Search

Abstract This study aimed to evaluate the adaptation and effectiveness of telemedicine with the diffuse-dual-channel system (DDS) and tiered-gatekeeper system (TGS) across different tiers of the healthcare system on technical-organizational-environmental (TOE) framework. The telemedicine services were extended as Tiered-gatekeeper system (TGS) by Primary and Secondary Healthcare (PSHC) and Diffuse-dual-channel system (DDS) by King Edward Medical University (KEMU) in 2020 benefiting 2605 and 21,905 patients, respectively. This cross-sectional survey is based on a structured questionnaire conducted on 172 healthcare practitioners (HCP) from KEMU and 76 from PSHC selected by purposive sampling and analysis is conducted through descriptive analysis and the Boruta features selection method. The diffuse-dual-channel system is found to be flexible, easy to implement, and impactful due to innate compatibility with prevailing healthcare practices, however, it is a parallel effort to the existing healthcare system. The tiered-gatekeeping system is found to be complex in adaptation initially, but is more organized, deeply integrated, accountable and channelizes the flow of patients, resultantly aiding the preservation of resources.

Abstract The root architecture, more seminal roots, and Deeper roots help the plants to uptake the resources from the deeper soil layer to ensure better growth. The Gibberellic acid-sensitive (GA-sensitive) Rht genes are well known for increasing drought tolerance in wheat. Much work has been performed on the effect of these genes on the plant agronomic traits and little work has been done on the effect of Rht genes on seminal roots and root architecture. This study was designed to evaluate 200 wheat genotypes under normal and osmotic stress. The genotypes were sown in the solution culture and laid under CRD factorial arrangement with three replications and two factors i.e., genotypes and treatments viz. normal and osmotic stress (20% PEG-6000) applied one week after germination. The data was recorded for the root traits. Results demonstrated that out of 200 genotypes, the GA-sensitive Rht13 gene was amplified in 21 genotypes with a fragment length of 1089 bp. In comparison, the GA-insensitive Rht1 gene was amplified in 24 genotypes with a band size of 228 bp. From 200 wheat genotypes, 122 genotypes produced 5 seminal roots, 4 genotypes 4 seminal roots, and 74 genotypes 3 seminal roots. The genotypes G-3 (EBW11TALL#1/WESTONIA-Rht5//QUAIU#1), G-6 (EBW01TALL#1/SILVERSTAR-Rht13B//ROLF07) and G-8 (EBW01TALL#1/SILVERSTAR-Rht13B//NAVJ07) produced 5 seminal roots and have longer coleoptile (> 4.0 cm), root (> 11.0 cm) and shoot (> 17 cm) under normal and osmotic stress. Furthermore, Ujala 16, Galaxy-13, and Fareed-06 produced 3 seminal roots and have short coleoptile (

Abstract Wheat commands attention due to its significant impact on culture, nutrition, the economy, and the guarantee of food security. The anticipated rise in temperatures resulting from climate change is a key factor contributing to food insecurity, as it markedly reduces wheat harvests. Terminal heat stress mostly affects spike fertility in wheat, specifically influencing pollen fertility and anther morphology. This research especially focuses on the shape of anthers and examines the effects of heat stress. The DinoLite Microscope’s high-resolution images are used to measure the length and width of wheat anthers. By using object identification techniques, the research accurately measures the length and width of each anther in images, offering valuable insights into the differences between various wheat varieties. Furthermore, Deep Learning (DL) methodologies are utilized to enhance agriculture, specifically employing record categorization to advance plant breeding management. Given the ongoing challenges in agriculture, there is a belief that incorporating the latest technologies is crucial. The primary objective of this study is to explore how Deep Learning algorithms can be beneficial in categorizing agricultural records, particularly in monitoring and identifying variations in spring wheat germplasm. Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. LeNet demonstrates optimized accuracy in classifying the records, outperforming CNN by 52% and Inception-V3 by 70%. Moreover, Precision, Recall, and F1 Measure are utilized to ascertain accuracy levels. The investigation also enhances our comprehension of the distinct roles played by various genes in abiotic stress tolerance among diverse wheat varieties. The outcomes of the research hold the potential to transform agricultural practices by introducing a more effective, data-driven approach to plant breeding management.

