Search Results (2,771)

This study aims to develop eco-efficient ceramic tiles through the valorization of recycled glass (GW; soda–lime glass cullet) as a partial raw material substituent, enabling a reduction in sintering temperature and, consequently, a decrease in thermal energy demand, carbon-equivalent emissions, and the depletion of virgin mineral resources. Ceramic tiles were elaborated by partially substituting natural bentonite with 30–50 wt.% GW and fired at 900 °C and 950 °C. Use of GW promoted liquid-phase sintering, driving significant densification evidenced by a marked reduction in open porosity and water absorption. SEM images confirm a denser, more homogeneous structure with reduced porosity, leading to improved mechanical strength and chemical durability. Compositions containing 30–35 wt.% bentonite exhibit the most optimized microstructure, characterized by well-dispersed crystalline phases embedded within a dense vitreous matrix. These findings demonstrate that high-performance ceramic tiles meeting standard classification thresholds can be manufactured at sub-1000 °C firing temperatures through judicious incorporation of recycled glass waste. This approach offers a viable pathway toward reduced energy consumption, diminished reliance on primary mineral resources, and enhanced circularity within the construction ceramics industry. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Truck–drone collaborative delivery (TDCD) refers to a coordinated logistics paradigm in which drones are deployed from delivery trucks to serve nearby customers, enabling parallelized last-mile operations. Much of the existing TDCD literature relies on synthetic datasets and manufacturer-declared drone specifications, which may overestimate performance in real-world operations. This study develops an empirically informed, route-based Mixed-Integer Linear Programming (MILP) framework that integrates empirically derived drone performance models with constrained fleet allocation decisions. Using delivery routes from the Amazon Last Mile Routing Dataset (2021), we consider three electric trucks departing from a common depot, each equipped with drones drawn from a shared fleet of 10 units. Drone flight time and energy consumption are modeled using regression functions calibrated with real flight test data from a DJI Matrice 100 platform, capturing observed variations due to payload and operational conditions. The optimization jointly determines truck stop selection, customer assignments, and drone allocation while minimizing a weighted combination of route makespan, total energy consumption, and fleet size under operational and energy constraints. The results indicate that coordinated truck–drone delivery can achieve substantial reductions in both delivery completion time and energy consumption relative to conventional truck-only delivery. These findings demonstrate the effectiveness of coordinated truck–drone operations under realistic constraints and highlight the importance of data-driven modeling and fleet-level resource allocation in improving last-mile delivery performance. Full article

Harvesting remains one of the main bottlenecks in microalgae-based technologies. Although microalgae hold great promise for industrial biotechnology, their growth in dilute suspensions makes biomass recovery challenging. Conventional harvesting methods are often energy-intensive and costly, limiting large-scale implementation. This study applies a life cycle assessment (LCA) to evaluate the environmental performance of a laboratory-scale magnetic harvesting process of Chlorella vulgaris (C. vulgaris) using Fe₃O₄ microparticles in combination with polyaluminum chloride (PAC) and polyacrylamide (PAM), followed by magnetic oscillation for particle detachment and subsequent reuse. Electricity consumption was identified as the dominant environmental hotspot across most impact categories, with the detachment step accounting for nearly two-thirds of the total energy demand, a step often overlooked in previous LCA studies. The global warming potential (GWP) is consistent with typical laboratory-scale assessments and is mainly driven by energy inefficiencies associated with small processing volumes. The values obtained and the scale-up literature indicate that further optimization and future industrial-scale production will decrease these values into a realistic and competitive range. Sensitivity analysis showed that replacing grid electricity with photovoltaic power significantly reduces environmental impacts. The use of NaOH as a reagent also contributed substantially to environmental impacts. Reusing magnetic particles (4 cycles) reduced material resource depletion by up to fourfold, which is a very relevant result bearing in mind the principles of sustainability and circularity. Full article

