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Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice science 2024年第1期31卷 47-61页

作者： Norhashila HASHIM Maimunah Mohd ALI Muhammad Razif MAHADI Ahmad Fikri ABDULLAH Aimrun WAYAYOK Muhamad Saufi Mohd KASSIM Askiah JAMALUDDIN department of Biological and Agricultural Engineering/SMART Farming Technology Research Centre Faculty of EngineeringUniversiti Putra MalaysiaSelangor 43400Malaysia department of Food Sciences Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaSelangor 43600Malaysia department of resource management&consumer studies Faculty of Human EcologyUniversiti Putra MalaysiaSelangor 43400Malaysia

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is *** review provides an insight into the role of smart farming in enhancing rice *** applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are *** challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are *** efforts have been exerted to combat the issues in rice production in order to promote rice sector *** effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial *** future prospects of smart farming in transforming existing rice production practices are also *** the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food ***,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

关键词： rice production smart farming food security agriculture sustainability

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Examining the characteristics between time and distance gaps of secondary crashes

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Transportation Safety and Environment 2024年第1期6卷 116-131页

作者： Xinyuan Liu Jinjun Tang Chen Yuan Fan Gao Xizhi Ding Smart Transport Key Laboratory of Hunan Province School of Transport and Transportation EngineeringCentral South UniversityChangsha 410075China department of Computer Science City University of Hong Kong999077Kowloon TongHong KongChina department of Geography and resource management The Chinese University of Hong KongShatinNT 999077Hong KongChina

Understanding the characteristics of time and distance gaps between the primary(PC)and secondary crashes(SC)is crucial for preventing SC ccurrences and improving road *** previous studies have tried to analyse the variation of gaps,there is limited evidence in quantifying the relationships between different gaps and various influential *** study proposed a two-layer stacking framework to discuss the time and distance ***,the framework took random forests(RF),gradient boosting decision tree(GBDT)and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression(LR)as a combiner in the second *** this basis,the local interpretable model-agnostic explanations(LIME)technology was used to interpret the output of the stacking model from both local and global *** SC dentification and feature selection,346 SCs and 22 crash-related factors were collected from California interstate *** results showed that the stacking model outperformed base models evaluated by accuracy,precision,and recall *** explanations based on LIME suggest that collision type,distance,speed and volume are the critical features that affect the time and distance *** volume can prolong queue length and increase the distance gap from the SCs to *** collision types,peak periods,workday,truck involved and tow away likely induce a long-distance ***,there is a shorter distance gap when secondary roads run in the same direction and are close to the primary *** speed is a significant factor resulting in a long-time gap,while the higher speed is correlated with a short-time *** results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs.

关键词： secondary crash(SC) time and distance gaps stacking framework local interpretable model-agnostic explanations(LIME)

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Relationship Between Individuals’Epidemic Risk Perception Within Living Space and Subjective Well-Being:Empirical Evidence from China after the First Wave of COVID-19

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Chinese Geographical Science 2024年第2期34卷 369-382页

作者： SONG Jiangyu ZHOU Suhong KWAN Mei-Po ZHENG Zhong School of Geography and Planning Sun Yat-sen UniversityGuangzhou 510006China Guangdong Provincial Engineering Research Center for Public Security and Disaster Guangzhou 510275China Institute of Space and Earth Information Science the Chinese University of Hong KongHong Kong 999077China department of Geography and resource management the Chinese University of Hong KongHong Kong 999077China Institute of Future Cities the Chinese University of Hong KongHong Kong 999077China Center for Territorial Spatial Planning and Real Estate studies Beijing Normal UniversityZhuhai 519000China

It is common to observe the epidemic risk perception(ERP)and a decline in subjective well-being(SWB)in the context of public health events,such as Corona Virus Disease 2019(COVID-19).However,there have been few studies exploring the impact of individuals’ERP within living space on their SWB,especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the *** this paper,we conducted a study with 789 participants in urban China,measuring their ERP within living space and examining its influence on their SWB using path *** results indicated that individuals’ERP within their living space had a significant negative effect on their *** density of certain types of facilities within their living space,such as bus stops,subway stations,restaurants,fast food shops,convenience shops,hospitals,and public toilets,had a significantly negative impact on their SWB,mediated by their ERP within living ***,participation in out-of-home work and other activities not only increased individuals’ERP within living space,but also strengthened its negative effect on their SWB.

关键词： subjective well-being epidemic risk perception living space the density of facilities out-of-home activities

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Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2869-2889页

作者： Adéla Hamplová Alexey Lyavdansky TomášNovák Ondrej Svojše David Franc Arnošt Veselý Czech University of Life Sciences in Prague(CULS) Faculty of Economics and ManagementDepartment of Information EngineeringPrague16500Czech Republic National Research University Higher School of Economics(HSE) Faculty of HumanitiesInstitute for Oriental and Classical StudiesMoscow101000The Russian Federation

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow *** goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological *** research culminates in several key findings and scientific *** comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this *** also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the *** dataset serves for training and evaluating the segmentation *** employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation *** study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future *** availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

关键词： Optical character recognition instance segmentation Palmyrene ancient languages computer vision

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Greenhouse gas emissions from Daihai Lake,China:Should eutrophication and salinity promote carbon emission dynamics?

