Search Results (12,637)

The growing demand for customized pet diets highlights the shortcomings of commercial dog foods designed for all breeds, especially when it comes to addressing breed-specific diseases, metabolic disorders, and health risks. This research presents the development and evaluation of a hybrid system for formulating wet canine food recipes. The system combines data on ingredients, veterinary feeds, and breed-related diseases; the architecture includes a recommendation module for ingredient selection and a linear programming block for recipe optimization, considering veterinary nutrient restrictions. The evaluation of the system included automatic classification of foods by specialization, visual analysis of recipe clustering, and comparison of formulas obtained by different models. The average precision of label recovery was 85.4% for TF-IDF and 88.2% for the E5 model. A comparison of ingredient extraction methods showed that machine learning produces more stable recipes, while the statistical approach provides greater variability. The developed system demonstrates potential for automating recipe creation, filling in missing data, and developing veterinary decision support platforms aimed at personalized diet selection based on the physiological needs of animals. Full article

Damage to tunnel structures under seismic action severely affects engineering safety and post-earthquake rescue, making it crucial to enhance the seismic capacity of tunnels. Current seismic approaches for tunnel engineering mainly include seismic isolation (shock absorption layer technology), damping, and anti-seismic, among which shock absorption layer technology has attracted considerable attention due to its economic efficiency and effectiveness. However, existing research has primarily focused on single shock absorption layer materials, lacking systematic classification frameworks and multi-dimensional comparative analyses, making it difficult to provide comprehensive guidance for material selection and engineering applications. This paper systematically reviews the research status of tunnel shock absorption layers. First, it elucidates three core mechanisms through which shock absorption layers function: wave-impedance mismatch and energy reflection, material damping and energy dissipation, and system stiffness reduction with natural period elongation. This study proposes categorizing the existing materials for tunnel shock absorption layers into five main types: foam concrete, other types of concrete, polymer materials, asphalt materials, and porous metallic materials. A detailed introduction is provided for each material category, covering their physical properties, shock absorption performance, advantages and disadvantages, as well as relevant optimization studies conducted to address material limitations. By comprehensively comparing the mechanical properties, shock absorption performance, durability, constructability, recyclability, and economy of these five types of materials, revealing their unique advantages and applicable limitations in tunnel shock absorption. Finally, the limitations of existing research are summarized, development directions for tunnel shock absorption layer materials are proposed, and the future research trend of tunnel damping layer technology is envisioned. This paper provides a reference for the research, selection, and standard formulation of tunnel shock absorption layer materials. Full article

The rising environmental burden of Portland cement production has intensified the demand for eco-friendly binders that support sustainable construction. This study investigates the development and performance of eco-friendly self-compacting geopolymer concrete (SCGC) produced from industrial by-products, including fly ash (FA), ground granulated blast furnace slag (GGBFS), silica fume (SF), metakaolin (MK), and glass waste powder (GWP). Twenty-one binder formulations were evaluated for fresh-state workability, mechanical performance, durability, and microstructural characteristics under different curing regimes. Fresh properties were assessed using slump flow, V-funnel, L-box, and J-ring tests, while hardened-state evaluations included compressive and flexural strength, Young’s modulus, and water absorption. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis were performed on selected mixes to examine microstructural features and crystalline phase development. Results highlight a strong dependency of SCGC performance on binder composition and curing conditions. Mixes rich in GGBFS and SF demonstrated superior mechanical and durability performance, achieving compressive strengths of up to 102.4 MPa under water curing and 107.6 MPa under heat curing, along with negligible water absorption, reflecting a dense and well-developed gel matrix. SEM micrographs confirmed homogeneous, compact microstructures in high-performing mixes, while XRD analysis revealed broad amorphous humps indicative of well-formed N-A-S-H and C-A-S-H gel phases with minimal crystalline residues. In contrast, FA-dominant mixes displayed delayed strength development, and MK-GWP-rich systems exhibited higher porosity and reduced strength. This study underscores the significance of precursor synergy, optimized curing strategies, and microstructural refinement in tailoring SCGC for high-performance, durable, and low-carbon applications in sustainable construction with values ranged from 38.64 GPa (Mix 21) to 25.04 GPa (Mix 19) at 28 days. Stiffer mixes corresponded to denser matrices containing GGBFS and silica fume, whereas lower values were linked to weaker bonding and higher porosity. Full article

