Search

Background: Cardioversion is the treatment of choice in an unstable patient with arrhythmia. It requires analgesia and sedation, which is usually given via intravenous route. However, when there is a failure to get vascular access, alternatives routes to intravenous (IV) sedation and analgesia are needed. Case Presentation: A 59 year old male patient was presented to the emergency department (ED) complaining of chest pain and palpitation. His electrocardiogram (ECG) showed new onset rapid narrow complex atrial fibrillation, which then converted to wide complex tachycardia. Initially, the patient remained hemodynamically stable, but his blood pressure dropped, when cardioversion was indicated. In this case, intranasal (IN) and intraosseous (IO) routes were used, both of which are uncommon to be used for sedation in an unstable patient. The sedatives bioavailability and dosage via non intravenous (non-IV) routes are not commonly used, hence, literature was reviewed to find out the appropriate sedative dosage via non-IV routes specifically in IN and IO route for moderate sedation in ED, to facilitate procedures like cardioversion. Conclusion: It was believed that IN and IO routes are good alternatives to intravenous routes when timely vascular access can't be secured and when emergency medication administration is required. [SJEMed 2024; 5(2.000): 156-161]

Abstract Modern natural language processing (NLP) state-of-the-art (SoTA) deep learning (DL) models have hundreds of millions of parameters, making them extremely complex. Large datasets are required for training these models, and while pretraining has reduced this requirement, human-labelled datasets are still necessary for fine-tuning. Few-shot learning (FSL) techniques, such as meta-learning, try to train models from smaller datasets to mitigate this cost. However, the tasks used to evaluate these meta-learners frequently diverge from the problems in the real world that they are meant to resolve. This work aims to apply meta-learning to a problem that is more pertinent to the real world: class incremental learning (IL). In this scenario, after completing its training, the model learns to classify newly introduced classes. One unique quality of meta-learners is that they can generalise from a small sample size to classes that have never been seen before, which makes them especially useful for class incremental learning (IL). The method describes how to emulate class IL using proxy new classes. This method allows a meta-learner to complete the task without the need for retraining. To generate predictions, the transformer-based aggregation function in a meta-learner that modifies data from examples across all classes has been proposed. The principal contributions of the model include concurrently considering the entire support and query sets, and prioritising attention to crucial samples, such as the question, to increase the significance of its impact during inference. The outcomes demonstrate that the model surpasses prevailing benchmarks in the industry. Notably, most meta-learners demonstrate significant generalisation in the context of class IL even without specific training for this task. This paper establishes a high-performing baseline for subsequent transformer-based aggregation techniques, thereby emphasising the practical significance of meta-learners in class IL.

Abstract The recent over production of municipal solid waste (MSW) poses a significant threat to both the ecosystem and human health. Utilizing MSW for agricultural purposes has emerged as a promising strategy to reduce solid waste disposal while simultaneously increasing soil fertility. To explore this potential solution further, an experiment was designed to assess the impact of varying concentrations of MSW (25%, 50%, and 75%) on the proximate composition of 15 different vegetable species. The experiment, conducted between 2018 and 2019, involved treating soil with different levels of solid waste and analyzing the proximate components, such as crude protein, dry matter, crude fiber, crude fat, and moisture content, in the 15 selected crops. The results indicate that the application of 25% MSW significantly increased the levels of crude protein, crude fiber, dry matter, and fat in Spinacia oleracea, Solanum tuberosum, Solanum melongena, and Abelmoschus esculentus. Conversely, the addition of 75% MSW notably elevated the moisture and ash content in Cucumis sativus. Correlation and scatter matrix analyses were conducted to elucidate the relationships between the protein, fiber, dry matter, ash, and fat contents. Principal component analysis and clustering confirmed the substantial impact of Treatment_1 (25% MSW) and Treatment_3 (75% MSW) on the proximate composition of the aforementioned vegetables, leading to their categorization into distinct groups. Our study highlights the efficacy of using 25% MSW to enhance the proximate composition and nutritional value of vegetables. Nonetheless, further research is warranted to investigate the mineral, antioxidant, vitamin, and heavy metal contents in the soil over an extended period of MSW application.

