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Phosphorus limitation on CO_(2)fertilization effect in tropical forests informed by a coupled biogeochemical model

Forest Ecosystems 2024年第4期11卷 502-515页

作者： Zhuonan Wang Hanqin Tian Shufen Pan Hao Shi Jia Yang Naishen Liang latif kalin Christopher Anderson Natural Resources Ecology Laboratory Colorado State UniversityFort CollinsCO80523USA Center for Earth System Science and Global Sustainability Schiller Institute for Integrated Science and SocietyBoston CollegeChestnut HillMAUSA Department of Earth and Environmental Sciences Boston CollegeChestnut HillMAUSA Department of Engineering Boston CollegeChestnut HillMAUSA Research Center for Eco-Environmental Sciences State Key Laboratory of Urban and Regional EcologyChinese Academy of SciencesBeijingChina Department of Natural Resource Ecology and Management Oklahoma State UniversityStillwaterOKUSA Center for Global Environmental Research(CGER) National Institute for Environmental Studies(NIES)TsukubaIbaraki305-8506Japan College of Forestry Wildlife and EnvironmentAuburn UniversityAuburnALUSA

Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivity in tropical forests throughout the 21st century due to CO_(2)***,phosphorus(P)liaitations on vegetation photosynthesis and productivity could significantly reduce the CO_(2)fertilization ***,we used a carbon-nitrogen-phosphorus coupled model(Dynamic Land Ecosystem Model;DLEM-CNP)with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests *** model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem *** further compared the simulations from DLEM-CNP,DLEM-CN,and DLEMC and the results showed that the inclusion of P processes reduced the CO_(2)fertilization effect on gross primary production(GPP)by 25%and 45%,and net ecosystem production(NEP)by 28%and 41%,respectively,relative to CN-only and C-on ly *** the 1860s to the 2010s,the DLEM-CNP estimated that in tropical forests GPP increased by 17%,plant respiration(Ra)increased by 18%,ecosystem respiration(Rh)increased by 13%,NEP increased by 121%per unit area,***,factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO_(2) fertilization effect had been offset by 135%due to deforestation from the 1860s to the *** study highlights the importance of P limitation on the C cycle and the weakened CO_(2)fertilization effect resulting from P limitation in tropical forests.

关键词： Tropical forests Carbon-nitrogen-phosphorus model Phosphorus limitation CO_(2)fertilization effect Terrestfial ecosy stem model

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Combined and synergistic effects of climate change and urbanization on water quality in the Wolf Bay watershed,southern Alabama

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Journal of Environmental Sciences 2018年第2期30卷 107-121页

作者： Ruoyu Wang latif kalin Purdue University Department of Agricultural and Biological Engineering Auburn University School of Forestry and Wildlife Sciences

This study investigated potential changes in flow, total suspended solid（TSS） and nutrient（nitrogen and phosphorous） loadings under future climate change, land use/cover（LULC）change and combined change scenarios in the Wolf Bay watershed, southern Alabama,USA. Four Global Circulation Models（GCMs） under three Special Report Emission Scenarios（SRES） of greenhouse gas were used to assess the future climate change（2016–2040）. Three projected LULC maps（2030） were employed to reflect different extents of urbanization in future. The individual, combined and synergistic impacts of LULC and climate change on water quantity/quality were analyzed by the Soil and Water Assessment Tool（SWAT）.Under the ＂climate change only＂ scenario, monthly distribution and projected variation of TSS are expected to follow a pattern similar to streamflow. Nutrients are influenced both by flow and management practices. The variation of Total Nitrogen（TN） and Total Phosphorous（TP） generally follow the flow trend as well. No evident difference in the N：P ratio was projected. Under the ＂LULC change only＂ scenario, TN was projected to decrease,mainly due to the shrinkage of croplands. TP will increase in fall and winter. The N：P ratio shows a strong decreasing potential. Under the ＂combined change＂ scenario, LULC and climate change effect were considered simultaneously. Results indicate that if future loadings are expected to increase/decrease under any individual scenario, then the combined change will intensify that trend. Conversely, if their effects are in opposite directions, an offsetting effect occurs. Science-based management practices are needed to reduce nutrient loadings to the Bay.

