Search Results (49)

In countries and regions where Asian elephants are distributed, human–elephant conflict has become an important ecological and socio-economic issue. As one of the major habitats of Asian elephants, China faces severe challenges. Based on the theory of planned behavior and the risk perception theory, this study takes the survey data of 449 smallholder farmers in the Asian elephant distribution areas of Pu’er City, Yunnan Province as samples and uses the Tobit model and the mediating effect model to empirically analyze the impact of human–elephant conflict on farmers’ land use efficiency and its mechanism. The results show the following: (1) The human–elephant conflict risk perception has a significant negative impact on farmers’ land use efficiency. A one-unit increase in risk perception decreases land use efficiency by 250.34 CNY/mu. (2) Social networks positively moderate the negative impact of the human–elephant conflict risk perception on farmers’ land use efficiency, further strengthening the negative impact of risk perception. (3) From the perspective of the mechanism, the human–elephant conflict risk perception increases the likelihood of farmers changing their land use behavior. Farmers with high risk perception tend to reduce agricultural capital investment, which in turn leads to a decline in land use efficiency. In view of this, this paper puts forward suggestions in terms of strengthening ecological monitoring and control, increasing support for agricultural production, and guiding rational social network communication, providing theoretical support and practical guidance for alleviating human–elephant conflict and improving farmers’ land resource use efficiency. Full article

In the context of global efforts to combat antimicrobial resistance (AMR), the importance of comprehensive AMR data is more crucial than ever. AMR surveillance networks, such as the European Antimicrobial Resistance Surveillance Network (EARS-Net) and the Central Asian and European Surveillance of Antimicrobial Resistance (CAESAR), support member states in obtaining high-quality AMR data. Nevertheless, data gaps persist in some countries, including those in the Western Balkans (WBs), a region with high AMR rates. This review analyzed existing research on carbapenem-resistant Enterobacterales (CRE) to better understand the AMR landscape in the WB countries. The most prevalent CRE was Klebsiella pneumoniae, followed by Escherichia coli, Enterobacter cloacae, and Proteus mirabilis, with sporadic cases of Morganella morganii, Providencia spp., Klebsiella oxytoca, and Citrobacter sedlakii. Carbapenemase production was identified as the most common mechanism of carbapenem resistance, but other resistance mechanisms were not investigated. An increasing trend in carbapenem resistance has been observed over the last decade, alongside a shift in carbapenemase epidemiology from the NDM type in 2013–2014 to the OXA-48 type in recent years. Few studies have applied whole-genome sequencing for CRE analysis, which has demonstrated the spread of resistance determinants across different niches and over time, emphasizing the importance of molecular-based research. The overall low number of studies in the WB countries can be attributed to limited resources, highlighting the need for enhanced support in education, training, technology, and equipment to improve data collection and evaluation. Full article

Insufficient milk supply is a widespread issue faced by women globally and associated with a higher risk of health problems in infants and mothers. Hemerocallis citrina Baron, commonly known as daylily, is a perennial edible plant often used in traditional Asian cuisine to promote lactation. However, the active compound(s) and mechanism of its lactation-promoting effect remain unclear. This study aimed to confirm the traditional use of daylily in promoting lactation and investigate its potential active components and underlying molecular mechanisms. Our results showed that the aqueous extracts of H. citrina Baroni (HAE) significantly enhanced milk production, and the serum levels of lactation-related hormones, and promoted mammary gland development in lactating rats, as well as increased the levels of milk components in bovine mammary epithelial cells (BMECs) (p < 0.05). UHPLC-Q-Exactive Orbitrap-MS analysis revealed that hexamethylquercetin (HQ) is the representative flavonoid component in HAE, accounting for 42.66% of the total flavonoids. An integrated network pharmacology and molecular docking analysis suggested that HQ may be the potential active flavonoid in HAE that promotes lactation, possibly supporting lactation by binding to key target proteins such as STAT5A, PIK3CA, IGF1R, TP53, CCND1, BCL2, INS, AR, and DLD. Cell experiments further demonstrated that HQ could promote cell proliferation and the synthesis of milk proteins, lactose, and milk fat in BMECs. Transcriptomic analysis combined with a quantitative reverse transcription polymerase chain reaction (RT-qPCR) revealed that both HAE and HQ exert a lactation-promoting function mainly through regulating the expression of key genes in the PI3K-Akt signaling pathway. Full article

Detecting surface defects in bamboo strips is essential for producing Asian bamboo products. Currently, the detection of surface defects in bamboo strips mainly relies on manual labor. The labor intensity is high, and the detection efficiency is low. Improving the speed and accuracy of identifying bamboo strip defects is crucial in enhancing enterprises’ production efficiency. Hence, this research designs a lightweight YOLOv5s neural network algorithm using the Ghost module to identify surface defects of bamboo strips. The research introduces an attention mechanism CA module to improve the recognition ability of the model target; the research also implements a C2f model to enhance the network performance and the surface quality of bamboo strips. The experimental results show that after training with the acquired image dataset, the YOLOv5s model can exert an intelligent detection effect on five common types of defects in bamboo strips, and the Ghost module makes YOLOv5s lightweight, which can effectively reduce model parameters and improve detection speed while maintaining recognition accuracy. Meanwhile, the C2f module and CA module can further leverage the model’s ability to identify specific defects in bamboo strips after lightweight improvement. Full article