The impact of nickel (Ni) metal toxicity, on public health was assessed analyzing forage samples (Acacia nilotica, Zea mays, Pennisetum glaucum, Capparis decidua and Medicago sativa), soils and blood samples of cow, buffalo and sheep (blood plasma, fecal, and hair) collected from three different agro-ecological zones and analyzed through atomic absorption spectrophotometer. Results showed that nickel values differed in soil samples ranging from 4.49 to 9 to 25 mg/kg, in forages from 3.78 to 9.53 mg/kg and in animal samples from 0.65 to 2.42 mg/kg. Nickel concentration, in soil and forage samples, was below the permitted limits. Soil with the minimum nickel level was found under C. decidua while the maximum concentration was reached under the A. nilotica. Among the animals, nickel was maximum in buffaloes that grazed on the Z. mays fodder. Ni was more accumulated in feces than other body tissues. The sheep and buffaloes showed high vulnerability to Ni pollution due to the highest contamination levels at site II and III. Bioconcentration factor, pollution load index and enrichment factor were found to be higher in buffaloes than cows, respectively. The daily intake and health risk index ranged from 0.0056 to 0.0184 mg/kg/day and 0.186–0.614 mg/kg/day respectively. In short, the results of this study evidenced that Nickel-containing fertilizers should never not be used to grow forage species. Government should to lessen the toxic metal accessibility to animals. Although general values were lower than the admitted limit, nickel can be accumulated and the consume of food containing nickel can increase health risks. General monitoring of soil and vegetation pollution load, as well as the use of other non-conventional water like canal water for forage irrigation could be a sustainable solution to decrease the access of nickel in the food chain.

Abstract The Internet of Things (IoT) and portable electronic devices are pivotal in enhancing living standards, with battery efficiency and compact design being critical for these devices. Analogue sensors, integral to advanced artificial intelligence, often necessitate complex real‐time processing and actuation. This study examines the performance of one‐dimensional armchair graphene nanoribbons in graphene nanoribbon field‐effect transistors (GNRFETs). It compares graphene nanoribbon‐based triple cascode operational transconductance amplifiers (GNRFET‐TCOTAs) with conventional CMOS‐based TCOTA. The results reveal substantial enhancements in the GNRFET‐based TCOTAs: the pure GNR‐TCOTA variant shows a remarkable 33.8% increase in DC gain and significant improvements in transconductance, slew rate, and gain‐bandwidth, with enhancements of 8.48, 5.85, and 8.56 times, respectively. Furthermore, the pure GNRFET TCOTA exhibits higher speed, lower energy‐delay product, and settling time compared to Si‐CMOS‐based TCOTA. The study also investigates the impact of critical design parameters on circuit performance. Overall, the research highlights the potential of GNRFETs to optimize TCOTA circuits, offering a path towards more efficient and compact electronic devices, thereby advancing the state of nanoelectronics and supporting the growth of high‐performance IoT systems.

Abstract In many countries with wastewater irrigation and intensive use of fertilizers (minerals and organics), heavy metal deposition by crops is regarded as a major environmental concern. A study was conducted to determine the impact of mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse on soil’s trace Pb content and edible parts of vegetables. It also evaluated the risk of lead (Pb) contamination in water, soil, and food crops. Six vegetables (Daucus carota, Brassica oleracea, Pisum sativum, Solanum tuberosum, Raphanus sativus, and Spinacia oleracea) were grown in the field under twelve treatments with different nutrient and water inputs. The lead concentrations in soil, vegetables for all treatments and water samples ranged from 1.038–10.478, 0.09346–9.0639 mg/kg and 0.036–0.26448 mg/L, The concentration of lead in soil treated with wastewater in treatment (T6) and vegetable samples was significantly higher, exceeding the WHO’s permitted limit. Mineral and organic fertilizers combined with wastewater treatment reduced lead (Pb) concentrations in vegetables compared to wastewater application without organic fertilizers. Health risk indexes for all treatments except wastewater treatment (T6) were less than one. Pb concentrations in mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse treated were determined to pose no possible risk to consumers.

The cultivation of forage crops on wastewater-irrigated soils, while common in many developing countries, poses significant risks due to heavy metal pollution, particularly Lead (Pb) and Nickel (Ni). This practice, aimed at addressing water scarcity challenges and providing affordable irrigation, was investigated for its ecological and human health implications across three diverse sites (site A, site B, and site C). Our study unveiled increases in Pb concentrations in contaminated soil, cultivated with Sesbania bispinosa showing the highest Pb accumulation. The Ni concentrations ranged from 5.34 to 10.43 across all forage crop samples, with S. fruticosa from site C displaying the highest Ni concentration and S. bicolor from site A exhibiting the lowest. Trace element concentrations in the specimens were determined using an atomic absorption spectrophotometer. The Pb levels in the blood, hair, and feces of farm ruminants (cows, buffaloes, and sheep) varied across the sites, with buffaloes consistently displaying the highest Pb levels. Insights into daily Pb intake by ruminant’s highlighted variations influenced by plant species, animal types, and sites, with site C, the cows exhibiting the highest Health Risk Index (HRI) associated with lead exposure from consuming forage crops. Soil and forage samples showed Pb concentrations ranging from 8.003 to 12.29 mg/kg and 6.69–10.52 mg/kg, respectively, emphasizing the severe health risks associated with continuous sewage usage. Variations in Ni concentrations across animal blood, hair, and feces samples underscored the importance of monitoring Ni exposure in livestock, with sheep at site B consistently showing the highest Ni levels. These findings highlight the necessity of vigilance in monitoring trace element (Pb and Ni) exposure in forage crops and livestock, to mitigate potential health risks associated with their consumption, with variations dependent on species, site, and trace element concentrations.

Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.

Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing Denoising Autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs’ unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.

Distance learning has grown rapidly in recent years. E-learning can aid teachers of students with disabilities, particularly visually impaired students (VISs), by offering versatility, accessibility, enhanced communication, adaptability, and a wide range of multimedia and non-verbal teaching methods. However, the shift from traditional face-to-face instruction to online platforms, especially during the pandemic, introduced unique challenges for VISs, with respect to including instructional methodologies, accessibility, and the integration of suitable technology. Recent research has shown that the resources and facilities of educational institutions pose challenges for teachers of visually impaired students (TVISs). This study conducts a literature review of research studies from the years 2000 to 2024 to identify significant issues encountered by TVISs with online learning to show the effects of distance learning before, during, and after the pandemic. This systematic literature review examines 25 publications. The evaluation reveals technological problems affecting the educational experience of visually impaired educators through a methodical categorization and analysis of these papers. The results emphasize important problems and suggest solutions, providing valuable knowledge for experts in education and legislation. The study recommends technology solutions to support instructors in providing inclusive online learning environments for VISs.

Automobile exhaust releases different types of pollutants that are at great risk to the air quality of the environment and incidental distress to the nature of roadside plants. Mimusops elengi L. is an evergreen medicinal tree cultivated along the roadside of Lahore City. This research aimed to investigate physiological, morphological and genomorphic characteristics of M. elengi under the influence of air pollution from vehicles. Healthy and mature leaves were collected from trees on Canal Bank and Mall roads of Lahore as the experimental sites and control sites were 20 km away from the experimental site. Different physiochemical, morphological, air pollution tolerance index (APTI) and molecular analysis for the detection of DNA damage were performed through comet assay. The results demonstrated the mean accumulated Cd, Pb, Cu and Ni heavy metal contents on the leaves were higher than the control plants (1.27, 3.22, 1.32 and 1.46 μg mg−1). APTI of trees was 9.04. Trees in these roads significantly (p

For the study of heavy metals impact on human beings, areas in Sargodha city that were supplied with various types of fertilizers were chosen. The three industrial areas; (Bhalwal, Sillanwali, and Sahiwal) of this city were explored for research reasons. The researchers wanted to know how much heavy metal was in the soil, food crops, and human. Excess fertilizer use contributes to global pollution. Farmyard manure, urea, and potassium chloride were used on Site 1; urea phosphate, manure, and ammonium sulphate were used on Site 2; and super phosphate, ammonium phosphate, and nitrate phosphate were used on Site 3. Samples of commonly used food crops, their respective soils and blood of residents who ingested the food crops of the studied area were collected. The zinc and lead levels in soil (8.30-16.80 and 1.80-12.71 mg/kg) and food crops (0.26-2.02 and 2.26-4.70 mg/kg) were far lower than WHO permitted limits. Blood mean concentration of both Zn (2.30-4.30 mg/L) and Ni (0.24-0.70 mg/L) were found maximum in residents of Site 3. The values of pollution load index, bioconcentration factor, enrichment factor for both zinc and lead were (0.18- 0.37 a n d 0.220-0.948), (0.027-0.138 and 0.316-1.705), (0.020-0.144 and 0.515-2.780), respectively. Daily intake of metal (0.004-0.008 and 0.001-0.002 mg/kg/day) and health risk index (0.0001-0.016 and 0.005-0.115) values were observed to be lower in individuals for Zn and Pb, respectively. In present work values of all pollution indices were

The members of genus Moniezia are the common parasites of livestock in tropical areas. The tapeworm, Moniezia expansa is commonly found in the gastrointestinal tract of the small and large ruminants. The present study focused on reporting the prevalence of M. expansa in small ruminants of southern Punjab: sheep and goats, in relation with epidemiological factors like age and gender. An overall prevalence of 27.2% was estimated for the small ruminants with higher infection rates in males (29.8%) and younger age group (