Background/Objectives: Reproducible evaluation of aerosol dispersibility remains a key challenge in the development of dry powder inhalers (DPIs), where small variations in particle cohesion, morphology, or device resistance can lead to large differences in aerodynamic performance. In passive DPIs, the forces required for powder fluidization and aerosolization arise from the interaction of patient inspiratory airflow with device geometry and must overcome strong interparticle cohesive forces to enable effective lung delivery. Cascade impaction is the gold standard for determining aerodynamic particle size distribution (APSD), but its low throughput and experimental burden limit its utility for systematic formulation and device screening. Prior studies have explored laser diffraction-based particle sizing under varying dispersion energies as indirect metrics of powder dispersibility. Here, we extend this approach by introducing a mathematically rigorous, distribution-based framework that applies the first-order Wasserstein distance (Earth Mover’s Distance) to quantify relative dispersibility with respect to a material-specific maximally dispersed reference state. Methods: Mannitol, trehalose, and inulin were spray-dried under matched conditions to generate model dry powders. Particle size distributions were measured by laser diffraction (Sympatec HELOS/R) using both a RODOS dry dispersion module to define a maximally dispersed reference state and an INHALER module to generate aerosols under clinically relevant dispersion conditions spanning multiple device resistances and pressure drops. For each condition, the Wasserstein-1 distance (W₁) was computed between cumulative volume-based size distributions obtained under reference and inhaler-based dispersion. Cascade impaction was used as an orthogonal method to characterize aerodynamic performance under a representative dispersion condition. Results: W₁ captured formulation-, device-, and flow-dependent differences in dispersibility that were not readily separable by visual inspection of particle size distributions alone. Crystalline mannitol exhibited the largest and most flow-rate-dependent W₁ values, whereas amorphous trehalose and polymeric inulin showed smaller W₁ values with distinct, non-monotonic pressure responses that depended on device resistance. W₁ qualitatively aligned with cascade impaction metrics, exhibiting a positive association with mass median aerodynamic diameter and an inverse association with fine particle fraction, while also demonstrating that efficient dose emission can occur despite incomplete deagglomeration. Conclusions: This study establishes the Wasserstein distance as a physically interpretable, formulation-agnostic metric for quantifying aerosol dispersibility relative to a material-specific reference state. This framework enables systematic comparison of dispersion efficiency across devices and operating conditions using standard laser diffraction data and provides a reproducible basis for mechanistic optimization of DPI formulations and inhaler designs. Full article

The accelerating transition toward carbon neutrality necessitates the synergistic integration of power and hydrogen systems to mitigate renewable intermittency; however, coordinating regulatory policies with the operational flexibility of these coupled systems remains a critical challenge under deep uncertainty. Motivated by this gap, this study develops a bilevel carbon price-coupled optimization framework for integrated power–hydrogen systems, aiming to coordinate environmental policy design with operational scheduling under deep uncertainty. The upper-level model represents the decision-making of a market regulator that determines the optimal carbon price and emission allowances to maximize overall social welfare, while the lower-level model captures the coordinated operation of electricity and hydrogen subsystems that minimize total dispatch cost, including renewable utilization, electrolyzer conversion, and fuel-cell recovery.To address stochastic variations in renewable generation and load demand, a Distributionally Robust Optimization (DRO) formulation is introduced using Wasserstein ambiguity sets, ensuring decision feasibility against worst-case probability distributions. The bilevel structure is efficiently solved via a Benders–Column-and-Constraint Generation (CCG) algorithm, which decomposes policy and operation layers into tractable subproblems with provable convergence. Case studies on a 33-bus integrated power–hydrogen network demonstrate that the proposed framework effectively balances economic efficiency and carbon reduction. Results show that the optimal carbon price of approximately 45 $/tCO₂ achieves a 27% emission reduction with only a 9% cost increase, revealing a near-optimal social welfare equilibrium. Hydrogen subsystems operate flexibly, with electrolyzer utilization increasing by 30% and storage cycling deepening by 15%, enabling enhanced renewable absorption. Sensitivity analyses confirm that the DRO layer reduces operational risk by 4% compared with stochastic optimization, validating robustness against distributional shifts. The study provides a rigorous and computationally efficient paradigm for policy-coordinated decarbonization, highlighting the synergistic role of carbon pricing and cross-energy scheduling in the next generation of resilient low-carbon energy systems. Full article

