Influence of agro-wastes derived biochar and their composite on reducing the mobility of toxic heavy metals and their bioavailability in industrial contaminated soils.

The agro-waste derived valuable products are prime interest for effective management of toxic heavy metals (THMs). The present study investigated the efficacy of biochars (BCs) on immobilization of THMs (Cr, Zn, Pb, Cu, Ni and Cd), bioaccumulation and health risk. Agro-wastes derived BCs including wheat straw biochar (WSB), orange peel biochar (OPB), rice husk biochar (RHB) and their composite biochar (CB) were applied in industrial contaminated soil (ICS) at 1% and 3% amendments rates. All the BCs significantly decreased the bioavailable THMs and significantly (p < 0.001) reduced bioaccumulation at 3% application with highest efficiency for CB followed by OPB, WSB and RHB as compared to control treatment. The bioaccumulation factor (BAF), concentration index (CI) and ecological risk were decreased with all BCs. The hazard quotient (HQ) and hazard index (HI) of all THMs were <1, except Cd, while carcer risk (CR) and total cancer risk index (TCRI) were decreased through all BCs. The overall results depicted that CB at 3% application rate showed higher efficacy to reduce significantly (p < 0.001) the THMs uptake and reduced health risk. Hence, the present study suggests that the composite of BCs prepared from agro-wastes is eco-friendly amendment to reduce THMs in ICS and minimize its subsequent uptake in vegetables.

The present study has a scientific research scope, based on reduction of bioavailability and bioaccumulation of toxic heavy metals (THMs) by the addition of biochars derived from agro-wastes and their composite biochar (CB), thereby decreasing the potential health risk. Limited study has been conducted, especially on the impact of CB in THMs-contaminated soil. This study could fill the scientific research gap and provides useful information for mitigation of THMs present in contaminated soil, which could be followed by the Environmental Protection Agency, Ministry of Agriculture and farmers in degraded lands.

Investigation of heavy metals uptake in root-shoot of native plant species adjoining wastewater channels.

Metal pollution in water, soil, and vegetation is an emerging environmental issue. Therefore, this study investigated the abundance of heavy metals (HMs) within roots and shoots of native plant species i.e., Bromus pectinatus, Cynodon dactylon, Poa annua, Euphorbia heliscopa, Anagallis arvensis, and Stellaria media grown in the adjoining area of municipal wastewater channels of a Pakistani city of Abbottabad. HMs concentrations (mg L-1) in municipal wastewater were: chromium (Cr) (0.55) > nickel (Ni) (0.09) > lead (Pb) (0.07) > cadmium (Cd) (0.03). Accumulation of HMs in both roots and shoots of plant species varied as B. pectinatus > C. dactylon > P. annua > E. heliscopa > A. arvensis > S. media. Irrespective of the plant species, roots exhibited higher concentrations of HMs than shoots. Higher amount of Cr (131.70 mg kg-1) was detected in the roots of B. pectinatus and the lowest amount (81 mg kg-1) in A. arvensis, Highest Cd concentration was found in the shoot of B. pectinatus and the lowest in the E. heliscopa. The highest concentration of Ni was found in the roots of S. media (37.40 mg kg-1) and the shoot of C. dactylon (15.70 mg kg-1) whereas the lowest Ni concentration was achieved in the roots of A. arvensis (12.10 mg kg-1) and the shoot of E. heliscopa (5.90 mg kg-1). The concentration of HMs in individual plant species was less than 1000 mg kg-1. Considering the higher values (> 1) of biological concentration factor (BCF), biological accumulation co-efficient (BAC), and translocation factor (TF), B. pectinatus and S. media species showed greater potential for HMs accumulation than other species. Therefore, these plants might be helpful for the remediation of HM-contaminated soil.

Occurrence of toxic elements in river areas along drains and groundwater resources: source of contamination and associated health risk.

