Search Results (13,212)

This review examines the transformative impact of innovative artificial intelligence (AI) methods on energy productivity, industrial transformation, and digitalization in the context of energy economics, energy efficiency, and sustainability. AI-based tools are revolutionizing energy systems by optimizing production, reducing waste, and enabling predictive maintenance in industrial processes. Integrating AI increases operational efficiency across various sectors, significantly contributing to energy savings and cost reductions. Using deep learning (DL), machine learning (ML), and generative AI (genAI), companies can model complex energy consumption patterns and identify efficiency gaps in real time. Furthermore, AI supports the renewable energy transition by improving grid management, forecasting, and smart distribution. The review highlights how AI-assisted digitalization fosters smart production, resource allocation, and decarbonization strategies. Economic analyses indicate that AI implementation correlates with improved energy intensity indicators and long-term sustainability benefits. However, challenges such as data privacy, algorithm transparency, and infrastructure investment remain key barriers. This article synthesizes current literature and case studies to provide a comprehensive understanding of AI’s evolving role in transforming energy-intensive industries. These findings highlight AI’s crucial contribution to sustainable economic development through improved energy efficiency and digital innovation. Full article

This study addressed the challenges posed by wet-grade maize distiller’s dried grains with solubles (DDGS), which are characterized by high moisture and complex fibers that limit their storage and utilization in poultry feed. Three experiments were conducted to enhance their nutritional value through enzymatic and solid-state fermentation treatments. In vitro pre-digestion using multiple enzymes significantly improved dry matter solubility (DMS) and reducing sugar yield for maize DDGS and the ingredient maize germ meal (MGM). Using optimized parameters, wet-based DDGS-MGM was subjected to solid-state fermentation with 500 mg/kg of cellulase and 200 mg/kg of the X1 enzyme (a laboratory-developed multi-enzyme complex), and this treatment enhanced both DMS and reducing sugar yield, and the resulting fermented product was subsequently applied in further experiments. In the broiler trial, forty 22-day-old Arbor Acres broilers with similar body weights were randomly assigned to five treatment groups, including the control group, (50% DDGS + 50% MGM) unfermented group, (62.5% DDGS + 37.5% MGM) unfermented group, (50% DDGS + 50% MGM) fermented group, and (62.5% DDGS + 37.5% MGM) fermented group, with eight replicates per treatment (one broiler per replicate). Replacement of 30% of the basal diet with fermented 50:50 DDGS-MGM material significantly increased apparent metabolizable energy (AME) and nitrogen-corrected AME by 2.74 MJ/kg and 2.73 MJ/kg, respectively, corresponding to improvements of 39.60% and 40.81% compared to the unfermented control (p < 0.05). Economic analysis indicated that using 5% fermented DDGS-MGM in feed reduced cost by 20.45 RMB per metric ton. These findings demonstrate that bioprocessing can improve the utilization and economic value of maize processing by-products, although further validation under practical conditions is needed. Full article

This review focuses on five native Australian brown algae species—Cystophora torulosa, Durvillaea potatorum, Ecklonia radiata, Hormosira banksii, and Phyllospora comosa—evaluating their environmental adaptability, biochemical composition, bioactive compounds, and potential for commercial development. Species-specific differences in temperature and light tolerance influence their habitat distribution. Nutritional assessments reveal that these algae are rich in proteins, polysaccharides, polyunsaturated fatty acids, and essential trace elements. Bioactive compounds, including polyphenols and fucoidans, exhibit antioxidant, anti-inflammatory, and anti-diabetic properties. D. potatorum extracts have considerable economic value in agriculture by enhancing crop yield, improving nutritional value, and promoting root development. C. torulosa is predominantly found in cooler marine environments and is comparatively more thermally sensitive. In contrast, H. banksii has a higher heat tolerance of up to 40 °C and thrives in warmer environments. E. radiata is widely distributed, highly tolerant of environmental stresses, and exhibits notable disease-resistant activities. P. comosa, due to its high polysaccharide content, demonstrates strong potential for industrial applications. Consumer studies indicate growing acceptance of seaweed-based products in Australia, although knowledge gaps remain. This study highlights the need for continued research, optimized processing methods, and targeted education to support the sustainable development and utilization of Australia’s native brown algae resources. Full article

