Search Results (55,075)

This study investigates the influence of PLA flowability and talc content on the performance of compostable thermoplastic starch/poly(butylene succinate) (TPS/PBS)-based systems for rigid applications. Different PLA grades with varying melt flow index (PLA23, PLA8 and PLA70) and talc contents (0, 5 and 10 wt%) were incorporated. Twelve formulations were compounded by twin-screw extrusion and processed by injection moulding. FTIR confirmed the coexistence of TPS, PBS and PLA phases without evidence of chemical interactions. Morphological analysis showed that PLA flowability plays a key role in phase distribution, with higher-flow PLA promoting improved dispersion and interfacial adhesion, while talc addition (5 and 10 wt%) increased structural heterogeneity; at higher loadings, particularly, DSC analysis revealed that talc acted as a nucleating agent for the PBS phase, increasing crystallisation temperatures from approximately 73 °C to 81 °C depending on formulation. Mechanical results showed that Young’s modulus increased from approximately 1.4 GPa to 2.7 GPa with decreasing PLA flowability and increasing talc content. Formulations containing low-flow PLA reached tensile strengths close to 32 MPa, although elongation at break decreased to values near 2%. In contrast, high-flow PLA formulations exhibited a more balanced mechanical response, with elongation values up to approximately 8%, associated with improved phase dispersion. Hybrid PLA systems showed intermediate behaviour, reaching elongations up to 22% while maintaining modulus values around 1.8 GPa. Talc provided additional reinforcement but reduced deformation capacity. HDT values remained relatively constant, indicating limited improvement in thermomechanical resistance despite increased stiffness. These results demonstrate that the combined control of PLA molecular characteristics and talc content enables tuning of the mechanical and thermomechanical performance of TPS/PBS/PLA/talc systems for rigid packaging applications. Full article

The high energy consumption of conventional mixers equipped with active mixing elements necessitates the development of more efficient technologies for mixing bulk materials and feed mixtures. This study presents a gravity-driven mixing approach based on the rotation of an inclined cylindrical chamber, eliminating the need for active mixing elements. During chamber rotation, the mixture components move toward both end walls while simultaneously undergoing a circular motion along the inner cylindrical surface. This movement intensifies the mixing process and reduces energy consumption, thereby providing an energy-efficient gravity-based mixing approach that operates without active mixing elements. Laboratory experiments were conducted to determine the key physical and mechanical properties of the sapropel, organic fertilizer, and compound feed (formulation K-60-1). The measured values were as follows: velocity on an inclined steel surface, 0.65–1.21 m/s; coefficient of friction, 0.40–0.91; bulk density, 453–1166 kg/m³; and angle of repose, 36–39°. The experimental results confirmed the validity and adequacy of the developed analytical relationships. A structural and technological design of the gravity mixer was developed, and an experimental prototype was manufactured. Analytical relationships were obtained to determine the critical rotational speed of the chamber, particle movement velocity, and the power required for the mixing process. Under optimal operating conditions, the mixture uniformity reached 95.7% after 4 min of mixing. The mixer productivity was 0.95 t/h, while the specific energy consumption was 0.5 kWh/t, which is 2.5 times lower than that of conventional mixers equipped with active mixing elements. The obtained results confirm the feasibility and effectiveness of the proposed gravity-based mixing method for the preparation of feed and organomineral mixtures under the operating conditions of small-scale farms. Full article

To reveal the controlling mechanism of cooling rate on the continuous cooling transformation, microstructure evolution and mechanical performances of Q580R low-temperature pressure vessel steel, this study took industrial-scale Q580R steel as the research object. The JMatPro thermodynamic software was utilized for simulating and calculating its equilibrium phase diagram, TTT diagram, CCT diagram and mechanical property evolution. Continuous cooling experiments with a wide range of cooling rates between 0.1 and 50 °C/s were executed on a Gleeble-3500 thermal simulator. Combined with optical microscopy, scanning electron microscopy and Vickers hardness tester for microstructure characterization and property testing, the measured CCT diagram was constructed and contrasted with the simulation results for verification. Experimentally, the phase composition of Q580R steel evolves at regular intervals with cooling rate. As the cooling rate rises, the ferrite content constantly decreases, the bainite content first increases and subsequently decreases, and the martensite content constantly increases. When the cooling rate reaches 30 °C/s, the martensite proportion can exceed 90%, and the microstructure is significantly refined. The hardness of the material first increases rapidly and subsequently trends to be steady as the cooling rate rises, reaching 308 HV₁₀ at 50 °C/s. The measured transformation law, microstructure evolution and hardness change exceedingly corresponds to the JMatPro simulation results. This validates the credibility of the simulation prediction. This study clarifies the quantitative relationship among “cooling rate-microstructure-properties” of Q580R steel, which can provide theoretical basis and data support for the precise design of heat treatment process and the optimization of strength and toughness. The established relationship can directly guide the formulation of controlled cooling parameters during hot rolling and off-line quenching and tempering production of Q580R pressure vessel plates, helping manufacturers optimize industrial heat-treatment procedures to satisfy low-temperature toughness requirements for petrochemical and cryogenic pressure vessel service. Full article

