The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure t...The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.展开更多
Soil and plant samples were collected from roadside sites (along with primary, secondary and tertiary roads) and reference site to investigate the contamination of soils and old common plant species with lead (Pb) and...Soil and plant samples were collected from roadside sites (along with primary, secondary and tertiary roads) and reference site to investigate the contamination of soils and old common plant species with lead (Pb) and cadmium (Cd) in Peshawar City, Pakistan. All the data were analyzed using ANOVA analysis that showed a significant (P ≤ 0.01) variation in Pb and Cd concentrations in the roadside soils and plants as compared to the reference site. The mean concentrations of Pb and Cd were 53.9 and 6.0 mg kg-1 in soils and 49.1 and 10.9 mg kg-1 in plants, respectively. Significant variation (P ≤ 0.01) in concentrations of Pb and Cd in soil and plant samples along with primary, secondary and tertiary roads might be due to different traffic densities. The highest value (9.4) of metal accumulation index (MAI) was observed for Eucalyptus camaldulensis. In selected plant species, the Pb and Cd accumulation was found in the order of E. camaldulensis > Ficus elastica > Dalbergia sissoo > Alstonia scholaris. The roadside soils and plants were highly contaminated with Pb and Cd as compared to the reference site.展开更多
Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but earl...Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches.展开更多
Application of plant growth-promoting rhizobacteria （PGPR） has been shown to increase legume growth and development under field and controlled environmental conditions. The present study was conducted to isolate pla...Application of plant growth-promoting rhizobacteria （PGPR） has been shown to increase legume growth and development under field and controlled environmental conditions. The present study was conducted to isolate plant growth-promoting rhizobacteria （PGPR） from the root nodules of lentil （Lens culinaris Medik.） grown in arid/semi-arid region of Punjab, Pakistan and examined their plant growth-promoting abilities. Five bacterial isolates were isolated, screened in vitro for plant growth-promoting （PGP） characteristics and their effects on the growth of lentil were assessed under in vitro, hydroponic and greenhouse （pot experiment） conditions. All the isolates were Gram negative, rod-shaped and circular in form and exhibited the plant growth-promoting attributes of phosphate solubilization and auxin （indole acetic acid, IAA） production. The IAA production capacity ranged in 0.5-11.0μg mL-1 and P solubilization ranged in 3-16 mg L-1. When tested for their effects on plant growth, the isolated strains had a stimulatory effect on growth, nodulation and nitrogen （N） and phosphorus （P） uptake in plants on nutrient-deficient soil. In the greenhouse pot experiment, application of PGPR significantly increased shoot length, fresh weight and dry weight by 65%, 43% and 63% and the increases in root length, fresh weight and dry weight were 74%, 54% and 92%, respectively, as compared with the uninoculated control. The relative increases in growth characteristics under in vitro and hydroponic conditions were even higher. PGPR also increased the number of pods per plant, 1 000-grain weight, dry matter yield and grain yield by 50%, 13%, 2870 and 29%, respectively, over the control. The number of nodules and nodule dry mass increased by 170% and 136%, respectively. After inoculation with effective bacterial strains, the shoot, root and seed N and P contents increased, thereby increasing both N and P uptake in plants. The root elongation showed a positive correlation （R2 = 0.67） with the IAA production and seed yield exhibited a positive correlation （R2 = 0.82） with root nodulation. These indicated that the isolated PGPR rhizobial strains can be best utilized as potential agents or biofertilizers for stimulating the growth and nutrient accumulation of lentil.展开更多
The 0.8 Me V copper ( Cu) ion beam irradiation-induced effects on structural, morphological and optical properties of tin dioxide nanowires (SnO_(2)NWs) are investigated. The samples are irradiated at three different ...The 0.8 Me V copper ( Cu) ion beam irradiation-induced effects on structural, morphological and optical properties of tin dioxide nanowires (SnO_(2)NWs) are investigated. The samples are irradiated at three different doses 5 × 10^12 ions/cm^(2), 1 ×10^(13) ions/cm^(2) and 5 × 10^(13) ions/em^(2) at room temperature. The XRD analysis shows that the tetragonal phase of SnO_(2)NWs remains stable after Cu ion irradiation, but with increasing irradiation dose level the crystal size increases due to ion beam induced coalescence of NWs. The FTIR spectra of pristine SnO_(2)NWs exhibit the chemical composition of SnO_(2)while the Cn-O bond is also observed in the FTIR spectra after Cu ion beam irradiation. The presence of Cu impurity in SnO_(2)is further confirmed by calculating the stopping range of Cu ions by using TRM/SRIM code. Optical properties of SnO_(2)NWs are studied before and after Cu ion irradiation. Band gap analysis reveMs that the band gap of irradiated samples is found to decrease compared with the pristine sample. Therefore, ion beam irradiation is a promising technology for nanoengineering and band gap tailoring.展开更多