Despite the fact that fertilizers have been used for millennia for sustainable crop production, this high and considerable dependence on fertilizers heightens environmental concerns with the indirect human exposure due to accumulation of toxins in food chain via soil contamination. The purpose of this study is to evaluate the application of fertilizers to the soil and their effect on the accumulation of copper and nickel in spinach (Spinacia oleracea), garlic (Allium sativum), wheat (Triticum aestivum), maize (Zea mays), and barley (Hordeum vulgare); as well as potential health concerns associated with consuming vegetables cultivated on this contaminated land. Samples of available soil, food crops, and human blood were collected from three different Tehsils: Bhalwal, Sahiwal, and Silanwali and were regarded as site 1, site 2 and site 3 respectively. Urea, farmyard manure, and potassium chloride were delivered to Site 1; urea phosphate, manure, and ammonium sulphate were delivered to Site 2; and superphosphate, ammonium phosphate, and nitrate phosphate were delivered to Site 3. Data was subjected to statistical analysis for computing out ANOVA and correlation. Analysis revealed that minimum copper concentration was found in the soil of T. aestivum grown at Site-1 while the inhabitants of Site 3 had the highest concentration of Cu in their blood. The highest level of HIR was found in the human beings that ate the S. oleracea grown at Site 3. It is strongly advised that fertilizers be used sparingly, as their excessive use can cause human health risks.

Wastewater sources contain enormous amounts of nutrients for plant growth. This study aimed to define the metal accumulation in the goosefoot plant (Chenopodium album L.) of wastewater use in agricultural irrigation and to evaluate the risks of this accumulation to human health. The present research was performed in field conditions in Khushab, Pakistan. The Cd, Cu, Cr, Fe, Zn, Ni, and Mn concentrations were determined with the analysis performed using Atomic Absorption Spectrophotometer-AAS. Heavy metal concentrations in goosefoot samples irrigated with groundwater (GWI), canal water (CWI) and sugar mill water (MWI) ranged from 0.84 to 1.08, 0.55 to 0.78, 0.23 to 0.70, 2.09 to 5.56, 2.84 to 13.53, 0.53 to 1.13 and 0.32 to 0.39 mg/kg for Cd, Cr, Cu, Fe, Ni, Zn, and Mn, respectively. According to the statistical analyses, wastewater applications had a non-significant effect on Cr, Cu, and Zn concentrations in C. album samples collected from three sites, and a significant effect on Cd, Fe, Mn, and Ni concentrations (p>0.05). The results also showed that the health risk index value of cadmium was higher than 1. According to these results, long-term consumption of C. album samples grown in the study area may cause an accumulation of Cd in the human body and diseases in many tissues and organs.

Nickel (Ni) is essential for plant metabolism but is dangerous to most plants and other organisms when present in large concentrations. In this study, nickel (Ni) was chosen as an important environmental pollutant and toxic substance due to its widespread availability, and high persistence, causing soil pollution and serious environmental problems. The main aim of the study is to determine the Ni content in various wheat varieties and soil grown in various water regimes (sewage water, groundwater and industrial water) in Punjab, Pakistan. The Ni concentrations were determined via the ecological environment (water, soils) and below (roots) and above-ground plant organs (shoots and wheat grains) and phytostabilization potential assessment using various pollution indices. The results showed that the mean Ni concentration in different wheat varieties varied from 1.35 to 2.45 mg/kg, 1.17 to 2.42 mg/kg, 1.09 to 2.10 mg/kg and 0.99 to 1.54 mg/kg in soil, root, shoot, and grains, respectively. Research findings also revealed that the nickel concentration in soil and grains is within the safe limits and all the indices for Ni are less than 1. As a result, the studied wheat cultivars appear to be safe for consumption. However, it can be said that the continuous use of wastewater for irrigation may pose health risks for living things in the food chain.

Apple (Malus pumila L) of the family Rosaceae, most cultivated fruit in temperate regions of the world and is used fresh or processed. The apple production is affected by several pathogens including fungi. The present study was designed to identify disease-causing agents that reduce fruit production in the district Qilla Abdullah of Balochistan, Pakistan, which is the main apple production area of the province. Three varieties of apple: ‘Tur-Kulu’ (‘Red Delicious’), ‘Shin-Kulu’ (‘Golden Delicious’), and ‘Kaja’ were selected. Infected leaf samples were collected from eight different sites of tehsil Gulistan, district Qilla Abdullah. The cultures of fungal micro-flora were grown on two media, potato dextrose agar (PDA), and malt extract agar (MEA) followed by incubation for one week. The resulting colonies were observed under a microscope and identified based on morphological characters. Predominant fungal species was identified through ITS marker and PCR amplification. The isolated pathogens belonged to Zygomycota and Ascomycota divisions. The pathogens found were Aspergillus niger, A. oryzae, A. terrus, Colletotrichum gloeosporioides, Fusarium oxysporum, Mucor spp., Penicillium expansum, and one species of Absidia as well as Rhizopus. Colletotrichum gloeosporioides were predominantly found in all varieties. Morphological and phylogenetic analysis confirmed the identity of Colletotrichum gloeosporioides. As a result of this study, the predominant pathogen species Colletotrichum gloeosporioides is one of the causes of leaf infection in apple varieties.

