Search Results (332)

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

In the era of climate change, the strategic importance of forestry products for sustainable development is increasingly recognized. Amid a global resurgence of industrial policy aimed at addressing environmental challenges, this study investigates the impact of China’s central and provincial green industrial policies on the export competitiveness of wood-processing enterprises. Utilizing firm-level data from the China Industrial Enterprise Database and China Customs Export Database (2000–2013), we apply a double machine learning (DML) approach and construct a heterogeneous competitiveness model to evaluate policy effects along two dimensions: export quantity (volume and intensity) and export quality (product complexity and consumer-perceived quality). Our findings reveal a clear dichotomy in policy outcomes. While industrial policies have significantly improved export product complexity—reflecting China’s comparative advantage in labor-intensive production—they have had limited or even negative effects on export volume, intensity, and product quality. This suggests that current policy frameworks disproportionately reward horizontal innovation (product diversification) while neglecting vertical upgrading (quality enhancement), thereby hindering comprehensive export performance gains. Those results highlight the need for more balanced and targeted policy design. By aligning industrial policy instruments with both complexity and quality objectives, policymakers can better support the sustainable transformation of China’s forestry sector and enhance its competitiveness in global value chains. Full article

[Objective] Digital infrastructure, with its fundamental and public good characteristics, can have a significant impact on export trade. This paper aims to analyze the impact and mechanism of digital infrastructure construction on the added value of agricultural exports by combining theory and empirical analysis. [Methodology] Based on the construction of the theoretical framework and the panel data of 61 economies from 2007 to 2021, the fixed effect model was used to explore the impact of the level of digital infrastructure on the added value of agricultural trade exports and the moderating effect of participation in the global agricultural value chain. [Results] (1) The construction of digital infrastructure is conducive to increasing the added value of agricultural exports. Specifically, a 1% increase in the level of digital infrastructure will promote a 0.159% increase in the added value of agricultural exports. (2) The construction of digital infrastructure affects the added value of agricultural exports through three mechanisms: enhancing labor productivity, optimizing the business environment, and promoting technological innovation. (3) Digital infrastructure has a more significant effect on enhancing the added value of agricultural exports in developed economies and those with higher levels of digital infrastructure. (4) Participation in the global value chain of agriculture has a moderating effect on the impact of digital infrastructure on the added value of agricultural exports. Full article

Developing agricultural new productive forces (ANPF) according to local conditions is a key strategy for agricultural modernization. Using panel data from 16 prefecture-level cities in Anhui Province from 2010 to 2022, this study constructed indicator systems for ANPF and the construction of a strong agricultural province (CSAP). The entropy-weight TOPSIS method was used to calculate the levels of ANPF and the SAP index. This study employed a modified gravity model and social network analysis (SNA) to investigate the spatial correlation and evolutionary characteristics of these networks. Geographical detectors were also used to identify the driving factors behind agricultural transformation. The findings indicate that both ANPF and CSAP showed an upward trend during the study period, with significant regional heterogeneity, with Central Anhui being the most prominent. This study revealed spatial spillover effects and strong network correlations between ANPF and CSAP, with the spatial network structure exhibiting characteristics of multi-core, multi-association, and multidimensional connections. The entities within the network are tightly connected, with no “isolated island” phenomenon, and Hefei, as the central hub, showed the highest number of connections. Laborer quality, tangible means of production, and new-quality industries emerged as the core driving forces, working in synergy to propel CSAP. This study contributes new insights into the spatial network dynamics of agricultural development and offers actionable recommendations for policymakers to enhance agricultural modernization globally. Full article

