Search Results (1,616)

This paper examines the effect of digital innovation on cost stickiness in manufacturing firms, focusing on the underlying mechanisms and contextual factors. Using data from Chinese A-share listed manufacturing firms from 2012 to 2023, we find that, first, for each one-unit increase in the level of digital technology, the cost stickiness index of enterprises decreases by an average of 0.4315 units, primarily through digital process innovation and digital business model innovation, whereas digital product innovation does not exhibit a statistically significant impact. Second, manufacturing servitization and the optimization of human capital structure are identified as key mediating mechanisms. Digital innovation promotes servitization by transitioning firms from product-centric to service-oriented business models, thereby reducing fixed costs and improving resource flexibility. It also optimizes human capital by increasing the proportion of high-skilled employees and reducing labor adjustment costs. Third, the effect of digital innovation on cost stickiness is found to be heterogeneous. Firms with high financing constraints benefit more from the cost-reducing effects of digital innovation due to improved resource allocation efficiency. Additionally, mid-tenure executives are more effective in leveraging digital innovation to mitigate cost stickiness, as they balance short-term performance pressures with long-term strategic investments. These findings contribute to the understanding of how digital transformation reshapes cost behavior in manufacturing and provide insights for policymakers and firms seeking to achieve sustainable development through digital innovation. Full article

Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a focus on the operational setting of the train crew depot in Česká Třebová, a city in the Czech Republic. The seven-step methodology includes identifying available train shifts, defining scheduling constraints, creating roster variants, and calculating personnel and time requirements for each option. The proposed roster reduced staffing needs by two employees, increased the average shift duration to 9 h and 42 min, and decreased non-productive time by 384 h annually. These improvements enhance sustainability by optimizing human resource use, lowering unnecessary energy consumption, and improving employees’ work–life balance. The model also provides a quantitative assessment of operational feasibility and economic efficiency. Compared to existing rosters, the proposed model offers clear advantages and remains applicable even in settings with limited technological support. The findings show that a well-designed rostering system can contribute not only to cost savings and personnel stabilization, but also to broader objectives in sustainable public transport, supporting resilient and resource-efficient rail operations. Full article

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

Extensive beef systems in the tropics are the cheapest but require more land and longer rearing times with environmental impact. This study was carried out to evaluate three beef bull’s production systems in tropics: pasture-based system (PASTU), feedlot system immediately after weaning (FELOT) and a system with the combination of rearing in pasture and finishing in feedlot (PRIME) on animal performance and carcass characteristics of 30 bulls crossbred Angus x Nellore. The final weight, average daily gain and carcass weight (hot and cold) were higher (p < 0.050) for the FELOT system, intermediate for the PRIME system and lowest for the PASTU system. The carcass dressing (hot and cold), dripping losses, ratio (Longissimus dorsi) and degree of finishing were similar (p > 0.050). The carcass pH_24h was higher for the PRIME system (p < 0.010). Subcutaneous fat thickness (mm) was lower for the PASTU system (p < 0.050). Marbling was better for the PRIME system. The tissular composition was similar among systems related to muscle percentage but PASTU showed the highest bone percentage (p < 0.050) and lowest of adipose (p < 0.050). PRIME enable cost-effective, fast beef production with less environmental impact. Full article

The aim of this study was to improve the efficiency of in situ uranium leaching by developing a specialized methodology for selecting rational parameters for the chemical treatment of production wells. This approach was designed to enhance the filtration properties of ores and extend the uninterrupted operation period of wells, considering the clay content of the productive horizon, the geological characteristics of the ore-bearing layer, and the composition of precipitation-forming materials. The mineralogical characteristics of ore and precipitate samples formed during the in situ leaching of uranium under various mining and geological conditions at a uranium deposit in the Syrdarya depression were identified using an X-ray diffraction analysis. It was established that ores of the Santonian stage are relatively homogeneous and consist mainly of quartz. During well operation, the precipitates formed are predominantly gypsum, which has little impact on the filtration properties of the ore. Ores of the Maastrichtian stage are less homogeneous and mainly composed of quartz and smectite, with minor amounts of potassium feldspar and kaolinite. The leaching of these ores results in the formation of gypsum with quartz impurities, which gradually reduces the filtration properties of the ore. Ores of the Campanian stage are heterogeneous, consisting mainly of quartz with varying proportions of clay minerals and gypsum. The leaching of these ores generates a variety of precipitates that significantly reduce the filtration properties of the productive horizon. Effective compositions and concentrations of decolmatant (clog removal) solutions were selected under laboratory conditions using a specially developed methodology and a TESCAN MIRA scanning electron microscope. Based on a scanning electron microscope analysis of the samples, the effectiveness of a decolmatizing solution based on hydrochloric and hydrofluoric acids (taking into account the concentration of the acids in the solution) was established for the destruction of precipitate formation during the in situ leaching of uranium. Geological blocks were ranked by their clay content to select rational parameters of decolmatant solutions for the efficient enhancement of ore filtration properties and the prevention of precipitation formation. Pilot-scale testing of the selected decolmatant parameters under various mining and geological conditions allowed the optimal chemical treatment parameters to be determined based on the clay content and the composition of precipitates in the productive horizon. An analysis of pilot well trials using the new approach showed an increase in the uninterrupted operational period of wells by 30%–40% under average mineral acid concentrations and by 25%–45% under maximum concentrations with surfactant additives in complex geological settings. As a result, an effective methodology for ranking geological blocks based on their ore clay content and precipitate composition was developed to determine the rational parameters of decolmatant solutions, enabling a maximized filtration performance and an extended well service life. This makes it possible to reduce the operating costs of extraction, control the geotechnological parameters of uranium well mining, and improve the efficiency of the in situ leaching of uranium under complex mining and geological conditions. Additionally, the approach increases the environmental and operational safety during uranium ore leaching intensification. Full article

