Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis...Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.展开更多
Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by reta...Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast.展开更多
The worship places in India are usually situated in and around the hilly regions and/or mountainous area and a number of devotees used to visit the holy places in every area for worship. The stability slopes upon whic...The worship places in India are usually situated in and around the hilly regions and/or mountainous area and a number of devotees used to visit the holy places in every area for worship. The stability slopes upon which these worship places are located in the hilly and mountainous region are always a major concern. Moreover, heavy precipitation, weathering conditions, seismic disturbances and human activities could cause problem to the stability of such slopes. The effect of slope instability could cause delay in traffic, loss of life of the devotees at pilgrim sites and the loss of the properties. The Saptashrungi gad temple (SGT) situated on basaltic hills belongs to Deccan volcanic and is one among the 51 Shakti Peeths and the most holy place for pilgrims. In this research, the slope stability analysis at SGT hill is assessed using Phase 2, a finite element program along the two parikrama paths: Parikrama Path 1 (or the Badi Parikrama Path “BPP”), and Parikrama Path 2 (or the Chhoti Parikrama Path “CPP”). On the basis of extensive field work, topographic survey, complex geology, orientations of joint sets, hill slope faces and geotechnical conditions, the study area has been divided into eight zones (Zone#01 to Zone#08), and eighteen topographic profiles (AA′ to RR′) are taken from these eight zones for detailed slope stability analysis. The analysis helps to identify the potentially vulnerable slope and zone of instability.展开更多
Rockfall is a major problem in high hill slopes and rocky mountainous regions and construction of highways at these rockfall prone areas often require stable slopes. The causes of rockfall are presence of discontinuit...Rockfall is a major problem in high hill slopes and rocky mountainous regions and construction of highways at these rockfall prone areas often require stable slopes. The causes of rockfall are presence of discontinuities, high angle cut slopes, heavy rainfall, and unplanned slope geometry etc. Slope geometry is one of the most triggering parameters for rockfall, when there are variations in slope angle along the profile of slope. The Present study involves rockfall hazard assessment of road cut slopes for 15 km distance starting from Mahabaleshwar town along State Highway-72 (SH-72). The vertical to subvertical cut slopes are prone to instability due to unfavorable orientation of discontinuities in slope face of weathered and altered basaltic rockmass. The predominant type of instability has been found as wedge type failure involving medium to large size blocks. In order to investigate the existing stability conditions, analyses were carried out at two locations under different slope conditions. The kinematic analysis was performed using stereographic projection method. RockFall 4.0 numerical simulator software was used to calculate the maximum bounce heights, total kinetic energies and translational velocities of the falling rockmass blocks, and a comparative analysis is presented with increasing the mass of blocks and height of the slope. The result of numerical analysis shows that varying slope angle geometry creates more problems as compared to the mass of blocks in the scenario of rockfall.展开更多
Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic syste...Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.展开更多
Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,t...Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.展开更多
Densely populated region of Ganga Plain is facing aquifer vulnerability through waterborne pollutants and groundwater stress due to indiscriminate abstraction,causing environmental and socio-economic instabilities.To ...Densely populated region of Ganga Plain is facing aquifer vulnerability through waterborne pollutants and groundwater stress due to indiscriminate abstraction,causing environmental and socio-economic instabilities.To address long-term groundwater resilience,it is crucial to understand aquifer heterogeneity and connectivity,groundwater recharge sources,effects of groundwater abstraction etc.In this con-text,present study aims to understand factors responsible for vertical and spatial variability of groundwater chemistry and to identify groundwater recharge sources in an intensively exploited agrarian region of the Ganga Plain.Interpretation of chemometric,statistical,and isotopic analysis categorises the alluvial aquifer into zone 1(G1;ground surface to 100 m)and zone 2(G2;>100 m-210 m).The group G1 samples are characterized by a wide variation in hydrochemical species,noted with pockets of F-and NO3-rich groundwater,and fresh to more evolved water types,while group G2 groundwater is characterized by a sharp increase in freshwater types and limited variation in their isotopic and hydrochemical species.The G1 groundwater chemistry is governed by soil mineralogy,local anthropogenic inputs(SO42-,Cl-,and NO3-),and manifested by multiple recharge sources(local precipitation,river,canal water,pond).The G2 group is dominated by geogenic processes and mainly recharged by the local precipitation.Geospatial signatures confirm more evolved water type for group G1 in northwestern region,while fresh-water type covers the rest of the study area.Fluoride rich groundwater is attributed to sodic water under alkaline conditions and enrichedδ18O values emphasizing role of evaporation in F-mobilization from micas and amphiboles abundant in the soil.The findings provide insight into potential groundwater vulnerability towards inorganic contaminants,and ground water recharge sources.The outcome of this study will help to develop aquifer resilience towards indiscriminate groundwater extraction for agricultural practices and aim towards sustainable management strategies in a similar hydrogeological setting.展开更多
Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express...Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social networks.Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions.This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown.The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.In this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter posts.The results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral polarity.The evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization.展开更多
In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disea...In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as COVID-19.There is a big list of infections of this coronavirus which is present in the form of a big family.This virus can cause several diseases that usually develop with a serious problem.According to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its existence.Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus.In this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete genome.This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties.This paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.展开更多
Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cas...Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.展开更多
文摘Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.