The objective of the current research was to examine the water quality of the River Ravi and the River Sutlej, with a specific focus on potentially toxic elements (PTEs). Additionally, we sought to monitor the sources of pollution in these rivers by gathering samples from the primary drains that carry industrial and municipal waste into these water bodies. Furthermore, we aimed to evaluate the impact of PTEs in surface water on groundwater quality by collecting groundwater samples from nearby populated areas. A total of 30 samples were collected from these three sources: rivers (6 samples), drains (9 samples), and groundwater (15 samples). The analysis revealed that the levels of PTEs in the samples from these three resources having a mean value: arsenic (As) 23.5 µg/L, zinc (Zn) 2.35 mg/L, manganese (Mn) 0.51 mg/L, lead (Pb) 6.63 µg/L, and chromium (Cr) 10.9 µg/L, exceeded the recommended values set by the World Health Organization (WHO). Furthermore, PTEs including (As 84%), (Zn 65%), (Mn 69%), (Pb 53%), (Cr 53%), and (Ni 27%), samples were beyond the recommended values of WHO. The results of the Principal Component Analysis indicated that surface water and groundwater exhibited total variability of 83.87% and 85.97%, respectively. This indicates that the aquifers in the study area have been contaminated due to both natural geogenic factors and anthropogenic sources. These sources include the discharge of industrial effluents, wastewater from municipal sources, mining activities, agricultural practices, weathering of rocks, and interactions between rocks and water. Spatial distribution maps clearly illustrated the widespread mobilization of PTEs throughout the study area. Furthermore, a health risk assessment was conducted to evaluate the potential adverse health effects of PTEs through the ingestion of drinking groundwater by both children and adults. Health risk assessment result show the mean carcinogenic values for As, Cr, Pb and Ni in children are calculated to be (1.88E-04), (2.61E-04), (2.16E-02), and (5.74E-05), respectively. Similarly, the mean carcinogenic values for the above mentioned PTEs in adults were recorded to be (2.39E-05), (3.32E-05), (1.19E-03), and (7.29E-06) respectively. The total hazard index values for As, Zn, Cr, Pb, Mn, Cu, and Ni in children were observed to be (9.07E + 00), (9.95E-07), (4.59E-04), (5.75E-04), (4.72E-05), (2.78E-03), and (5.27E-05) respectively. The analysis revealed that As has an adverse effect on the population of the study area as compared to other PTEs investigated in this study.

Biochemical and phenological characterization of diverse wheats and their association with drought tolerance genes.

Drought is one of the most important wheat production limiting factor, and can lead to severe yield losses. This study was designed to examine the effect of drought stress on wheat physiology and morphology under three different field capacities (FC) viz. 80% (control), 50% (moderate) and 30% (severe drought stress) in a diverse collection of wheat germplasm including cultivars, landraces, synthetic hexaploid and their derivatives. Traits like grain weight, thousand grain weight and biomass were reduced by 38.23%, 18.91% and 26.47% respectively at 30% FC, whereas the reduction rate for these traits at 50% FC were 19.57%, 8.88% and 18.68%. In principal component analysis (PCA), the first two components PC1 and PC2 accounted for 58.63% of the total variation and separated the cultivars and landraces from synthetic-based germplasm. Landraces showed wide range of phenotypic variations at 30% FC compared to synthetic-based germplasm and improved cultivars. However, least reduction in grain weight was observed in improved cultivars which indicated the progress in developing drought resilient cultivars. Allelic variations of the drought-related genes including TaSnRK2.9-5A, TaLTPs-11, TaLTPs-12, TaSAP-7B-, TaPPH-13, Dreb-B1 and 1fehw3 were significantly associated with the phenological traits under drought stress in all 91 wheats including 40 landraces, 9 varieties, 34 synthetic hexaploids and 8 synthetic derivatives. The favorable haplotypes of 1fehw3, Dreb-B1, TaLTPs-11 and TaLTPs-12 increased grain weight, and biomass. Our results iterated the fact that landraces could be promising source to deploy drought adaptability in wheat breeding. The study further identified drought tolerant wheat genetic resources across various backgrounds and identified favourable haplotypes of water-saving genes which should be considered to develop drought tolerant varieties.

Water stress effects on stay green and chlorophyll fluorescence with focus on yield characteristics of diverse bread wheats.