Laccases, a class of multicopper oxidases found in diverse biological sources, have emerged as key green biocatalysts with significant potential for eco-friendly pollutant degradation. Their ability to drive electron transfer reactions using oxygen, converting pollutants into less harmful products, positions laccases as promising tools for scalable and sustainable treatment of wastewater, soil, and air pollution. This review explores laccase from a translational perspective, tracing its journey from laboratory discovery to real-world applications. Emphasis is placed on recent advances in production optimization, immobilization strategies, and nanotechnology-enabled enhancements that have improved enzyme stability, reusability, and catalytic efficiency under complex field conditions. Applications are critically discussed for both traditional pollutants such as synthetic dyes, phenolics, and pesticides and emerging contaminants, including endocrine-disrupting chemicals, pharmaceuticals, personal care products, microplastic additives, and PFAS. Special attention is given to hybrid systems integrating laccase with advanced oxidation processes, bioelectrochemical systems, and renewable energy-driven reactors to achieve near-complete pollutant mineralization. Challenges such as cost–benefit limitations, limited substrate range without mediators, and regulatory hurdles are evaluated alongside solutions including protein engineering, mediator-free laccase variants, and continuous-flow bioreactors. By consolidating recent mechanistic insights, this study underscores the translational pathways of laccase, highlighting its potential as a cornerstone of next-generation, scalable, and eco-friendly remediation technologies aligned with circular bioeconomy and low-carbon initiatives. Full article

Algal biorefineries constitute an emerging platform for the sustainable production of renewable bioproducts; however, their economic viability remains constrained by the high costs associated with microalgal cultivation and biomass harvesting. This study investigated an integrated strategy combining macronutrient optimization with electrocoagulation–flocculation (ECF) harvesting for Chlorella vulgaris. A Central Composite Design (CCD) was employed to optimize concentrations of NaNO₃, KH₂PO₄, and MgSO₄ with the dual objective of maximizing biomass yield and enhancing biocompound content. Subsequently, the ECF process parameters—current density, electrolysis duration, pH, and electrolyte concentration—were optimized to improve harvesting efficiency. Under the optimal macronutrient conditions (NaNO₃: 100.00 mg/L; KH₂PO₄: 222.12 mg/L; MgSO₄: 100.84 mg/L), the model predicted a maximum biomass concentration of 0.475 g/L, along with 32.79% w/w carbohydrates and 6.79 mg/L chlorophyll-a. Optimal ECF harvesting conditions (current: 0.57 A; pH: 4.00; electrolysis time: 12.70 min; electrolyte: 1.74 g/L) achieved a biomass recovery efficiency of 89.51% w/v. These results demonstrate that coupling nutrient optimization with ECF-based harvesting offers a synergistic, scalable, and cost-effective pathway to improve the sustainability of algal biorefineries. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

In the process of digital transformation, the performance diagnosis of server systems is crucial for ensuring service continuity and enhancing user experience. Addressing the issues of invasiveness, poor universality, and difficulty in precisely locating abnormal bottlenecks in service requests with traditional performance analysis methods, this paper proposes a nonintrusive diagnosis method named Cross-Environment Server Diagnosis with Fusion (CSDF), which is based on the fusion of network traffic and Application Performance Management (APM) metrics. This CSDF method uses a traffic replay tool to reproduce real service requests captured via network cards in a production environment at a 1:1 ratio in a replay environment, comparing performance differences between the two environments to identify abnormal bottlenecks. By integrating Key Performance Indicator (KPI) metrics collected from APM systems, a correlation model between metrics and bottlenecks is established using the Random Forest algorithm within CSDF to pinpoint the root cause at the host resource layer. Simultaneously, it supplements network layer bottleneck analysis by parsing network transmission characteristics of data packets as an important part of CSDF. Experimental results demonstrate that this CSDF method can effectively identify abnormal bottlenecks in specific service requests, verifying its effectiveness in China Tower’s production system—the correlation coefficient between 1 min average load and latency reached 0.87, and the optimization effect was significant. This study provides a general framework for the precise diagnosis and optimization of server systems via CSDF, possessing strong practical value and promising application prospects. Full article