Accurate estimation aboveground biomass (AGB) in winter wheat is crucial for yield assessment but remains challenging to achieve non-destructively. Unmanned aerial vehicle (UAV)-based remote sensing offers a promising solution at the plot level. Traditional field sampling methods, such as random plant selection or full-quadrat harvesting, are labor intensive and may introduce substantial errors compared to the canopy-level estimates obtained from UAV imagery. This study proposes a novel method using Fractional Vegetation Coverage (FVC) to adjust field-sampled AGB to per-plant biomass, enhancing the accuracy of AGB estimation using UAV imagery. Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. The aim was to evaluate the performance of multimodal data in estimating winter wheat leaves, spikes, stems, and total AGB. Results demonstrated that (1) FVC-adjusted per-plant biomass significantly improved correlations with most indicators, particularly during the filling stage, when the correlation between leaf biomass and NDVI increased by 56.1%; (2) RF and NN models outperformed SVM, with the optimal accuracies being R² = 0.709, RMSE = 0.114 g for RF, R² = 0.66, RMSE = 0.08 g for NN, and R² = 0.557, RMSE = 0.117 g for SVM. Notably, the RF model achieved the highest prediction accuracy for leaf biomass during the flowering stage (R² = 0.709, RMSE = 0.114); (3) among different water treatments, the R² values of water and drought treatments were higher 0.723 and 0.742, respectively, indicating strong adaptability. This study provides an economically effective method for monitoring winter wheat growth in the field, contributing to improved agricultural productivity and fertilization management. Full article

Packaging equipment plays a crucial role in the semiconductor industry by enhancing product quality and reducing labor costs through automation. Research was conducted on IGBT module packaging equipment (an automatic pin insertion machine) during the pin assembly process of insulated gate bipolar transistor (IGBT) modules to improve productivity and product quality. First, the manual pin assembly process was divided into four stages: feeding, stabilizing, clamping, and inserting. Each stage was completed by separate cams, and corresponding step timing diagrams are drawn. The profiles of the four cams were designed and verified through theoretical calculations and kinematic simulations using a seventh-degree polynomial curve fitting method. Then, image algorithms were developed to detect pin tilt defects, pin tip defects, and to provide visual guidance for pin insertion. Finally, a pin insertion machine and its human–machine interaction interface were constructed. On-machine results show that the pin cutting pass rate reached 97%, the average insertion time for one pin was 2.84 s, the pass rate for pin insertion reached 99.75%, and the pin image guidance accuracy was 0.02 mm. Therefore, the designed pin assembly machine can reliably and consistently perform the pin insertion task, providing theoretical and experimental insights for the automated production of IGBT modules. Full article

This study aims to identify and quantify the critical factors influencing construction labor productivity. A systematic review and meta-analysis of 27 empirical studies published between 2000 and 2024 were conducted in accordance with PRISMA guidelines. This study synthesizes findings from a variety of global studies and calculates the relative importance index of various factors affecting construction labor productivity. The findings indicate that 66 CFs, categorized into 12 groups, influence construction labor productivity. The results findings underscore the pivotal role of labor-related factors, particularly “worker experience and skills”, and site management factors, such as “competent supervisors” and “effective communication”. Additionally, environmental factors, such as “weather conditions”, have been demonstrated to play a significant role. The meta-analysis identified substantial regional variations and an increasing importance of factors like worker motivation and technological advancements. Moreover, in light of the evident disparities among regional influential factors, including but not limited to climate, economics, and culture, the findings of this study underscore the imperative for customized, localized management methodologies to enhance construction labor productivity, which will provide practical suggestions for project managers in the region and globally. Full article

The construction sector is confronted with significant challenges, such as reduced productivity, high injury rates, and labor deficits, driving research into autonomous robotics as a viable solution. This study delivers a comprehensive review of recent advancements in AI-driven autonomous construction robotics, organized within the sense–think–act (STA) framework. A rigorous bibliometric analysis of 319 selected publications from 2015 to 2024 highlights key research trends and notable contributors. A systematic content analysis elaborates on advancements in each STA component, including technologies for perception and environmental understanding, decision-making algorithms for reasoning and planning, and varied actuation methods addressing scale and collaborative robotics. The study also explores challenges such as environmental unpredictability, specialized task demands, and structural safety concerns. Finally, it underscores future research priorities, focusing on balanced robotic system design, dataset standardization, domain-specific knowledge incorporation, and enhanced robustness to support the broader implementation of autonomous construction robotics. Full article

