Search Results (450)

Driven by the United Nations Sustainable Development Goals (SDGs) and the global “Bamboo as a Substitute for Plastic” initiative, China has become a key bamboo industry player by leveraging abundant resources and an integrated supply chain. To enhance international competitiveness, optimizing product structure and market resilience is essential. Using descriptive statistics, visualization, trade concentration index, and K-means clustering, this study analyzed China’s bamboo trade spatiotemporal patterns and market resilience based on 2015–2024 China customs data. Results revealed major revisions in the Harmonized System (HS) codes for bamboo products in 2017, yet existing classifications remain insufficiently detailed. Imports declined overall, characterized by fragmented primary products mainly sourced from the Taiwan region of China and Vietnam. In contrast, exports grew steadily, led by Bamboo Tableware, with the United States, Japan, and Europe as key markets, and notable expansion into Southeast Asia. In 2024, bamboo products accounted for over 99% of China’s total bamboo trade value, and the export–import gap kept widening. Compared with 2015, export concentration declined: low- and medium-concentration markets increased, highly concentrated ones decreased, and overall resilience improved. Cluster analysis split core destinations into seven groups in 2015 but only five in 2024, signalling broader demand diversity and fewer single-category-dominated markets. The study recommends refining HS codes to reflect new bamboo innovations; consolidating markets in Europe and America while expanding differentiated demand in Southeast Asia; upgrading Bamboo Tableware through technology; and boosting core product competitiveness to support global bamboo trade and the “Bamboo as a Substitute for Plastic” initiative. Full article

In the context of fuzzy logic speed control for switching reluctance motor (SRM) applications, the objective of this work is to propose a unique zone-based simplification technique. Using the procedure that has been outlined, it is made easier to reduce membership functions as well as rule sets in a logical manner. This is accomplished by splitting the error–change-of-error plane into discrete decision zones. This method is separate from heuristic or adaptive reduction strategies since it employs a systematic framework that reduces the number of rules from 49 in the standard design to 9 and 5 without compromising the accuracy of the control. This is accomplished without adversely affecting the performance of the control. The simplified controller that was produced as a consequence of this study decreases the amount of overshoot, enhances the speed at which a dynamic response happens, and makes it simpler to use on digital platforms that are affordable. All of these capabilities were achieved by the controller. Based on simulations and testing carried out in the real world, it has been determined that the zone-based simplified fuzzy controller that was proposed has a superior performance to traditional PID and full-rule fuzzy systems in terms of reaction time, stability, and energy efficiency. Taking all of this into consideration, it is evident that it has the potential to be useful in real-world applications for SRM drives that demand a high level of speed while maintaining a low cost factor. Full article

The increasing demand for sustainable construction materials has prompted the recycling of construction and demolition waste in concrete manufacturing. This study investigates the feasibility of utilizing porcelain and brick waste as partial substitutes for natural sand in concrete with the objective of improving sustainability and preserving mechanical and durability characteristics. The experimental program was conducted in three consecutive phases. During the initial phase, natural sand was partially substituted with porcelain waste powder (PWP) and brick waste powder (BWP) in proportions of 25%, 50%, and 75% of the weight of the fine aggregate. During the second phase, polypropylene fibers were mixed at a dosage of 0.5% by volume fraction to enhance tensile and flexural properties. During the third phase, zinc oxide nanoparticles (ZnO-NPs) were utilized as a partial substitute for cement at concentrations of 0.5% and 1% to improve microstructure and strength progression. Concrete samples were tested at curing durations of 7, 28, and 91 days. The assessed qualities encompassed workability, density, water absorption, porosity, compressive strength, flexural strength, and splitting tensile strength. Microstructural characterization was conducted utilizing X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). The findings indicated that porcelain waste powder markedly surpassed brick waste powder in all mechanical and durability-related characteristics, particularly at 25% and 50% sand replacement ratios. The integration of polypropylene fibers enhanced fracture resistance and ductility. Moreover, the incorporation of zinc oxide nanoparticles improved hydration, optimized the pore structure, and resulted in significant enhancements in compressive and tensile strength throughout prolonged curing durations. The best results were obtained with a mix of 50% porcelain sand aggregate, 1% zinc oxide nanoparticles as cement replacement, and 0.5% polypropylene fibers, for which the improvements in compressive strength, flexural strength, and splitting tensile strength were 39.5%, 46.2%, and 60%, respectively, at 28 days. The results confirm the feasibility of using porcelain and brick waste as sand replacements in concrete, as well as polypropylene fiber-reinforced concrete and polypropylene fiber-reinforced concrete mixed with zinc oxide nanoparticles as a sustainable option for construction purposes. Full article

