Search Results (6,329)

Under the guidance of the “community pension” initiative and adhering to the people-centered principle, this study focuses on the public interaction spaces within eight communities in the central urban area of Jiaozuo City. It assesses the current state of these spaces and the needs of the elderly population. A comprehensive evaluation system, integrating both subjective and objective factors, is developed for this purpose. Utilizing the railway station community as an illustrative case, this paper delves into the interaction behavior characteristics of various elderly groups and proposes optimal design directions for various public interaction spaces, encompassing functional diversity, space aging adaptability, and construction refinement. Full article

The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is reflected in the chlorophyll content of leaves that are strongly linked to the lamina coloration. Pests and diseases (virus or bacterial infections) also cause symptoms on the foliage. These symptoms induced by biotic and abiotic stressors often have a specific pattern, which allows for their prediction based on remote sensing. In this report, an RGB (red, green and blue) image processing system is presented to determine leaf lamina color variability based on RGB-based color indices. LeafLaminaMap was developed in Scilab with the Image Processing and Computer Vision toolbox, and the code is available freely at GitHub. The software uses RGB images to visualize 29 color indices and the R, G and B values on the lamina, as well as to calculate the statistical parameters. In this case study, symptomatic (senescence, fungal infection, etc.) and healthy grapevine (Vitis vinifera L.) leaves were collected, digitalized and analyzed with the LeafLaminaMap software according to the mean, standard deviation, contrast, energy and entropy of each channel (R, G and B) and color index. As an output for each original image in the sample set, the program generates 32 images, where each pixel is constructed using index values calculated from the RGB values of the corresponding pixel in the original image. These generated images can subsequently be used to help the end-user identify locally occurring symptoms that may not be visible in the original RGB image. The statistical evaluation of the samples showed significant differences in the color pattern between the healthy and symptomatic samples. According to the F value of the ANOVA analysis, energy and entropy had the largest difference between the healthy and symptomatic samples. Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. The case study showed that the LeafLaminaMap software is an effective environment for the leaf lamina color pattern analysis; moreover, the results underline that energy and entropy are valuable features and could be more effective than the mean and standard deviation of the color properties. Full article

Negative air ions (NAIs) have the effect of improving environmental quality and human health. This study for the first time constructed an evaluation system for forest release of NAIs employing five capacity indicators: release contribution rate (L), release coefficient (n), release rate (s), instantaneous standing stock (v), and total release amount (Z). These were applied to evaluate the forest’s ability to release NAIs in the suburban urban green space of Beijing—Xishan National Forest Park. The results showed that: (1) during the growing season of the forest, the value ranges of these indicators were as follows: L: 6.04~9.71%, n: 6.63~11.05%, s: 4.53 × 10³~7.49 × 10³/cm²/min, v: 4.48 × 10⁴~7.34 × 10⁴/cm², Z: 2.70 × 10⁵~4.40 × 10⁵/cm², with the spring and autumn “noon and evening” and summer “morning and evening” forests having the strongest effect and the highest release capacity of NAIs; (2) the daily changes of L, n, s, v, and Z are generally in a “bimodal” pattern, and the overall trend of “rise and fall, rise and fall” among various indicators is consistent, showing a “linkage”; (3) weather characteristics affect release capacity in the order of sunny > rainy > cloudy, with the strongest NAI release ability from forests at 6:00 on cloudy days (0.53%, 1.7%, 877.19/cm²/min, 3.56 × 10⁴/cm², 9.67 × 10⁴/cm²) and at 18:00 on rainy days (4.58%, 4.83%, 3.16 × 10³/cm²/min, 3.16 × 10⁴/cm², 1.90 × 10⁵/cm²), with poorer NAI release ability in the afternoon on cloudy and rainy days; (4) forests can produce over 100 million levels of NAIs throughout the year, with an average daily production of over one million levels of NAIs. From 2019 to 2021, NAI production showed an increasing trend year by year, and the increase rate increased year by year to 19.6% and 56.5%.; and (5) the five indicators are significantly positively correlated with solar radiation and temperature in the range of 0–200 w/m² and 5–20 °C, respectively. This study provides a new method to reveal the ability of forests to release NAIs, providing strong evidence for creating a livable ecological environment. Full article