Graphene and carbon nanotube (CNT) membranes are promising for filtration, desalination, and water treatment, yet their performance requires the joint interpretation of their architecture, nanoconfined transport, selectivity, fouling, swelling, defects, stability, and operating conditions. Here, GCMembrane-LLM was developed as an evidence-grounded domain-specific large language model. A curated 582-paper corpus generated 12,208 cleaned membrane-specific question–answer pairs for Low-Rank Adaptation (LoRA)-based supervised fine-tuning of Llama-3.1-8B-Instruct, and retrieval-augmented generation provided article-title and page-level traceability. GCMembraneBench included 100 application-oriented questions on graphene oxide (GO) membranes, CNT membranes, GO/CNT hybrids, and cross-material reasoning. Under direct answering without retrieval context, the anonymized and shuffled automatic evaluation showed that GCMembrane-LLM achieved a mean weighted score of 4.237/5.0, exceeding Llama-3.1-8B-Instruct and Doubao-1.5-lite. A stratified 30-question blinded manual assessment showed the same ranking. The application cases further yielded membrane science conclusions: CNT-assisted GO/CNT transport should be evaluated with dispersion, interfacial compatibility, defects, and stability; GO desalination depends on swelling control, interlayer spacing, and defect suppression; and CNT high flux requires joint examination of pore diameter, entrance chemistry, hydration barriers, ion rejection, and operating conditions. GCMembrane-LLM supports source-traceable evidence organization and preliminary hypothesis formulation before experimental validation. Full article

Background/Objectives: With the increasing incidence of drug counterfeiting and the emergence of personalized medicine, the need for unique marking of solid dosage forms, e.g., tablets, has attracted considerable interest in the current research and development landscape. Besides traditional printing methods, laser marking offers several advantages, as it eliminates the need for organic solvents and enables the generation of precise patterns. However, laser exposure may raise safety concerns regarding the stability of photosensitive drugs in the irradiated dosage forms. Therefore, the aim of the present study was to test the photoprotective effect of titanium dioxide (TiO₂) and its various alternatives, e.g., talc, calcium carbonate (CaCO₃), zinc oxide (ZnO), and black iron oxide (Fe₃O₄), alongside a ready-to-use reference formulation, Opadry^® Brown, which contains TiO₂ (titanium-containing, TC) on nifedipine, a light-sensitive model drug. Methods: Laser marking or short-term laser ablation at different wavelengths (193 nm, 248 nm, 532 nm, and 781 nm) was applied to different coating formulations. As a positive control, prolonged exposure to daylight was applied. The properties and photostability of these formulations were evaluated using several analytical methods (i.e., surface profilometry, Raman spectroscopy, and high-performance liquid chromatography (HPLC)). Results: The TiO₂, ZnO, Fe₃O₄, and Opadry^® TC Brown coatings maintained their color during the long-term study under all conditions. Furthermore, the prepared formulations exhibited different ablation depths and morphological changes depending on the coating and laser type. HPLC measurements confirmed significant differences in the protective ability of various pigments against sunlight and different types of lasers. Nevertheless, the obtained Raman spectra were not in complete agreement with HPLC results, which can be attributed to spectral overlap between key nifedipine degradation markers and excipient signals in the tablet core. Conclusions: Overall, laser treatment of tablets containing photosensitive drugs may induce API decomposition; however, this effect can be minimized or avoided by careful selection of the appropriate combination of laser type and photoprotective pigment. Under the applied experimental conditions, Ti:Sa laser treatment was associated with the lowest degree of nifedipine degradation among all formulations, while ZnO-containing coatings demonstrated the most consistent photoprotective performance against the majority of the tested laser types, while Fe₃O₄-containing coatings provided superior protection during prolonged sunlight exposure and Nd:YAG laser irradiation. Full article

