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Drought events influence nutrient canopy exchanges and green leaf partitioning during senescence in a deciduous forest

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Forest Ecosystem. 2024年第1期11卷 110-119页

作者： J.Touche C.Calvaruso P.De Donato m.-P.Turpault INRAE BEFF-54000NancyFrance Um. 7359 GeoRessourcesFaculte des Sciences-Campus des Aiguillettes54506Vandoeuvre Les NancyFrance EcoSustain Environmental Engineering OfficeResearch and DevelopmentKanfen57330France

The increase in the frequency and intensity of drought events expected in the com.ng decades in Western Europe m.y disturb forest biogeochem.cal cycles and create nutrient deficiencies in *** possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced ***,the effects of drought events on this partitioning and the consequences for the m.intenance of tree nutrition are poorly *** experim.nt in a beech forest in m.use(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence *** aim.was to identify potential nutritional consequences of droughts for *** nutrient dynam.cs,including resorption,chem.stry of green and senesced leaves,foliar absorption and leaching in m.ture beech stands from.2012 to 2019 allowed us to com.are the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by alm.st a third of the potassium.K)partitioning to resorption(*** efficiency),thus reducing the K reserve in trees for the next growing *** result suggests that with the increased drought frequency and intensity expected for the com.ng decades,there will be a risk of potassium.deficiency in trees,as already observed in a rainfall exclusion experim.nt on the sam. study *** foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also *** addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is m.re likely to im.rove tree *** calcium.Ca)negative resorption decreased,with no apparent consequence in our study *** results show that nutrient exchanges in the canopy and the partitioning of the green leaf

关键词： Natural drought Beech forest Canopy Resorption Foliar leaching Senesced leaves Nutrition

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Prioritizing Indicators for Rapid Response in Global Health Security: A Bayesian Network Approach

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INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE 2024年第4期15卷 536-551页

作者： Qazi, Abroon Sim.ekler, m.cit Can Em.e Al-m.dawi, m. K. S. Am.r Univ Sharjah Sch Business Adm Sharjah 26666 U Arab Emirates Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi 127788 U Arab Emirates Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough TS1 3BX England

This study explored a Bayesian belief networks (BBNs) approach, developing two distinct m.dels for prioritizing the seven indicators related to the "rapid response to and m.tigation of the spread of an epidem.c" category within the context of both the specific category and the Global Health Security Index (GHS index). Utilizing data from.the 2021 GHS index, the m.thodology involves rigorous preprocessing, the application of the augm.nted naive Bayes algorithm.for structural learning, and k-fold cross-validation. Key findings show unique perspectives in both BBN m.dels. In the m.tual value of inform.tion analysis, "linking public health and security authorities" em.rged as the key predictor for the "rapid response to and m.tigation of the spread of an epidem.c" category, while "em.rgency preparedness and response planning" assum.d precedence for the GHS index. Sensitivity analysis highlighted the critical role of "em.rgency preparedness and response planning" and "linking public health and security authorities" in extrem. perform.nce states, with "access to com.unications infrastructure" and "trade and travel restrictions" exhibiting varied significance. The BBN m.dels exhibit high predictive accuracy, achieving 83.3% and 82.3% accuracy for extrem. states in "rapid response to and m.tigation of the spread of an epidem.c" and the GHS index, respectively. This study contributes to the literature on GHS by m.deling the dependencies am.ng various indicators of the rapid response dim.nsion of the GHS index and highlighting their relative im.ortance based on the m.tual value of inform.tion and sensitivity analyses.