关键词： Climate change Urbanization Sediment Nutrients SWAT

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Impacts of urban headwater disturbances on downstream sediment loading where streamside management zones for forest harvesting activities are present downstream

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International Journal of Sediment Research 2017年第4期32卷 555-563页

作者： Ilkim Cavus latif kalin Ferhat Kara Ministry of Forestry and Water Affairs.General Directorate of Combating Desertification and Erosion Auburn University School of Forestry and Wildlife Sciences Kastamonu University Faculty of Forestry

Watershed disturbances caused by land use/land cover(LU/LC) change, intensive forestry practices, and other forms of watershed disturbance are known to negatively affect water quality, if best management practices(BMPs) are not present. Disturbances near or around headwaters where BMPs are absent or are inadequate may impact downstream water quality and stream ecology regardless of the presence of downstream BMPs. In this study, the recovery of two small forest-dominated watersheds(Ww and Ew) in Auburn, Alabama, USA, was monitored 6-years after a partial clearcut was done. The streamside management zone(SMZ) in Ww was also partially harvested shortly after the partial clearcut to improve its sediment trapping effectiveness over the long-term. The hydrology and sediment transport in these watersheds were expected to stabilize over this period, however, new disturbances in the headwaters of both watersheds changed the watershed dynamics. Streamflow and sediment load were examined over a15-month period from February 2014 to May 2015. The clearcut in Ww and Ew, and the partially harvested SMZ in Ww seemed to have recovered; however, substantial amount of sediment load were measured across all monitoring sites in both watersheds as a result of upstream disturbances. This study emphasizes the necessity of watershed management as a whole, and the importance of BMP applications within the entire watershed. It also highlights the fact that watershed management and BMP implementation is a dynamic process, thus, strategies and techniques need to be continuously adapted in response to environmental changes.

关键词： Headwater disturbances Best management practices Sediment load Southeast USA

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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Intelligent Automation & Soft Computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema Akkasha latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West High School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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Redefining biofuels:Investigating oil palm biomass as a promising cellulose feedstock for nitrocellulose-based propellant production

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Defence Technology（防务技术） 2024年第7期37卷 111-132页

作者： Khoirul Solehah binti Abdul Rahim Alinda binti Samsuri Siti Hasnawati binti Jamal Siti Aminah binti Mohd Nor Siti Nor Ain binti Rusly Hafizah binti Ariff Nur Shazwani binti Abdul latif Centre for Tropicalization(CENTROP) Defence Research InstituteUniversiti Pertahanan Nasional MalaysiaKem Sungai Besi57000 Kuala LumpurMalaysia Department of Chemistry and Biology Centre for Defence Foundation StudiesUniversiti Pertahanan Nasional MalaysiaKem Sungai Besi57000 Kuala LumpurMalaysia

This review paper explores the potential of oil palm biomass as a valuable cellulose source for the production of nitrocellulose-based propellants,contributing to the green revolution and sustainable energy *** highlights the availability of the corresponding biomass in Malaysia and in line with global studies,the chemical compositions,as well as a brief description of current technologies for converting biomass of oil palm into value added products specifically *** to achieve maximum utilization of biomass from oil palm industry for cellulose production and prospective source for nitrocellulose-based propellant are also *** methodology section outlines the pretreatment of lignocellulosic fibres,cellulose extraction,and nitrocellulose production ***,the review underscores the prospective of palm oil biomass as a sustainable cellulose source for propellant manufacturing,while acknowledging the need for further research and advancements in the field.

关键词： Biopolymer Cellulose nitrate Energetic materials Green technology Palm oil

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Geospatial Variability of Cholera Cases in Malawi Based on Climatic and Socioeconomic Influences

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Journal of Geographic Information System 2024年第1期16卷 1-20页

作者： Emmanuel Chinkaka Francis Chauluka Ruth Chinkaka Billy Kachingwe Esther Banda latif Department of Geography and Spatial Sciences University of Delaware Newark USA Department of Earth Sciences Malawi University of Science and Technology Limbe Malawi Department of Water Resources Management Malawi University of Science and Technology Limbe Malawi Department of Obstetrics and Gynecology Queen Elizabeth Central Hospital Blantyre Malawi Department of Public Health Nkhoma Mission Hospital Nkhoma Malawi