At present, the evaluation perspective of the gel plugging agent assessment method is incomprehensive, due to which the experimental results deviate from the field data. By analyzing the current indoor evaluation methods and the factors controlling the sealing capability of gel plugging agents, an experimental device and method for evaluating the blocking effect of oilfield gel plugging agents has been designed. In contrast to traditional assessment methods, the proposed approach offers advantages such as simple operation, rapid experimentation, and accurate results. The experimental results show that gels selected using conventional methods are inconsistent with the results of plugging displacement tests. This discrepancy can be attributed to the fact that these methods focus solely on cohesive strength while neglecting adhesive strength. Considering that the evaluation perspective of conventional methods is relatively limited, an evaluation method for the sealing effect of the plugging agent was developed. This method comprehensively incorporates factors such as cohesion strength, adhesion capability, shear resistance, and the long-term anti-dehydration performance of the gel. The evaluation results of the method were consistent with the results of the plugging displacement experiments. The newly constructed method defines Γ as the comprehensive evaluation parameter for the gel. A new experimental system with a comprehensive evaluation index (Γ) of 8.97 Pa² was selected. After the profile control of the system, the effluent ratio of the high and low permeability layers reached 1:9, and its erosion resistance was greater than 20 PV. Meanwhile, the profile control effect was also stable. Through verification based on field data, the injection pressure of the system optimized by the proposed method was found to be 2.5 times higher than that of the original system. Meanwhile, the plugging validity period was >2 times of the original system. The test results were consistent with the plugging capability evaluation index. In summary, the performance evaluation method of the designed gel plugging agent was reasonable in principle and the results were accurate and reliable. Therefore, it is considered to be of guiding significance for the selection of efficient profile control plugging agents in oilfields. Full article

The livestock high-quality development (LHIQUD) is an important guarantee for promoting livestock modernization and sustainability. The scientific evaluation of LHIQUD and its regulations is significant to solving the key problems in livestock sustainability and promoting LHIQUD. This paper holds that LHIQUD is influenced by the cutting-edge view of economic development and ecological civilization construction, driven by innovation and the change in quality and efficiency. Its fundamental goal is to satisfy people’s growing demand for safe, high-quality agricultural products. Finally, it will realize the sharing of development achievements and enhance competitiveness of the livestock. Based on this, the study sets up an evaluation system for LHIQUD, including indicators of quality and efficiency improvement, coordination and sharing, green development, and innovative development. The entropy method, exploratory spatial data analysis, and kernel density estimation method are used to evaluate the regional LHIQUD of China from 2010 to 2019. The dynamic evolution and spatial auto-correlation analysis results show that (a) Regional LHIQUD in China has generally improved, and there is a decreasing trend from the East, Northeast, and central region to the West, with the characteristics of spatial non-equilibrium. (b) LHIQUD is positively correlated with the regional economic development. (c) The spatial auto-correlation of LHIQUD is not obvious, generally showing a weak agglomeration pattern. Provincial LHIQUD is interdependent in geographical space, with agglomeration characteristics. The findings of this study are invaluable for governments at all levels to accurately comprehend the true state of regional LHIQUD in China, thereby providing a solid foundation for formulating corresponding policies. Full article

Accounting professionals play a pivotal role in reducing environmental impact through systems like activity-based costing (ABC). This study offers a thorough review of research on ABC and environmental impact, providing insights into the current literature and guiding future developments. It systematically reviews 58 articles published in the Web of Science from 1998 to 2023, using Excel and the R package Bibliometrix for data analysis. The findings indicate a steady increase in research on ABC and environmental impact. Key contributions highlight the advantages of ABC in minimizing environmental impact across industries such as sustainable construction, metallurgy, transportation, and manufacturing. Emerging research directions include developing costing systems to reduce environmental impact, optimizing supply chain cost management models, and applying new technologies to tackle environmental challenges in production processes. Two primary research themes, identified as “motor themes,” are crucial for advancing this field: life-cycle assessment management models, which integrate environmental factors throughout a product or service’s life cycle; and the performance and impact of environmental cost management systems, which evaluate the effectiveness of these systems in reducing ecological footprints while maintaining profitability. These areas are essential for driving future research and innovation at the intersection of cost management and environmental sustainability. Full article

In order to elucidate the active ingredients, potential targets, and mechanisms of action of peppermint in treating bovine mastitis, this study utilized network pharmacology analysis and molecular docking to conduct an exploratory, prospective investigation. Using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, all compounds and targets of peppermint were retrieved. After removing duplicates, a total of 133 compounds and 272 targets were obtained. Targets were then standardized to gene names using the UniProt database to construct a drug–component-target network. A total of 183 disease targets related to bovine mastitis were retrieved from the GeneCards database. We obtained 28 cross targets of peppermint targets and bovine mastitis targets, and constructed a protein–protein interaction (PPI) network using the STRING database. A visual network was built using Cytoscape 3.10.0 software, and seven core targets were analyzed and obtained. GO and KEGG pathway enrichment analysis was performed using the Metascape database. Molecular docking was conducted using AutoDockTools–1.5.6 software on some small–molecule compounds and the seven targets to evaluate the stability of binding between peppermint and core targets. Apigenin, luteolin, and ursolic acid are the three main components in peppermint. Core targets (TNF, IL–6, STAT–3, IL–1β, FGF–2, IFNG, and ESR–1) were selected based on the PPI network. The enrichment analysis suggested that the major signaling pathways in network pharmacology may include AGEs–RAGE, IL–17, NF–κB, TLRs, HIF–1, TGF–β, PI3K–Akt, and MAPK. The molecular docking results showed that one of the main components of mint, ursolic acid, exhibited good binding activity with all core targets of bovine mastitis. Other constituents also produced favorable binding with some core targets. This study elucidates the mechanisms of mint in treating bovine mastitis, providing data to support the potential development of new therapies for bovine mastitis using mint and its constituents. Full article