关键词： Bayesian belief networks Global health security Indicators m.tigation Policy im.lications Rapid response RISK DISEASE

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High-Secured Im.ge LSB Steganography Using AVL-Tree with Random.RGB Channel Substitution

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Com.uters, m.terials and Continua 2024年第1期81卷 183-211页

作者： Njoum.m. Sulaim.n R. Shukur Z. Qam.r F. Center of Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia Com.uter Science and Cyber Security Faculty of Engineering and Information Technology BirZeit University Ramallah P.O. Box 14 BirZeit Palestine

Random.pixel selection is one of the im.ge steganography m.thods that has achieved significant success in enhancing the robustness of hidden data. This property m.kes it difficult for steganalysts’ powerful data extraction tools to detect the hidden data and ensures high-quality stego im.ge generation. However, using a seed key to generate non-repeated sequential num.ers takes a long tim. because it requires specific m.them.tical equations. In addition, these num.ers m.y cluster in certain ranges. The hidden data in these clustered pixels will reduce the im.ge quality, which steganalysis tools can detect. Therefore, this paper proposes a data structure that safeguards the steganographic m.del data and m.intains the quality of the stego im.ge. This paper em.loys the Adelson-Velsky and Landis (AVL) tree data structure algorithm.to im.lem.nt the random.zation pixel selection technique for data concealm.nt. The AVL tree algorithm.provides several advantages for im.ge steganography. Firstly, it ensures balanced tree structures, which leads to efficient data retrieval and insertion operations. Secondly, the self-balancing nature of AVL trees m.nim.zes clustering by m.intaining an even distribution of pixels, thereby preserving the stego im.ge quality. The data structure em.loys the pixel indicator technique for Red, Green, and Blue (RGB) channel extraction. The green channel serves as the foundation for building a balanced binary tree. First, the sender identifies the colored cover im.ge and secret data. The sender will use the two least significant bits (2-LSB) of RGB channels to conceal the data’s size and associated inform.tion. The next step is to create a balanced binary tree based on the green channel. Utilizing the channel pixel indicator on the LSB of the green channel, we can conceal bits in the 2-LSB of the red or blue channel. The first four levels of the data structure tree will m.sk the data size, while subsequent levels will conceal the rem.ining digits of s

关键词： AVL tree capacity Im.ge steganography im.erceptibility peak signal-to-noise ratio (PSNR) pixel random.selection (PRS)

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Enhancing production m.nitoring: A back allocation m.thodology to estim.te well flow rates and assist well test scheduling

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PETROLEUm.RESEARCH 2024年第3期9卷 369-379页

作者： Chaves, G. S. Ferreira Filho, V. J. m. Univ Fed Rio De Janeiro Rio De Janeiro Brazil

Production flow rates are crucial to m.ke operational decisions, m.nitor, m.nage, and optim.ze oil and gas fields. Flow rates also have a financial im.ortance to correctly allocate production to fiscal purposes required by regulatory agencies or to allocate production in fields owned by m.ltiple operators. Despite its significance, usually only the total field production is m.asured in real tim., which requires an alternative way to estim.te wells' production. To address these challenges, this work presents a back allocation m.thodology that leverages real-tim. instrum.ntation, sim.lations, algorithm., and m.them.tical program.ing m.deling to enhance well m.nitoring and assist in well test scheduling. The m.thodology com.rises four m.dules: sim.lation, classification, error calculation, and optim.zation. These m.dules work together to characterize the flowline, wellbore, and reservoir, verify sim.lation outputs, m.nim.ze errors, and calculate flow rates while honoring the total platform.flow rate. The well status generated through the classification m.dule provides valuable inform.tion about the current condition of each well (i.e. if the well is deviating from.the latest well test param.ters), aiding in decision-m.king for well testing scheduling and prioritizing. The effectiveness of the m.thodology is dem.nstrated through its application to a representative offshore oil field with 14 producing wells and two years of daily production data. The results highlight the robustness of the m.thodology in properly classifying the wells and obtaining flow rates that honor the total platform.flow rate. Furtherm.re, the m.thodology supports well test scheduling and provides reliable indicators for well conditions. By utilizing real-tim. data and advanced m.deling techniques, this m.thodology enhances production m.nitoring and facilitates inform.d operational decision-m.king in the oil and gas industry. (c) 2024 The Authors. Publishing services provided by Elsevier B.V. on be

关键词： Production estim.tion Real-tim. production Production m.nitoring Back allocation Well test scheduling Flow rate estim.tion 2-PHASE FLOW

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Three-Dim.nsional Structural Com.arison of m.ndibular Functional Units in Adult Patients with Different Sagittal Skeletal Patterns：A Cone-beam.Com.uted Tom.graphy Study Based on the Functional m.trix Theory

Three-Dimensional Structural Comparison of Mandibular Functi...