文摘Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis.It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases.However,recent image fusion techniques have encountered several challenges,including fusion artifacts,algorithm complexity,and high computing costs.To solve these problems,this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance.First,the method employs a cross-bilateral filter(CBF)that utilizes one image to determine the kernel and the other for filtering,and vice versa,by considering both geometric closeness and the gray-level similarities of neighboring pixels of the images without smoothing edges.The outputs of CBF are then subtracted from the original images to obtain detailed images.It further proposes to use edge-preserving processing that combines linear lowpass filtering with a non-linear technique that enables the selection of relevant regions in detailed images while maintaining structural properties.These regions are selected using morphologically processed linear filter residuals to identify the significant regions with high-amplitude edges and adequate size.The outputs of low-pass filtering are fused with meaningfully restored regions to reconstruct the original shape of the edges.In addition,weight computations are performed using these reconstructed images,and these weights are then fused with the original input images to produce a final fusion result by estimating the strength of horizontal and vertical details.Numerous standard quality evaluation metrics with complementary properties are used for comparison with existing,well-known algorithms objectively to validate the fusion results.Experimental results from the proposed research article exhibit superior performance compared to other competing techniques in the case of both qualitative and quantitative evaluation.In addition,the proposed method advocates less computational complexity and execution time while improving diagnostic computing accuracy.Nevertheless,due to the lower complexity of the fusion algorithm,the efficiency of fusion methods is high in practical applications.The results reveal that the proposed method exceeds the latest state-of-the-art methods in terms of providing detailed information,edge contour,and overall contrast.
文摘The worship places in India are usually situated in and around the hilly regions and/or mountainous area and a number of devotees used to visit the holy places in every area for worship. The stability slopes upon which these worship places are located in the hilly and mountainous region are always a major concern. Moreover, heavy precipitation, weathering conditions, seismic disturbances and human activities could cause problem to the stability of such slopes. The effect of slope instability could cause delay in traffic, loss of life of the devotees at pilgrim sites and the loss of the properties. The Saptashrungi gad temple (SGT) situated on basaltic hills belongs to Deccan volcanic and is one among the 51 Shakti Peeths and the most holy place for pilgrims. In this research, the slope stability analysis at SGT hill is assessed using Phase 2, a finite element program along the two parikrama paths: Parikrama Path 1 (or the Badi Parikrama Path “BPP”), and Parikrama Path 2 (or the Chhoti Parikrama Path “CPP”). On the basis of extensive field work, topographic survey, complex geology, orientations of joint sets, hill slope faces and geotechnical conditions, the study area has been divided into eight zones (Zone#01 to Zone#08), and eighteen topographic profiles (AA′ to RR′) are taken from these eight zones for detailed slope stability analysis. The analysis helps to identify the potentially vulnerable slope and zone of instability.