Faisalabad is a major industrial area in Pakistan’s Punjab province that discharges wastewater into the Chenab River. Industrial effluents in Faisalabad are predicted to pose a significant threat to the riparian vegetation of the Chenab River and nearby vegetation. Heavy metal pollution of plants, water, and soils is one of the biggest problems worldwide that needs to be addressed because heavy metals above normal levels are extremely dangerous to both riparian vegetation and wildlife. The results indicated high levels of pollution in the industrial effluents as well as in the river in terms of salinity, metal toxicity, TSS, TDS, SAR, the acidic and alkaline nature of the industrial effluents, and the spread of industrial effluents up to 15 square kilometres in the Chenab River. Despite the higher pollution, four plants were found at all sites: Calotropis procera, Phyla nodiflora, Eclipta alba and Ranunculus sceleratus. It was found that most of the selected plants were phytoaccumulators, making them best suited to survive in harsh environments such as those with industrial pollution. The Fe concentration in the plant constituents was the highest, along with Zn, Pb, Cd, and Cu, all of which were above the permissible limits of the WHO. The metal transfer factor (MTF) was higher in most of the plants studied, and even exceeded 10 at some severely affected sites. Calotropis procera proved to be the most suitable plant for growth on drainage systems and also at river sites, as it had the highest importance value across all sites and seasons.

The increased use of waste water for agriculture purposes has increased around the globe. There are toxic metals present in waste water which affect plants, animals and human health. But at the same time it also contains useful nutrients which increase growth of plants. Different indices were applied to evaluate the metals present in water soil plant and milk. Various and correlation were determined with the help of SPSS, mean significance was found at the probability levels of 0.05, 0.001 and 0.01. The highest value of Cr was found in Avena sativa (0.7872 mg/kg) collected from site 5 while lower concentration of Cr was observed in Brassica campestris (0.0743 mg/kg) at site 4. In soil samples, Trifolium alexandrinum showed highest value of Cr (0.9887 mg/kg) at site 1 while lowered concentration was observed in Zea mays (0.1862 mg/kg) at site 3. Milk samples of site 5 had higher value of Cr (0.2898 mg/kg) and lowest at (0.1540 mg/kg) site 2. Water samples of site 3 had high value of Cr in them (1.849 mg/kg) and lowered concentration was found in water samples of site 5 (0.219 mg/kg). Cr concentration in fodders ranges from 0.0743 to 0.7872 mg/kg, soil 0.1862 to 0.9887 mg/kg, milk 0.1540 to 0.2898 mg/kg and water 0.219 to 1.849 mg/kg. In water samples, Cr level were above than permissible limit. In milk samples, concentration of Cr was greater than permissible limit which shows that it is hazardous to human health, may be due to pollution of environment like air, water and soil. This study concluded that if animals are allowed to graze on contaminated fodders and drink wastewater then metals were accumulated in their tissues and milk which causes toxicity to human health.

The primary objective of this study was to determine the heavy metal contents in the water–soil–coriander samples in an industrial wastewater irrigated area and to assess the health risks of these metals to consumers. Sampling was done from areas adjoining the Chistian sugar mill district Sargodha and two separate sites irrigated with groundwater (Site 1), and sugar mill effluents (Site 2) were checked for possible metal contamination. The water–soil–coriander continuum was tested for the presence of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Ni), lead (Pb), and zinc (Zn). The mean concentrations of all metals were higher than the permissible limits for all studied metals except for Mn in the sugar mill wastewater, with Fe (8.861 mg/L) and Zn (9.761 mg/L) exhibiting the highest values. The mean levels of Fe (4.023 mg/kg), Cd (2.101 mg/kg), Cr (2.135 mg/kg), Cu (2.180 mg/kg), and Ni (1.523 mg/kg) were high in the soil at Site 2 in comparison to the groundwater irrigated site where Fe (3.232 mg/kg) and Cd (1.845 mg/kg) manifested high elemental levels. For coriander specimens, only Cd had a higher mean level in both the groundwater (1.245 mg/kg) and the sugar mill wastewater (1.245 mg/kg) irrigated sites. An estimation of the pollution indices yielded a high risk from Cd (health risk index (HRI): 173.2), Zn (HRI: 7.012), Mn (HRI: 6.276), Fe (HRI: 1.709), Cu (HRI: 1.282), and Ni (HRI: 1.009), as all values are above 1.0 indicating a hazard to human health from consuming coriander irrigated with wastewater. Regular monitoring of vegetables irrigated with wastewater is strongly advised to reduce health hazards to people.