Amidst the dual challenges of escalating ecological environmental pressures and economic transformation globally, green innovation emerges as a pivotal pathway toward achieving high-quality sustainable development. To elucidate how digitalization and novel productive forces synergistically drive the green transition, the research utilizes panel data from 30 provincial-level administrative regions in China spanning 2009 to 2022, constructing a green innovation efficiency measurement frame-work grounded in the Super Slack-Based Measure (Super-SBM)model, alongside a novel productive forces evaluation system based on the triad of laborers, labor objects, and means of production. Employing spatial difference-in-differences and double machine learning methodologies within a quasi-natural experimental design, the research investigates the causal mechanisms through which digital empowerment and novel productive forces influence regional green innovation efficiency. The findings reveal that both digital empowerment and novel productive forces significantly enhance regional green innovation efficiency, exhibiting pronounced positive spatial spillover effects on neighboring regions. Heterogeneity analyses demonstrate that the promotive impacts are more pronounced in eastern provinces compared to central and western counterparts, in provinces participating in carbon trading relative to those that do not, and in innovation-driven provinces versus non-innovative ones. Mediation analysis indicates that digital empowerment operates by fostering the aggregation of innovative talent and elevating governmental ecological attentiveness, whereas new-type productivity exerts its influence primarily through intellectual property protection and the clustering of high-technology industries. The results offer empirical foundations for policymakers to devise coordinated regional green development strategies, refine digital transformation policies, and promote industrial structural optimization. Furthermore, this research provides valuable data-driven insights and theoretical guidance for local governments and enterprises in cultivating green innovation and new-type productivity. Full article

The forestry sector is increasingly dealing with a significant lack of labor and faces the difficult task of securing a professional, stable and sustainable manpower. In this study, different strategic directions for strengthening forestry workforce sustainability are presented and evaluated. The considered strategic directions were developed with respect to forestry employees’ views on necessary measures for making the forestry occupation more appealing. Those measures were observed in three categories: (I) stronger recruiting, (II) stronger retention and (III) higher work commitment. The findings of the survey and other performed analyses resulted in the creation of four different strategic directions: (1) the direct financial strategy, implying increased direct monetary compensation as the main instrument and putting focus on labor productivity; (2) the indirect financial strategy, stressing worker wellbeing through indirect material benefits and aiming at performance quality; (3) the educational strategy, focusing on worker training and education and (4) the technical–technological strategy, aiming at the increased utilization of modern machinery and advanced technologies in forest operations. The results of the study include a comparison of the defined strategies by SWOT analysis and the construction of An analytic Hierarchy Process (AHP) model as the multi-criteria tool for strategy evaluation. Considering the possibility and conditions of its implementation in the national forestry sector, the technical–technological strategy has been evaluated as best option to pursue. The objective of the study is to contribute to enhancing the sustainability of forestry workforce by defining critical issues and pointing to specific cornerstones that can assist in formulating effective future policies and strategies in the forestry sector. Full article

The driving mechanism of new qualitative productivity forces for coordinated development, which constitutes an inherent requirement of high-quality development, requires creative factor allocation through spatial flows, and the same is true for new maritime qualitative productivity forces. In this study, we constructed an evaluation indicator system to assess the impact of spatial flows of marine new qualitative productivity force factors on economic coordinated development in China’s coastal regions. Using panel data from 11 coastal provinces (2003–2022), we quantified new qualitative productivity force factor spatial flows and marine economic coordinated development levels, visualized their spatial–temporal patterns, and empirically examined their interaction mechanisms. The key findings include the following: (1) From 2013 to 2022, marine new qualitative productivity force factor spatial flows in coastal China transitioned from clustered “block-style” to scattered “multi-point” distribution patterns, with marine economic coordination exhibiting steady growth alongside pronounced spatial polarization. (2) Marine new qualitative productivity force factor spatial flows demonstrate significant positive direct effects on local marine economic coordination. (3) The notable spatial spillover effects of marine new qualitative productivity force factor spatial flows enhance coordinated development in neighboring regions. (4) Heterogeneous impacts emerge across marine new qualitative productivity force factor dimensions, where the spatial flows of new-type marine objects of labor and means of labor exert particularly significant influences. These findings provide policy insights for optimizing the spatial allocation of marine new qualitative productivity force factors to advance China’s marine economic coordination. Full article

