Search Results (47,603)

Injection molding plays a pivotal role in modern manufacturing, enabling the mass production of complex components with high precision. However, traditional tooling methods often face challenges related to thermal management, design constraints, and material efficiency. This study examines the use of additive manufacturing (AM) in the development and optimization of injection molding tools to overcome these limitations. A novel prototype was fabricated using AM techniques, incorporating integrated cooling channels and optimized lattice structures to enhance thermal performance and simplify the manufacturing process. Experimental validation demonstrated the prototype’s effective integration into a vacuum-assisted resin transfer molding (VA-LRTM) system without requiring modifications to existing tooling setups. The results showed significant improvements in temperature regulation, reduced cycle times, and consistent mechanical properties of the molded components compared to conventional approaches. By reducing the number of tool components and eliminating the need for support structures during manufacturing, AM also minimized material waste and post-processing requirements. This research highlights the transformative potential of additive manufacturing in injection molding tool design, offering increased flexibility, cost efficiency, and enhanced functionality to meet the evolving demands of modern industrial applications. Full article

The ultrasound-assisted extraction process with microwave pretreatment was modeled and optimized to maximize the yield of antioxidants from green walnut husks using a response surface methodology with Box–Behnken design. In this design, the ultrasound-assisted extraction time (10–40 min), ultrasound-assisted extraction temperature (40–60 °C), and microwave pretreatment time (20–60 s) were selected as the factors, while the total antioxidant content was defined as the response. The solvent of choice for extracting antioxidants was 50% (v/v) ethanol. After optimization using the desirability function, an ultrasound-assisted extraction time of 23 min, ultrasound-assisted extraction temperature of 60 °C, and microwave pretreatment time of 60 s were proposed as the optimal conditions and their validity was verified. Under these conditions, the experimentally determined total antioxidant content was 3.69 g of gallic acid equivalent per 100 g of dry matter. In addition to phenolics, UHPLC–ESI–MS/MS analysis indicated the presence of lipids, quinones, terpenoids, and organic acids in the extract. After the antioxidant extraction, the solid residue was further processed to isolate cellulose in line with the concept of sustainable manufacturing. The structural characterization and hydration properties of cellulose were analyzed to identify its key features and assess its potential for value-added applications. The results demonstrate that green walnut husks are a valuable and cost-effective agro-industrial byproduct for extracting antioxidants and isolating cellulose. This aligns with the principles of a circular economy and the sustainable production of natural compounds. Full article

To address the issues of low recognition rates and poor detection accuracy for road negative obstacles caused by insufficient feature representation, we propose a novel detection framework: the Negative Road Obstacles Segmentation Network (NROSegNet). The detection performance of the algorithm is improved through a data enhancement strategy based on feature splicing and an adaptive feature enhancement module. Specifically, the data augmentation strategy introduces negative obstacle features into other datasets through geometric transformations and random splicing, effectively increasing the diversity of training data. This can solve the problem of an uneven distribution of data features while improving the performance of the model in capturing illumination changes and local details. The framework further adopts a dynamic multi-scale feature enhancement module to improve the perception of local details and global semantics. A robust multimodal data fusion mechanism and edge-aware optimization strategy are designed to effectively alleviate the problems of noise interference and boundary blur. The experimental results show that the NROSegNet proposed in this paper achieves 70.6 and 83.0 in mIoU and mF1, respectively, which is 2.8 percentage points and 2.9 percentage points higher than other methods. The results fully demonstrate its excellent performance in precise segmentation and boundary detail processing. Full article