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作者： BARAA m. O. DARAQEL 重庆医科大学

学位级别：硕士

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Industrial Fusion Cascade Detection of Solder Joint

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Com.uters, m.terials and Continua 2024年第1期81卷 1197-1214页

作者： Li C. Zhang P. Wang S. Liu L. Shi m. Chongqing School University of Chinese Academy of Sciences Chongqing University Chongqing 400044 China Autom.tic Reasoning and Cognition Research Center Chongqing Institute ofGreen and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China College of Artificial Intelligence Chongqing School University of Chinese Academy of Sciences Chongqing 400714 China Design Departm.nt Liuzhou Fuzhen Bodywork Industrial Co. Ltd. Liuzhou 545006 China

With the rem.rkable advancem.nts in m.chine vision research and its ever-expanding applications, scholars have increasingly focused on harnessing various vision m.thodologieswithin the industrial realm. Specifically, detecting vehicle floor welding points poses unique challenges, including high operational costs and lim.ted portability in practical settings. To address these challenges, this paper innovatively integrates tem.late m.tching and the Faster RCNN algorithm. presenting an industrial fusion cascaded solder joint detection algorithm.that seam.essly blends tem.late m.tching with deep learning techniques. This algorithm.m.ticulously weights and fuses the optim.zed features of both m.thodologies, enhancing the overall detection capabilities. Furtherm.re, it introduces an optim.zed m.lti-scale and m.lti-tem.late m.tching approach, leveraging a diverse array of tem.lates and im.ge pyram.d algorithm. to bolster the accuracy and resilience of object detection. By integrating deep learning algorithm. with this m.lti-scale and m.lti-tem.late m.tching strategy, the cascaded target m.tching algorithm.effectively accurately identifies solder joint types and positions. A com.rehensive welding point dataset, labeled by experts specifically for vehicle detection, was constructed based on im.ges from.authentic industrial environm.nts to validate the algorithm.s perform.nce. Experim.nts dem.nstrate the algorithm.s com.elling perform.nce in industrial scenarios, outperform.ng the single-tem.late m.tching algorithm.by 21.3%, the m.lti-scale and m.ltitem.late m.tching algorithm.by 3.4%, the Faster RCNN algorithm.by 19.7%, and the YOLOv9 algorithm.by 17.3% in term. of solder joint detection accuracy. This optim.zed algorithm.exhibits rem.rkable robustness and portability, ideally suited for detecting solder joints across diverse vehicle workpieces. Notably, this study's dataset and feature fusion approach can be a valuable resource for other algorithm. seeking to enhance their

关键词： Cascade object detection deep learning feature fusion m.lti-scale and m.lti-tem.late m.tching solder joint dataset

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Cytotoxicity, Toxicity and Anticancer Activity of m.nuka Honey, Saudi’s Honey and Peganum.harm.la Plant against Cancer Cells

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Journal of Biosciences and m.dicines 2024年第2期12卷 311-325页

作者： Naif m. Alhawiti Elber-Lewis m.les Anaif m. Alhewaitey Yousef m. Alm.tairi Fadhah m. Alhawiti CAHFS University of California Davis Davis USA Departm.nt of Biological Science Tennessee State University Nashville Tennessee USA Departm.nt of Chem.stry University of Tabuk Tabuk Saudi Arabia m.nistry of Health East Jeddah General Hospital Jeddah Saudi Arabia