Heavy metals are considered as most important contaminations due to industrialization of countries and an influence on its existence in soil, plant and milk. A study was carried out to check manganese content in soil, forage and milk at three sites of city Jhang, Punjab, Pakistan. All samples (milk, soil, water, fodder plants and ground water) were analyzed for manganese by Atomic Absorption Spectrophotometer. Different health indices were also studied to check Mn flow in food chain. Level of Mn in samples was found within acceptable limits. Manganese level was higher in soil samples collected from Site-III than other sites. Manganese showed higher value (2.595 to 10.402 mg/kg) in soil than other samples. Fodders were found to accumulate manganese from 0.008 to 0.022 mg/kg. Manganese concentration was found to be 0.1482 to 1.241 mg/L, 0.164 to 0.9708 mg/L in water and milk, respectively. BCF and PLI values for manganese were also found to be less than 1. Estimated daily intake (EDI) and THQ of manganese are found within permissible limits in milk of cows feeding on fodders irrigated with wastewater and ground water. So, use of wastewater for irrigation purpose should be properly checked due to possible toxic effects.

In current work, we examined the absorption of metal Copper in generally grown food crops (Maize & Millet), cultivated in the waste water irrigated different Districts of Punjab, Pakistan. Analyzed samples of Water, Soil, shoot and grain were processed through atomic absorption spectrometric method. Our findings of Copper in water and Shoot were highest for Millet of Sargodha, irrigated with wastewater (1.69 mg/kg and 1.43 mg/kg respectively). In soil and grain samples maximum absorption of Cu was obtained in the waste water irrigated Maize (Sargodha) and Millet (Sheikhupura). Overall, Cu level was within the acceptable parameters set by FAO/WHO (2001). In this study, we also find out different indices such as Enrichment factor, Translocation factor, Pollution Load Index, Bio-concentration factor, Daily intake of metal and Health Risk Index. The study determined that frequently intake of waste watered forages in experimenting zone may carry possible health uncertainties in inhabitants.

Wastewater contains a surplus amount of trace metals that contaminate the soil and crops. A pot trial was performed to determine the impact of wastewater on the zinc accumulation in forages and their associated health risk. Forages both of summer (Zea mays, Echinochloa colona, Pennisetum typhoideum, Sorghum vulgare, Sorghum bicolor, Sesbania rostrata, and Cyamopsis tetragonoloba) and winter (Trifolium alexandrinum, Medicago sativa, Brassica campestris, Trifolium resupinatum, Brassica juncea, and Brassica napus) were grown with sewage water and tap water treatment. The experiment was laid down in a completely randomized design with five replicates. The concentration of zinc in water, root and forage samples were analysed by atomic absorption spectrophotometer. In tap water, the zinc value was 0.498 mg/L and in wastewater 0.509 mg/L, respectively. The maximum level of zinc in the forages leaves was 3.582 mg/kg found in Brassica napus grown in the winter season. The maximum observed value for zinc bioconcentration factor in Brassica juncea was (2.88) grown in winter. The values of pollution load index for zinc were found less than 1. The values of daily intake of metal and health risk index for zinc in all forages were less than 1 indicated that consumption of these forages was free of risk.

The present research determines the effect of wastewater for irrigation on heavy metal accumulation in vegetables in the example of spinach (Spinacia oleracea L.) and to evaluate human health risk from consumption. Trace metal values of Cd, Cu, Cr, Fe, Zn, Ni and Mn, were determined in the water, soil and plant samples by Atomic Absorption Spectrophotometer. Trace metal concentrations in spinach samples ranged from 0.29 to 0.37, 0.14 to 1.25, 0.07 to 0.67, 1.12 to 2.48, 0.33 to 0.38, 1.92 to 2.90 and 0.51 to 0.63 mg/kg for Cd, Cr, Cu, Fe, Ni, Zn and Mn, respectively. These values of trace metals were lower than the permissible limits except for Cd. All health risk index (HRI) values except for Cd were less than 1. However, the HRI values related to spinach samples irrigated with canal water and sugar mill water were generally higher than the values of the samples irrigated with groundwater. The HRI value of Cd was higher than 1 and consumers of such vegetables in which HRI of metal was greater than 1 will be at risk.

In this paper, two new versions of modified active disturbance rejection control (MADRC) are proposed to stabilize a nonlinear quadruple tank system and control the water levels of the lower two tanks in the presence of exogenous disturbances, parameter uncertainties, and parallel varying input set-points. The first proposed scheme is configured from the combination of a modified tracking differentiator (TD), modified super twisting sliding mode (STC-SM), and modified nonlinear extended state observer (NLESO), while the second proposed scheme is obtained by aggregating another modified TD, a modified nonlinear state error feedback (MNLSEF), and a fal-function-based ESO. The MADRC schemes with a nonlinear quadruple tank system are investigated by running simulations in the MATLAB/SIMULINK environment and several comparison experiments are conducted to validate the effectiveness of the proposed control schemes. Furthermore, the genetic algorithm (GA) is used as a tuning algorithm to parametrize the proposed MADRC schemes with the integral time absolute error (ITAE), integral square of the control signal (ISU), and integral absolute of the control signal (IAU) as an output performance index (OPI). Finally, the simulation results show the robustness of the proposed schemes with a noticeable reduction in the OPI.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay–energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.