Additive manufacturing (AM) processes, such as Fused Filament Fabrication (FFF), enable the production of lightweight parts with high stiffness-to-weight ratios, making them highly suitable for a wide range of engineering applications. However, ensuring the mechanical reliability of these components, particularly for load-bearing purposes, requires systematic mechanical testing of well-designed specimens to asses their suitability. While the tensile properties of additively manufactured materials have been extensively studied, the compressive behavior of components produced via AM, particularly those made from thermoplastic materials, remains comparatively underexplored and insufficiently characterized in the existing body of research. Among these materials, polylactic acid (PLA)—a biodegradable thermoplastic derived from renewable resources—has gained prominence in AM applications. Recent studies have investigated the compression properties of PLA in reinforced materials; however, the focus has primarily been on solid, semi-solid, or porous specimens. These investigations largely overlook thin-walled structures, which are integral to weight-saving designs and commonly feature in topology-optimized structures. Understanding the mechanical behavior of monolayers, the fundamental building blocks of most AM components, is essential for accurately predicting the overall performance of multilayer structures. Monolayers represent the smallest, most basic structural elements of AM parts, and their properties directly influence the behavior of the final, more complex assemblies. Establishing a methodology that correlates monolayer properties with those of multilayer components could significantly streamline testing procedures. By performing mechanical tests on monolayers, instead of on more intricate multilayer specimens, manufacturers could reduce testing complexity and cost while accelerating the development process. The current literature reveals a gap in the design and analysis of thin-walled AM specimens, especially monolayers, under compressive loads. Specifically, the design of monolayer or thin-walled AM compression specimens without infill has not been thoroughly explored. This article addresses this gap by investigating the design and testing of AM monolayer compression specimens produced using FFF of PLA. Three distinct specimen geometries are considered—circular, helicoidal, and S-shaped—to evaluate their potential for understanding and predicting the compressive behavior of AM monolayer structures. Full article

Beetroot (Beta vulgaris), a root vegetable known for its vivid natural color and nutritional profile, is a source of a wide range of bioactive compounds, including betalains, phenolics, vitamins, and antioxidants. These bioactive compounds are associated with many health-promoting properties, including antihypertensive, antioxidant, anti-inflammatory, and anticancer effects. The beetroot processing industry produces substantial by-products abundant in phytochemicals and betalains, presenting valuable opportunities for utilization. Therefore, it can replace synthetic additives and enhance the nutritional value of foods. By reducing waste and supporting a circular economy, beetroot by-products improve resource efficiency, cut production costs, and lessen the food industry’s environmental impact. Beetroot and its by-products are rich in phytochemicals that provide various wellness advantages. They support cardiovascular health, inhibit microbe-induced food spoiling, aid liver function, and reduce inflammation and oxidative stress. This paper presents a detailed review of current knowledge on beetroot and its by-products, focusing on their biochemical components, extraction and stabilization techniques, health benefits, and potential applications in the food industry. It underscores the versatility and importance of red beetroot and its derivatives, advocating for further research into optimized processing methods and innovative uses to enhance their industrial and nutritional value. By providing valuable insights, this review aims to inspire food scientists, nutritionists, and the agricultural sector to integrate beetroot and its by-products into more sustainable and health-oriented food systems. Full article

The implementation of sponge cities in China modifies the hydrological conditions of the underlying surface, effectively alleviating the urban heat island effect. However, in planning and construction, heat island mitigation targets are difficult to quantify and lack quantitative design and evaluation methods. To address this issue, two planning schemes were proposed based on sponge city management and control indicators. The WRF-UCM model was used to conduct numerical simulations of the current conditions (case 1) and the sponge city planning schemes (cases 2 and 3), analyzing the impact of sponge city initiatives on the mitigation of the heat island effect. The results indicated that by changing the structure of the underlying surface and increasing the water content of the underlying surface, the sponge city affects the urban energy distribution process and regional horizontal advection pattern. This not only reduces heat accumulation within the urban area but also suppresses regional convection during high-temperature periods, thereby mitigating the urban heat island effect. Moreover, different schemes following the same sponge city design requirements have varying impacts on urban microclimate elements and heat island distributions. Notably, a higher subsurface water content yields a more pronounced inhibition of the heat island effect. Finally, a sponge city planning method with the consideration of heat island mitigation was proposed, facilitating pre-simulation optimization and decision-making in sponge city planning. Full article