A short-term performance test of blended biodiesel (FAME), green diesel (HVO), and diesel was experimentally assessed in a 100 kW Cummins 6BTAA5.9-G12 diesel engine under multiple load conditions. The objective was to determine the technical feasibility, operational trade-offs, and optimal blend formulations for renewable energy deployment in diesel power plants. All tested blends operated stably without engine modification, confirming the “drop-in capability” of FAME–HVO mixtures for existing diesel engines. Specific fuel consumption (SFC) increased notably at high loads, with penalties up to 15.15% for B30D20 and B35D15 relative to neat diesel, although overall efficiency improved with load. Among the ternary fuels, B30D10 and B30D20 provided the most balanced compromise between combustion reactivity and flow properties. Exhaust gas temperatures rose with load for all fuels, with FAME-rich blends exhibiting higher temperatures than neat diesel, while coolant-side analysis showed D100 and D50 as thermally favorable and B50–B100 imposing the highest cooling demand. The results emphasize the need for injection system recalibration on an energy basis for HVO-rich fuels, and for strengthened filtration and maintenance practices for FAME-rich blends to avoid filter clogging and injection instability. Considering performance, operability, and system stability up to 100 kW, B30D10 and B35D15 are identified as optimal compromise blends. The study highlights the necessity of future work on long-term durability, fuel system compatibility, supply chain robustness, and techno-economic viability to safely scale green diesel use in Indonesian stationary power generation. Full article

Objectives: An integrated methodological framework was developed for modeling and multiobjective optimization of sustained-release drug delivery systems. Methods: The cumulative release percentage was fitted as a function curve, and functional principal component analysis was subsequently used to transform the function curves into functional principal component scores (FPCs). FPCs were then treated as dependent variables, while the proportions of the formulation factors were used as independent variables to construct Scheffé polynomial regression models. Finally, Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to perform multi-objective optimization. Results: FPC1, FPC2, and FPC3 captured 95.18%, 4.39%, and 0.32% of the total variation, respectively. Corresponding Scheffé polynomial regression models were established, including quadratic models for FPC1 (adjusted R² = 0.751, AIC = 168.557) and FPC2 (adjusted R² = 0.592, AIC = 119.302), and a special cubic model for FPC3 (adjusted R² = 0.597, AIC = 64.574). The NSGA-III algorithm generated a Pareto optimal set, yielding stable formulation compositions with mean (SD) values of X₁ = 0.123 (0.015), X₂ = 0.821 (0.032), X₃ = 0.012 (0.017), and X₄ = 0.045 (0.015). The corresponding FPCs were −41.787 (2.544), 10.009 (0.168), and 8.264 (0.010) for FPCs1–FPCs3, respectively. The reconstructed cumulative release percentages were 42.471 (1.661), 52.623 (2.868), 69.942 (1.200), 84.275 (1.010), and 93.330 (0.832), demonstrating good agreement with the target release profiles. Conclusions: The integrated FDA–Scheffé–NSGA-III framework provides a robust and effective approach for accurately modeling release behavior and optimizing sustained-release formulations. Full article

During the stage of high-quality economic development, the synergy between advancing county industrial structure and employment growth has become a key issue in county governance. Although existing studies confirm that industrial structure has both creation and substitution effects on employment, few have adopted a configurational perspective to reveal how combinations of multiple factors can jointly promote both advanced county industrial structure and employment growth, thereby achieving industry-employment synergy. From the perspective of inclusive growth, this study incorporates six factors-economic level, financial level, innovation level, human capital, fiscal expenditure, and agricultural resources-into a unified analytical framework under the dimensions of efficiency and equity. Using a mixed method that combines dynamic QCA and regression analysis, and taking 1128 Chinese counties as the sample, this study explores configurational pathways that can simultaneously achieve advanced county industrial structure and inclusive employment growth. The findings are as follows: (1) Four configurational pathways lead to advanced county industrial structure: market-driven with efficiency priority (C1), endowment-substituted with factor concentration (C2), endowment-dependent with efficiency-equity coordination (C3), and talent–innovation dual-driven with government assistance (C4). (2) These four pathways differ in their effectiveness in promoting industry–employment synergy. Configurations C1, C2, and C3 achieve coordinated development of county industry and employment, whereas configuration C4 promotes advanced county industrial structure but inhibits employment growth. The conclusions reveal multiple equivalent pathways for synergistically enhancing county industry and employment, providing a basis for local governments to formulate context-specific industry–employment coordination policies. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