OBJECTIVE: To find a correlation between total serum cholesterol (TSC) in acute ischemic stroke and their prognosis is assessed employing the Barthel Index (BI) score. METHODOLOGY: This prospective study was conducted at Medical Emergency of Allied Hospital Faisalabad and Social Security Hospital Lahore from 2020 to 2021. The written informed consent was obtained. In 58 sudden-onset acute ischemic stroke subjects, 36 males and 22 females were selected by purposive sampling. Patients were aged between 18-50 years. The BI scoring system was used instantly on admission day, followed up at 48 hours, two weeks and one month of stroke to determine everyday routine in Personal care. Patients with a previous history of some stroke, transient ischemic attack (TIA), diabetes mellitus (DM), renal dysfunction, cardiac malfunctions, or valvular heart diseases were excluded. Stroke patients having an acute ischemic stroke for the first time were diagnosed and confirmed by non-contrast computed tomography (CT) scan and statistical analysis using SPSS version 17.0. RESULTS: The mean BI score taken at four intervals was significantly correlated to the levels of TSC. The acute ischemic stroke patients with TSC level > 200 mg/dl showed better prognosis in comparison to those with TSC level < 200 mg/dl. CONCLUSION: The present study on acute ischemic stroke patients suggests that TSC is correlated to a better future outcome as assessed by the BI score. This study demonstrates that high total serum cholesterol (HTSC) can be considered as a marker to anticipate the prognosis in patients of acute ischemic stroke.

Abstract Background Cotton is one of the topmost fiber crops throughout the globe. During the last decade, abrupt changes in the climate resulted in drought, heat, and salinity. These stresses have seriously affected cotton production and significant losses all over the textile industry. The GhAGC kinase, a subfamily of AGC group and member of serine/threonine (Ser/Thr) protein kinases group and is highly conserved among eukaryotic organisms. The AGC kinases are compulsory elements of cell development, metabolic processes, and cell death in mammalian systems. The investigation of RNA editing sites within the organelle genomes of multicellular vascular plants, such as Gossypium hirsutum holds significant importance in understanding the regulation of gene expression at the post-transcriptional level. Methods In present work, we characterized twenty-eight GhAGC genes in cotton and constructed phylogenetic tree using nine different species from the most primitive to the most recent. Results In sequence logos analyses, highly conserved amino acid residues were found in G. hirsutum, G. arboretum, G. raimondii and A. thaliana. The occurrence of cis-acting growth and stress-related elements in the promoter regions of GhAGCs highlight the significance of these factors in plant development and abiotic stress tolerance. Ka/Ks levels demonstrated that purifying selection pressure resulting from segmental events was applied to GhAGC with little functional divergence. We focused on identifying RNA editing sites in G. hirsutum organelles, specifically in the chloroplast and mitochondria, across all 28 AGC genes. Conclusion The positive role of GhAGCs was explored by quantifying the expression in the plant tissues under abiotic stress. These findings help in understanding the role of GhAGC genes under abiotic stresses which may further be used in cotton breeding for the development of climate smart varieties in abruptly changing climate.

Abstract The situation of global warming imparts negative impacts on crop growth and development. Cotton is the most important fiber crop around the globe. However, frequent drought episodes pose serious threats to cotton production worldwide. Due to the complex genetic structure of drought tolerance, the development of a tolerant cultivar is cumbersome via conventional breeding. Multiple omics techniques have appeared as successful tool for cotton improvement in drought tolerance. Advanced omics-based biotechniques have paved the way for generation of omics data like transcriptomics, genomics, metabolomics and proteomics, which greatly expand the knowledge of cotton response to drought stress. Omics methodologies and have provided ways for the identification of quantitative trait loci (QTLs), gene regulatory networks, and other regulatory pathways against drought stress in cotton. These resources could speed up the discovery and incorporation of drought tolerant traits in the elite genotypes. The genome wide association study (GWAS), gene-editing system CRISPER/Cas9, gene silencing through RNAi are efficient tools to explore the molecular mechanism of drought tolerance and facilitate the identification of mechanisms and candidate genes for the improvement of drought tolerance in cotton.