文摘Rockfall is a major problem in high hill slopes and rocky mountainous regions and construction of highways at these rockfall prone areas often require stable slopes. The causes of rockfall are presence of discontinuities, high angle cut slopes, heavy rainfall, and unplanned slope geometry etc. Slope geometry is one of the most triggering parameters for rockfall, when there are variations in slope angle along the profile of slope. The Present study involves rockfall hazard assessment of road cut slopes for 15 km distance starting from Mahabaleshwar town along State Highway-72 (SH-72). The vertical to subvertical cut slopes are prone to instability due to unfavorable orientation of discontinuities in slope face of weathered and altered basaltic rockmass. The predominant type of instability has been found as wedge type failure involving medium to large size blocks. In order to investigate the existing stability conditions, analyses were carried out at two locations under different slope conditions. The kinematic analysis was performed using stereographic projection method. RockFall 4.0 numerical simulator software was used to calculate the maximum bounce heights, total kinetic energies and translational velocities of the falling rockmass blocks, and a comparative analysis is presented with increasing the mass of blocks and height of the slope. The result of numerical analysis shows that varying slope angle geometry creates more problems as compared to the mass of blocks in the scenario of rockfall.
基金This work was funded and supported by the Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif University,Taif,Saudi Arabia.
文摘Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status.
基金funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-136-611-1443)DSR technical and financial support.
文摘Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.
基金extend their gratitude to Banaras Hindu University for the financial assistance granted through the Institute of Eminence(IoE,Dev Scheme 6031)Research GrantAppreciation is conveyed to the Head,Department of Geology,B.H.U.,Varanasi,for his valuable support during this study+1 种基金This study constitutes a segment of Mr.Abhinav’s Ph.D.thesis and would like to thank UGCCSIR(F.No.16-9 June 2018/2019)for the fellowship(JRF,SRF)for the financial assistance during this studyThe authors are thankful to the National Institute of Hydrology,Roorkee for carrying out major ions and stable isotopic analysis.
文摘Densely populated region of Ganga Plain is facing aquifer vulnerability through waterborne pollutants and groundwater stress due to indiscriminate abstraction,causing environmental and socio-economic instabilities.To address long-term groundwater resilience,it is crucial to understand aquifer heterogeneity and connectivity,groundwater recharge sources,effects of groundwater abstraction etc.In this con-text,present study aims to understand factors responsible for vertical and spatial variability of groundwater chemistry and to identify groundwater recharge sources in an intensively exploited agrarian region of the Ganga Plain.Interpretation of chemometric,statistical,and isotopic analysis categorises the alluvial aquifer into zone 1(G1;ground surface to 100 m)and zone 2(G2;>100 m-210 m).The group G1 samples are characterized by a wide variation in hydrochemical species,noted with pockets of F-and NO3-rich groundwater,and fresh to more evolved water types,while group G2 groundwater is characterized by a sharp increase in freshwater types and limited variation in their isotopic and hydrochemical species.The G1 groundwater chemistry is governed by soil mineralogy,local anthropogenic inputs(SO42-,Cl-,and NO3-),and manifested by multiple recharge sources(local precipitation,river,canal water,pond).The G2 group is dominated by geogenic processes and mainly recharged by the local precipitation.Geospatial signatures confirm more evolved water type for group G1 in northwestern region,while fresh-water type covers the rest of the study area.Fluoride rich groundwater is attributed to sodic water under alkaline conditions and enrichedδ18O values emphasizing role of evaporation in F-mobilization from micas and amphiboles abundant in the soil.The findings provide insight into potential groundwater vulnerability towards inorganic contaminants,and ground water recharge sources.The outcome of this study will help to develop aquifer resilience towards indiscriminate groundwater extraction for agricultural practices and aim towards sustainable management strategies in a similar hydrogeological setting.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-209-830-1443).The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social networks.Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions.This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown.The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.In this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter posts.The results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral polarity.The evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-111-611-1443)The authors,therefore,gratefully acknowledge DSR technical and financial support.
文摘In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as COVID-19.There is a big list of infections of this coronavirus which is present in the form of a big family.This virus can cause several diseases that usually develop with a serious problem.According to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its existence.Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus.In this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete genome.This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties.This paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.
基金This project was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-15-611-1443).
文摘Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.