Background: Vertebrogenic low back pain (LBP) is a distinct subtype of chronic LBP (cLBP) arising from nociceptive sensitization of the basivertebral nerve (BVN) within pathologically altered vertebral endplates. Modic type 1 and type 2 changes on MRI are primary imaging biomarkers for patient selection. Basivertebral nerve ablation (BVNA), a minimally invasive intraosseous radiofrequency procedure, has emerged as a targeted treatment for this condition. This narrative review aims to synthesize current evidence on the pathophysiology of vertebrogenic LBP, patient selection criteria, procedural outcomes, safety profile, and cost-effectiveness of BVNA. Methods: We conducted this narrative review of the literature, encompassing randomized controlled trials (including the SMART and INTRACEPT studies), prospective registries, and real-world cohort studies evaluating BVNA for vertebrogenic LBP. Clinical and imaging-based selection criteria, procedural techniques, outcome measures, adverse events, opioid utilization, and healthcare utilization data were examined. Results: Evidence demonstrates consistent and durable reductions in pain and disability following BVNA, with a favorable safety profile. Complication rates are low, with vertebral compression fracture and procedure-related radicular pain reported as the most frequent adverse events. BVNA is associated with reduced opioid consumption and decreased overall healthcare utilization. Moreover, emerging data suggest efficacy beyond originally defined inclusion criteria, including cases of osteoporosis, multilevel Modic changes, adult spinal deformity, and complex comorbid presentations. Conclusions: BVNA represents an effective and safe treatment option within the multimodal management of vertebrogenic LBP. Current evidence supports a gradual expansion of procedural indications, with implications for healthcare resource optimization and opioid stewardship. 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

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

In the field of stereo depth sensing, modern research predominantly prioritizes accuracy, yet inference speed remains a critical bottleneck for practical, real-time applications on resource-constrained platforms. Existing acceleration approaches often rely on lighter network architectures or runtime-specific optimizations, which may require architectural redesign, platform-specific tuning, or accuracy trade-offs. However, a common inefficiency remains in many stereo pipelines: feature extraction is typically performed using two separate forward passes, one for the left image and one for the right, even though both passes use the same network weights. We address this redundancy by concatenating the left and right images into a single combined tensor, enabling feature extraction in one batched pass while preserving the original network architecture. By reducing feature extraction time by up to 48.4%, our results demonstrate that this method accelerates the overall inference rate by 10% to 39% on average on Nvidia V100 and up to 28.4% on edge device, depending on the model architecture. This speedup is achieved at the expense of only a moderate increase in runtime memory consumption, while retaining the original accuracy. Because the method does not alter the core stereo network, it can be applied as a plug-and-play enhancement to both existing and newly developed stereo matching models. Full article

Stereo vision is essential for passive three-dimensional perception in resource-constrained applications that require low power consumption, predictable latency, and explainable geometry. Although deep learning architectures dominate recent benchmarks, the classical block-matching pipeline remains a foundational approach. Optimizing this pipeline involves navigating complex trade-offs among matching robustness, map density, and computational efficiency. This study systematically surveys and physically validates the classical stereo framework. After revisiting geometric first principles, three matching costs (SAD, NCC, ZNCC) are benchmarked alongside Sobel preprocessing and structural refinements, with subsequent validation using a calibrated consumer webcam rig. Middlebury benchmarks (2001–2021) indicate that while SAD fails under complex radiometric distortion, NCC consistently achieves superior quantitative metrics, incurring only a 1.2-fold computational overhead. Extending the disparity search range improves foreground localization, while block size imposes a trade-off between resolving the aperture problem and preserving fine geometric detail. To bridge theoretical analysis and practical deployment, the pipeline is validated using a custom-calibrated consumer stereo rig. The optimized Sobel-NCC architecture is then evaluated for real-time edge deployment on constrained hardware (NVIDIA Jetson Nano) and narrow-baseline sensors (OAK-D SR) in the context of agricultural robotic manipulation. By prioritizing metric precision over dense prediction, the classical pipeline reconstructs target surfaces with approximately 1 cm depth accuracy at 21 frames per second. These results demonstrate that optimized local algorithms offer deterministic and reliable geometric foundations for real-time edge-computed robotics. Although neural networks are essential for dense reconstructions in ill-posed regions, the foundational principles established here remain indispensable for advanced stereo vision system deployment. Full article

This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and $7.365\times {10}^{-4}$, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

In present recommendation system research, most approaches rely on analyzing the consumption behavior of large numbers of users to generate recommendations. However, this strategy requires extensive computational resources and often leads to considerable delays in producing recommendation results, which negatively affect the user experience. To overcome these limitations, we developed an innovative segmented data-based recommendation method for user region optimization, offering an effective alternative to traditional big-data recommendation strategies. The developed method divides the data into multiple smaller segments according to user regions and then performs specialized analysis within each segment. This segmentation substantially reduces computational time while simultaneously improving the relevance and accuracy of recommendations. By lowering computational complexity, the system is able to respond more rapidly to user requests, making more efficient use of computational resources without compromising recommendation quality. Through this segmented computation approach, the system shows faster response speeds and maintains high recommendation performance. Ultimately, the method provides new insights into optimizing recommendation systems and highlights promising directions for future improvements. Full article

