Determination of uncertainties of geomechanical parameters of metamorphic rocks using petrographic analyses

Geomechanical parameters of intact metamorphic rocks determined from laboratory testing remain highly uncertain because of the great intrinsic variability associated with the degrees of metamorphism.The aim of this paper is to develop a proper methodology to analyze the uncertainties of geomechanical characteristics by focusing on three domains,i.e.data treatment process,schistosity angle,and mineralogy.First,the variabilities of the geomechanical laboratory data of Westwood Mine(Quebec,Canada)were examined statistically by applying different data treatment techniques,through which the most suitable outlier methods were selected for each parameter using multiple decision-making criteria and engineering judgment.Results indicated that some methods exhibited better performance in identifying the possible outliers,although several others were unsuccessful because of their limitation in large sample size.The well-known boxplot method might not be the best outlier method for most geomechanical parameters because its calculated confidence range was not acceptable according to engineering judgment.However,several approaches,including adjusted boxplot,2MADe,and 2SD,worked very well in the detection of true outliers.Also,the statistical tests indicate that the best-fitting probability distribution function for geomechanical intact parameters might not be the normal distribution,unlike what is assumed in most geomechanical studies.Moreover,the negative effects of schistosity angle on the uniaxial compressive strength(UCS)variabilities were reduced by excluding the samples within a specific angle range where the UCS data present the highest variation.Finally,a petrographic analysis was conducted to assess the associated uncertainties such that a logical link was found between the dispersion and the variabilities of hard and soft minerals.

Phylogenetic, phylogeographic and divergence time analysis of Anopheles subpictus species complex using ITS2 and COI sequences

Objective:To address the phylogenetic and phylogeographic relationship between different lineages of Anopheles(An.)subpictus species complex in most parts of the Asian continent by maximum utilization of Internal Transcriber Spacer 2(ITS2)and cytochrome C oxidase I(COI)sequences deposited at the GenBank.Methods:Seventy-five ITS2,210 COI and 26 concatenated sequences available in the NCBI database were used.Phylogenetic analysis was performed using Bayesian likelihood trees,whereas median-joining haplotype networks and time-scale divergence trees were generated for phylogeographic analysis.Genetic diversity indices and genetic differentiation were also calculated.Results:Two genetically divergent molecular forms of An.subpictus species complex corresponding to sibling species A and B are established.Species A evolved around 37-82 million years ago in Sri Lanka,India,and the Netherlands,and species B evolved around 22-79 million years ago in Sri Lanka,India,and Myanmar.Vietnam,Thailand,and Cambodia have two molecular forms:one is phylogenetically similar to species B.Other forms differ from species A and B and evolved recently in the above mentioned countries,Indonesia and the Philippines.Genetic subdivision among Sri Lanka,India,and the Netherlands is almost absent.A substantial genetic differentiation was obtained for some populations due to isolation by large geographical distances.Genetic diversity indices reveal the presence of a long-established stable mosquito population,at mutation-drift equilibrium,regardless of population fluctuations.Conclusions:An.subpictus species complex consists of more than two genetically divergent molecular forms.Species A is highly divergent from the rest.Sri Lanka and India contain only species A and B.

Matrix Assisted Laser Desorption Ionization Time of Flight(MALDI-TOF)-Mass Spectrometry and^(13)C-NMR-Identified New Compounds in Paraberlinia bifoliolata(Ekop-Beli)Bark Tannins

Extracts of plant origin,particularly tannins,are attracting growing interest for the sustainable development of materials in the industrial sector.The discovery of new tannins is therefore necessary.The aim of this work was to contribute to the understanding of the properties of Paraberlinia bifoliolata tannin by Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectroscopy MALDI-TOF/MS and Carbon 13 Nuclear Magnetic Resonance(13C NMR).The chemical composition of tannin extracted from Paraberlinia bifoliolata bark was determined,as was the mechanical strength of the resin hardened with Acacia nilotica extracts.Yield by successive water extraction was 35%.MALDI-TOF/MS analysis revealed the presence of three new compounds in this tannin,previously unknown in this family of extracts.These are 3-hydroxyproline acid,N-methyl-4-hydroxypipecolic acid and N-methyl-5-dihydroxypipecolic acid.The identification of the above molecules means that this tannin can be used for industrial applications,as a resin in the manufacture of particleboard and in the formulation of green corrosion inhibitors.This information is reinforced by 13C NMR spectrometry,which indicates the presence of several polyflavonoid units,confirming the condensed nature of the tannin.Thermomechanical analysis of the resin formed by the purified tannin of Paraberlinia bifoliolata to which a vegetal biohardener has been added provided a Modulus of Elasticity(MOE)value of 4840 MPa at 150℃,confirming its possible use as a binder resin in the manufacture of wood panels as well as for the formulation of a corrosion inhibitor.