Genetic programming (GP) has been widely used in quantitative finance for discovering formulaic alpha factors that can predict asset returns. However, GP often produces overgrown expressions that are difficult to interpret and expensive to evaluate. This paper proposes a large language model (LLM)-assisted pruning framework that reviews expression trees generated by GP, with the LLM acting as a semantic reviewer that flags redundant or financially implausible branches based on structural complexity and contextual reasoning. The proposed method is formalized as a closed-loop Trigger–Evaluate–Decide–Execute (TEDE) process. We present mathematical formulations, algorithmic design, and examples showing how redundant nested functions can be simplified while monitoring predictive performance. Experiments with high-frequency cryptocurrency market data, using DeepSeek-V4-Flash as the semantic engine, show lower expression complexity and higher rubric-based interpretability scores for the pruned symbolic factors. Under the reported test setup, the LLM-pruned configuration has higher Information Ratio (IR) values than the listed baselines and more compact expression trees than the GP baselines. Full article

Background: Lepidium meyenii Walp (L. meyenii), traditionally known as maca, is widely recognized for its health-promoting properties, including potential protection against exercise-induced muscle damage (EIMD). However, its precise effect on post-exercise blood biomarkers remains unclear. Objective: This study aimed to qualitatively review research published until April 2026 examining L. meyenii supplementation to reduce blood markers of muscle damage and protein degradation post-exertion in animal studies. Specifically, the effect size (ES) of L. meyenii supplementation on post-exercise levels of creatine kinase (CK), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN) was estimated. Methods: This systematic review and meta-analysis were conducted in accordance with the PRISMA guidelines. The certainty of the evidence was assessed using the GRADE framework. Relevant studies were identified through Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE. Eligible studies included in vivo experiments in animals with controlled designs and pre-/post-intervention assessments. Methodological quality and risk of bias were evaluated using the CAMARADES tool. Statistical analysis involved standardized mean differences (SMD) using Hedges’ g with 95% confidence intervals. Results: 15 studies were included in the systematic review, and 14 studies in animals in the meta-analysis. The CAMARADES scores ranged from 5 to 7 points, indicating moderate methodological quality. Supplementation with L. meyenii was not associated with statistically significant changes in LDH (SMD = −1.37; 95% CI −3.34 to 0.59), BUN (SMD = −0.37; 95% CI −2.16 to 1.42) nor CK (SMD = 0.29; 95% CI −5.45 to 6.03), with very high heterogeneity (I² > 97%). Exploratory subgroup analyses and meta-regression analyses by formulation type and dose did not identify any moderators that could robustly explain this heterogeneity. Conclusions: The available evidence does not support a robust overall effect of L. meyenii supplementation on blood biomarkers of muscle damage or protein catabolism in animals subjected to physical stress. The high degree of heterogeneity could not be robustly explained by either the type of formulation or the dose. These findings, which are exploratory and hypothesis-generating in nature, highlight the need for standardized, well-characterized formulations and trials with adequate statistical power. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

A six-week feeding experiment was carried out to investigate the suitability of wasted tofu meal (WTM) as a substitute protein source for fish meal (FM) in diets for juvenile yellowtail (Seriola quinqueradiata). A diet containing FM as the principal protein source served as the control (C), while WTM was incorporated to replace 20%, 35%, and 50% of the FM protein in the experimental diets, referred to as T20, T35, and T50, respectively. Juvenile fish with an initial average body weight of approximately 30.99 g were randomly distributed into 500-L tanks at a density of 20 fish per tank, with triplicate groups assigned to each dietary treatment. At the end of the feeding trial, fish fed the T20 diet showed no significant differences from the control group in final body weight, specific growth rate, daily feed intake, feed efficiency, or survival. However, fish receiving the T35 and T50 diets exhibited significant reductions in most growth performance indices compared with those fed the control diet. Although nutrient retention efficiency and plasma biochemical indicators associated with fish health were not significantly influenced by dietary treatment, alterations were observed in whole-body lipid composition and fatty acid profiles, including reductions in EPA, DHA, total n-3 fatty acids, and the n-3/n-6 fatty acid ratio with increasing WTM inclusion. Overall, the findings suggest that, under the dietary formulations tested, WTM can replace up to 20% of FM protein in diets for juvenile yellowtail without negatively affecting growth performance or physiological health; however, supplementation with n-3 HUFA-rich lipid sources may be required to maintain optimal whole-body fatty acid composition and product nutritional quality. Full article