Early pest detection in edible crops demands sensing solutions that can run at the edge under tight power, budget, and maintenance constraints. This review synthesizes peer-reviewed work (2015–2025) on three modality families—vision/AI, spectroscopy/imaging spectroscopy, and indirect sensors—restricted to edible crops and studies reporting some implementation or testing (n = 178; IEEE Xplore and Scopus). Each article was scored with a modality-aware performance–cost–implementability (PCI) rubric using category-specific weights, and the inter-reviewer reliability was quantified with weighted Cohen’s $\kappa$. We translated the evidence into compact decision maps for common deployment profiles (low-power rapid rollout; high-accuracy cost-flexible; and block-scale scouting). Across the corpus, vision/AI and well-engineered sensor systems more often reached deployment-leaning PCI (≥3.5: 32.0% and 33.3%, respectively) than spectroscopy (18.2%); the median PCI was 3.20 (AI), 3.17 (sensors), and 2.60 (spectroscopy). A Pareto analysis highlighted detector/attention models near $(P,C,I)\approx (4,5,4)$; sensor nodes spanning balanced $(4,4,4)$ and ultra-lean $(2,5,4)$ trade-offs; and the spectroscopy split between the early-warning strength $(5,4,3)$ and portability $(4,3,4)$. The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic $\kappa$ = 0.73–0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: ${\kappa }_{\mathrm{quadratic}}=0.30$–$0.44$), supporting rubric stability with adjacency-dominated disagreements. The decision maps operationalize these findings, helping practitioners select a fit-for-purpose modality and encouraging a minimum PCI metadata set to enable reproducible, deployment-oriented comparisons. Full article

To achieve a scientific and efficient emergency response, a dynamic route-planning strategy for emergency vehicles based on government–enterprise collaboration was studied. Firstly, a hybrid evaluation approach was developed, integrating the Analytic Hierarchy Process, Entropy Weight Method, and Gray Relation Analysis-TOPSIS to quantitatively assess the urgency of demands at disaster sites. Secondly, a government–enterprise coordinated route-planning strategy was designed, leveraging the government’s strong mobilizing capabilities and enterprises’ flexible operational mechanisms. Thirdly, to optimize scheduling efficiency, a dynamic route-planning model was proposed, incorporating multiple distribution conditions to minimize scheduling time, delay penalties, and unmet demand rates. A two-stage cellular genetic algorithm was employed to address realistic constraints such as demand splitting, soft time windows, open scheduling, and differentiated services. Numerical simulations of potential flooding in Hunan Province revealed that the collaborative strategy significantly improved performance: the demand satisfaction rate rose from 70.1% (government-led) to 92.3%, while the material transportation time per unit decreased by 23.6% (from 1.61 to 1.23 min/unit). Vehicle path characteristics varied under different operational behaviors, aligning with theoretical expectations. Even under sudden road disruptions, the model maintained a 98% demand satisfaction rate with only a negligible 0.076% increase in system loss. This research fills the gaps in previous studies by comprehensively addressing multiple factors in emergency vehicle route planning, offering a practical and efficient solution for post-disaster emergency response. Full article