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Journal of Environmental Sciences 2024年第1期135卷 407-423页

作者： Xiangwei Li Ruihong Yu Jun Wang Heyang Sun Xinyu Liu Xiaohui Ren Shuai Zhuang Zhiwei Guo Xixi Lu Inner Mongolia Key Laboratory of River and Lake Ecology School of Ecology and EnvironmentInner Mongolia UniversityHohhot 010070China Key Laboratory of Mongolian Plateau Ecology and resource Utilization Ministry of EducationHohhot 010070China Autonomous Region Collaborative Innovation Center for Integrated management of Water resources and Water Environment in the Inner Mongolia Reaches of the Yellow River Hohhot 010018China department of Geography National University of Singapore117570Singapore

Greenhouse gases(GHGs)emitted or absorbed by lakes are an important component of the global carbon ***,few studies have focused on the GHG dynamics of eutrophic saline lakes,thus preventing a comprehensive understanding of the carbon ***,we conducted four sampling analyses using a floating chamber in Daihai Lake,a eutrophication saline lake in Inner Mongolia Autonomous Region,China,to explore its carbon dioxide(CO_(2))and methane(CH_(4))*** mean CO_(2)emission flux(FCO_(2))and CH_(4)emission flux(FCH_(4))were 17.54±14.54 mmol/m^(2)/day and 0.50±0.50 mmol/m^(2)/day,*** results indicated that Daihai Lake was a source of CO_(2)and CH_(4),and GHG emissions exhibited temporal *** mean CO_(2)partial pressure(p CO_(2))and CH_(4)partial pressure(p CH_(4))were 561.35±109.59μatm and 17.02±13.45μatm,which were supersaturated relative to the *** regression and correlation analysis showed that the main influencing factors of p CO_(2)were wind speed,dissolved oxygen(DO),total nitrogen(TN)and Chlorophyll a(Chl.a),whereas the main influencing factors of p CH_(4)were water temperature(WT),Chl.a,nitrate nitrogen(NO_(3)^(-)-N),TN,dissolved organic carbon(DOC)and water *** regulated carbon mineralization and organic matter decomposition,and it was an important influencing factor of p CO_(2)and p CH_(4).Additionally,the trophic level index(TLI)significantly increased p CH_(4).Our study elucidated that salinity and eutrophication play an important role in the dynamic changes of GHG ***,research on eutrophic saline lakes needs to be strengthened.

关键词： Eutrophication Salinity Greenhouse gas emissions Inland lake Inner Mongolia Autonomous Region

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Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

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International Journal of Disaster Risk Science 2024年第1期15卷 134-148页

作者： Yuran Sun Shih‑Kai Huang Xilei Zhao department of Civil and Coastal Engineering University of FloridaGainesvilleFL 32611USA department of Emergency management and Public Administration Jacksonville State UniversityJacksonvilleAL 36265USA

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency *** studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in *** present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological *** enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new *** findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive *** outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

关键词： Artifcial Intelligence(AI) Decision-making modeling Hurricane evacuation Interpretable machine learning Nonlinearity and interaction detection

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DNA Barcoding of Insects and Its Direct Application for Plant Protection

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Open Journal of Applied Sciences 2024年第3期14卷 676-686页

作者： Peter Quandahor Iddrisu Yahaya Francis Kusi Issah Sugri Julius Yirzagla Abdul Karim Alhassan Jerry A. Nboyine George Y. Mahama Godwin Opoku Mohammed Mujitaba Dawuda Asieku Yahaya Theophilus Kwabla Tengey Rofela Combey John Abraham CSIR-Savanna Agricultural Research Institute Tamale Ghana Faculty of Agriculture Food and Consumer Science University for Development Studies Tamale Ghan department of Conservational Biology and Entomology School of Biological Sciences University of Cape Coast Ghana

The introduction of invasive insect pests across national borders has become a major concern in crop production. Accordingly, national plant protection organizations are challenge to reinforce their monitoring strategies, which are hampered by the weight and size of inspection equipment, as well as the taxonomic extensiveness of interrupted species. Moreover, some insect pests that impede farmer productivity and profitability are difficult for researchers to address on time due to a lack of appropriate plant protection measures. Farmers’ reliance on synthetic pesticides and biocontrol agents has resulted in major economic and environmental ramifications. DNA barcoding is a novel technology that has the potential to improve Integrated Pest management decision-making, which is dependent on the ability to correctly identify pest and beneficial organisms. This is due to some natural traits such as phenology or pesticide susceptibility browbeaten by IPM strategies to avert pest establishment. Specifically, Deoxyribonucleic acid (DNA) sequence information was applied effectively for the identification of some micro-organisms. This technology, DNA barcoding, allows for the identification of insect species by using short, standardized gene sequences. DNA barcoding is basically based on repeatable and accessible technique that allows for the mechanisation or automation of species discrimination. This technique bridges the taxonomic bio-security gap and meets the International Plant Protection Convention diagnostic standards for insect identification. This review therefore discusses DNA barcoding as a technique for insect pests’ identification and its potential application for crop protection.