Real-time dynamic capture of a single moving target is one of the most crucial and representative tasks in UAV capture problems. This paper proposes a multi-UAV real-time dynamic capture strategy based on a differential game model to address this challenge. In this paper, the dynamic capture problem is divided into two parts: pursuit and capture. First, in the pursuit–evasion problem based on differential games, the capture UAVs and the target UAV are treated as adversarial parties engaged in a game. The current pursuit–evasion state is modeled and analyzed according to varying environmental information, allowing the capture UAVs to quickly track the target UAV. The Nash equilibrium solution in the differential game is optimal for both parties in the pursuit–evasion process. Then, a collaborative multi-UAV closed circular pipeline control method is proposed to ensure an even distribution of capture UAVs around the target, preventing excessive clustering and thereby significantly improving capture efficiency. Finally, simulations and real-flight experiments are conducted on the RflySim platform in typical scenarios to analyze the computational process and verify the effectiveness of the proposed method. Results indicate that this approach effectively provides a solution for multi-UAV dynamic capture and achieves desirable capture outcomes. Full article

Aiming at the saline soil in Binhai New Area, which is solid and sclerotic, and addressing the problem of poor quality and low efficiency of traditional rotary tillage, this research designed a high-speed rotary tiller that can realize the high-speed rotation of knife rollers to cut. The average operating speed is higher than that of the ordinary rotary tiller. We analyzed the rotary tiller operating conditions and rotary tiller knife cutting process and conducted a movement trajectory theoretical analysis to determine the rotary tiller’s high-speed operating speed relationship. The working process of a high-speed rotary tiller was simulated using EDEM software. The experimental indicators included the soil-crushing rate and surface smoothness after tilling. The experimental factors included the forward speed of the machine, the rotational speed of the blade roller, and the tilling depth. An orthogonal experiment was performed to establish regression equations for the soil-crushing rate and surface smoothness. Using Design-Expert analysis software, we obtained the following optimal combination of parameters: a knife roller speed at 310 r/min, tillage depth of 13.2 cm, and machine forward speed of 4.8 km/h. At this time, the simulation values of the soil fragmentation rate and surface flatness were 90.6% and 18.2 mm, respectively. When determining the optimal knife roller speed of 310 r/min, a transient structural simulation under the mesh bevel gear transient was conducted. The simulation analysis showed that the maximum equivalent stress value was 584.57 MPa, which was smaller than the permissible stress of 695.8 MPa, meeting the bevel gear meshing strength requirements. Under the optimal combination determined by a field comparison test, the results show that the values of the high-speed rotary tiller operation after the soil-breaking rate, tillage depth, the tillage depth stability coefficient, and vegetation cover were 89.3%, 14.2 cm, 92.8%, and 90.3%. The land surface flatness was 16.4 mm, which is superior to the ordinary rotary tiller operation effects, meeting the agronomic requirements for pre-sowing land preparation for peanuts in the saline land of Binhai New Area. Full article

With rapid urbanization and industrialization, Vietnam is facing many challenges in solid waste management and increasing energy demand. In this context, the development of renewable energy from solid waste not only solves the problem of environmental pollution but also makes an important contribution to energy security and sustainable economic development. Solid waste to energy is a system of solid waste reatment by thermal methods, in which the heat generated from this treatment process is recovered and utilized to produce energy. Site selection is one of the biggest challenges for renewable energy projects. In addition to technical factors, this decision must also consider environmental impacts, including protecting ecosystems, minimizing noise, and limiting impacts on public health. To solve this problem, multi-criteria decision making (MCDM) methods combined with fuzzy numbers are often used. These methods allow planners to evaluate and balance competing factors, thereby determining the most optimal location for the project. In this study, the authors proposed a Spherical Fuzzy Multi-Criteria Decision-making Model (SFMCDM) for site selection in solid waste-to-energy projects. In the first stage, all criteria affecting the decision-making process are defined based on literature review, experts and triple bottom line model (social, environmental, and economic), and analytic hierarchy process (AHP), and fuzzy theory is applied for calculating the weights in the second stage. The weighted aggregated sum product assessment (WASPAS) method is utilized for ranking four potential locations in the final stage. The contribution of the proposed process is its structured, systematic, and innovative approach to solving the location selection problem for renewable energy projects. Choosing the right location not only ensures the success of the project but also contributes to the sustainable development of renewable energy. Full article