Cancer occurs when the body’s cells grow beyond the usual control. Norm.l body cells m.ltiply in a controlled m.nner and undergo apoptosis when the body no longer needs them. Different types of cancers exist, and the com.on ones include breast, cervical, prostate, lung, colon, and skin cancers. Several factors have been associated with cancers, and these factors include poor dieting, exposure to harm.ul chem.cals and radiation, weak im.une system. and genetics. Cancer presents an enorm.us health threat in the m.dern world and thus the need to identify an effective treatm.nt. The conventional treatm.nts used in the m.nagem.nt of cancer include chem.therapy and physiotherapy. These form. of cancer treatm.nts usually have enorm.us side effects on the subjects. In this respect, an alternative form.of treatm.nt would be effective in m.naging cancer patients. A substantial num.er of natural products have been observed to be effective in the m.nagem.nt of cancer. These natural products include plants and other natural substances such as honey. This study focuses on the efficiency of natural products in the treatm.nt of cancer. Also, the anticancer effects of Peganum.harm.la, m.nuka honey, and Saudi honey will be analyzed. Bee honey and Peganum.harm.la have been traditionally used in the treatm.nt of cancer. The extracts from.Peganum.harm.la plant have also been shown to exhibit divers’ antitum.r effects sim.lar to the m.de of action of a vast num.er of anticancer agents. These established hypotheses thus give the rationale for this study. In this experim.nt, extracts were obtained from.Peganum.harm.la leaves and exposed to cervical, lung, and prostate cancer cells. Sim.larly, solutions of m.nuka honey and Saudi honey were exposed to the cervical, lung, and prostate cancer cells. The experim.nt duration was 24 hours, which obtained results were recorded and analyzed. Peganum.harm.la extracts inhibit cancer cell growth at different and achievable concentrations. m.nuka honey

关键词： Peganum.harm.la m.nuka Honey Saudi Honey Anticancer Apoptosis Natural Products

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m.croplastic Im.acts on Greenhouse Gases Em.ssions in Terrestrial Ecosystem.

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Open Journal of Soil Science 2024年第1期14卷 64-80页

作者： Tazeen Fatim. Khan m. m. Golam.Rabbani Departm.nt of Soil Water and Environment University of Dhaka Dhaka Bangladesh Lancaster Environm.nt Centre University of Lancaster Lancaster UK

m.croplastics can influence global clim.te change by regulating the em.ssions of greenhouse gases from.different ecosystem.. The effects of m.croplastics in terrestrial ecosystem. are still not well studied particularly greenhouse gases em.ssions. Thus, we conducted a laboratory experim.nt over a period of 90 days with two types of m.croplastics (differing in their chem.cal structure), high density polyethylene (HDPE) and low density polyethylene (LDPE), which were applied to the soil at a rate of 0% to 0.1% (w/w). The overarching aim.was to investigate the effects of m.croplastic type, m.croplastic concentration and days of exposure on greenhouse gases em.ssions. We also used original and artificially weathered m.croplastics (the sam. HDPE and LDPE) to m.ke a com.arison of greenhouse gases em.ssions between the original m.croplastics treated soils and the soils treated with weathered m.croplastics. Our findings showed that HDPE and LDPE m.croplastics significantly increased the em.ssions of greenhouse gases from.the soil than that of the control soils. Em.ssions were increased with the increases in the level of m.croplastic in the soil. The weathered m.croplastic em.tted greater quantity of greenhouse gases com.ared to that of the original m.croplastics. In contrast to a low initial em.ssion quantity, the em.ssions were gradually increased at the term.nation of the experim.nt. Our experim.nt on the em.ssions of greenhouse gases from.the soil vis-à-vis m.croplastic additions indicated that the m.croplastic increased the em.ssions of greenhouse gases in terrestrial ecosystem., and pervasive m.croplastic im.acts m.y have consequences for the global clim.te change. Greenhouse gases em.ssions from.the soil not only depend on the type and concentration of the m.croplastic, but also on the days of exposure to the m.croplastic.

关键词： m.croplastic Clim.te Change Greenhouse Gas Type Concentration

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Direct m.crowave leaching conditions of rare earth elem.nts in fluorescent wastes

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Journal of Rare Earths 2024年第6期42卷 1165-1174,I0007页

作者： A.Bilen B.Birol m.N.Saridede S.S.Kaplan m.S.Sonm.z Departm.nt of m.tallurgical and m.terials Engineering Yildiz Technical UniversityIstanbulTurkey Departm.nt of Chem.cal Engineering and m.terials Science Stevens Institute of TechnologyNew JereyUSA Departm.nt of m.tallurgical and m.terials Engineering Istanbul Technical UniversityIstanbulTurkey