MAIN CONCLUSION: The study provided an insight toward better understanding of stay-green mechanisms for drought tolerance improvement and identified that synthetic-derived wheats proved as a promising germplasm for improved tolerance against water stress. Stay-green (SG) trait is considered to be related with the ability of wheat plants to maintain photosynthesis and CO2 assimilation. The present study explored the interaction of water stress with SG expression through physio-biochemical, agronomic and phenotypic responses among diverse wheat germplasm comprising of 200 synthetic hexaploids, 12 synthetic derivatives, 97 landraces and 16 conventional bread wheat varieties, for 2 years. The study established that variation of SG trait existed in the studied wheat germplasm and there was positive association between SG trait and tolerance to water stress. The relationship of SG trait with chlorophyll content (r = 0.97), ETR (r = 0.28), GNS (r = 0.44), BMP (r = 0.34) and GYP (r = 0.44) was particularly promising under water stress environment. Regarding chlorophyll fluorescence, the positive correlation of фPSII (r = 0.21), qP (r = 0.27) and ETR (r = 0.44) with grain yield per plant was noted. The improved ΦPSII and Fv/Fm of PSII photochemistry resulted in the high photosynthesis activity in SG wheat genotypes. Regarding relative water content and photochemical quenching coefficient, synthetic-derived wheats were better by maintaining 20.9, 9.8 and 16.1% more RWC and exhibiting 30.2, 13.5 and 17.9% more qP when compared with landraces, varieties and synthetic hexaploids, respectively, under water stress environment. Synthetic derived wheats also exhibited relatively more SG character with good yield and were more tolerant to water stress in terms of grain yield, grain weight per plant, better photosynthetic performance through chlorophyll fluorescence measurement, high leaf chlorophyll and proline content, and hence, may be used as novel sources for breeding drought tolerant materials. The study will further facilitate research on wheat leaf senescence and will add to better understanding of SG mechanisms for drought tolerance improvement.

Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models.

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 µg/L, with an average of 11.64 µg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 µg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.

Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.

Characterizing cutaneous leishmaniasis in a conflict-affected region: a study from North Waziristan, Pakistan.

Background/aim: Cutaneous leishmaniasis is an emerging tropical disease that remains a serious public health issue in Pakistan, particularly in North Waziristan. The current research was carried out to investigate the presence of cutaneous leishmaniasis in this region. Materials and methods: This prospective observational study was conducted from October 2018 to December 2020 at District Head Quarter Hospital Miranshah in North Waziristan with the collaboration of the Pathology Department of Gomal Medical College Dera Ismail Khan, Khyber Pakhtunkhwa. Needle aspirates were used for the microscopic Giemsa-stained slides. SPSS was used for data analysis. Results: Of the 5406 clinically-suspected cases, 2603(48.2%) were positive by microscopic examination. Of these 2603 patients, 1474 (57%) were male and 1129 (43%) were female. Most of the lesions were on the face, followed by upper and lower limbs. The 5-10-year age group had the highest percentage of 1167 (45%). A single lesion affected 96.6% of the patients, while 2.7% had double lesions and 0.7% had triple lesions. A high number of cutaneous leishmaniasis were seen from April to August, while the lowest number was seen November to December. Conclusion: This study provides extensive information in relation to the existence of cutaneous leishmaniasis in the North Waziristan district of Pakistan, as well as the detailed demographic features of those affected by the disease.

Contamination level, source identification and health risk assessment of potentially toxic elements in drinking water sources of mining and non-mining areas of Khyber Pakhtunkhwa, Pakistan.

Accelerated mining activities have increased water contamination with potentially toxic elements (PTEs) and their associated human health risk in developing countries. The current study investigated the distribution of PTEs, their potential sources and health risk assessment in both ground and surface water sources in mining and non-mining areas of Khyber Pakhtunkhwa, Pakistan. Water samples (n = 150) were taken from selected sites and were analyzed for six PTEs (Ni, Cr, Zn, Cu, Pb and Mn). Among PTEs, Cr showed a high mean concentration (497) µg L-1, followed by Zn (414) µg L-1 in the mining area, while Zn showed the lowest mean value (4.44) µg L-1 in non-mining areas. Elevated concentrations of Ni, Cr and a moderate level of Pb in ground and surface water of Mohmand District exceeded the permissible limits set by WHO. Multivariate statistical analyses showed that the pollution sources of PTEs were mainly from mafic-ultramafic rocks, acid mine drainage, open dumping of mine wastes and mine tailings. The hazard quotient (HQ) was the highest for children relative to that for adults, but not higher than the USEPA limits. The hazard index (HI) for ingestions of all selected PTEs was lower than the threshold value (HIing < 1), except for Mohmand District, which showed a value of HI >1 in mining areas through ingestion. Moreover, the carcinogenic risk (CR) values exceeded the threshold limits for Ni and Cr set by the USEPA (1.0E-04-1.0E-06). In order to protect the drinking water sources of the study areas from further contamination, management techniques and policy for mining operations need to be implemented.