This paper details the enhancement of the optoelectronic properties of Cu-(In, Ga)-Se₂ (CIGSe) solar cells through a two-segment process in the ultraviolet (UV)–visible spectral range. These include fine-tuning the DC sputtering power of the absorber layer (ranging from 20 to 40 W at segment I) and thoroughly checking the trace micro-chemistry composition of the absorber layer (CdS, ZnO/CdS, ZnMgO/CdS, and ZnMgO at segment II). After segment I of treatment, the optimal 30 W CIGSe absorber layer (i.e., with a 0.95 CGI ratio) can be obtained, it can be seen that the Cu-rich film exhibits the ability to significantly promote grain growth and can effectively reduce its trap state density. After the segment II process aimed at replacing toxic CdS, the optimal metal alloy (Zn_0.9Mg_0.1O) composition (buffer layer) achieved the highest conversion efficiency (η) of 8.70%, also emphasizing its role in environmental protection. Especially within the tunable bandgap range (2.48–3.62 eV), the developed overall internal and external quantum efficiency (IQE/EQE) is significantly improved by 13.15% at shorter wavelengths. A photovoltaic (PV) module designed with nine optimal CIGSe cells demonstrated commendable stability. Variation remained within ±5% throughout the 60-day experiment. The PV modules in this study represent a breakthrough benchmark toward a significant advance in the scientific understanding of renewable energy. Furthermore, this research clearly promotes the practical application of PV modules, harmonizes with sustainable goals, and actively contributes to the creation of eco-friendly communities. Full article

Accurate flow stress prediction is vital for optimizing the manufacturing of lightweight materials under high-temperature conditions. In this study, a boron nitride (BN)-reinforced AZ80 magnesium composite was subjected to hot compression tests at temperatures of 300–400 °C and strain rates ranging from 0.01 to 10 s⁻¹. A data-driven Support Vector Regression (SVR) model was developed to predict flow stress based on temperature, strain rate, and strain. Trained on experimental data, the SVR model demonstrated high predictive accuracy, as evidenced by a low mean squared error (MSE), a coefficient of determination (R²) close to unity, and a minimal average absolute relative error (AARE). Sensitivity analysis revealed that strain rate and temperature exerted the greatest influence on flow stress. By integrating machine learning with experimental observations, this framework enables efficient optimization of thermal deformation, supporting data-driven decision-making in forming processes. The results underscore the potential of combining advanced computational models with real-time experimental data to enhance manufacturing efficiency and improve process control in next-generation lightweight alloys. Full article

Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model’s reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH₄ production by 5.4% and 13.5%, respectively, while H₂, H₂S, and NH₃ decreased by 28.2%, 43.6%, and 42.5%, respectively. Full article

This article examines the impact of fluid flow dynamics on microbial growth, distribution, and control within food processing systems. Fluid flows, specifically laminar and turbulent flows, significantly influence microbial behaviors, such as biofilm development and microbial adhesion. Laminar flow is highly conducive to biofilm formation and microbial attachment because the flow is smooth and steady. This smooth flow makes it much more difficult to sterilize the surface. Turbulent flow, however, due to its chaotic motion and the shear forces that are present, inhibits microbial growth because it disrupts attachment; however, it also has the potential to contaminate surfaces by dispersing microorganisms. Computational fluid dynamics (CFD) is highlighted as an essential component for food processors to predict fluid movement and enhance numerous fluid-dependent operations, including mixing, cooling, spray drying, and heat transfer. This analysis underscores the significance of fluid dynamics in controlling microbial hazards in food settings, and it discusses some interventions, such as antimicrobial surface treatments and properly designed equipment. Each process step from mixing to cooling, which influences heat transfer and microbial control by ensuring uniform heat distribution and optimizing heat removal, presents unique fluid flow requirements affecting microbial distribution, biofilm formation, and contamination control. Food processors can improve microbial management and enhance product safety by adjusting flow rates, types, and equipment configurations. This article helps provide an understanding of fluid–microbe interactions and offers actionable insights to advance food processing practices, ensuring higher standards of food safety and quality control. Full article