Abstract Zinc oxide nanoparticles have wide range biological, biomedical and environmental applications. However, traditional nanofabrication of ZnONPs uses various toxic chemicals and organic solvents which limit their bio-applications. To overcome this hurdle, Bauhinia variegata derived buds extract was utilized to fabricate ZnONPs. The greenly generated ZnONPs were successfully prepared and extensively characterized using different analytical tools and the average crystalline size was calculated as 25.47 nm. Further, bioengineered ZnONPs were explored for multiple biological activities that revealed excellent therapeutic potentials. The antibacterial potential was determined using different bacterial strains. Pseudomonas aeruginosa (MIC: 137.5 µg/mL) was reported to be the most resistant variant while Bacillus subtilis (MIC: 34.38 µg/mL) was observed to be most susceptible bacterial strain. DPPH radical scavenging potential was measured to determine the antioxidant capacity of ZnONPs and the highest scavenging potential was observed as 82% at highest of 300 µg/mL. The fungicidal effect of green ZnONPs in comparison with Amphotericin B was assessed against five selected pathogenic fungal strains. The results revealed, Fusarium solani (MIC: 46.875 µg/mL) was least resistant and Aspergillus flavus (MIC: 187.5 µg/mL) was most resistant in fungicidal examination. Cytotoxicity potential of B.V@ZnONPs was analyzed against newly hatched nauplii of brine shrimps. The results for greenly produced ZnONPs was recorded as 39.78 µg/mL while 3.006 µg/mL was reported for positive control vincristine sulphate. The results confirmed the category of general cytotoxic for greenly synthesized nano sized B.V@ZnONPs.

Abstract Drought is one of the major environmental issues that reduce crop yield. Seed germination is a crucial stage of plant development in all crop plants, including soybean. In soybean breeding, information about genetic mechanism of drought tolerance has great importance. However, at germination stage, there is relatively little knowledge on the genetic basis of soybean drought resistance. The objective of this work was to find the quantitative trait nucleotides (QTNs) linked to drought tolerance related three traits using a genome-wide association study (GWAS), viz., germination rate (GR), root length (RL), and whole seedling length (WSL), using germplasm population of 240 soybean PIs with 34,817 SNPs genotype data having MAF > 0.05. It was observed that heritability (H2 ) for GR, WSL, and RL across both environments (2020, and 2019) were high in the range of 0.76–0.99, showing that genetic factors play a vital role in drought tolerance as compared to environmental factors. A number of 23 and 27 QTNs were found to be linked to three traits using MLM and mrMLM, respectively. Three significant QTNs, qGR8-1, qWSL13-1, and qRL-8, were identified using both MLM and mrMLM methods among these QTNs. QTN8, located on chromosome 8 was consistently linked to two traits (GR and RL). The area (± 100 Kb) associated with this QTN was screened for drought tolerance based on gene annotation. Fifteen candidate genes were found by this screening. Based on the expression data, four candidate genes i.e. Glyma08g156800, Glyma08g160000, Glyma08g162700, and Glyma13g249600 were found to be linked to drought tolerance regulation in soybean. Hence, the current study provides evidence to understand the genetic constitution of drought tolerance during the germination stage and identified QTNs or genes could be utilized in molecular breeding to enhance the yield under drought stress.

Abstract Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann–Kendall trend test, and Sen’s Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience “moderate drought (MD)” in the twenty-first century.

Abstract Plant growth-promoting rhizobacteria (PGPR) boost crop yields and reduce environmental pressures through biofilm formation in natural climates. Recently, biofilm-based root colonization by these microorganisms has emerged as a promising strategy for agricultural enhancement. The current work aims to characterize biofilm-forming rhizobacteria for wheat growth and yield enhancement. For this, native rhizobacteria were isolated from the wheat rhizosphere and ten isolates were characterized for plant growth promoting traits and biofilm production under axenic conditions. Among these ten isolates, five were identified as potential biofilm-producing PGPR based on in vitro assays for plant growth-promoting traits. These were further evaluated under controlled and field conditions for their impact on wheat growth and yield attributes. Surface-enhanced Raman spectroscopy analysis further indicated that the biochemical composition of the biofilm produced by the selected bacterial strains includes proteins, carbohydrates, lipids, amino acids, and nucleic acids (DNA/RNA). Inoculated plants in growth chamber resulted in larger roots, shoots, and increase in fresh biomass than controls. Similarly, significant increases in plant height (13.3, 16.7%), grain yield (29.6, 17.5%), number of tillers (18.7, 34.8%), nitrogen content (58.8, 48.1%), and phosphorus content (63.0, 51.0%) in grains were observed in both pot and field trials, respectively. The two most promising biofilm-producing isolates were identified through 16 s rRNA partial gene sequencing as Brucella sp. (BF10), Lysinibacillus macroides (BF15). Moreover, leaf pigmentation and relative water contents were significantly increased in all treated plants. Taken together, our results revealed that biofilm forming PGPR can boost crop productivity by enhancing growth and physiological responses and thus aid in sustainable agriculture.