The aim of the present research was to determine the trace metal accumulations in Brassica campestris irrigated with three different water regimes (groundwater, canal water, and sugar mill water). The analysis was conducted by Atomic Absorption Spectrophotometer to evaluate the concentration of minerals in the soil and in vegetables. The heavy metals investigated in this study were Cd, Cu, Cr, Fe, Zn, Ni, and Mn. Trace metal concentrations in soil samples ranged from 0.30 to 0.39, 0.14 to 0.30, 0.25 to 0.39, 1.47 to 3.98, 0.37 to 0.41, 0.8 to 8.78 and 0.36 to 0.36 mg/kg for Cd, Cr, Cu, Fe, Ni, Zn and Mn, respectively. Among the three treatments, the mean concentrations of Fe and Zn were higher than other metal accumulations for all treatments. The contents of Cd, Cr, Cu, Fe, Ni, Zn and Mn in Brassica campestris ranged from 0.35 to 0.44, 0.32 to 0.89, 0.09 to 0.73, 1.93 to 3.02, 1.11 to 1.82, 0.36 to 0.43 and 0.37 to 0.40 mg/kg, respectively. Statistical analyses showed that the treatments have a non-significant effect (p>0.05) on concentrations of metals in Brassica campestris collected from three sites for Cd, Cr, Cu and Mn and significant effect on Fe, Zn, and Ni.

The aim of this study was to determine the levels of potential toxic metals, cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), zinc (Zn), and lead (Pb) in the wheat variety (Fsd-2008) irrigated by sewage water. Various indices were examined such as bioconcentration factor, enrichment factor and health risk index in order to better understand the transport and accumulation of metals through the food chain. Seeds of wheat variety were sown toward the end of October 2014 and flooded with different combinations of local sewage water and groundwater. Determination of metal concentration in soil and wheat samples was carried out by atomic absorption spectrophotometer. Analysis of mean metal concentration in soil that was irrigated by five different water treatments revealed that the mean concentration of Cd was less in the soil of treatment III, IV and V. Results also indicated that the mean concentrations of Cd, Cu and Cr increased and that of Ni and Pb decreased by sewage water irrigation.

Synthetic food colors are important ingredients in the food industry. These synthetic food colorants are azo dyes, majorly acidic in nature such as Allura red and Tartrazine. They are present in sweets, carbonated drinks, meat products, and candies to attract the consumers. This review article is an attempt to explain the adverse effects of azo dyes and their association with oral cavities and cardiovascular disorders. These synthetic dyes (azo dyes) have staining effects on dentin. Poor dental care accelerates the bacterial accumulation on the dental crown (Gram-negative bacteria P. gingivalis, T. denticola, and T. forsythia and Gram-positive bacteria Strep. Gordonii), causing the washing of enamel, forming dental plaque. Bacterial pathogens (P. ginigivalis and F. nacleatum) release different chemicals (FadA and Fap2) that bind to protein on the cell by producing an inflammatory response through different line-host defenses, such as Gingival epithelial cells (ECs), Hemi-desmosomes, and desmosomes, which helps the bacterium migration from the cell–cell junction. This makes the junctions slightly open up and makes the whole vessel permeable, through which the bacterium enters into the blood stream line. This leads to different major arteries, such as the carotid artery, and causes the accumulation of plaque in major cardiac arteries, which causes different cardiovascular disorders. These bacterial species present in gums cause cardiovascular diseases, such as ischemic heart disease, coronary artery disease, heart attacks and strokes, and arrhythmias, which can lead to death.

Buffer Overflow (BOF) has been a ubiquitous security vulnerability for more than three decades, potentially compromising any software application or system. This vulnerability occurs primarily when someone attempts to write more bytes of data (shellcode) than a buffer can handle. To date, this primitive attack has been used to attack many different software systems, resulting in numerous buffer overflows. The most common type of buffer overflow is the stack overflow vulnerability, through which an adversary can gain admin privileges remotely, which can then be used to execute shellcode. Numerous mitigation techniques have been developed and deployed to reduce the likelihood of BOF attacks, but attackers still manage to bypass these techniques. A variety of mitigation techniques have been proposed and implemented on the hardware, operating system, and compiler levels. These techniques include No-EXecute (NX) and Address Space Layout Randomization (ASLR). The NX bit prevents the execution of malicious code by making various portions of the address space of a process inoperable. The ASLR algorithm randomly assigns addresses to various parts of the logical address space of a process as it is loaded in memory for execution. Position Independent Executable (PIE) and ASLR provide more robust protection by randomly generating binary segments. Read-only relocation (RELRO) protects the Global Offset Table (GOT) from overwriting attacks. StackGuard protects the stack by placing the canary before the return address in order to prevent stack smashing attacks. Despite all the mitigation techniques in place, hackers continue to be successful in bypassing them, making buffer overflow a persistent vulnerability. The current work aims to describe the stack-based buffer overflow vulnerability and review in detail the mitigation techniques reported in the literature as well as how hackers attempt to bypass them.