Rice is one of the three primary staple crops worldwide. The accurate monitoring of its key growth stages is crucial for agricultural management, disaster early warning, and ensuring food security. The effective collection of ground reference data is a critical step for monitoring rice growth stages using satellite imagery, traditionally achieved through labor-intensive field surveys. Here, we propose utilizing UAVs as an alternative means to collect spatially continuous ground reference data across larger areas, thereby enhancing the efficiency and scalability of training and validation processes for rice growth stage mapping products. The UAV data collection involved the Nanchuan, Yongchuan, Tongnan, and Kaizhou districts of Chongqing City, encompassing a total area of 377.5 hectares. After visual interpretation, centimeter-level high-resolution labels of the key rice growth stages were constructed. These labels were then mapped to Sentinel-2 imagery through spatiotemporal matching and scale conversion, resulting in a reference dataset of Sentinel 2 data that covered growth stages such as jointing and heading. Furthermore, we employed 30 vegetation index calculation methods to explore 48,600 spectral band combinations derived from 10 Sentinel-2 spectral bands, thereby constructing a series of novel vegetation indices. Based on the maximum relevance minimum redundancy (mRMR) algorithm, we identified an optimal subset of features that were both highly correlated with rice growth stages and mutually complementary. The results demonstrate that multi-feature modeling significantly enhanced classification performance. The optimal model, incorporating 300 features, achieved an F1 score of 0.864, representing a 2.5% improvement over models based on original spectral bands and a 38.8% improvement over models using a single feature. Notably, a model utilizing only 12 features maintained a high classification accuracy (F1 = 0.855) while substantially reducing computational costs. Compared with existing methods, this study constructed a large-scale ground-truth reference dataset for satellite imagery based on UAV observations, demonstrating its potential as an effective technical framework and providing an effective technical framework for the large-scale mapping of rice growth stages using satellite data. Full article

The evaluation of imagery style in industrial product design is inherently subjective, making it difficult for designers to accurately capture user preferences. This ambiguity often results in suboptimal market positioning and design decisions. Existing methods, primarily limited to single product categories, rely on labor-intensive user surveys and computationally expensive data processing techniques, thus failing to support cross-category collaboration. To address this, we propose an Imagery Style Evaluation (ISE) method that enables rapid, objective, and intelligent assessment of imagery styles across diverse industrial product forms, assisting designers in better capturing user preferences. By combining Kansei Engineering (KE) theory with four key visual morphological features—contour lines, edge transition angles, visual directions and visual curvature—we define six representative style paradigms: Naturalness, Technology, Toughness, Steadiness, Softness, and Dynamism (NTTSSD), enabling quantification of the mapping between product features and user preferences. A deep learning-based ISE architecture was constructed by integrating the NTTSSD paradigms into an enhanced YOLOv5 network with a Convolutional Block Attention Module (CBAM) and semantic feature fusion, enabling effective learning of morphological style features. Experimental results show the method improves mean average precision (mAP) by 1.4% over state-of-the-art baselines across 20 product categories. Validation on 40 product types confirms strong cross-category generalization with a root mean square error (RMSE) of 0.26. Visualization through feature maps and Gradient-weighted Class Activation Mapping (Grad-CAM) further verifies the accuracy and interpretability of the ISE model. This research provides a robust framework for cross-category industrial product style evaluation, enhancing design efficiency and shortening development cycles. Full article