In many different applications such as retail, energy, and tourism, forecasts for a set of related time series must satisfy both linear and non-negativity constraints, as negative values are meaningless in practice. Standard regression-based reconciliation approaches achieve coherence with linear constraints, but may generate negative forecasts, reducing interpretability and usability. This paper develops and evaluates three alternative strategies for non-negative forecast reconciliation. First, reconciliation is formulated as a non-negative least squares problem and solved with the operator splitting quadratic program, allowing flexible inclusion of additional constraints. Second, we propose an iterative non-negative reconciliation with immutable forecasts, offering a practical optimization-based alternative. Third, we investigate a family of set-negative-to-zero heuristics that achieve efficiency and interpretability at minimal computational cost. Using the Australian Tourism Demand dataset, we compare these approaches in terms of forecast accuracy and computation time. The results show that non-negativity constraints consistently improve accuracy compared to base forecasts. Overall, set-negative-to-zero achieve near-optimal performance with negligible computation time, the block principal pivoting algorithm provides a good accuracy–efficiency compromise, and the operator splitting quadratic program offers flexibility for incorporating additional constraints in large-scale applications. Full article

Potato production in the Andean highlands demands strategies that reduce dependence on synthetic inputs without sacrificing yield. We evaluated two microbial bioinputs—Bacillus subtilis and Trichoderma viride—applied once pre-plant to seed tubers, under three organo-mineral fertilization regimes (0%, 50%, and 100% of the recommended NPK rate) in two cultivars (INIA 303-Canchán and Yungay) in field conditions in Ayacucho, Peru, using a randomized complete block, split-plot design (three replicates). Agronomic traits (plant height, root dry weight, stems per plant, tubers per plant, and plot-level yield) were analyzed with robust two-way ANOVA and multivariate methods. Combining microbial inoculation with 50% NPK sustained growth responses comparable to 100% NPK for key traits: in Yungay with T. viride, plant height at 50% NPK (≈96.15 ± 1.71 cm) was not different from 100% NPK (≈98.87 ± 1.70 cm), and root dry weight at 50% NPK (≈28.50 ± 0.28 g) matched or exceeded 100% NPK (≈16.97–22.62 g depending on cultivar–treatment). Notably, T. viride increased root biomass even without mineral fertilizer (≈27.62 ± 0.29 g in Yungay), while B. subtilis enhanced canopy vigor and stem number at full NPK (≈4.5 ± 0.29 stems). Yungay out-yielded INIA 303-Canchán overall (≈57.5 ± 2.5 kg vs. ≈42.7 ± 2.5 kg per plot). The highest yields occurred with B. subtilis + 100% NPK (≈62.88 ± 6.07 kg per plot), followed by B. subtilis + 50% NPK (≈51.7 ± 6.07 kg per plot). Plant height was the strongest correlate of yield (Spearman ρ ≈ 0.60), underscoring its value as a proxy for productivity. Overall, a single pre-plant inoculation with B. subtilis or T. viride can halve mineral fertilizer inputs while maintaining growth and sustaining high, cultivar-dependent yields in highland potato systems. Full article

In autonomous-driving real-time semantic segmentation, simultaneously maximizing accuracy, minimizing model size, and sustaining high inference speed remains challenging. This tripartite demand poses significant constraints on the design of lightweight neural networks, as conventional frameworks often suffer from a trade-off between computational efficiency and feature representation capability, thereby limiting their practical deployment in resource-constrained autonomous driving systems. We introduce ISANet, an information supplementary aggregation framework that markedly elevates segmentation accuracy without sacrificing frame rate. ISANet integrates three key components: (i) the Spatial-Supplementary Lightweight Bottleneck Unit (SLBU) that splits channels and employs compensatory branches to extract highly expressive features with minimal parameters; (ii) the Missing Spatial Information Recovery Branch (MSIRB) that recovers spatial details lost during feature extraction; and (iii) the Object Boundary Feature Attention Module (OBFAM) that fuses multi-stage features and strengthens inter-layer information interaction. Evaluated on Cityscapes and CamVid, ISANet attains 76.7% and 73.8% mIoU, respectively, while delivering 58 FPS and 90 FPS with only 1.37 million parameters. Full article