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation. Full article

Entropy is a foundational concept across scientific domains, playing a role in understanding disorder, randomness, and uncertainty within systems. This study applies Shannon’s entropy in information theory to evaluate and manage complexity in industrial supply chain management. The purpose of the study is to propose a quantitative modeling method, employing Shannon’s entropy model as a proxy to assess the complexity in SCs. The underlying assumption is that information entropy serves as a proxy for the complexity of the SC. The research method is quantitative modeling, which is applied to four focal companies from the agrifood and metalworking industries in Southern Brazil. The results showed that companies prioritizing cost and quality exhibit lower complexity compared to those emphasizing flexibility and dependability. Additionally, information flows related to specially engineered products and deliveries show significant differences in average entropies, indicating that organizational complexities vary according to competitive priorities. The implications of this suggest that a focus on cost and quality in SCM may lead to lower complexity, in opposition to a focus on flexibility and dependability, influencing strategic decision making in industrial contexts. This research introduces the novel application of information entropy to assess and control complexity within industrial SCs. Future studies can explore and validate these insights, contributing to the evolving field of supply chain management. Full article

Southeast Asia relies heavily on hydropower from dams and reservoir projects, but this dependence comes at the cost of ecological damage and increased vulnerability to extreme events. This dilemma necessitates a choice between continued dam development and adopting alternative renewable options. Concerns over these environmental impacts have already led to halts in dam construction across the region. This study assesses the potential of run-of-river hydropower plants (RHPs) across 199 hydrometric stations in Mainland Southeast Asia (MSEA). The assessment utilizes power duration curves for the historical period and projections from the HBV hydrological model, which is driven by an ensemble of 31 climate models for future scenarios. Energy production was analyzed at four levels (minimum, maximum, balanced, and optimal) for both historical and future periods under varying Shared Socioeconomic Pathways (SSPs). To promote sustainable development, environmental flow constraints and carbon dioxide (CO₂) emissions were evaluated for both historical and projected periods. The results indicate that the aggregate energy production potential during the historical period ranges from 111.15 to 229.62 MW (Malaysia), 582.78 to 3615.36 MW (Myanmar), 555.47 to 3142.46 MW (Thailand), 1067.05 to 6401.25 MW (Laos), 28.07 to 189.77 MW (Vietnam), and 566.13 to 2803.75 MW (Cambodia). The impact of climate change on power production varies significantly across countries, depending on the level and scenarios. At the optimal level, an average production change of −9.2–5.9% is projected for the near future, increasing to 15.3–19% in the far future. Additionally, RHP development in MSEA is estimated to avoid 32.5 Mt of CO₂ emissions at the optimal level. The analysis further shows avoidance change of 8.3–25.3% and −8.6–25.3% under SSP245 and SSP585, respectively. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

Plastic (P) and microplastic (MP) pollution in marine and freshwater environments is an increasingly urgent issue that needs to be addressed at many levels. The Seabin (an easily operated and cost-effective floating debris collection device) can help clean up buoyant plastic debris in calm waters while monitoring water pollution. A Seabin was used to conduct a comparative analysis of plastic and microplastic concentrations in northern Lake Garda (Italy) during peak and low tourist seasons. The composition of the litter was further investigated using Fourier-Transform Infrared (FTIR) spectroscopy. The analysis showed a decreased mean amount of plastic from summer (32.5 mg/m³) to autumn (17.6 mg/m³), with an average number of collected microplastics per day of 45 ± 15 and 15 ± 3, respectively. Packaging and foam accounted for 92.2% of the recognized plastic waste products. The material composition of the plastic mass (442 pieces, 103.0 g) was mainly identified as polypropylene (PP, 47.1%) and polyethylene (PE, 21.8%). Moreover, 313 microplastics (approximately 2.0 g) were counted with average weight in the range of 1–16 mg. A case study of selected plastic debris was also conducted. Spectroscopic, microscopic, and thermal analysis of specimens provided insights into how aging affects plastics in this specific environment. The purpose of this study was to establish a baseline for further research on the topic, to provide guidelines for similar analyses from a multidisciplinary perspective, to monitor plastic pollution in Lake Garda, and to inform policy makers, scientists, and the public. Full article