Nitrogen mineralization in the oldest climax communities in the eastern Mediterranean region

In this study,we investigated how tree species affect N mineralization in connection to some soil properties and seconder metabolite levels of litter,in the soil of the old-est native forest communities.In the oldest pure communi-ties of Pinus nigra(PN),Fagus orientalis(FO),and Abies bornmuelleriana(AB)in the mountain range of Mount Uludağ,Bursa,Turkey,annual net yield and N mineraliza-tion in the 0-5-and 5-20-cm soil layers were determined in a field incubation study over 1 year.Sampling locations were chosen from 1300 to 1600 m a.s.l.,and moisture content(%),pH,water-holding capacity(%),organic C,total N,and C/N ratio,and annual net mineral N yield of the soil and hydro-lyzed tannic acid and total phenolic compounds in litter were compared for these forest communities.F.orientalis had the highest annual net Nmin yield(43.9±4.8 kg ha^(-1) a^(-1)),P.nigra the lowest(30.5±4.2 kg ha^(-1) a^(-1)).Our findings show that in the oldest forest ecosystems,the seasonal soil moisture content and tree species play an essential role in N cycling and that hydrolyzed tannic acids and total phenolic compounds effectively control N turnover.Tannic acid and total phenolics in the litter were found to inhibit nitrification,but total phenolics were found to stimulate ammonification.

Efficiency and effectiveness of systems for the treatment of domestic wastewater based on subsurface flow constructed wetlands in Jarabacoa, Dominican Republic

Constructed wetlands(CwW)are well known nature-based systems for water treatment.This study evaluated the efficiency and effectiveness of seven domestic wastewater treatment systems based on horizontal flow CWs in Jarabacoa,the Dominican Republic.The results showed that the CWs were efficient in reducing the degree of contamination of wastewater to levels below the Dominican wastewater discharge standards for parameters such as the 5-day biochemical oxygen demand(BOD5)and chemical oxygen demand,but not for the removal of phosphorus and fecal coliforms.In addition,a horizontal flow subsurface wetland in the peri-urban area El Dorado was evaluated in terms of the performance of wastewater treatment in tropical climatic conditions.The concentrations of heavy metals,such as zinc,copper,chromium,and iron,were found to decrease in the effluent of the wetland,and the concentrations for nickel and manganese tended to increase.The levels of heavy metals in the effluent were lower than the limit values of the Dominican wastewater discharge standards.The construction cost of these facilities was around 200 USD per population equivalent,similar to the cost in other countries in the same region.This study suggested some solutions to the improved performance of CWs:selection of a microbial flora that guarantees the reduction of nitrates and nitrites to molecular nitrogen,use of endemic plants that bioaccumulate heavy metals,combination of constructed wetlands with filtration on activated carbon,and inclusion of water purification processes that allow to evaluate the reuse of treated water.

Factors Contributing to Contamination of Street Foods in Bamako, Mali

The World Health Organization states that foodborne diseases are a worldwide public health issue. Although street foods can provide nutritious and affordable ready-to-eat meals for city dwellers, their health risks can outweigh the benefits. A cross-sectional study was conducted in the Bamako district, focusing on street food vendors near schools, universities, extensive markets, administrative centers, and major roads. We aimed to sample fifty (50) sellers per municipality, making 300 sellers for the Bamako district. We developed a survey sheet to collect data, and six teams rotated between the municipalities each month. Before starting the collection, the teams were provided administrative papers approved by the municipal authority. The survey revealed three types of sales sites: fixed (65%), semi-fixed (30%), and mobile (4.40%). The proportion of sellers was 26.8%, 23.2%, 19.7%, and 4.2% in municipalities III, IV, and I. In municipalities I, II, III, IV, and VI, respectively, 92%, 95.70%, 93%, 87.2%, and 100% of the sellers were female. The age distribution of sellers was 65.63%, 46.81%, 40.82%, 38.30%, 36.17%, 36%, and 32% in the 25-34 and 35 - 44 age groups. Illiteracy rates were 59.20%, 61.70%, 55.30%, 75%, and 56% in municipalities I, II, III, IV, and VI, respectively. The study identified two categories of sellers: 48.3% in type 1 and 51.7% in type 2. The first category comprised 154 sellers, and the second 165 sellers. The survey found that 66.00%, 56.00%, 48.90%, 44.90%, 38.30%, and 34.40% of municipal V, VI, III, I, II, and IV sales sites were open-air. In municipality I, 63.30% of the sites were under hangars, while in municipalities II and IV, the corresponding percentages were 51.10% and 59.40%, respectively. Moreover, 46.00%, 31.90%, 31.30%, 30.60%, and 27.70% of the sites in municipalities VI, II, IV, I, and III were located next to gutters. In conclusion, this study identified several factors that could compromise the quality of street foods sold in the six municipalities of Bamako.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.

