiation and vertical migration characteristics of pollutants under various coverage measures (high-density polyethylene (HDPE) film covering, soil covering, a composite of film–soil covering, and open-air storage) at different pH conditions were systematically analyzed. Results indicated that under open-air stockpiling conditions, the surface accumulation of TP and F⁻ was the most significant among all covering measures, corresponding to the highest environmental risk. In contrast, the membrane–soil composite covering exhibited the optimal inhibitory effect on the surface diffusion of TP and F⁻, but was less effective for metal and metalloid enrichment. Under acidic conditions (pH < 6), the vertical migration capacity of TP, F⁻, and MMs (Cu, Cd, Cr, Pb, and Zn) increased, leading to enrichment in the deep layers of the stack. With the increase in pH, the calcium-mediated precipitation–adsorption effect created a “geochemical barrier”, facilitating the solid-phase fixation of pollutants. A significant positive correlation among pollutants indicates synergistic release and fixation behaviors. In addition, a pH-controlled P-F-MM source-to-sink conceptual model was established, outlining the dissolution, precipitation, adsorption, fixation and re-enrichment pathway from fresh stock to leachate. This work provides insights for optimizing cover designs and pollution control strategies. Full article

Since 2021, the design and construction of nearly zero-energy buildings (nZEBs) have been mandatory for European Union Member States. Subsequent requirements for the building sector, characterized by high energy demand and significant environmental impact, include the minimization of carbon footprint and the introduction of climate-neutral building standards. The carbon footprint comprises both embodied emissions related to materials and construction processes and operational emissions resulting from building use. This paper analyzes both types of carbon footprint using a residential building that is part of an experimental housing estate consisting of 44 semi-detached buildings as a case study. Analyses of energy consumption optimization and carbon footprint reduction were conducted at both the individual building scale and the scale of the entire housing complex. The estate was developed in two stages. In the first stage (completion of construction in 2024), the primary criterion for technology selection was investment cost while maintaining compliance with applicable technical and building regulations. Prior to the implementation of the second stage, the investor conducted a social participation process in the form of a survey among future users. The survey addressed environmental aspects of the newly designed buildings and enabled the selection of materials, technologies, and energy sources aligned with user preferences. The results indicate that environmental aspects are important to future users; however, investment decisions are strongly balanced against economic factors. At the same time, the energy analyses demonstrate that a substantial reduction in the operational carbon footprint can be achieved, enabling a significant progression toward climate neutrality, both at the level of individual buildings and across the entire housing estate. Social participation, therefore, becomes an important element in the pursuit of climate neutrality in buildings. However, it must be taken into account already at the design stage. The results of the analyses carried out in the article showed that, taking into account public participation in the design process and user recommendations, the selected optimal variant (W5) allows for a reduction in the EP index by over 90% compared to the variant based on standard low-cost solutions (W0) (EP (W0) = 243.64 kWh/(m² year); EP (W5) = 18.42 kWh/(m² year). In terms of the embodied carbon footprint, the optimal option W5 allows for a reduction of over 30% in the embodied carbon footprint of the building structure (W0—51,585.32 [kgCO₂e]; W5—35,537.87 [kgCO₂e]). The optimal variant indicated by users (W5) allows for a reduction in the operational carbon footprint by approximately 80% compared to the basic variant (W0): W0—604,189.50 [kgCO₂e/kWh]; W5—247,402.0 [kgCO₂e/kWh]. The results obtained indicate that public participation is not only a complementary element of the design process, but it can also be a key component of the decarbonisation strategy in residential construction. Involving future users in the decision-making process increases the likelihood of achieving long-term greenhouse gas emission reductions and supports the implementation of long-term climate policy goals. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

Accurate mapping of near-surface air temperature ($T_{\mathrm{air}}$) at the fine spatial resolution is required for city-scale monitoring and remains a critical challenge in Earth Observation (EO). Reliance on ground-based measurements is constrained by their sparse spatial coverage and high operational costs. We present a novel, scalable machine learning framework designed to overcome this limitation. Our method utilizes interpretable Convolutional Neural Networks (CNNs) to fuse high-resolution Landsat data, integrating both thermal and reflective spectral bands, with contextual spatiotemporal metadata. This approach allows for inference, at 30 m resolution, of $T_{\mathrm{air}}$ fields without relying on dense, localized ground monitoring networks. Our hybrid CNN architecture is optimized for spatial generalization, maintaining strong and transferable performance (station-wise $R^2\approx 0.88$) across diverse environments from humid coasts ($R^2\approx 0.89$) to arid interiors ($R^2\approx 0.84$). Although focused on a specific geographical region, our results suggest a robust and reproducible pathway for generating spatially consistent temperature fields from globally available EO archives, directly supporting urban heat island mitigation, climate policy development, and high-resolution public health assessment worldwide. Full article

Polyphenols, abundant compounds found in natural sources, exhibit various biological activities, including immunomodulatory properties that can either stimulate or suppress immune responses, making them promising for therapeutic applications. However, their poor solubility, low bioavailability, rapid metabolism, and non-specific distribution require advanced drug delivery strategies to overcome limitations in clinical translations. Therefore, nano-drug delivery systems have been intensively studied to explore the full therapeutic potential of polyphenols. Distinct from conventional paradigms where polyphenols serve solely as active compounds, this review advances the concept of polyphenol-based nanomedicine as dual-functional platforms: bioactive structural components and intrinsic immune modulators. Recent strategies to improve the loading efficacy of polyphenols, enhance their cellular uptake, prolong circulation, and enhance specific delivery based on those nanocarriers are emphasized. In addition, polyphenol-based nanoparticles, in which polyphenols serve as structural components, were also studied as self-therapeutics or multifunctional nanocarriers for drug delivery. We intensively focus on their immunomodulatory applications and highlight their potential in preclinical as well as clinical settings for the treatment of various diseases and therapeutic purposes, including autoimmune diseases, cancer immunotherapy, vaccination, inflammation, and infectious diseases. Although polyphenol nanoparticle development has made significant advances, there remain challenges in formulation stability, unclear in vivo toxicity profiles, and clinical translation. Further studies on optimizing nanoparticle design and assessing long-term toxicity are necessary to materialize their application. A combination of polyphenol nanoparticles with other immunotherapies may promise a pronounced efficacy and safety profile. Full article

Metal-enhanced fluorescence (MEF) has found widespread application in biomedical sensing and in vivo tissue imaging systems. To enhance MEF efficiency, it is necessary to optimize the interaction between the metal nanoparticle plasmon and the fluorophore molecule. The size and shape of the nanoparticle, the nanoscale gap between the fluorescent molecule and the nanoparticle, and the excitation wavelength are critical parameters. In this study, we propose a model for a more complete and accurate description of the processes of molecular excitation and generation of the fluorescence spectral response, introducing a new concept of effective properties for the field enhancement factor, quantum yield, and fluorescence enhancement factor. The influence of the spectral properties of both the nanostructure plasmon and the fluorophore molecule on the optimal tuning of fluorescent complexes is studied. Particular attention is paid to the analysis of the spectral properties of plasmon resonance and calculations of the near-field intensity enhancement of the plasmonic nanostructure’s excitation field. Numerical results for optimizing the MEF of fluorescent complexes based on TagRFP and gold (silver) nanorod composites are presented. The advantages of the proposed model for the optimal design of new nanomaterials with unique fluorescent properties are discussed. Full article