The road construction industry aims to contribute to the protection of already compromised environment. Cold mix asphalt (CMA) is a measure initiated by the road industry to protect the environment and preserve energy. Despite having additional benefits, CMA has attracted little attention due to its inferior performance. CMA's performance is enhanced using a sustainable binder bio-modifier, natural cup lump rubber (CLR) is one of them. This study evaluated the tensile properties, rutting, moisture susceptibility, and adhesion properties of CLR-modified CMA (CMA-CR). The tensile property was enhanced by 26% due to CLR modification. CMA-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles, showing 70% improvement compared with conventional CMA. Moisture susceptibility evaluation indicated that CMA-CR had tensile strength ratio (TSR) value of 104%, satisfying the minimum 80% requirement of AASHTO T283. It also retained more than 96% bitumen coating. The moisture damage resistance was improved by 12% and 10% in terms of TSR and stripping, respectively. The durability results revealed that the CMA-CR mixture prevented higher mass loss, representing 14% improvement compared with conventional CMA.

Objective: To determine the depth of the curve of Spee in healthy permanent dentition for its application in complete denture wearers. Study Design : Cross-sectional study. Place and Duration of Study: The study was conducted at Lahore Medical and Dental College from 20 April to 20 October 2023. Materials and Methods: A total of two hundred patients aged 18 – 32 years were included in this study. Mandibular casts of patients were obtained. Each of the casts had fully developed dentitions except for the third molars. Independent sample t-test was used to determine mean depth of the curve of Spee between females and males. Data was collected, results were then statistically analyzed using SPSS version 25. A p-value ≤ 0.05 was considered significant. Results: The mean age of subjects was 25.19 ± 3.529 with minimum and maximum age as 18 and 32 years. The depth of the curve of Spee was found to be P=0.380 and t= -1.540, the curve of Spee is not significantly different in both genders. The curve of Spee mean value was 1.990 ± 0.6175 mm. Conclusion: This study has revealed that the depth of the curve of Spee in natural dentition remains constant irrespective of age and gender. Therefore, the curve of Spee values can be used in the rehabilitation of complete denture wearer.

Background: Agitation is frequently encountered in patients recovering from traumatic brain injury (TBI) in the intensive care unit (ICU). The etiopathology for agitation is multifactorial. Melatonin has been widely used to study the effects of delirium in ICU, however, its effect on agitation is not well studied. Aims and Objectives: The aim of this study was to assess the effect of melatonin administration on the prevalence of agitation and length of stay in patients with severe TBI, managed conservatively, or undergoing decompressive craniectomy. Materials and Methods: A retrospective observational study with 70 patients undergoing various decompressive craniotomy or managed conservatively, and admitted to neurosurgical ICU was included in the study. Thirty-six patients were recruited from the control group and 34 patients received melatonin during their stay in the ICU. In this study, records of 70 patients who had suffered isolated TBI with no associated injuries were analyzed. The patients had a Glasgow coma score of 3–14 on admission to ICU. The patients were managed as per the standard protocols, as per the existing guidelines, and based on TBI guidelines. Results: A non-significant decreasing trend of agitation and reduced duration of ICU stay was noted in patients who received melatonin compared with the control group. The prevalence of agitation observed on modified Ramsay scale (Mean±SD) in patients who received melatonin on Day 3, Day 4, Day 5, Day 6, Day 7, Day 8, and Day 9 were −1.67±3.01; −1.61±2.82; −1.2±2.55; −1.23±2.51; −1.23±2.11; −1.05±2.09; and 0.76±2.03, respectively. These scores were slightly lower than observed in the control group on Day 3, Day 4, Day 5, Day 6, Day 7, Day 8, and Day 9 as −1.58±3.16; −1.33±2.72; −1.08±2.46; −1.13±2.25; −0.94±1.87; −0.52±1.7; and 0.52±1.36, respectively. The mean±SD duration of stay in ICU of patients receiving melatonin (13.14±3.37) and not receiving melatonin (14.52±3.73) was comparable (P=0.1). Conclusion: Although there was a decreased prevalence of agitation and a decrease in the mean duration of the ICU stay in patients who received oral melatonin, these beneficial effects did not show any statistical significance once compared with the control group.

Globally, the petroleum industry plays a very significant role in producing oil to fill full the demand of the overgrowing population. The improper management of abandoned quantity of petroleum sludge that is one of the byproducts of petroleum industry has posed many environmental as well as socio-economic issues in most of the developing countries. The petroleum sludge contains various toxic substances like minerals, oil, and other chemicals which are very harmful for biotic as well as abiotic environment. Meanwhile, a huge quantity of livestock manure, especially buffalo dung, is produced in villages and burned as fuel after drying in open atmosphere for domestic application without any treatment which generates indoor air pollution. This study was formulated to analysis the biochemical methane potential of buffalo dung with petroleum sludge at different mixing ratios (i.e., 1:1, 1.5:0.5 and 0.5:1.5) through batch digestion system. The substrates were prepared and characterized before and after batch digestion by using standard methodology. The maximum methane was obtained as 268Nml/gVS, followed by 326Nml/gVS and 191Nml/gVS at mixing ratio of 1:1, 1.5:0.5 and 0.5:1.5 respectively. The results and finding of study lead to recommend that the codigestion of buffalo dung with petroleum sludge at mixing ratio of 1.5:0.5 through continuous batch digestion would be best option to enhance methane production.