Crop residue management and soil tillage (CRM and ST) are key steps in agricultural production. The effects of different CRM and ST modes on the quality of seedbed, seeding, and harvest yield are not well determined. In this study, the system of maize (Zea mays L.)–soybean (Glycine max (L.) Merr) rotation under ridge-tillage in the semi-arid regions of Northeast China was chosen as the study conditions. Four modes were investigated: deep tillage and seeding (DT and S), stubble field and no-tillage seeding (SF and NTS), three-axis rotary tillage and seeding (TART and S), and shallow rotary tillage and seeding (SRT and S). Results show that the DT and S mode produced the best quality of seedbed and seeding. Among the conservation tillage modes, the SRT and S mode produced the shortest average length of roots and straw, the best uniformity of their distribution in the seedbed, and the highest soybean yield. Both the SRT and S and SF and NTS modes yielded a higher net profit as their cost-effectiveness. When considering only the quality of seedbed and seeding under conservation tillage as a prerequisite, it can be concluded that the SRT and S mode is both advantageous and sustainable. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

There is still a need to develop scenarios and models aimed at substituting fuelwood and reducing the use of fossil fuels such as liquefied petroleum gas (LPG), on which low-income rural households in the Global South often depend. The use of these fuels for cooking and heating in domestic and productive activities poses significant health and environmental risks. This study validated, in three different phases, the sustainability of a model for the production and use of biogas from the treatment of swine-rearing wastewater (WWs) on a community farm: (i) A Multi-Criteria Analysis (MCA), incorporating environmental, social/health, technical, and economic criteria, identified the main weighted criterion to C8 (use of small-scale technologies and low-cost access), with a score of 0.44 points, as well as the Tubular biodigester (Tb) as the most suitable option for the study area, scoring 8.1 points. (ii) Monitoring of the Tb over 90 days showed an average biogas production of 2.6 m³ d⁻¹, with average correlation 0.21 m³ Biogas kg Biomass⁻¹. Using the experimental biogas production rate (k = 0.0512 d⁻¹), the process was simulated with the BgMod model, achieving an average deviation of only 10.4% during the final production phase. (iii) The quantification of benefits demonstrated significant reductions in firewood use: in Scenario S1 (kitchen energy needs), biogas replaced 83.1% of firewood, while in Scenario S2 (citronella essential oil production), the substitution rate was 24.1%. In both cases, the avoided emissions amounted to 0.52 tons of CO_2eq per month. Finally, this study proposes a synthesised, community-based rural biogas framework designed for replication in regions with similar socio-environmental, technical, and economic conditions. Full article

Interfacial solar evaporation has emerged as a promising strategy for freshwater production, where 3D evaporators offer distinct advantages in heat management and salt rejection. Freeze–thaw cycling is a widely adopted fabrication method for 3D hydrogel evaporators, yet the impact of preparation conditions (e.g., freezing temperature) on their evaporation performance remains poorly understood, hindering rational optimization of fabrication protocols. Herein, we report the fabrication of chitosan-based hydrogel evaporators via freeze–thaw cycles at different freezing temperatures (−20 °C, −40 °C, and −80 °C), leveraging its low cost and environmental friendliness. Characterizations of crosslinking density and microstructure reveal a direct correlation between freezing temperature and network porosity, which significantly influences evaporation rate, photothermal conversion efficiency, and anti-salt performance. It is noteworthy that the chitosan hydrogel prepared at −80 °C demonstrates an excellent evaporation rate in high-salinity environments and exhibits superior salt resistance during continuous evaporation testing. Long-term cyclic experiments indicate that there was an average evaporation rate of 3.76 kg m⁻² h⁻¹ over 10 cycles, with only a 2.5% decrease observed in the 10th cycle. This work not only elucidates the structure–property relationship of freeze–thaw fabricated hydrogels but also provides a strategic guideline for tailoring evaporator architectures to different salinity conditions, bridging the gap between material design and practical seawater desalination. Full article

The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R² of 78.32%, whereas the random forest regression model had an R² of 63.7%. The final model for predicting the remaining useful life had an R² of 99.6%. Full article