This research investigates integrating Building Information Modeling (BIM) and blockchain technology to enhance building information’s security, reliability, and accuracy in Martian environments. Given the unique challenges posed by extraterrestrial construction, this study evaluates the feasibility of this hybrid approach through a structured SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis. Expert inputs were collected through a comprehensive questionnaire identifying nine strengths, eight weaknesses, eight opportunities, and six threats to implementing BIM and blockchain technology in space projects. The Analytical Hierarchy Process (AHP) was used to prioritize these factors. Findings indicate that the strengths are cost calculation and budgeting (26.21), and the weaknesses are technology complexity (25.488). Increased productivity (19.16) is the most important criterion at the opportunity point, and defects in data security (20.68) are the most important at the threat point. The SWOT analysis places BIM and blockchain integration in a conservative strategy quadrant, indicating that the technology holds significant promise but requires further development and refinement. Ultimately, this research contributes to the growing knowledge about extraterrestrial construction technologies and provides a foundation for developing flexible and autonomous building systems for Martian habitats. Full article

Faults in valves that regulate fluid flow and pressure in industrial systems can significantly degrade system performance. In systems where multiple valves are used simultaneously, a single valve fault can reduce overall efficiency. Existing fault diagnosis methods struggle with the complexity of multivariate time-series data and unseen fault scenarios. To overcome these challenges, this study proposes a method based on a one-dimensional convolutional neural network (1D CNN) for diagnosing the location and severity of valve faults in a multi-valve system. An experimental setup was constructed with 17 sensors, including 8 pressure sensors at the inlets and outlets of 4 valves, 4 flow sensors, and 5 pressure sensors along the main pipe. Sensor data were collected to observe the sensor values corresponding to valve behavior, and foreign objects of varying sizes were inserted into the valves to simulate faults of different severities. These data were used to train and evaluate the proposed model. The proposed method achieved robust prediction accuracy (MAE: 0.0306, RMSE: 0.0629) compared to existing networks, performing on both trained and unseen fault severities. It identified the location of the faulty valve and quantified fault severity, demonstrating generalization capabilities. Full article

This article is dedicated to the construction of neural networks for the prediction of the current–voltage characteristic (CVC). CVC is the most important characteristic of the mass transfer process in electro-membrane systems (EMS). CVC is used to evaluate and select the optimal design and effective operating modes of EMS. Each calculation of the CVC at the given values of the input parameters, using developed analytical-numerical models, takes a lot of time, so the CVC is calculated in a limited range of parameter changes. The creation of neural networks allowed for the use of prediction to obtain the CVC for a wider range of input parameter values and much faster, saving computing resources. The regularities of the behavior of CVC for various values of input parameters were revealed. During this work, several different neural network architectures were developed and tested. The best predictive results on test samples are given by the neural network consisting of convolutional and LSTM (Long Short-Term Memory) layers. Full article

The increased concerns about climate change, diminishing natural resources, and environmental degradation call for deep research into new environmentally friendly building systems that use natural or recycled materials. The article presents an assessment of the environmental and climatic benefits associated with the construction of a tiny house made of quincha, a building system based on a wooden structure filled with locally sourced earth and straw. The tiny house is located in the Elqui Valley, in the Chilean region of Coquimbo, and it is designed to be compact, functional, comfortable, and efficient. The study uses a life cycle approach to assess the environmental impacts of building construction, maintenance, and end-of-life treatment, comparing the adopted quincha solution with four hypothetical scenarios using industrial, prefabricated, and/or synthetic construction materials currently adopted in the region. The thermal performance of all the analyzed solutions is also included in order to provide insights into the impact of the operational phase. This paper demonstrates that the quincha solution, in the face of lower thermal insulation compared to the other prefabricated solutions (the U-value of the quincha wall is 0.79 W/m²K while the U-value of the best prefabricated wall is 0.26 W/m²K), has higher thermal inertia (time lag (TL) and decrement factor (DF) are, respectively, 6.97 h and 0.60, while other systems have a TL below 4 h and DF higher than 0.81). For a quantitative environmental evaluation, the carbon footprint (global warming potential), water footprint, and embodied energy indicators are assessed through LCA, which takes into account the mass of the materials and their emission factors. The effectiveness of the quincha solution is also reflected in environmental terms; in fact, it is found to have the lowest carbon footprint (2635.47 kgCO₂eq) and embodied energy (42.7 GJ) and the second-lowest water footprint (2303.7 m³). Moreover, carbon sequestration values, which are assessed by estimating the carbon contained in building systems using wood and straw, demonstrate that the quincha tiny house is the only solution that can theoretically reach carbon neutrality (with its carbon storage value at −5670.21 kgCO₂eq). Full article