Evaluating a Hybrid Circuit Topology for Fault-Ride through in DFIG-Based Wind Turbines.

Large-scale wind power integration has raised concerns about the reliability and stability of power systems. The rotor circuit of a doubly fed induction generator (DFIG) is highly vulnerable to unexpected voltage dips, which can cause considerable electromotive force in the circuit. Consequently, the DFIG must fulfil the fault-ride through (FRT) criteria to ensure the system's performance and contribute to voltage regulation during severe grid outages. This paper provides a hybrid solution for DFIG wind turbines with FRT capabilities, using both a modified switch-type fault current limiter (MSFTCL) and a direct current (DC) chopper. The proposed system has the merit of keeping the rotor current and the DC-link voltage within the permissible limits, enhancing the FRT capability of generators. Moreover, the boundness of supply voltage into its reference value ensures dynamic stability during symmetric and asymmetric grid failures. Further, electromagnetic torque variations are significantly reduced during fault events. Finally, the performance validation of the proposed scheme is performed in a simulation setup, and the results are compared with the existing sliding mode control (SMC) and proportional-integral (PI) controller-based approaches. The comparison results show that a hybrid strategy with advanced controllers provides superior performance for all critical parameters.

Sensor Fault-Tolerant Control of Microgrid Using Robust Sliding-Mode Observer.

This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer. It aims to provide a diagnosis of multiple faults (i.e., magnitude, phase, and harmonics) occurring simultaneously or individually in current/potential transformers. A modified algorithm based on convex optimization is used to determine the gains of the sliding-mode observer, which utilizes the feasibility optimization or trace minimization of a Ricatti equation-based modification of H-Infinity (H∞) constrained linear matrix inequalities. The fault and disturbance estimation method is modified and improved with some corrections in previous works. The stability and finite-time reachability of the observers are also presented for the considered faulty and perturbed microgrid system. A proportional-integral (PI) based control is utilized for the conventional regulations required for frequency and voltage sags occurring in a microgrid. However, the same control block features fault-tolerant control (FTC) functionality. It is attained by incorporating a sliding-mode observer to reconstruct the faults of sensors (transformers), which are fed to the control block after correction. Simulation-based analysis is performed by presenting the results of state/output estimation, state/output estimation errors, fault reconstruction, estimated disturbances, and fault-tolerant control performance. Simulations are performed for sinusoidal, constant, linearly increasing, intermittent, sawtooth, and random sort of often occurring sensor faults. However, this paper includes results for the sinusoidal nature voltage/current sensor (transformer) fault and a linearly increasing type of fault, whereas the remaining results are part of the supplementary data file. The comparison analysis is performed in terms of observer gains being estimated by previously used techniques as compared to the proposed modified approach. It also includes the comparison of the voltage-frequency control implemented with and without the incorporation of the used observer based fault estimation and corrections, in the control block. The faults here are considered for voltage/current sensor transformers, but the approach works for a wide range of sensors.

Green Synthesis of Silver Nanoparticles Using Euphorbia wallichii Leaf Extract: Its Antibacterial Action against Citrus Canker Causal Agent and Antioxidant Potential.

Biologically synthesized silver nanoparticles are emerging as attractive alternatives to chemical pesticides due to the ease of their synthesis, safety and antimicrobial activities in lower possible concentrations. In the present study, we have synthesized silver nanoparticles (AgNPs) using the aqueous extract of the medicinal plant Euphorbia wallichii and tested them against the plant pathogenic bacterium Xanthomonas axonopodis, the causative agent of citrus canker, via an in vitro experiment. The synthesized silver nanoparticles were characterized by techniques such as UV-Vis spectroscopy, Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction analysis and transmission electron microscopy. Moreover, the plant species were investigated for phenolics, flavonoids and antioxidant activity. The antioxidant potential of the extract was determined against a DPPH radical. The extract was also evaluated for phenolic compounds using the HPLC technique. The results confirmed the synthesis of centered cubic, spherical-shaped and crystalline nanoparticles by employing standard characterization techniques. A qualitative and quantitative phytochemical analysis revealed the presence of phenolics (41.52 mg GAE/g), flavonoids (14.2 mg QE/g) and other metabolites of medicinal importance. Different concentrations (1000 µg/mL to 15.62 µg/mL-2 fold dilutions) of AgNPs and plant extract (PE) alone, and both in combination (AgNPs-PE), exhibited a differential inhibition of X. axanopodis in a high throughput antibacterial assay. Overall, AgNPs-PE was superior in terms of displaying significant antibacterial activity, followed by AgNPs alone. An appreciable antioxidant potential was recorded as well. The observed antibacterial and antioxidant potential may be attributed to eight phenolic compounds identified in the extract. The Euphorbia wallichii leaf-extract-induced synthesized AgNPs exhibited strong antibacterial activity against X. axanopodis, which could be exploited as effective alternative preparations against citrus canker in planta in a controlled environment. In addition, as a good source of phenolic compounds, the plant could be further exploited for potent antioxidants.