Diabetic wounds remain a major clinical challenge due to delayed healing caused by chronic inflammation and impaired fibroblast activity. Here, we present a thermoresponsive gel composed of chitosan (CS) and poloxamers (POL) incorporating silk fibroin (SFB) and Aloe vera gel extract (AV), developed for topical application and, for the first time, evaluated using an inflammation-induced diabetic fibroblast model. The optimized formulation exhibited rapid sol–gel transition at physiological temperature and suitable rheological properties for effective wound coverage. In vitro evaluation using human normal fibroblasts (HNF) and human diabetic fibroblasts (HDF), under both basal and inflammation-induced conditions, demonstrated good cytocompatibility and a significant enhancement of fibroblast migration, particularly in an inflammatory microenvironment simulated by high glucose, lipopolysaccharide (LPS), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α). These findings highlight the potential of the developed thermoresponsive gel as a promising biomaterial platform for improving diabetic wound healing under inflammation-relevant conditions. Full article

Background: Migraine is a highly prevalent and disabling primary headache disorder frequently accompanied by gastrointestinal symptoms and comorbid gastrointestinal diseases. Increasing evidence suggests that alterations in the gut microbiota and dysregulation of the microbiome–gut–brain axis may contribute to migraine pathophysiology through immune activation, oxidative stress, impaired intestinal barrier function, and neuroinflammatory signaling. Objectives: This narrative review aims to summarize current mechanistic and clinical evidence linking the gut–brain axis to migraine, with a particular focus on the potential roles of probiotics, prebiotics, and postbiotics as adjunctive strategies in migraine management. Methods: A narrative synthesis of experimental, translational, and clinical studies was performed, focusing on microbiome composition, gut barrier integrity, immune and oxidative pathways, and interventional trials evaluating probiotics, prebiotics, synbiotics, and microbiota-derived metabolites in adult and pediatric migraine populations. Results: Migraine has been associated with intestinal dysbiosis, increased gut permeability, and low-grade systemic inflammation. Probiotics, most commonly strains of Lactobacillus and Bifidobacterium, may modulate inflammatory cytokine profiles, enhance tight junction integrity, reduce oxidative stress, and influence neurotransmitter-related pathways along the gut–brain axis. Clinical trials evaluating probiotic supplementation report heterogeneous but promising signals, including reductions in migraine frequency, severity, disability scores, and analgesic use, particularly in chronic migraine and pediatric populations. Emerging evidence also supports a potential role for prebiotics (e.g., inulin-type fructans) and microbiota-derived metabolites such as short-chain fatty acids, although direct clinical data remain limited. Conclusions: Modulation of the microbiome–gut–brain axis represents a biologically plausible adjunct approach in migraine management. While probiotics, prebiotics, and postbiotics show potential benefits with favorable safety profiles, current evidence of their strain-, formulation-, and population-specific characteristics is lacking. Well-powered, placebo-controlled trials with standardized migraine endpoints and integrated microbiome and metabolomic analyses are needed to define responders, optimal interventions, and clinical relevance. Full article