In this study, we present an oncolytic virus (OV) evaluation system established using microfluidic organ-on-a-chip (OOC) systems and patient-derived organoids (PDOs), which was used in the development of a novel oncolytic virus, AD4-GHPE. An OV offers advantages such as good targeting ability and minimal side effects, and it has achieved significant breakthroughs when combined with immunotherapy in recent clinical trials. The development of OVs has become an emerging research focus. PDOs can preserve the heterogeneity of in situ tumor tissues, whereas microfluidic OOC systems can automate and standardize various experimental procedures. These systems have been applied in cutting-edge drug screening and cell therapy experiments; however, their use in functionally complex oncolytic viruses remains to be explored. In this study, we constructed a novel recombinant oncolytic adenovirus, AD4-GHPE, and evaluated OOC systems and PDOs through various functional validations in hypopharyngeal and breast cancer organoids. The results confirmed that AD4-GHPE exhibits three antitumor mechanisms, namely, tumor-specific cytotoxicity, a reduction in programmed death ligand 1 (PD-L1) expression in tumor cells to increase CD8⁺ T-cell activity, and granulocyte–macrophage colony-stimulating factor (GM-CSF) secretion. The evaluation system combining OOC systems and PDOs was efficient and reliable, providing personalized OV treatment recommendations for patients and offering industrialized and standardized research ideas for the development of OVs. Full article

The development of export–import activity at all levels of the national economic system is an important factor and the result of globalization and country integration in the world economy. The elaboration of sound public policy related to export–import activity is based on its assessment and analysis. The goal of this article is the systematization and development of methodological support for the evaluation of the export–import activity of the national economy, including the development of its technology and a conceptual assessment model, which is presented in a structural–logical–semantic form, and the analysis and forecasting of export–import activity using the proposed model. To achieve the research goal, quantitative methods were used: the method of integral taxonomic indicators of development and efficiency, the method of forecasting based on trend models, multivariate factor analysis, and the modified method of cointegration of time series. A set of indicators for the export–import activity “development–effectiveness” matrix was proposed, and their dichotomies were studied. To ensure the reliability and objectivity of the export–import activity assessment, a methodological basis, which is detailed by structural elements, was formed. The model was constructed and tested based on monthly data on the export–import activity of Ukraine for the years 2021–2023. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

With rapid economic development, the amount of the municipal solid waste (MSW) generated has increased dramatically. To improve the socio-economic benefits and environmental impacts of the low-carbon management of MSW, it is crucial to identify the drivers of Greenhouse Gas (GHG) emissions from MSW treatment and assess their systematic and comprehensive benefits. The factor decomposition method is one of the most commonly used methods focused on identifying GHG emission-influencing factors, while the system dynamics (SD) method is commonly used to analyze the causal relationships between linear and nonlinear variables in complex dynamic systems. Unlike existing studies that account for and evaluate MSW from a static perspective, this paper innovatively combines the LMDI-SD model to identify and quantify the GHG emission drivers of MSW and evaluate the benefits of decarbonizing the MSW management in China from a comprehensive and systematic perspective. The results show that the dominant factor driving MSW GHG emissions from 2010 to 2022 is the economic development factor, ∆E_ED, while the intensity of MSW generation ∆E_GI and the structure of MSW treatment ∆E_TS play a stronger inhibiting role. Based on this, the SD model is constructed to simulate different scenarios, and the analysis shows that increasing the waste separation rate (S3) is the most effective measure to improve the socio-economic benefits and environmental impacts of the system. Compared with the base scenario, the socio-economic benefits and environmental impacts in 2050, for example, are increased by 82.8% and 43.4%, respectively. Improving the utilization rate of landfill gas (S1), reducing the per capita amount of MSW generated (S4) and increasing the incineration rate of MSW (S2) also have significant advantages for the improvement of benefits. Finally, some policy recommendations for the improvement of the comprehensive benefits of low-carbon MSW management systems are proposed to help policymakers make appropriate decisions. Full article