Methane pyrolysis produces hydrogen (H₂) with solid carbon black as a co-product, eliminating direct CO₂ emissions and enabling a low-carbon supply when combined with renewable or low-carbon heat sources. In this study, we propose a hybrid geothermal pyrolysis configuration in which an enhanced geothermal system (EGS) provides base-load preheating and isothermal holding, while either electrical or solar–thermal input supplies the final temperature rise to the catalytic set-point. The work addresses four main objectives: (i) integrating field-scale geothermal operating envelopes to define heat-integration targets and duty splits; (ii) assessing scalability through high-pressure reactor design, thermal management, and carbon separation strategies that preserve co-product value; (iii) developing a techno-economic analysis (TEA) framework that lists CAPEX and OPEX, incorporates carbon pricing and credits, and evaluates dual-product economics for hydrogen and carbon black; and (iv) reorganizing state-of-the-art advances chronologically, linking molten media demonstrations, catalyst development, and integration studies. The process synthesis shows that allocating geothermal heat to the largest heat-capacity streams (feed, recycle, and melt/salt hold) reduces electric top-up demand and stabilizes reactor operation, thereby mitigating coking, sintering, and broad particle size distributions. High-pressure operation improves the hydrogen yield and equipment compactness, but it also requires corrosion-resistant materials and careful thermal-stress management. The TEA indicates that the levelized cost of hydrogen is primarily influenced by two factors: (a) electric duty and the carbon intensity of power, and (b) the achievable price and specifications of the carbon co-product. Secondary drivers include the methane price, geothermal capacity factor, and overall conversion and selectivity. Overall, geothermal-assisted methane pyrolysis emerges as a practical pathway to turquoise hydrogen, if the carbon quality is maintained and heat integration is optimized. The study offers design principles and reporting guidelines intended to accelerate pilot-scale deployment. Full article

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article

Pulmonary embolism (PE) on CT pulmonary angiography (CTPA) demands rapid, accurate assessment, yet small, low-contrast clots in distal arteries remain challenging. We benchmarked ten fully convolutional network (FCN) backbones and introduced Consensus Intersection-Optimized Fusion (CIOF)—a K-of-M, pixel-wise mask fusion with the voting threshold K* selected on training patients to maximize IoU. Using the FUMPE cohort (35 patients; 12,034 slices) with patient-based random splits (18 train, 17 test), we trained five FCN architectures (each with Adam and SGDM) and evaluated segmentation with IoU, Dice, FNR/FPR, and latency. CIOF achieved the best overall performance (mean IoU 0.569; mean Dice 0.691; FNR 0.262), albeit with a higher runtime (~63.7 s per case) because all ten models are executed and fused; the strongest single backbone was Inception-ResNetV2 + SGDM (IoU 0.530; Dice 0.648). Stratified by embolization ratio, CIOF remained superior across <10⁻⁴, 10⁻⁴–10⁻³, and >10⁻³ clot burdens, with mean IoU/Dice = 0.238/0.328, 0.566/0.698, and 0.739/0.846, respectively—demonstrating gains for tiny, subsegmental emboli. These results position CIOF as an accuracy-oriented, interpretable ensemble for offline or second-reader use, while faster single backbones remain candidates for time-critical triage. Full article

Waxy maize (Zea mays L. ceratina) is extensively cultivated and exhibits substantial market demand in China; however, its yield and quality improvement remain constrained by relatively underdeveloped cultivation techniques. Optimizing plant density and row spacing is critical to improving the yield and nutritional quality of waxy maize, yet their combined effects remain insufficiently explored. A split-plot design evaluated two plant densities, i.e., 5.25 × 10⁴ plants ha⁻¹ (PD_5.25) and 6.75 × 10⁴ plants ha⁻¹ (PD_6.75), and three row configurations, i.e., 80 + 40 cm wide–narrow rows (RS_8-4), 100 + 20 cm wide–narrow rows (RS_10-2) and conventional 60 + 60 cm equal rows (RS_6-6). This study aims to identify the optimal cultivation configuration for waxy maize in the Loess Plateau region. Results showed that the RS_8-4 configuration maximized agronomic traits, dry matter accumulation, and yield relative to RS_6-6 and RS_10-2 treatments. Specifically, RS_8-4 reduced the insertion angle of the lower ear leaf by 12.4% (p < 0.05) and ear height by 8.3% while increasing yield by 19.86–20.00% compared to RS_6-6 and RS_10-2 treatments. At fresh-market maturity, dry matter accumulation under RS_8-4 treatment increased significantly by 34.0% with higher plant density. Under PD_6.75, RS_8-4 boosted dry matter by 29.8% and 39.4% versus RS_6-6 and RS_10-2, respectively. Under the RS_8-4 and PD_6.5 configurations, dry matter accumulation reached 13.56 t ha⁻¹ and a yield of 9.94 t ha⁻¹ was achieved in 2022. In summary, the combination of the PD_6.75 density and the RS_8-4 row spacing configuration achieved the optimal yield for the ‘Jinnuo 20’ cultivar in the Loess Plateau region. This approach provides a scalable planting framework for high-yield waxy maize production in the area, while demonstrating that optimized plant density and row spacing represent not only a key technical measure for enhancing productivity but also a core agronomic strategy for improving resource-use efficiency. Full article