Every year worldwide, citrus processing generates large volumes of by-products, often wasted, although rich in bioactive compounds. In this study, mandarin peel (Citrus reticulata) was used as a source of functional compounds for the development of guar gum/chitosan functionalized edible films. The response surface methodology was used for both bioactive extraction and edible film formulation. For extraction, the optimization focused on extraction time, solvent composition (acetone/water ratio), and solvent/solid ratio, while for edible film, the guar gum/chitosan ratio, glycerol content, and mandarin peel extract concentration were selected as critical formulation variables. The predictive models exhibited high statistical significance (p < 0.05), adequate predictive ability, and good consistency of predicted and experimental values. The extraction optimization allowed significant results in total polyphenols (329.59 mg GAE/g), flavonoids (42.6 mg QE/g), and total carotenoids (1.53 mg/g) associated with significant antioxidant activity. Mandarin peel bioactive compounds integrated into composite edible film resulted in excellent functional properties in terms of swelling index (65.83%), water absorption (65.48%), weight loss (41.91%) and visual appearance (L* 89.30). These findings support formulating chitosan–guar gum films with mandarin peel bioactives, advancing biopolymer-based approaches toward next-generation sustainable packaging. Full article

Background/Objectives: Obesity remains a major global health burden, with metabolic–bariatric surgery being the most efficient long-term treatment strategy. However, both variability in perioperative care and postoperative complications persist. To address these challenges, the EUropean PErioperative MEdical Networking (EUPEMEN) protocol for bariatric surgery was developed to standardize care and enhance perioperative outcomes across European healthcare settings. Methods: The protocol was formulated through close collaboration among experts from multiple disciplines, involving surgeons, anesthetists, nurses, and nutritionists. Its development included a literature review, expert consensus, and the creation of structured perioperative guidelines covering the preoperative, intraoperative, and postoperative phases. Focus areas include patient education, nutritional optimization, early mobilization, opioid-sparing analgesia, and minimally invasive surgical techniques, supported by educational materials and manuals. Technical activities included the development of detailed multimodal rehabilitation manuals translated into five languages, the creation of an open-access online learning platform, training of future educators through a “train the trainer” approach, organization of multiplier promotional events, international collaboration meetings to refine the protocol, and revision and standardization of existing perioperative care guidelines to ensure evidence-based, unified practices across Europe. Results: Implementation of the EUPEMEN protocol aims to reduce postoperative complications, enhance recovery, and decrease hospitalization time. Standardized rehabilitation pathways and access to free educational platforms promote consistent care delivery across diverse healthcare environments. Key strategies include early oral intake, limited use of invasive devices, and comprehensive patient preparation. Conclusions: The EUPEMEN protocol introduces an evidence-based, multidisciplinary framework for optimizing perioperative management in bariatric surgery. While variability in resources and adherence may present potential obstacles, its application holds significant promise for improving perioperative outcomes. Future studies are necessary to assess its long-term impact and adaptability in different healthcare settings. Full article

Reactive structural adhesives—epoxies, polyurethanes, and acrylics—are essential in high-performance applications, yet their development remains complex due to multiscale adhesion mechanisms, combinatorial formulation spaces, and stringent performance requirements. Traditional trial-and-error approaches are time- and resource-intensive. Machine learning (ML) provides a powerful framework to accelerate adhesive design by capturing nonlinear relationships between formulation, processing, and performance, while enabling predictive modeling, optimization, and experiment prioritization. This review presents a process-oriented guide for ML-assisted adhesive development, covering component selection, feature engineering, initial dataset design, model choice, and iterative workflows integrating classical design-of-experiments, active learning, and Bayesian optimization. Emphasis is placed on interpreting ML outputs through the lens of polymer chemistry, reaction kinetics, and fracture mechanics to extract mechanistic insights and guide rational formulation design. Key challenges—including small, noisy datasets, multi-component interactions, and multi-objective trade-offs—are discussed, along with emerging directions such as collaborative databases, automated knowledge extraction, and hybrid ML–chemistry approaches to further enhance structural adhesive development. The review underscores the potential of integrating ML into adhesive R&D to reduce experimental burden, improve formulation efficiency, and enable data-driven exploration of complex chemistries. Full article