In South Asia, the high costs of synthetic fertilizers have imposed research on alternative nutrient inputs. We aimed to identify potential trace elements (PTE) present in some organic manure that might be a source of environmental pollutions and risk to public health following consumption. The study aims to evaluate how different organic waste (poultry waste, PW; press mud, PM; and farmyard manure, FYM) influences the heavy metal migration in soil, the accumulation in garlic, and their potential health risks. Organic waste caused a higher accumulation of certain metals (Zn, Cu, Fe, and Co), whereas Mn, Cd, Cr, and Pb were in lower concentrations in soil. Amendments of soil with PM revealed a higher accumulation of Cd, Cr, Fe, and Pb, whereas PW resulted in Cu and Zn accumulation in garlic. Treatment of soil with FYM exhibited higher metal concentration of Co and Mn. An environmental hazard indices study revealed that pollution load index (PLI) was highest for Cu following treatment with PM. The health risk index (HRI) was greater for Cd following amendment with PM. Maximum daily intake of metals (DIM) was observed for Zn after treatment with PW. The Pb exhibited maximum bioconcentration factor (BCF) in PM-amended plants. Based on these findings, we concluded that garlic grown on contaminated soil with organic waste may pose serious health hazards following consumption.

Salinity is a major threat to agricultural productivity worldwide. The selection and evaluation of crop varieties that can tolerate salt stress are the main components for the rehabilitation of salt-degraded marginal soils. A field experiment was conducted to evaluate salinity tolerance potential, growth performance, carbon (δ13C) and nitrogen isotope composition (δ15N), intrinsic water use efficiency (iWUE), harvest index, and yield stability attributes in six barley genotypes (113/1B, 59/3A, N1-10, N1-29, Barjouj, Alanda01) at three salinity levels (0, 7, and 14 dS m−1). The number of spikes m−2 was highest in Alanda01 (620.8) while the lowest (556.2) was exhibited by Barjouj. Alanda01 produced the highest grain yield (3.96 t ha−1), while the lowest yield was obtained in 59/3A (2.31 t ha−1). Genotypes 113/1B, Barjouj, and Alanda01 demonstrate the highest negative δ13C values (−27.10‰, −26.49‰, −26.45‰), while the lowest values were obtained in N1-29 (−21.63‰) under salt stress. The δ15N was increased (4.93‰ and 4.59‰) after 7 and 14 dS m−1 as compared to control (3.12‰). The iWUE was higher in N1-29 (144.5) and N1-10 (131.8), while lowest in Barjouj (81.4). Grain protein contents were higher in 113/1B and Barjouj than other genotypes. We concluded that salt tolerant barley genotypes can be cultivated in saline marginal soils for food and nutrition security and can help in the rehabilitation of marginal lands.

The present study was designed to estimate the zinc (Zn) requirement of Labeo rohita juveniles fed practical diet. Treatments used for the study were consisted of six experimental diets supplemented with graded levels of Zn (0, 21, 42, 63, 84 and 104 mg/kg diet) from Zn gluconate. For each experimental diet, two replicates were allocated, and 18 fish were stocked in each replicate. The feeding trial was lasted for 90 days. Results showed that final weight, absolute weight gain, weight gain% and specific growth rate increased with increasing dietary Zn levels up to 42 mg/kg and started to decrease with further increase in dietary Zn level. Quadratic regression analysis of weight gain% data indicated that L. rohita juveniles required 62.58 mg/kg Zn for normal growth. Maximum Zn absorption was observed in fish fed diet supplemented with 42 mg/kg Zn compared with other dietary treatments. Alkaline Phosphatase (ALP) activity in kidney and spleen of L. rohita juveniles increased with the increase in dietary Zn levels up to 42 and 63 mg/kg, respectively. Conclusively, supplementation of graded levels of dietary Zn-gluconate improved the growth performance and increased the Zn bioavailability and ALP activity up to a certain limit in L. rohita juveniles.

ABSTRACT Objective. This study was conducted during winter season at rural livestock farm of district Sargodha, Pakistan, in order to define the macrominerals status in blood plasma of lactating and dry Desi breed cows . Material and methods. Twenty lactating and twenty dry cows were used and their blood plasma was analyzed for Ca, Mg, Zn, Cu and Fe using atomic absorption spectrophotometer. Results. In lactating cows, the plasma Ca concentration was lower than dry cows (6.8 vs. 7.6 mg/dl, p