COVID-19 persuaded lockdown impact on local environmental restoration in Pakistan.

Coronavirus disease 2019 (COVID-19) pandemic adversely affected human beings. The novel coronavirus has claimed millions of lives all over the globe. Most countries around the world, including Pakistan, restricted people's social activities and ordered strict lockdowns throughout the country, to control the fatality of the novel coronavirus. The persuaded lockdown impact on the local environment was estimated. In the present study, we assessed air quality changes in four cities of Pakistan, namely Islamabad, Karachi, Lahore, and Peshawar, based on particulate matter (PM2.5), using "Temtop Airing 1000," which is capable of detecting and quantifying PM2.5. The Air Quality Index (AQI) was evaluated in three specific time spans: the COVID-19 pandemic pre- and post-lockdown period (January 1, 2020 to March 20, 2020, and May 16, 2020 to June 30, 2020 respectively), and the COVID-19 pandemic period (March 21 2020 to May 15, 2020). We compared land-monitored AQI levels for the above three periods of time. For validation, air quality was navigated by the Moderate Resolution Imaging Spectrometer (MODIS) satellite during the first semester (January 1 to June 30) of 2019 and 2020. It is seen that the concentration of PM2.5 was considerably reduced in 2020 (more than 50%), ranging from ~ 0.05 to 0.3 kg⋅m3, compared to the same period in 2019. The results revealed that the AQI was considerably reduced during the lockdown period. This finding is a very promising as the inhabitants of the planet Earth can be guaranteed the possibility of a green environment in the future.

Occurrence and Distribution of Heavy Metals in Mining Degraded Soil and Medicinal Plants: A Case Study of Pb/Zn Sulfide Terrain Northern Areas, Pakistan.

Mining activities have serious environmental impacts, thus releasing heavy metals (HMs) such as cadmium (Cd), lead (Pb), chromium (Cr), zinc (Zn) and nickel (Ni) into the surrounding environment. The current paper investigated the impacts of mining activities of Pb-Zn sulfide on soil and medicinal plants. Hence, soil samples (n = 36) and medicinal plants (n = 36) samples were collected, acid extracted and were analyzed through Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for HMs quantification. Our results showed that mineralized zones showed high HMs enrichment levels as compared to non-mineralized zones. Various Indices for HMs assessment revealed that the contaminated soil of the study area had low to extreme level. The mean concentrations of HMs in mining degraded soil and medicinal plants were significantly higher (p ≤ 0.01) and were found in order of Zn > Pb > Cr > Ni > Cd and Cr > Zn > Pb > Ni > Cd respectively. Similarly, some widely consumable medicinal plants showed good metal accumulation for Cd, Cr and Pb i.e., 3.57 mg kg1, 350 mg kg-1 and 335 mg kg-1 in C. sativa, while 5.9 mg kg-1, 276.9 mg kg-1 and 188.7 mg kg-1 in R. hestatus respectively. Hence, the present study recommended that medicinal plants grown in mining areas should be analyzed for HMs concentration before consumption.

Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study.

BACKGROUND: The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. OBJECTIVE: The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. METHODS: This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. RESULTS: The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. CONCLUSIONS: Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

Bi-Directional Mutual Energy Trade between Smart Grid and Energy Districts Using Renewable Energy Credits.