Background/Objectives: Fosfomycin is an old antimicrobial agent historically used in its oral formulation for uncomplicated urinary tract infections. In the current context of rising antimicrobial resistance and limited antimicrobial options, fosfomycin has attracted renewed interest. Methods: A comprehensive review on the IV fosfomycin use focusing on critically ill patients and/or severe infections due to difficult-to-treat (DTR) Gram-negative bacilli (GNB). Results: Fosfomycin’s IV formulation is now being used more widely, particularly in critically ill patients with multidrug-resistant (MDR) or DTR-GNB infections. It offers several attractive features: a unique mechanism of action that minimizes cross-resistance; a broad spectrum of activity, covering both Gram-negative and Gram-positive pathogens; and consistent synergy with multiple pivotal antimicrobials. Its pharmacokinetic/pharmacodynamic (PK/PD) profile is favorable, with extensive tissue penetration, including the central nervous system. The ratio of area under the concentration–time curve to the minimum inhibitory concentration of the pathogen (AUC/MIC) is considered the optimal PK/PD target for fosfomycin. The adverse events are mainly non-serious (most frequently, hypernatremia and hypokalemia), although safety data for higher dosing regimens remain limited. Growing clinical evidence supports IV fosfomycin as an effective and well-tolerated component of combination therapy for severe infections in critically ill patients, including those infections caused by extended-spectrum beta-lactamases-, carbapenemase-producing Enterobacterales, and DTR non-fermentative GNB. Nevertheless, as with many rediscovered antimicrobials, its expanded role requires confirmation through rigorously designed clinical trials to better define its efficacy, optimal use, and safety profile in the treatment of severe DTR-GNB infections. Full article

The increasing adoption of digital light processing (DLP) three-dimensional (3D) printing in prosthodontics has enabled the rapid fabrication of denture bases with improved dimensional accuracy and reproducibility. However, the mechanical performance and surface characteristics of 3D-printed denture base resins remain inferior to those of conventional heat-polymerized polymethyl methacrylate (PMMA), limiting their long-term clinical reliability. This study aimed to investigate the effect of incorporating zinc oxide (ZnO) and samarium oxide (Sm₂O₃) nanoparticles, individually and as hybrid nanofiller systems, on the mechanical and wettability properties of a DLP 3D-printed denture base resin. ZnO and Sm₂O₃ nanoparticles were incorporated into a photopolymerizable denture base resin at concentrations of 1 and 2 wt.%, producing seven experimental formulations, including a control group. A total of 280 specimens were fabricated using a DLP 3D printer and subjected to standardized post-processing. Nanoparticle dispersion and morphology were examined using field-emission scanning electron microscopy (FE-SEM), while Fourier-transform infrared spectroscopy (FTIR) was employed to assess possible chemical interactions between the nanofillers and the polymer matrix. Mechanical performance was evaluated through impact strength, transverse strength, and flexural strength tests, and surface wettability was assessed using static water contact angle measurements. Statistical analysis was conducted using one-way ANOVA followed by Tukey’s post hoc test (α = 0.05). The results demonstrated that all nanoparticle-reinforced groups exhibited significantly enhanced mechanical properties compared with the unmodified control resin. The incorporation of 1 wt.% nanofillers yielded the most pronounced improvements, with the 1 wt.% ZnO group achieving the highest transverse strength and the 1 wt.% ZnO–Sm₂O₃ hybrid group exhibiting the maximum flexural strength. Increasing the nanofiller concentration to 2 wt.% resulted in partial reductions in impact and flexural strength, which were attributed to nanoparticle agglomeration and increased light scattering during photopolymerization. FTIR analysis revealed no evidence of chemical bonding between the resin matrix and the nanofillers, indicating that the observed enhancements were primarily governed by physical reinforcement mechanisms. Wettability analysis showed that Sm₂O₃-containing formulations significantly reduced the water contact angle, indicating increased surface hydrophilicity, whereas ZnO incorporation produced more hydrophobic surfaces. Within the limitations of this in vitro study, the findings suggest that low-concentration incorporation of ZnO and Sm₂O₃ nanoparticles represents an effective strategy to enhance the mechanical integrity and tailor the surface properties of DLP 3D-printed denture base resins. These results suggest potential clinical relevance of nanoparticle-reinforced printed denture bases, emphasizing the importance of optimized filler loading to avoid agglomeration-induced performance degradation. Full article