The aim of this paper is to evaluate changes in Italy’s competitiveness in the global wood products market, with a particular focus on wooden furniture and wood panels, both final and intermediate products of the crucial wooden furniture supply chain. The analysis is conducted through a cross-country comparison using trade flow matrices and various descriptive indices: Market Share, Trade Competitiveness Index, Balassa’s Revealed Comparative Advantage Index, and the Symmetric Balassa Index. Furthermore, this study also examines intra-industry trade using the Grubel–Lloyd Index. While each index has its limitations when used individually, their combined analysis can provide a more comprehensive view. The study covers the period from 1996 to 2019, using data from FAO and COMTRADE sources. The results show that Italy maintains a significant position in the international furniture market, although this position has deteriorated over time. Conversely, Italy remains a net importer of wood panels. Trade flows have become more concentrated, with Canada and Germany still holding importance in the international market. However, Asian countries have now become the core of the commercial network. China has emerged as the leading exporting country in all product categories considered, with Vietnam and Malaysia also increasing in importance. Noteworthy progress has also been recorded by Russia and Poland in Europe. Additionally, the study discusses the implications of these findings for rural development, particularly in regions dependent on the wood-product sectors. Full article

In recent years, trade protectionism and unilateralism have prevailed, and countries around the world have imposed restrictions on log exports. It has also become more difficult for China to import wood resources and export deep-processed wood forest products. Based on panel data from 2000 to 2019, this study uses social network analysis to measure the level of the Chinese wood forest product trade network, takes the Chinese free trade agreements (FTAs) as the natural experiment, and uses the multi-stage double-difference method to investigate the impact of the signed FTAs on China’s wood forest product trade. The study finds that the trade network of Chinese wood forest products is becoming increasingly complex, and the central position of China and the Association of Southeast Asian Nations (ASEAN) in the network is increasing year by year. The signing of FTAs has had a significant positive impact on the trade of wood forest products in China and a significant trade creation effect. This finding remains true after conducting the placebo test and propensity score-matched regression control. At the same time, the import of wood forest products in China will have a significant trade transfer effect due to the signing of FTAs, and this will not affect exports. Although FTAs show significant trade creation and trade transfer effects in China’s wood forest product trade, they also increase, to a certain extent, the mismatch of forest resources worldwide. Full article

In Asian regions, areca nuts are tropical fruits that are extensively consumed. The areca nut contains a lot of polyphenols and its safety is unknown. In this research, we investigated the effects of lipopolysaccharides (LPS) and areca nut polyphenols (ANP) on normal RAW264.7 cells. The results showed that LPS stimulated adverse effects in normal cells by affecting cytokine production. The GO analysis results mainly affected DNA repair, cell division, and enzyme activities. In the KEGG analysis results, the NOD-like receptor signaling pathway, which is related to NF-κB, MAPK, and the pro-inflammatory cytokines, is the most significant. In the protein–protein interaction network (PPI) results, significant sub-networks in all three groups were shown to be related to cytokine–cytokine receptor interaction. Collectively, our findings showed a comprehensive understanding of LPS-induced toxicity and the protective effects of ANP by RNA sequencing. Full article

The profound impact of deep learning technology is poised to revolutionize various industries, marking the fourth industrial revolution. Thus, we combined efficiency and productivity research (data envelopment analysis, DEA), artificial intelligence and deep learning (artificial neural networks, ANN), a system integrating DEA and ANNs, and simultaneous longitudinal research (time series) to determine comprehensive research trends and create relevant applications. We addressed mega-sports events’ performance assessment systems that predict the efficiency of nations participating in the Asian Games from 1990 to 2023 and analyzed the outcomes, applying them to practical issues of national sports policies and development. Performance assessment systems to diagnose, plan, monitor, and revise the impact of implementing measures in Asian nations represent a step forward. The PAS findings point out future research recommendations by addressing national sports policies and development issues, transforming the predictions of performance assessment systems in mega-sports events into practical management recommendations. In this way, the system for enhanced predictive analytics developed in the study can rapidly analyze large, medium, and small datasets, reveal insights that humans may overlook, and refine the likelihood of predicting future events with greater precision and accuracy. Full article