Dysregulated microRNAs in type 2 diabetes and breast cancer:Potential associated molecular mechanisms

Type 2 diabetes(T2D)is a multifaceted and heterogeneous syndrome associated with complications such as hypertension,coronary artery disease,and notably,breast cancer(BC).The connection between T2D and BC is established through processes that involve insulin resistance,inflammation and other factors.Despite this comprehension the specific cellular and molecular mechanisms linking T2D to BC,especially through microRNAs(miRNAs),remain elusive.miRNAs are regulators of gene expression at the post-transcriptional level and have the function of regulating target genes by modulating various signaling pathways and biological processes.However,the signaling pathways and biological processes regulated by miRNAs that are associated with T2D and BC have not yet been elucidated.This review aims to identify dysregulated miRNAs in both T2D and BC,exploring potential signaling pathways and biological processes that collectively contribute to the development of BC.

Distribution of Atmospheric Pollution in Southern Benin

This study focuses on air quality in southern Benin in order to show public authorities what the Beninese populations are exposed to for efficient decision-making. Two sampling campaigns were carried out, one in the wet period and the other in the dry season. The measurements were taken using a monitor called an “Air Quality Monitor”. For data processing, the multiple comparison methods of Dun (1961) and the Wilcoxon test were used. To maintain legitimacy, all spatial data were included in the official cartographic repository of Benin: WGS 1984, Transverse Mercator Universe Projection (UTM), Zone 31 North. The Moran statistic was used to measure the levels of spatial autocorrelation of the variables studied and to test the significance. In order to locate the spatial subsets, the local spatial association indices of Anselin Local Moran and Getis-Ord, Gi* were used. In terms of results, on the 13 monitoring sites and the 8 parameters chosen to determine air quality, we do not note any significant inter-seasonal difference. Of the eight parameters, only three parameters present spatial autocorrelation leading to predictions of ambient air quality over the entire study area based on the distance separating the points, namely, PM2.5, PM10 and ambient air quality index (AQI). The localities affected by atmospheric pollution in South Benin are located in the south-western part of Benin, headed by Cotonou, which is heavily polluted by CO2, TCOV, PM10 and PM2.5.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

Identification of Microorganisms in Poultry Farms in N’djamena and the Border Areas of Hadjer-Lamis and Chari-Baguirmi Chad

Introduction: On the outskirts of Ndjamena, semi-industrial poultry farming and traditional poultry farming are practised informally on almost all poultry farms in Chad. This type of poultry farming is faced with real health problems attributable to a lack of monitoring of the vaccination schedule, inadequate compliance with biosecurity measures and poor application of the Ichikawa rule based on the 5 M’s. Objective: The aim of this article is to identify the microorganisms responsible for contamination of poultry farms in the study area. Method: The study was carried out from 28/04/2022 to 31/01/2023 on the basis of 300 samples taken from feed, drinking water, droppings and scrapings from poultry housing surfaces in the 30 farms that served as a framework for our research. Sampling was of the simple random type, and farms were selected on the basis of the farmers’ consent. The data were recorded on pre-established survey forms. Our study was cross-sectional, descriptive and prospective. Bacteria were isolated using the reference method NF EN ISO 6579 for Salmonella spp. and cultured on the specific medium eosin methylene blue (EMB) for Escherichia coli, Pseudomonas and Citrobacter freundii. Results: The following results emerged from this study: Escherichia coli (5.33%), Pseudomonas (1.33%), Citrobacter freundii (12%) and Salmonella paratyphi (21.68%). Conclusion: Of the 300 samples analysed, 121 (40.33%) were contaminated with pathogens. This high level of contamination is a health problem. The study shows that biosecurity is less satisfactory on the farms visited. Nevertheless, farms with a very satisfactory level of biosafety ensure food safety and variety for the population.

RELAXATION TIME LIMITS PROBLEM FOR HYDRODYNAMIC MODELS IN SEMICONDUCTOR SCIENCE

In this article, two relaxation time limits, namely, the momentum relaxation time limit and the energy relaxation time limit are considered. By the compactness argument, it is obtained that the smooth solutions of the multidimensional nonisentropic Euler-Poisson problem converge to the solutions of an energy transport model or a drift diffusion model, respectively, with respect to different time scales.