With the rapid development of artificial intelligence and robotics, the application of robotics in the chemical domain is driving a transformation toward intelligent and large-scale research in chemistry and material science. However, sample weighing and synthesis reactions constitute critical stages in chemical experiments, which presents significant challenges for robotic gripping of reagent tubes to achieve precise measurements and collision-free path planning autonomously. Therefore, this study aims to address automation of manipulation in chemical experiments, achieving collision-free path planning and optimization under multi-objective constraints. Specifically, the trajectory planning problem for such tasks is formulated as a multi-objective optimization to minimize motion time, joint jerk and energy consumption. Then, an improved optimized multi-objective particle swarm optimization (OMOPSO) algorithm that incorporates seventh-order polynomial interpolation is proposed to improve the smoothness of robotic motion trajectory. A uniform Pareto front is obtained through a reference vector-guided leader selection mechanism, and an update strategy based on $\epsilon$-domination, and inflection point selection is proposed to balance the convergence and diversity of the solution set. Finally, simulation results and demonstrations on a manipulation platform have fully validated the feasibility and practicality of the proposed method, which further provides a reference for robotic execution of chemical experiments. Full article

Physical layer security (PLS) is a fundamental challenge for sixth-generation (6G) wireless networks, particularly in integrated sensing and communication (ISAC) systems, where sensing targets may simultaneously act as potential eavesdroppers. In this paper, we investigate PLS in a reconfigurable intelligent surface (RIS)-assisted cell-free ISAC system, where distributed access points collaboratively serve users and actively sense potential eavesdroppers. We formulate a weighted sum secrecy rate maximization problem through joint ISAC beamforming design. The resulting non-convex problem is first transformed into a semidefinite programming (SDP) formulation and then solved via convex optimization techniques. To further enhance secure communication performance, we extend the framework by incorporating RIS phase shift optimization and propose an alternating optimization algorithm that jointly optimizes active ISAC beamforming and passive RIS configurations. This joint design exploits the controllable wireless propagation environment provided by RISs to enhance legitimate links while suppressing eavesdropping channels. Extensive simulation results demonstrate that the proposed approach significantly outperforms baseline approaches. Specifically, the proposed joint ISAC method improves the communication signal to interference plus noise ratio (SINR) by approximately $1.8$ dB and the sensing signal-noise ratio (SNR) by $4.8$ dB compared to sensing-priority and communication-priority baselines, respectively. Furthermore, the RIS-assisted framework improves a weighted sum secrecy rate gain of approximately $2.2$ dB compared to the frameworks without RIS, validating the proposed framework as a promising solution for secure and spectrum-efficient cell-free ISAC systems in future 6G networks. Full article

To determine the optimal mix proportion of steel fiber-reinforced concrete in cold regions, this study adopted a multi-factor orthogonal experimental design method. A series of mix proportion schemes was formulated based on different water-to-binder ratios, steel fiber volume fractions, and combinations of mineral admixtures such as silica fume. Mechanical performance tests and freeze–thaw cycle tests were conducted to obtain the strength, deformation characteristics, and durability degradation patterns of specimens with different mix proportions before and after freeze–thaw exposure. Meanwhile, scanning electron microscopy (SEM) was employed to observe the microscopic surface morphology of specimens, both pre- and post-freeze–thaw cycles, and to analyze the damage evolution in pore structures and the fiber–matrix interfacial transition zone, thereby elucidating the microscopic mechanism of freeze–thaw damage. Ultimately, by comprehensively comparing the macro-mechanical properties, freeze–thaw durability, and microstructural characteristics, the experimental results of different groups were evaluated to identify the optimal mix proportion for steel fiber-reinforced concrete, which exhibits excellent mechanical performance and durability under freeze–thaw conditions. The results indicated that freeze–thaw cycles significantly reduced the mechanical properties of the concrete. The optimal mix proportion was achieved with a water-to-binder ratio of 0.4, a silica fume content of 10%, and a steel fiber volume fraction of 1.5%. This optimal mix proportion can provide a direct reference for the material design and application of steel fiber-reinforced concrete in engineering projects located in cold regions. Full article