Silver carp,Hypopthalmichthys molitrix is one of the most economically valuable fish species in Bangladesh.However,its production is often hindered by parasite-induced mortality.The present study reports the intensity...Silver carp,Hypopthalmichthys molitrix is one of the most economically valuable fish species in Bangladesh.However,its production is often hindered by parasite-induced mortality.The present study reports the intensity of parasitic infestation in 216 specimens of H.molitrix collected from different fish markets in Rajshahi City,Bangladesh.Nine different parasite species (Trichodina pediculatus,Dactylogyrus vastator,Ichthyophthirius multifilis,Gyrodactylus elegans,Lernaea sp.,Apiosoma sp.,Myxobolus rohitae,Camallanus ophiocephali,and Pallisentis ophiocephali) were recovered from the gill,skin,stomach,and intestine of host fish.The highest level of infection was observed for host skin,while lower levels were observed for host gill,stomach,and intestine.The results also revealed that the intensity of parasite infection in different organs of H.molitrix varied with the season.In particular,the highest levels of infection were recorded during the winter period (November-February),when fish are most susceptible to parasites.The findings of the study will help in the management and conservation of H.molitrix.展开更多
Earthquake 2001 in Bhuj region of Kachchh district of Gujarat(India) was one of the most devastating earthquakes in the Indian history. This earthquake has caused severe damage to human life and properties. The impact...Earthquake 2001 in Bhuj region of Kachchh district of Gujarat(India) was one of the most devastating earthquakes in the Indian history. This earthquake has caused severe damage to human life and properties. The impact of earthquake on groundwater resources at many locations was significant. Steep rise in static water level due to earthquake was observed at Bhachau and Chandarani. At other location groundwater followed the declining trend.展开更多
An experiment upon an agri-silvicultural system involving Willow （Salix alba） tree, Kale （Brassica oleracea var. acephala） and Knol khol （Brassica oleracea var. caularapa） was laid in randomized block designed a...An experiment upon an agri-silvicultural system involving Willow （Salix alba） tree, Kale （Brassica oleracea var. acephala） and Knol khol （Brassica oleracea var. caularapa） was laid in randomized block designed at farmers＇ willow field at Shalimar near Sher-e- Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar India during 2005 and 2006. The main plot was divided into sub-spots with 8 m × 2 m in size each in which four two-year-old willow （Salix alba） trees were at a spacing of 2 m ×2 m in a sub-spot. The intercrops were maintained at recommended spacing and supplied with recommended doses of fertilizers. The benefit-cost ratio in willow plantation intercropped with vegetable crops of Kale and Knol Khol was analyzed and compared with the benefit-cost ratio of sole willow tree forestry. The results showed that every rupee invested in plantation of agri-silvicultural system generates benefit-cost ratio of 2.78 and 2.79 in case of Willow intercropping with Kale and Willow with Knol khol, respectively, while as for sole crop of willows benefit-cost ratio was calculated to be 2.66. These results provided circumstantial evidence in favour of adopting agroforestry involving willow instead of Sole tree forestry.展开更多
Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lowe...Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.展开更多
The electron flux oscillations in photo-detachment of a non-collinear tri-atomic anion have been studied by taking each atom of the system as a coherent source of detached-electron wave. These electron waves traversin...The electron flux oscillations in photo-detachment of a non-collinear tri-atomic anion have been studied by taking each atom of the system as a coherent source of detached-electron wave. These electron waves traversing along three different trajectories result in a quantum interference. An analytical expression of detached-electron flux is evaluated for various detached-electron energies and for different geometrical shapes of the system. The results show that the electron flux distributions exhibit molecular shape-induced oscillatory structures. These oscillations are explained using the semi- classical closed-orbit theory; the outgoing electron waves produced from one center are propagated in the vicinity of the sources at other centers. It is also observed that in a particular case our non-collinear tri-atomic system reduces to the collinear tri-atomic system recently published.展开更多
Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.H...Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.展开更多
This work provides the design and analysis of a single layer,linearly polarized millimeter wave reflectarray antenna with mutual coupling optimization.Detailed analysis was carried out at 26GHz design frequency using ...This work provides the design and analysis of a single layer,linearly polarized millimeter wave reflectarray antenna with mutual coupling optimization.Detailed analysis was carried out at 26GHz design frequency using the simulations of the reflectarray unit cells as well as the periodic reflectarray antenna.The simulated results were verified by the scattering parameter and far-fieldmeasurements of the unit cell and periodic arrays,respectively.Aclose agreement between the simulated and measured results was observed in all the cases.Apart from the unit cells and reflectarray,the waveguide and horn antenna were also fabricated to be used in the measurements.The measured scattering parameter results of the proposed circular ring unit cells provided a maximum reflection loss of 2.8 dB with phase errors below 10°.On the other hand,the measured far-field results of the 20×20 reflectarray antenna provided a maximum gain of 26.45 dB with a maximum 3 dB beam width of 12°and 1 dB gain drop bandwidth of 13.1%.The performance demonstrated by the proposed reflectarray antenna makes it a potential candidate to be used in modern-day applications such as 5th Generation(5G)and 6th Generation(6G)communication systems.展开更多
Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find ...Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.展开更多
Zn1-xCrxO （x = 0.00, 0.01, 0.03, 0.05, 0.07, and 0.09） nanoparticles were synthesized, by an auto-com- bustion method. Structural, optical, and magnetic characteristics of Cr-doped ZnO samples calcined at 600 ℃ hav...Zn1-xCrxO （x = 0.00, 0.01, 0.03, 0.05, 0.07, and 0.09） nanoparticles were synthesized, by an auto-com- bustion method. Structural, optical, and magnetic characteristics of Cr-doped ZnO samples calcined at 600 ℃ have been analyzed by using X-ray diffraction （XRD）, field emission scanning electron microscope （FESEM）, UV-Vis spectroscopy and vibrating sample magnetometer （VSM）. The XRD data confirmed the hexagonal wurtzite structure of pure and Cr-doped ZnO nanoparticles. The calculated values of grain size using Scherrer＇s formula are in the range of 30.7-9.2 nm. The morphology of nanopowders has been observed by FESEM, and EDS results con- firmed a systematic increase of Cr content in the samples and clearly indicate with no impurity element. The band gaps, computed by UV-Vis spectroscopy, are in the range of 2.83-2.35 eV for different doping concentrations. By analyzing VSM data, significantly enhanced room temperature ferromagnetism is identified in Cr-doped ZnO samples. The value of magnetization is a 12 times increased of the value reported by Daun et al. （2010）. Room temperature ferromagnetism of the nanoparticles is of vital prominence for spintronics applications.展开更多
文摘The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.
基金Supported by the University of Peshawar, Pakistan
文摘Soil and plant samples were collected from roadside sites (along with primary, secondary and tertiary roads) and reference site to investigate the contamination of soils and old common plant species with lead (Pb) and cadmium (Cd) in Peshawar City, Pakistan. All the data were analyzed using ANOVA analysis that showed a significant (P ≤ 0.01) variation in Pb and Cd concentrations in the roadside soils and plants as compared to the reference site. The mean concentrations of Pb and Cd were 53.9 and 6.0 mg kg-1 in soils and 49.1 and 10.9 mg kg-1 in plants, respectively. Significant variation (P ≤ 0.01) in concentrations of Pb and Cd in soil and plant samples along with primary, secondary and tertiary roads might be due to different traffic densities. The highest value (9.4) of metal accumulation index (MAI) was observed for Eucalyptus camaldulensis. In selected plant species, the Pb and Cd accumulation was found in the order of E. camaldulensis > Ficus elastica > Dalbergia sissoo > Alstonia scholaris. The roadside soils and plants were highly contaminated with Pb and Cd as compared to the reference site.
文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches.