Satellite precipitation products (SPPs) have advanced remarkably in recent decades. However, the bias correction of SPPs still performs unsatisfactorily in the case of a limited rain-gauge network. This study proposes a new real-time bias correction approach that includes three steps to improve the precipitation quality with limited gauges and facilitate the hydrological simulation in the Min River Basin, China. This paper employed 66 gauges as available ground observation precipitation, Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) as the historical precipitation to correct Global Satellite Mapping of Precipitation NOW (GNOW) and Global Satellite Mapping of Precipitation NRT (GNRT) in 2020. A total of 1020 auto-rainfall stations were used as the benchmark to evaluate the original and corrected SPPs with six criteria. The results show that the statistic and dynamic bias correction method (SDBC) improved the SPPs significantly and the cumulative distribution function matching method (CDF) could reduce the overcorrection error from SDBC. The inverse error variance weighting method (IEVW) integrations of GNOW and GNRT did not have noticeable improvement as they use similar hardware and software processes. The corrected SPPs show better performance in hydrological simulations. It is recommended to employ different SPPs for integration. The proposed bias correction approach is significant for precipitation estimation and flood prediction in data-sparse basins worldwide. Full article

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols’ impact and trends. Full article

Plant resistance to drought stress is a parameter that should be studied with more emphasis in the search for higher agricultural yields. In this scenario, research within breeding programs should be directed toward specific mechanisms of action and important agricultural crops in worldwide agribusiness. From this perspective, this study carried out a bibliographic investigation regarding the advances in genetic improvement aimed at drought stress in crops using a hybrid model of analysis of scientific articles. The analysis employed bibliometric parameters for qualitative and quantitative discussion of scientific production and the methodological process of systematic review for the synthesis of the results obtained. The work was divided into four stages: the search for articles in databases, meta-analysis, bibliometric analysis, and systematic analysis. Scientific articles were searched for on the Scopus, Scielo, and Web of Science databases within a 20-year timeframe. Most authors and institutions were from Asian countries, demonstrating the need for global expansion of research on the subject. With regard to the co-occurrence networks between the keywords used in the search, a focus was observed on the following terms: drought resistance, drought stress; drought, and drought tolerance. Evidently, the primary mechanism of tolerance or even resistance studied in breeding programs is associated with the expression of genes and genetically modified organisms that confer resistance to plants. Also, the crops addressed in the research retrieved are highly diverse. Full article

Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution ($3840\times 2160$) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users. Full article

Rice is a staple food in many Asian countries, but its production requires a high water demand. Moreover, more attention should be paid to the water management of rice due to global climate change and frequent droughts. To address this problem, we propose a rice water stress identification system. Since water irrigation usually affects the opening and closing of rice leaf stomata which directly affects leaf temperature, rice leaf temperature is a suitable index for evaluating rice water stress. The proposed rice water stress identification system uses a CNN (convolutional neural network) to identify water stress in thermal images of rice fields and to classify the irrigation situation into three classes: 100%, 90%, and 80% irrigation. The CNN was applied to extract the temperature level score from each thermal image based on the degree of difference between the three irrigation situations, then these scores were used to further classify the water-stress situation. In the experiments in this study, we compare CNN classification results without considering the degree between each class. The proposed method considerably improves water stress identification. Since rice leaf temperature is relative to air temperature and is not an absolute value, the background temperature is also important reference information. We combine two different methods for background processing to extract more features and achieve more accurate identification. Full article

Agribusiness trade is a complex network of commercial relations among countries, and it is influenced by on-shore and off-shore logistics. Therefore, it is essential to comprehend these relationships to improve decision-making regarding production and logistical development. This paper investigates Brazilian agricultural and livestock exports between 2013 and 2022 to understand logistical bottlenecks based on trade partners. To do so, we performed descriptive statistics and social network analysis (SNA) considering measures such as degree centrality, k-core, and tie strength. Our results indicate Brazil’s dependency on Asian markets whereby eight of ten are located on this continent. We observe an unexpected result regarding the low purchase of these products byimportant Brazilian partners such as the United States, the UK, and the European Union. Finally, the study confirms the Brazilian logistical bottleneck where two logistical corridors correspond to 76% of all agricultural exports in the period, with Santos, the busiest port, moving more than 46% of the cargo. Full article

With the development of technology and the widespread adoption of digital technology, the trade volume of electronic products keeps improving. For a country’s trade situation, it is important to study the global trade of electronic products. In this paper, the data on global trade in electronic products from 240–246 countries and regions from 2000 to 2021 are used to create complex network models. Characteristic indicators, such as the network density, average clustering coefficient, average path length, and centrality are used to analyze the evolution of the global electronic product trade network pattern. The results of the complex network analysis show the following: (1) Since 2000, global electronic products have shown a trend of fluctuating growth, showing a state of three-pole differentiation. In addition, the trade volume is unevenly distributed, with the United States and China in the leading positions. (2) The global electronics trade network has significant scale-free and small-world characteristics, with high network density and close ties between countries. (3) There are differences between the closeness centrality and the betweenness centrality of the global electronic product trade network. The core countries are mainly in Europe and North America, while the influence of Asian countries is rising. (4) The global electronic product trade network has a clear division of communities and undergoes dynamic evolution. (5) Global electronic product trade is influenced by natural resources, economic and technological strength, political culture, and other factors. Finally, three policy suggestions are made for the development of China’s electronics trade. Full article