Cholera remains a public health threat in most developing countries in Asia and Africa including Malawi with seasonal recurrent outbreaks. Malawi’s recent Cholera outbreak in 2022 and 2023, exhibited higher morbidity and mortality rates than the past two decades. Lack of spatiotemporal-based technology and variability assessment tools in Malawi’s Cholera monitoring and management, limit our understanding of the disease’s epidemiology. The present work developed a spatiotemporal variability model for Cholera disease at district level and its relationship to socioeconomic and climatic factors based on cumulative confirmed Cholera cases in Malawi from March 2022 to July 2023 using Z-score statistic and multiscale geographically weighted regression (MGWR) in a Geographical Information System (GIS). We found out that socioeconomic factors such as access to safe drinking water, population density and poverty level, and climatic factors including temperature and rainfall strongly influenced Cholera prevalence in a complex and multifaceted manner. The model shows that Lilongwe, Mangochi, Blantyre and Balaka districts were highly vulnerable to Cholera disease followed by lakeshore districts of Salima, Nkhotakota, Nkhata-Bay and Karonga than other districts. We recommend strategic measures such as Water, Sanitation, and Hygiene (WASH) interventions, community awareness on proper water storage, Cholera case management, vaccination campaigns and spatial-based surveillance systems in the most affected districts. This research has shown that MGWR, as a surveillance system, has the potential of providing insights on the disease’s spatial patterns for public health authorities to identify high-risk districts and implement early response interventions to reduce the spread of the disease.

关键词： Cholera Geospatial Variability Prevalence GIS MGWR Vulnerability Malawi

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Recognition of the inducible,secretory small protein OsSSP1 by the membrane receptor OsSSR1 and the co-receptor OsBAK1 confers rice resistance to the blast fungus

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Molecular Plant 2024年第5期17卷 807-823页

作者： Tianfeng Zhao Shijie Ma Ziying Kong Haimiao Zhang Yi Wang Junzhe Wang Jiazong Liu Wanzhen Feng Tong Liu Chunyan Liu Suochen Liang Shilin Lu Xinyu Li Haipeng Zhao Chongchong Lu Muhammad Zunair latif Ziyi Yin Yang Li Xinhua Ding State Key Laboratory of Crop Biology Shandong Provincial Key Laboratory for Biology of Vegetable Diseases and Insect PestsCollege of Plant ProtectionShandong Agricultural UniversityTaian 271018ShandongChina College of Breeding and Multiplication(Sanya Institute of Breeding and Multiplication) Hainan UniversitySanya 572024HainanChina

The plant apoplast,which serves as the frontline battleground for long-term host–pathogen interactions,harbors a wealth of disease resistance ***,the identification of the disease resistance proteins in the apoplast is relatively *** this study,we identified and characterized the rice secretory protein OsSSP1(Oryza sativa secretory small protein 1).OsSSP1 can be secreted into the plant apoplast,and either in vitro treatment of recombinant OsSSP1 or overexpression of OsSSP1 in rice could trigger plant immune *** expression of OsSSP1 is suppressed significantly during Magnaporthe oryzae infection in the susceptible rice variety Taibei 309,and OsSSP1-overexpressing lines all show strong resistance to *** the knockout and overexpression results,we found that OsSSP1 positively regulates plant immunity in response to fungal ***,the recognition and immune response triggered by OsSSP1 depend on an uncharacterized transmembrane OsSSR1(secretory small protein receptor 1)and the key co-receptor OsBAK1,since most of the induced immune response and resistance are lost in the absence of OsSSR1 or ***,the OsSSP1 protein is relatively stable and can still induce plant resistance after 1 week of storage in the open environment,and exogenous OsSSP1 treatment for a 2-week period did not affect rice ***,our study reveals that OsSSP1 can be secreted into the apoplast and percepted by OsSSR1 and OsBAK1 during fungal infection,thereby triggering the immune response to enhance plant resistance to *** findings provide novel resources and potential strategies for crop breeding and disease control.

关键词： rice plant apoplast OsssP1 receptor plant resistance Magnaporthe oryzae

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