关键词： DNA Barcoding Integrated Pest management Taxonomy Biosecurity Crop Protection

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Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China

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Journal of Arid Land 2024年第6期16卷 798-815页

作者： LU Haitian ZHAO Ruifeng ZHAO Liu LIU Jiaxin LYU Binyang YANG Xinyue College of Geography and Environmental Science Northwest Normal UniversityLanzhou 730070China Key Laboratory of resource Environment and Sustainable Development of Oasis Gansu ProvinceLanzhou 730070China School of Geography Planning and Spatial SciencesUniversity of TasmaniaHobart TAS 7005Australia School of Chinese Language and Literature Xi'an International Studies UniversityXi'an 710128China department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China School of Design and Built Environment Curtin UniversityPerth 6102Australia

Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid *** study took Gansu Province,China,a typical area with complex terrain and variable climate,as the research *** on Google Earth Engine,we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022,and quantitatively analyzed the main causes of regional differences in surface water *** findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to *** surface water area exhibited significant fluctuations,while permanent surface water area showed a steady ***,terrestrial water storage exhibited a trend of first decreasing and then increasing,correlated with the dynamics of surface water *** change and human activities jointly affected surface hydrological processes,with the impact of climate change being slightly higher than that of human ***,climate change affected the'source'of surface water to a greater extent,while human activities tended to affect the'destination'of surface *** of surface water resources faced by inland arid and semi-arid areas like Gansu Province are ***,we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water'supply-demand'balance *** study not only sheds light on the dynamics of surface water area in Gansu Province,but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.

关键词： surface water area terrestrial water storage Open-surface Water Detection Method with Enhanced Impurity Control method Google Earth Engine climate change human activities inland arid and semi-arid areas

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全球变暖加剧青藏高原湖泊效应

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Science Bulletin 2024年第7期69卷 968-977页

作者：戴玉凤汪涛盛永伟王磊陈宏斌张潇文李香钰王伟财邬俊飞陈文锋姚檀栋 State Key Laboratory of Tibetan Plateau Earth System Environment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China department of Geography University of CaliforniaLos Angeles CA 90095USA College of Information Science and Engineering Huaqiao UniversityXiamen 361021China department of resource management Tangshan Normal UniversityTangshan 063000China department of Atmospheric Science School of Environmental StudiesChina University of GeosciencesWuhan 430074China Center for Pan-third Pole Environment Lanzhou UniversityLanzhou 730000China

Lake-effect snowfall(LES)occurs when cold air moves across open *** is expected to occur more frequently over the TP,due to the intensified lake expansion caused by intensified global ***,there is an urgent need to comprehensively assess the LES over the ***,we revealed that the LES is triggered by westerly southward shift leading to the drop in air temperature and is positively correlated with lake area,wind speed and longitude across 12 large lakes(>300 km^(2))based on satellite observations and reanalysis *** a sensitivity model simulation,we determined that large lakes in the southern TP contributed to more than 50%of the snowfall in the downwind area in *** indicate that the westerly-triggered LES will increase under the future RCP4.5 climate warming scenario,highlighting the importance of developing adaptive policies to address the growing risks associated with future LES.

关键词： Lake-effect snowfall Westerly Tibetan Plateau Climate warming

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An Improved Enterprise resource Planning System Using Machine Learning Techniques

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Journal of Software Engineering and Applications 2024年第5期17卷 203-213页

作者： Ahmed Youssri Zakaria Elsayed Abdelbadea Atef Raslan Tarek Ali Mervat Gheith Al-Sayed Khater Essam A. Amin department of Software Engineering Faculty of Graduate Studies for Statistical Research (FGSSR) Cairo University Giza Egypt

Traditional Enterprise resource Planning (ERP) systems with relational databases take weeks to deliver predictable insights instantly. The most accurate information is provided to companies to make the best decisions through advanced analytics that examine the past and the future and capture information about the present. Integrating machine learning (ML) into financial ERP systems offers several benefits, including increased accuracy, efficiency, and cost savings. Also, ERP systems are crucial in overseeing different aspects of Human Capital management (HCM) in organizations. The performance of the staff draws the interest of the management. In particular, to guarantee that the proper employees are assigned to the convenient task at the suitable moment, train and qualify them, and build evaluation systems to follow up their performance and an attempt to maintain the potential talents of workers. Also, predicting employee salaries correctly is necessary for the efficient distribution of resources, retaining talent, and ensuring the success of the organization as a whole. Conventional ERP system salary forecasting methods typically use static reports that only show the system’s current state, without analyzing employee data or providing recommendations. We designed and enforced a prototype to define to apply ML algorithms on Oracle EBS data to enhance employee evaluation using real-time data directly from the ERP system. Based on measurements of accuracy, the Random Forest algorithm enhanced the performance of this system. This model offers an accuracy of 90% on the balanced dataset.

关键词： ERP HCM Machine Learning Employee Performance Pythonista Pythoneer

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