A central authority, in a conventional centralized energy trading market, superintends energy and financial transactions. The central authority manages and controls transparent energy trading between producer and consumer, imposes a penalty in case of contract violation, and disburses numerous rewards. However, the management and control through the third party pose a significant threat to the security and privacy of consumers'/producers' (participants) profiles. The energy transactions between participants involving central authority utilize users' time, money, and impose a computational burden over the central controlling authority. The Blockchain-based decentralized energy transaction concept, bypassing the central authority, is proposed in Smart Grid (SG) by researchers. Blockchain technology braces the concept of Peer-to-Peer (P2P) energy transactions. This work encompasses the SolarCoin-based digital currency blockchain model for SG incorporating RE. Energy transactions from Prosumer (P) to Prosumer, Energy District to Energy District, and Energy District to SG are thoroughly investigated and analyzed in this work. A robust demand-side optimized model is proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to maximize Prosumer Energy Surplus (PES), Grid revenue (GR), percentage energy transactions accomplished, and decreased Prosumer Energy Cost (PEC). Real-time averaged energy data of Australia are employed, and a piece-wise energy price mechanism is implemented in this work. The graphical analysis and tabular statistics manifest the efficacy of the proposed model.

Nasal carriage of highly resistant methicillin resistant Staphylococcus aureus (MRSA) strains by hospital staff in Hazara region of Pakistan.

OBJECTIVE: To isolate and characterise multidrug resistant strains of Staphylococcus aureus from healthcare workers who are at potential risk of nosocomial infections. METHODS: The observational, cross-sectional study was conducted from November 2014 to April 2015 at different hospitals of Haripur and Abbottabad, Pakistan, and comprised ward and operation theatre staff. The isolates were identified on the basis of microbiological and biochemical tests and further confirmed by polymerase chain reaction. Disc diffusion method was used for antibiotic sensitivity testing, and panton valentine leukocidin and methicillin resistance mecA genes were detected using polymerase chain reaction. RESULTS: Of 208 isolates, 108(52%) were from the ward staff and 100(48%) were from the operation theatre staff. Overall, 167(80.3%) isolates were positive for Staphylococcus aureus, and 75(36%) were methicillin-resistant Staphylococcus aureus. The number of antibiotic-resistant isolates was 75(45%) cefoxitin, 60(36%) ofloxacin, 152(91%) erythromycin, 52(31%) doxycycline, 127(76%) lincomycin, 53(32%) amoxicillin-clavulanate, 67(40%) ciprofloxacin, and 89(53%) ceftriaxone. CONCLUSIONS: A high number of hospital staff, including those working in operation theatres, were found to be carrying methicillin-resistant Staphylococcus aureus and multidrug resistant strains in their nasal passage that may be a source of infection to patients.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Microbial reactions and environmental factors affecting the dissolution and release of arsenic in the severely contaminated soils under anaerobic or aerobic conditions.

The soils near the abandoned Shimen Realgar Mine are characterized by containing extremely high contents of total and soluble arsenic. To determine the microbial reactions and environmental factors affecting the mobilization and release of arsenic from soils phase into pore water, we collected 24 soil samples from the representative points around the abandoned Shimen Realgar Mine. They contained 8310.84 mg/kg total arsenic and 703.21 mg/kg soluble arsenic in average. The soluble arsenic in the soils shows significant positive and negative correlations with environmental SO42-/TOC/pH/PO43-, and Fe/Mn, respectively. We found that diverse dissimilatory As(V)-respiring prokaryotes (DARPs) and As(III)-oxidizing bacteria (AOB) exist in all the examined soil samples. The activities of DARPs led to 65-1275% increase of soluble As(III) in the examined soils after 21.0 days of anaerobic incubation, and the microbial dissolution and releases of arsenic show significant positive and negative correlations with the environmental pH/TN and NH4+/PO43-, respectively. In comparison, the activities of AOB led to 24-346% inhibition of the dissolved oxygen-mediated dissolution of arsenic in the soils, and the AOB-mediated releases of As(V) show significant positive and negative correlations with the environmental SO42- and pH/NH4+, respectively. The microbial communities of 24 samples contain 54 phyla of bacteria that show extremely high diversities. Total arsenic, TOC, NO3- and pH are the key environmental factors that indirectly controlled the mobilization and release of arsenic via influencing the structures of the microbial communities in the soils. This work gained new insights into the mechanism for how microbial communities catalyze the dissolution and releases of arsenic from the soils with extremely high contents of arsenic.