基金Supported by the University of Azad Jammu and Kashmir, Pakistan and the Pakistan Agriculture Research Council, Pakistan (No. ALP NR-27)
文摘Application of plant growth-promoting rhizobacteria （PGPR） has been shown to increase legume growth and development under field and controlled environmental conditions. The present study was conducted to isolate plant growth-promoting rhizobacteria （PGPR） from the root nodules of lentil （Lens culinaris Medik.） grown in arid/semi-arid region of Punjab, Pakistan and examined their plant growth-promoting abilities. Five bacterial isolates were isolated, screened in vitro for plant growth-promoting （PGP） characteristics and their effects on the growth of lentil were assessed under in vitro, hydroponic and greenhouse （pot experiment） conditions. All the isolates were Gram negative, rod-shaped and circular in form and exhibited the plant growth-promoting attributes of phosphate solubilization and auxin （indole acetic acid, IAA） production. The IAA production capacity ranged in 0.5-11.0μg mL-1 and P solubilization ranged in 3-16 mg L-1. When tested for their effects on plant growth, the isolated strains had a stimulatory effect on growth, nodulation and nitrogen （N） and phosphorus （P） uptake in plants on nutrient-deficient soil. In the greenhouse pot experiment, application of PGPR significantly increased shoot length, fresh weight and dry weight by 65%, 43% and 63% and the increases in root length, fresh weight and dry weight were 74%, 54% and 92%, respectively, as compared with the uninoculated control. The relative increases in growth characteristics under in vitro and hydroponic conditions were even higher. PGPR also increased the number of pods per plant, 1 000-grain weight, dry matter yield and grain yield by 50%, 13%, 2870 and 29%, respectively, over the control. The number of nodules and nodule dry mass increased by 170% and 136%, respectively. After inoculation with effective bacterial strains, the shoot, root and seed N and P contents increased, thereby increasing both N and P uptake in plants. The root elongation showed a positive correlation （R2 = 0.67） with the IAA production and seed yield exhibited a positive correlation （R2 = 0.82） with root nodulation. These indicated that the isolated PGPR rhizobial strains can be best utilized as potential agents or biofertilizers for stimulating the growth and nutrient accumulation of lentil.
基金Supported by the Department of Physics,the University of AJKHigh Tech.Centralized Instrumentation Lab,the University of AJK,Pakistanthe Experimental Physics Division,and the National Center for Physics,Islamabad Pakistan
文摘The 0.8 Me V copper ( Cu) ion beam irradiation-induced effects on structural, morphological and optical properties of tin dioxide nanowires (SnO_(2)NWs) are investigated. The samples are irradiated at three different doses 5 × 10^12 ions/cm^(2), 1 ×10^(13) ions/cm^(2) and 5 × 10^(13) ions/em^(2) at room temperature. The XRD analysis shows that the tetragonal phase of SnO_(2)NWs remains stable after Cu ion irradiation, but with increasing irradiation dose level the crystal size increases due to ion beam induced coalescence of NWs. The FTIR spectra of pristine SnO_(2)NWs exhibit the chemical composition of SnO_(2)while the Cn-O bond is also observed in the FTIR spectra after Cu ion beam irradiation. The presence of Cu impurity in SnO_(2)is further confirmed by calculating the stopping range of Cu ions by using TRM/SRIM code. Optical properties of SnO_(2)NWs are studied before and after Cu ion irradiation. Band gap analysis reveMs that the band gap of irradiated samples is found to decrease compared with the pristine sample. Therefore, ion beam irradiation is a promising technology for nanoengineering and band gap tailoring.
基金Project supported by the Universiti Kebangsaan Malaysia (UKM)through Young Researcher Incentive Grant (No. GGPM-2011-057)UKM Research Grant (No. UKM-OUP-FST-2012)
文摘Silver carp,Hypopthalmichthys molitrix is one of the most economically valuable fish species in Bangladesh.However,its production is often hindered by parasite-induced mortality.The present study reports the intensity of parasitic infestation in 216 specimens of H.molitrix collected from different fish markets in Rajshahi City,Bangladesh.Nine different parasite species (Trichodina pediculatus,Dactylogyrus vastator,Ichthyophthirius multifilis,Gyrodactylus elegans,Lernaea sp.,Apiosoma sp.,Myxobolus rohitae,Camallanus ophiocephali,and Pallisentis ophiocephali) were recovered from the gill,skin,stomach,and intestine of host fish.The highest level of infection was observed for host skin,while lower levels were observed for host gill,stomach,and intestine.The results also revealed that the intensity of parasite infection in different organs of H.molitrix varied with the season.In particular,the highest levels of infection were recorded during the winter period (November-February),when fish are most susceptible to parasites.The findings of the study will help in the management and conservation of H.molitrix.
文摘Earthquake 2001 in Bhuj region of Kachchh district of Gujarat(India) was one of the most devastating earthquakes in the Indian history. This earthquake has caused severe damage to human life and properties. The impact of earthquake on groundwater resources at many locations was significant. Steep rise in static water level due to earthquake was observed at Bhachau and Chandarani. At other location groundwater followed the declining trend.