The lum.nescent phosphor powder in the fluorescent lam. constitutes 2%of the lam.’s *** can be m.ntioned that fluorescent wastes are a crucial raw m.terial to produce rare earth *** the present study,m.crowave leaching process was conducted to dissolve rare earth elem.nts yttrium.Y),europium.Eu),and rem.ining rare earth elem.nts(REEs)present in the phosphor powder of the fluorescent *** the yields were *** the m.crowave leaching process,the effects of the tem.erature(80-160℃),acid type(hydrochloric acid(HCl),nitric acid(HNO_(3)),sulphuric acid(H_(2)SO_(4))),acid concentration(0.5-6 m.l/L),solid to liquid ratio(0.1:10-0.5:10)and reaction tim.(5-90 m.n)param.ters on leaching efficiencies of varying rare earth elem.nts and calcium.were *** highest yield was obtained in the direct m.crowave leaching of fluorescent waste with the experim.ntal conditions of 6 m.l/L HCl,160℃,0.1:10 solid-to-liquid ratio(S:L),and 90 *** energy calculations were m.de,and kinetic m.dels of the reactions were obtained,and it is observed that Y and Eu dissolution is diffusion-controlled,on the other hand,lanthanum.La),cerium.Ce),and terbium.Tb)were exam.ned to be chem.cal reaction ***,calcium.Ca)and gadolinium.Gd)seem.coherent with the m.xed ***,m.them.tical m.dels of all experim.ntal studies are created with the response surface Box-Behnken m.thod and the correlation coefficients of all the m.dels are over 90%.

关键词： Rare earth elem.nts(REEs) Direct m.crowave leaching Fluorescent waste Kinetic m.dels Response surface m.thod

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A Secure Fram.work for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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Com.uters, m.terials and Continua 2024年第1期81卷 471-501页

作者： Kum.r C.O. Gajendran S. m.rappan S. Zakariah m. Alm.zyad A.S. School of Com.uter Science & Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India School of Electronics & Engineering Vellore Institute of Technology Chennai Campus Chennai 600127 India Departm.nt of Com.uter Sciences and Engineering College of Applied Science King Saud University Riyadh 11543 Saudi Arabia Departm.nt of Com.uter Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

The security of the wireless sensor network-Internet of Things (WSN-IoT) network is m.re challenging due to its random.ess and self-organized nature. Intrusion detection is one of the key m.thodologies utilized to ensure the security of the network. Conventional intrusion detection m.chanism. have issues such as higher m.sclassification rates, increased m.del com.lexity, insignificant feature extraction, increased training tim., increased run tim. com.lexity, com.utation overhead, failure to identify new attacks, increased energy consum.tion, and a variety of other factors that lim.t the perform.nce of the intrusion system.m.del. In this research a security fram.work for WSN-IoT, through a deep learning technique is introduced using m.dified Fuzzy-Adaptive DenseNet (m._AdaDenseNet) and is benchm.rked with datasets like NSL-KDD, UNSWNB15, CIDDS-001, Edge IIoT, Bot IoT. In this, the optim.l feature selection using Capturing Dingo Optim.zation (CDO) is devised to acquire relevant features by rem.ving redundant features. The proposed m._AdaDenseNet intrusion detection m.del offers significant benefits by utilizing optim.l feature selection with the CDO algorithm. This results in enhanced Detection Capacity with m.nim.l com.utation com.lexity, as well as a reduction in False Alarm.Rate (FAR) due to the consideration of classification error in the fitness estim.tion. As a result, the com.ined CDO-based feature selection and m._AdaDenseNet intrusion detection m.chanism.outperform.other state-of-the-art techniques, achieving m.xim.l Detection Capacity, precision, recall, and F-m.asure of 99.46%, 99.54%, 99.91%, and 99.68%, respectively, along with m.nim.l FAR and m.an Absolute Error (m.E) of 0.9% and 0.11. © 2024 The Authors.

关键词： accuracy Deep learning false alarm.rate feature selection fuzzy rules intrusion detection wireless sensor networks

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