Twin-roll strip casting (TRSC) is a key development in near-net-shape casting technology, offering the potential for high-efficiency and low-cost production. During the TRSC process, the solidification characteristics of the strip are largely governed by the configuration of the melt delivery system as well as by various process parameters. In this study, a three-dimensional model of low-carbon steel strip casting was developed using ProCAST software to investigate microstructure evolution under industrial-scale conditions. Simulation results revealed that the solidified strip exhibits a typical three-layer structure: a surface equiaxed grain zone in contact with the cooling rolls, a subsurface columnar grain zone, and a central equiaxed grain zone. Introducing side holes into the delivery system promoted the formation of a distinct columnar grain region near the side dams, resulting in a reduction in the average grain size in this region from 43.7 μm to 38.2 μm compared to the delivery system without side holes. Increasing the heat transfer coefficient at the interface between the molten pool and the cooling rolls significantly enlarged the columnar grain zone. This change had little effect on the average grain size and grain density, with the average grain size remaining close to 37 μm and the grain density variation being less than 0.7%. In contrast, when the casting speed was raised from 50 m min⁻¹ to 70 m min⁻¹, a reduction in the area of the columnar grain zone was observed, while the average grain size decreased slightly (by less than 0.5 μm), and the grain density increased accordingly. This study provides valuable insights for optimizing process parameters and designing more effective melt delivery systems in industrial twin-roll strip casting. Full article

Nucleic acid aptamers are single-stranded DNA or RNA molecules that can bind to a target with high specificity and affinity, as screened by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX). In recent years, SELEX technologies have been significantly advanced for the screening of aptamers for a variety of target molecules, cells, and even bacteria and viruses. By integrating recent advances of emerging technologies with SELEX, novel screening technologies for nucleic acid aptamers have emerged with improved screening efficiency, reduced production costs and enhanced aptamer performance for a wide range of applications in medical diagnostics, drug delivery, and environmental monitoring. Aptasensors utilize aptamers to detect a wide range of analytes, allowing for the accurate identification and determination of small molecules, proteins, and even whole cells with remarkable specificity and sensitivity. Further optimization of the aptasensor can be achieved by aptamer truncation, which not only maintains the high specificity and affinity of the aptamer binding with the target analytes, but also reduces the manufacturing cost. Predictive models also demonstrate the powerful capability of determination of the minimal functional sequences by simulation of aptamer–target interaction processes, thus effectively shortening the aptamer screening procedure and reducing the production costs. This paper summarizes the research progress of protein-targeted aptamer screening in recent years, introduces several typical aptasensors at present, discusses the optimization methods of aptasensors by combining efficient SELEX with advanced predictive algorithms or post-SELEX processes, as well as the challenges and opportunities faced by aptasensors. Full article

In Mexico, 95% of the population has access to drinking water sources, but only about 65% of domestic waste water is treated to safe levels. This study analyzes forty years of Mexican scientific production on water and waste water treatment through a bibliometric and conceptual approach, evaluating its contribution Sustainable Development Goal (SDG) 6. The analysis identified three major research clusters: (1) biological processes for water treatment, (2) development and optimization of physical–chemical processes, and (3) water quality and management. These themes reflect the evolution of biological approaches for identifying and removing organic contaminants, the application of advanced techniques for improving water quality, and the promotion of sustainable water use. The study also highlights the growing attention to emerging contaminants, nanotechnology, integrated water resource management, and persistent challenges in sanitation. With respect to SDG 6, Mexican research has mainly focused on targets 6.1 (universal and equitable access to drinking water), 6.3 (water quality), and 6.5 (water resources management), while targets 6.2 (sanitation), 6.a (international cooperation), and 6.b (community participation) remain underrepresented compared with the international benchmarks, where the research trend is on water management, resources, and the water–food–energy nexus. Finally, the findings also show synergies with SDGs 11 (sustainable cities and communities), 9 (industry, innovation, and infrastructure), and 3 (good health and well-being), although gaps persist in addressing equitable access to water and society participation. Full article

Handling delicate agricultural products, such as tomatoes, requires careful attention from workers during harvesting, sorting, and packaging processes. This labor-intensive approach is often inefficient and susceptible to human error. A potential solution to improve efficiency is the development of automated systems capable of replacing manual labor. However, such systems face significant challenges due to the irregular shapes and fragility of these products, requiring specialized adaptable and soft gripping mechanisms. In this context, this paper introduces a parametric design methodology for 3D-printed flexible fingers in soft grippers, tailored for agricultural applications. The approach was tested in a case study that targeted soft agricultural products with diameters between 45 and 75 mm. Three finger topologies were modeled and compared to identify an optimal configuration. A prototype was then developed using 3D printing with Z-SemiFlex. Experimental tests confirmed that the prototype could grasp different fruits reliably and without surface damage. It achieved an Average Precision (AP) of 87.5% for tomatoes and 92.5% for mandarins across 80 trials. These results validate the feasibility of the proposed design methodology for fingers in soft grippers. Full article