文摘An experiment upon an agri-silvicultural system involving Willow （Salix alba） tree, Kale （Brassica oleracea var. acephala） and Knol khol （Brassica oleracea var. caularapa） was laid in randomized block designed at farmers＇ willow field at Shalimar near Sher-e- Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar India during 2005 and 2006. The main plot was divided into sub-spots with 8 m × 2 m in size each in which four two-year-old willow （Salix alba） trees were at a spacing of 2 m ×2 m in a sub-spot. The intercrops were maintained at recommended spacing and supplied with recommended doses of fertilizers. The benefit-cost ratio in willow plantation intercropped with vegetable crops of Kale and Knol Khol was analyzed and compared with the benefit-cost ratio of sole willow tree forestry. The results showed that every rupee invested in plantation of agri-silvicultural system generates benefit-cost ratio of 2.78 and 2.79 in case of Willow intercropping with Kale and Willow with Knol khol, respectively, while as for sole crop of willows benefit-cost ratio was calculated to be 2.66. These results provided circumstantial evidence in favour of adopting agroforestry involving willow instead of Sole tree forestry.
文摘Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.
文摘The electron flux oscillations in photo-detachment of a non-collinear tri-atomic anion have been studied by taking each atom of the system as a coherent source of detached-electron wave. These electron waves traversing along three different trajectories result in a quantum interference. An analytical expression of detached-electron flux is evaluated for various detached-electron energies and for different geometrical shapes of the system. The results show that the electron flux distributions exhibit molecular shape-induced oscillatory structures. These oscillations are explained using the semi- classical closed-orbit theory; the outgoing electron waves produced from one center are propagated in the vicinity of the sources at other centers. It is also observed that in a particular case our non-collinear tri-atomic system reduces to the collinear tri-atomic system recently published.
文摘Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.
基金The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group No.RG-21-12-08.
文摘This work provides the design and analysis of a single layer,linearly polarized millimeter wave reflectarray antenna with mutual coupling optimization.Detailed analysis was carried out at 26GHz design frequency using the simulations of the reflectarray unit cells as well as the periodic reflectarray antenna.The simulated results were verified by the scattering parameter and far-fieldmeasurements of the unit cell and periodic arrays,respectively.Aclose agreement between the simulated and measured results was observed in all the cases.Apart from the unit cells and reflectarray,the waveguide and horn antenna were also fabricated to be used in the measurements.The measured scattering parameter results of the proposed circular ring unit cells provided a maximum reflection loss of 2.8 dB with phase errors below 10°.On the other hand,the measured far-field results of the 20×20 reflectarray antenna provided a maximum gain of 26.45 dB with a maximum 3 dB beam width of 12°and 1 dB gain drop bandwidth of 13.1%.The performance demonstrated by the proposed reflectarray antenna makes it a potential candidate to be used in modern-day applications such as 5th Generation(5G)and 6th Generation(6G)communication systems.
文摘Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for them.In the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business.In this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one year.The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future.The permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the future.The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC curve.RF model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree classifier.Hence,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour.Our research also reveals the importance of page value as a key feature for capturing online purchasing trends.This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.
基金Project supported by the Office of Research,Innovation,and Commercialization(ORIC),MUST Mirpur(AJK)
文摘Zn1-xCrxO （x = 0.00, 0.01, 0.03, 0.05, 0.07, and 0.09） nanoparticles were synthesized, by an auto-com- bustion method. Structural, optical, and magnetic characteristics of Cr-doped ZnO samples calcined at 600 ℃ have been analyzed by using X-ray diffraction （XRD）, field emission scanning electron microscope （FESEM）, UV-Vis spectroscopy and vibrating sample magnetometer （VSM）. The XRD data confirmed the hexagonal wurtzite structure of pure and Cr-doped ZnO nanoparticles. The calculated values of grain size using Scherrer＇s formula are in the range of 30.7-9.2 nm. The morphology of nanopowders has been observed by FESEM, and EDS results con- firmed a systematic increase of Cr content in the samples and clearly indicate with no impurity element. The band gaps, computed by UV-Vis spectroscopy, are in the range of 2.83-2.35 eV for different doping concentrations. By analyzing VSM data, significantly enhanced room temperature ferromagnetism is identified in Cr-doped ZnO samples. The value of magnetization is a 12 times increased of the value reported by Daun et al. （2010）. Room temperature ferromagnetism of the nanoparticles is of vital prominence for spintronics applications.