Green prepared dishes are an emerging food-consumption format that links convenience, food safety, and sustainable consumption. In this study, “green” denotes a sustainability-oriented product profile involving food-safety assurance, resource-conscious packaging or sourcing, and waste-reduction potential, rather than formal organic certification. However, existing studies have mainly relied on linear behavioral models and have paid limited attention to nonlinear and asymmetric consumer decision mechanisms. This study integrates the stimulus–organism–response framework with explainable machine learning to predict consumers’ sustainable purchase intention toward green prepared dishes. Based on 805 valid questionnaires collected in Jilin Province, China, predictors were organized into three dimensions: environmental and health cognition, socioeconomic and infrastructural conditions, and sustainable behavioral propensity. The sample represents a regional online consumer profile in Jilin Province rather than a national probability sample. Six classifiers were trained using SMOTE–Tomek resampling and Optuna-based hyperparameter optimization. XGBoost achieved the best predictive performance, with an F1-score of 0.894, an AUC of 0.934, and an MCC of 0.702. Unlike conventional black-box machine learning, the SHAP-based interpretation translated ensemble predictions into transparent feature-level and case-level explanations. Accordingly, the model interpretations are framed as predictive associations rather than causal mechanisms. The study reveals an asymmetric decision pattern in which core behavioral willingness functions as a non-compensatory barrier, while channel convenience, delivery efficiency, and after-sales support facilitate purchase intention among consumers who already show high behavioral readiness. The findings suggest that green prepared-dish strategies should prioritize trust-based advocacy and word-of-mouth, reliable channel design, low-risk trial experiences, and collaborative food-safety governance rather than relying only on short-term traffic acquisition. Full article

Year-round production of high-quality green forage using resource-efficient technologies is an important challenge for sustainable livestock farming. This study developed and experimentally evaluated an automated multi-tier hydroponic system integrating a sapropel-based bioactive medium, recirculating irrigation, and energy-efficient LED lighting. Experimental trials were conducted using feed barley (Hordeum vulgare L.) during a 10-day cultivation cycle. Resource consumption was assessed under light in-tensities of 200, 300, and 400 μmol m⁻² s⁻¹, while biomass productivity was evaluated using sapropel extract concentrations of 1.0%, 2.0%, and 2.5%. The highest biomass productivity was obtained at a 2.5% concentration, where fresh green mass reached 44.8 kg per tray (25.45 kg m⁻²), representing a 1.6-fold increase compared with the control treatment, which consisted of identical hydroponic cultivation conditions without sapropel extract addition. The recirculating irrigation system reduced specific water consumption, while optimized LED lighting improved energy-use efficiency. The results demonstrate that integration of natural bioactive supplementation with automated environmental control can significantly enhance hydroponic forage productivity while reducing specific resource inputs. Full article

In flexible job shop scheduling, industrial robots undertake both workpiece transportation and loading/unloading operations. Equipping each machine with dedicated buffers tends to increase transportation workload and further intensify transport bottlenecks. Shared buffers are therefore introduced to temporarily store workpieces and relieve congestion in the production process. This paper establishes a transport-integrated flexible job shop scheduling model with shared buffer constraints, which minimizes makespan, total energy consumption, and machine load range simultaneously. Correspondingly, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is developed to achieve better solution performance. A time-window-based path-planning decoding scheme is constructed to address buffer constraints and transportation conflicts in the coordinated production and transportation process. In parallel, four initialization rules are designed to improve the quality and diversity of the initial population, and a variable neighborhood search algorithm (VNS) is embedded to enhance the local exploitation ability of the proposed algorithm. The performance of the presented method is evaluated through two groups of numerical experiments. The first group is carried out on extended benchmark instances. Comparisons with the conventional Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization algorithms (MOPSO) validate the efficacy of the proposed strategies and demonstrate the superiority of ENSGA-II in both solution quality and computational efficiency. Experimental results on real-world cases further illustrate that the proposed method can effectively solve the integrated scheduling problem in flexible manufacturing systems where industrial robots are employed as the main transport resources. Full article