The search for higher efficiency in agribusiness supports the adoption of digital tools and Lean Production principles in agricultural spraying, a crucial operation for crops. Spraying is essential to ensure yield, quality, cost efficiency, and environmental protection. This study analyzed operational data from self-propelled sprayers in soybean and corn fields, classifying hours, calculating efficiencies, and applying statistical process control. Efficiencies were investigated by combining Lean Production principles with CAN-based digital monitoring, which enabled the identification of non-value-adding activities and supported the real-time management of spraying operations. The results showed that productive time accounted for 41.2% of total recorded hours, corresponding to effective operation and auxiliary tasks directly associated with the execution of spraying activities. A high proportion of unrecorded hours (21.2%) was also observed, reflecting discrepancies between administrative work schedules and machine-logged data. Additionally, coefficients of variation for operational speed and fuel consumption were 12.1% and 24.0%, respectively. Correcting special causes increased work capacity (4.9%) and reduced fuel consumption (0.9%). Economic simulations, based on efficiencies, operating parameters of the sprayer, and cost indicators, indicated that increasing scale reduces costs when installed capacity is carefully managed. Integrating telemetry with Lean Production principles enables real-time resource optimization and waste reduction. Full article

This study outlines a high-yield green method for synthesizing MBT using aniline, carbon disulfide and sulfur as raw materials via a one-step reaction combined with high–low-temperature extraction. The process is supported by experimental results and lab-scale tests, and the operating conditions of the amplification process are evaluated using Aspen Plus simulation software, supplemented with Gaussian09 calculations. The sensitivity analysis results indicate that the MBT yield reaches its maximum value when the feed mass ratio of S:CS₂:C₆H₇N:C₇H₈ is 6:17:20:90. Additionally, setting the reaction temperature to 240 °C and pressure to 10 MPa improves the MBT synthesis yield from 58% to 82.5%. Optimal condensation and extraction conditions are achieved at −30 °C and 1 atm, followed by a separation step at 40 °C. The simulation results provide valuable guidance for the industrial production of MBT. Full article

Cyclic Solvent Injection (CSI) is a method for enhanced heavy oil recovery, offering a reduced environmental impact. CSI processes typically involve fluid flow through both wormholes and the surrounding porous media in reservoirs. Therefore, understanding how foamy oil behavior differs between bulk phases and porous media is crucial for optimizing CSI operations. However, despite CSI’s advantages, limited research has explained why foamy oil, a key mechanism in CSI, displays weaker strength and stability in bulk phases than in porous media. To address this gap, three advanced visual micromodels were employed to monitor bubble behavior from nucleation through collapse under varying porosity with a constant pressure reduction. A sandpack depletion test in a large cylindrical model further validated the non-equilibrium bubble-reaction kinetics observed in the micromodels. Experiments showed that, under equivalent operating conditions, bubble nucleation in porous media required less energy and initiated more rapidly than in a bulk phase. Micromodels with lower porosity demonstrated up to a 2.5-fold increase in foamy oil volume expansion and higher bubble stability. Moreover, oil production in the sandpack declined sharply at pressures below 1800 kPa, indicating the onset of critical gas saturation, and yielded a maximum recovery of 37% of the original oil in place. These findings suggest that maintaining reservoir pressure above critical gas saturation pressure enhances oil recovery performance during CSI operations. Full article

Soft-magnetic materials can benefit significantly from additive manufacturing using Laser Powder Bed Fusion of metals with laser beam, as this technology allows the production of parts with complex geometries. In this study, two iron-based alloys were investigated: Fe-10%Ni (at%) and Fe-10%Si (at%), which are known for their promising soft-magnetic properties. A parameter study was first conducted to optimize the process settings with the goal of maximizing the relative density, which strongly influences magnetic performance. Using AI-based optimization software (xT-Saam by Exponential Technologies Ltd., Riga, Latvia), geometrically simple specimens with a relative density of ≥99.95% were successfully produced. Utilizing the developed parameter sets, toroids were manufactured and heat-treated to improve their magnetic properties. The best obtained ferromagnetic properties were H_C = 1621 A/m (coercivity) and µ_R = 305 (permeability) for Fe-10%Ni, and H_C = 300 A/m and µ_R = 1114 for Fe-10%Si. Full article