Rural economic development faces significant challenges in the context of rapid urbanization and agricultural transformation, particularly in developing countries like China. Agricultural socialization services (ASSs) play a crucial role in promoting rural economic sustainability by enhancing household income and fostering regional development. This study investigates the impact of ASSs on rural economic sustainability in China from both temporal and spatial perspectives, employing the entropy weight method, double fixed effects model, and Spatial Durbin Model. Analyzing panel data from 30 Chinese provinces from 2011 to 2021 reveals significant positive effects of ASSs on rural income, along with spatial spillovers to neighboring regions. The results highlight regional heterogeneity in the impact of ASSs, with the eastern region benefiting from local spillovers, while the central and western regions gain from intensification and scale effects. These findings suggest that policymakers should adopt region-specific ASSs strategies, such as facilitating technology transfer in the eastern regions while leveraging intensification and scale advantages in the central and western regions, to optimize the effectiveness of agricultural support measures. Moreover, the relationship between ASSs and rural income exhibits a non-linear trend across various urbanization stages, implying that ASS policies should be tailored to the specific challenges and opportunities associated with different levels of urbanization to maximize their impact on rural economic sustainability. These findings underscore the importance of optimizing ASSs, tailoring policies to local conditions, and harnessing the role of ASSs in the urbanization process to promote inclusive rural development and foster sustainable rural economic growth. Full article

Hijiki (Hizikia fusiformis) is a seaweed native to warm-temperate and subtropical regions that has a high edible value and economic value, with a production of about 2 × 10⁵ tons/year. Current research has clearly shown that the pharmacological activities of active ingredients from hijiki have covered a broad spectrum of areas, including antioxidant, hypoglycemic, antiviral, anticoagulant, anti-inflammatory, intestinal flora modulation, anti-aging, antineoplastic and antibacterial, and anti-Alzheimer’s disease areas. However, no studies have reported on the production of antioxidant peptides from hijiki proteins. The objectives of this study were to optimize the preparation process and explore the cytoprotective function and mechanisms of antioxidant peptides from hijiki protein. The results indicated that papain is more suitable for hydrolyzing hijiki protein than pepsin, trypsin, alkaline protease, and neutral protease. Under the optimized parameters of an enzyme dosage of 3%, a material–liquid ratio of 1:30, and an enzyme digestion time of 5 h, hijiki hydrolysate with a high radical scavenging activity was generated. Using ultrafiltration and serial chromatographic methods, ten antioxidant oligopeptides were purified from the papain-prepared hydrolysate and identified as DGPD, TIPEE, TYRPG, YTPAP, MPW, YPSKPT, YGALT, YTLLQ, FGYGP, and FGYPA with molecular weights of 402.35, 587.61, 592.64, 547.60, 532.53, 691.77, 523.57, 636.73, 539.58, and 553.60 Da, respectively. Among them, tripeptide MPW could regulate the Keap1/Nrf2 pathway to significantly ameliorate H₂O₂-induced oxidative damage of A549 cells by increasing cell viability and antioxidant enzyme (SOD, CAT, and GSH-Px) activity, decreasing ROS and MDA levels, and reducing the apoptosis rate. Molecular docking experiments show that HFP5 (MPW) exerts its inhibitory effect mainly through hydrogen bonds and hydrophobic interactions with the Kelch domain of the Keap1 protein, eventually facilitating the translocation of Nrf2 to the nucleus. Therefore, antioxidant peptides from hijiki can be applied to develop algae-derived health foods for treating diseases associated with oxidative stress. Full article