(1) Background: Soccer action recognition (SAR) is essential in modern sports analytics, supporting automated performance evaluation, tactical strategy analysis, and detailed player behavior modeling. Although recent advances in deep learning and computer vision have enhanced SAR capabilities, many existing methods remain limited to coarse-grained classifications, grouping actions into broad categories such as attacking, defending, or goalkeeping. These models often fall short in capturing fine-grained distinctions, contextual nuances, and long-range temporal dependencies. Transformer-based approaches offer potential improvements but are typically constrained by the need for large-scale datasets and high computational demands, limiting their practical applicability. Moreover, current SAR systems frequently encounter difficulties in handling occlusions, background clutter, and variable camera angles, which contribute to misclassifications and reduced accuracy. (2) Methods: To overcome these challenges, we propose TAT-SARNet, a structured framework designed for accurate and fine-grained SAR. The model begins by applying Sparse Dilated Attention (SDA) to emphasize relevant spatial dependencies while mitigating background noise. Refined spatial features are then processed through the Split-Stream Feature Processing Module (SSFPM), which separately extracts appearance-based (RGB) and motion-based (optical flow) features using ResNet and 3D CNNs. These features are temporally refined by the Multi-Granular Temporal Processing (MGTP) module, which integrates ResIncept Patch Consolidation (RIPC) and Progressive Scale Construction Module (PSCM) to capture both short- and long-range temporal patterns. The output is then fused via the Context-Guided Dual Transformer (CGDT), which models spatiotemporal interactions through a Bi-Transformer Connector (BTC) and Channel–Spatial Attention Block (CSAB); (3) Results: Finally, the Cascaded Temporal Classification (CTC) module maps these features to fine-grained action categories, enabling robust recognition even under challenging conditions such as occlusions and rapid movements. (4) Conclusions: This end-to-end architecture ensures high precision in complex real-world soccer scenarios. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article

Due to the global growth of the construction industry, the use of concrete has increased rapidly. Consequently, the depletion of natural aggregates, which are essential components of concrete, has emerged as a critical issue. Simultaneously, the construction of marine structures has recently increased due to population growth and climate change. This trend highlights the growing demand for durable and sustainable construction materials in aggressive environments. To address the depletion of natural aggregates, extensive research has focused on artificial lightweight aggregates produced from industrial waste. However, the high porosity and low compressive strength of artificial lightweight aggregates have limited their effectiveness in ensuring the performance of sustainable marine structures. In this study, a high-performance mortar (HPM) incorporating artificial lightweight fine aggregates (ALWFAs) was developed to address the depletion of natural aggregates and to serve as a protective layer material in marine environments. To enhance the physical properties of ALWFAs, the aggregates were coated with epoxy-TiO₂ coatings applied to both their internal voids and external surfaces. The effectiveness of this enhancement was assessed by comparing the performance of mortars prepared with uncoated and coated ALWFAs. The HPM was evaluated for its porosity, compressive strength, split tensile strength, and chloride diffusion coefficient. The results showed that increases in the ALWFA replacement ratio led to a general reduction in performance. However, a comparison between uncoated and coated ALWFAs revealed that the coated aggregates led to improvements of up to 4.13%, 49.3%, 28.6%, and 52.0% in porosity, compressive strength, split tensile strength, and chloride diffusion coefficient, respectively. The study results are discussed in detail in the paper. Full article