Seroprevalence of Helicobacter pylori in dyspeptic patients and its relationship with HIV infection,ABO blood groups and life style in a university hospital,Northwest Ethiopia

瞄准：在消化不良的病人之中决定 Helicobacter pylori (H pylori ) 的流行并且估计在 H pylori 感染，血型， HIV 感染和病人的生活方式之间的关系。方法：在基于医院的代表性的研究， Gondar 医院的大学的病人出席门诊病人部门被注册。Socio 人口统计的信息用问询表被收集。浆液用一个商业工具包为 anti-H pylori IgG 抗体被分析。HIV serostatus 被连接酶的免疫吸着剂试金(ELISA ) 决定。Blood grouping 被幻灯片凝集试验执行。结果：215 个消化不良的病人的一个总数在学习被包括。116 个病人(54%) 是女性，(46%) 99 是男性。Anti-H pylori IgG 抗体在重量的单位被检测一 of 184 (85.6%) 病人。流行在 50 年并且在上面的病人是显著地更高的。百分之五个病人被发现是为 HIV 的血清反应阳性的二十个点。没有重要协会为 H pylori 在性别，土佬血型，香的食谱的消费，社会经济的地位和 seropositivity 之间被发现。然而，白酒消费显著地与 H pylori 血清学被联系。结论：H pylori 感染的流行与白酒吸入和老年的历史被联系。象为 H pylori 感染的风险因素的不同饮食，酒精和社会经济的地位的效果需要进一步的学习。

Diffusion Coefficient in Silicon Solar Cell with Applied Magnetic Field and under Frequency: Electric Equivalent Circuits

In this paper, a theory on the determination of the diffusion coefficient of excess minority carriers in the base of a silicon solar cell is presented. The diffusion coefficient expression has been established and is related to both frequency modulation and applied magnetic field;the study is then carried out using the impedance spectroscopy method and Bode diagrams. From the diffusion coefficient, we deduced the diffusion length and the minority carriers’ mobility. Electric parameters were derived from the diffusion coefficient equivalent circuits.

基于遗传算法优化单类支持向量机的油田离心泵注水站异常检测

目前油田离心泵注水站多采用传统的人工巡检等方式进行异常检测,存在浪费大量资源且检测精度不高的情况。针对此问题,提出一种基于遗传算法优化单类支持向量机(genetic algorithm optimized one-class support vector machine,GA-OC-SVM)的注水站异常检测方法。首先,对注水站数据进行标准化、归一化处理以及特征提取;其次,使用遗传算法进行寻优得到最佳种群个体值作为单类支持向量机的参数,建立检测模型;最后,将GA-OC-SVM算法与孤立森林算法、局部离群因子算法等主流方法用于测试数据集的异常检测对比,并分析算法的精度,采用接受者操作特征(receiver operating characteristics,ROC)曲线进行模型评价。结果表明所提出的GA-OC-SVM算法更优,检测精度达到99%,同时能够节省大量的人力物力资源。

Rainwater harvesting systems: An urban flood risk mitigation measure in arid areas

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.

Review on the fully mulched ridge–furrow system for sustainable maize production on the semi-arid Loess Plateau

The fully mulched ridge–furrow(FMRF) system has been widely used on the semi-arid Loess Plateau of China due to its high maize(Zea mays L.) productivity and rainfall use efficiency. However, high outputs under this system led to a depletion of soil moisture and soil nutrients, which reduces its sustainability in the long run. Therefore, it is necessary to optimize the system for the sustainable development of agriculture. The development, yield-increasing mechanisms,negative impacts, optimization, and their relations in the FMRF system are reviewed in this paper. We suggest using grain and forage maize varieties instead of regular maize;mulching plastic film in autumn or leaving the mulch after maize harvesting until the next spring, and then removing the old film and mulching new film;combining reduced/notillage with straw return;utilizing crop rotation or intercropping with winter canola(Brassica campestris L.), millet(Setaria italica), or oilseed flax(Linum usitatissimum L.);reducing nitrogen fertilizer and partially replacing chemical fertilizer with organic fertilizer;using biodegradable or weather-resistant film;and implementing mechanized production. These integrations help to establish an environmentally friendly, high quality, and sustainable agricultural system, promote highquality development of dryland farming, and create new opportunities for agricultural development in the semi-arid Loess Plateau.

Developing a Secure Framework Using Feature Selection and Attack Detection Technique

Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.