Objective Telemedicine is a digital substitute for in-person healthcare service delivery systems that has gained popularity amid the global COVID-19 pandemic. The objective of this study was to evaluate telemedicine compatibility from the perspective of healthcare practitioners to enhance the effectiveness and spectrum of the Model for Assessment of Telemedicine. Method Primary and Secondary Healthcare and King Edward Medical University extended their respective telemedicine services in 2020 where 24,516 patients were benefited from the telemedicine services provided by 1273 doctors from different specializations. A cross-sectional survey via online questionnaire was conducted among purposively sampled 248 healthcare practitioners designated at telemedicine portals in the public sector; further analysed by descriptive analysis and Monte Carlo Feature Selection. Results Healthcare practitioner perception was analysed explicitly and found significant in addition to the existing domains under multidisciplinary assessment in the Model for Assessment of Telemedicine model. The variables of subdomains integration with healthcare system, patient facilitation, technology ease, capacity building, ethical integrity, outcome assessment and communication gap under proposed healthcare practitioner perception domain were found interdependent. The variables of patient satisfaction, resource preservation, healthcare practitioner satisfaction, digital connectivity, user-friendliness, and patient safety were found to be of higher importance (RI values). However, the compatibility of telemedicine with the healthcare system was also influenced by interdependencies (RI plot) and multifaceted interactions of variables derived from the healthcare practitioner perception. Conclusion The variables of healthcare practitioner perception were exhibiting various weightages of importance and interdependencies in determining the compatibility of telemedicine within the healthcare system and recommended to be considered in the Model for Assessment of Telemedicine framework.

Graphene-based hybrid filler polymer composites represent a significant advancement in materials science and engineering due to graphene’s remarkable mechanical, thermal, and electrical properties. This comprehensive review systematically evaluates both traditional (e.g., mechanical and solution mixing) and modern fabrication techniques (e.g., in situ polymerization, chemical modification, electrospinning, and layer-by-layer assembly) employed in the synthesis of these composites, comparing their effectiveness in enhancing material properties such as strength, thermal stability, and conductivity. Emphasizing the synergistic interactions between graphene and other fillers within polymer matrices, the paper discusses mechanisms that lead to superior composite performance. A thorough survey of the literature highlights successful examples where these composites have demonstrated significant advancements in mechanical, thermal, and electrical properties. Three detailed tables summarize findings from published studies, illustrating the enhanced properties and achievements at various filler concentrations across different preparation methods. The review concludes by outlining future research directions aimed at optimizing graphene hybrid filler polymer composites for broader scientific and industrial utilization. By consolidating current knowledge and providing a practical resource, this review aims to guide researchers and practitioners in harnessing the full potential of these advanced materials.

Synthetic plastics are in great demand in society due to their diversified properties, but they cause environmental pollution due to their non-biodegradable nature. Therefore, synthetic plastics are in need to be replaced with biodegradable plastics. Polyhydroxyalkanoates (PHAs), bacterial biopolymers are natural alternative to synthetic plastics. These are present inside the bacterial cytoplasm in granular form. Presently, the production cost of PHA is high due to expensive carbon substrates used in its biosynthesis. Therefore, this study focuses on the cost-effective production of PHA using waste carbon sources. Rice bran and sugarcane molasses were used as the carbon source for PHA production from Bacillus subtilis, Bacillus cereus, Alcaligenes sp. and Pseudomonas aeruginosa. PHA production from these bacterial strains was confirmed through Sudan Black-B screening. With rice bran, as carbon source, the highest PHA yield obtained was for P. aeruginosa, which yielded 93.7% and lowest was 35.5% for B. cereus. Surprisingly, B. cereus produced the highest cell dry mass (0.045 g/L) but its extracted PHA contents were lowest being only 0.02 g/L. Alcaligenes sp. with 0.031 g/L CDM yielded 87.1% PHA. B. subtilis had a CDM 0.029 g/L, 0.02 g/L PHA content and a yield of 69.10%. In the case of sugarcane molasses, P. aeruginosa produced 95% PHA yield, 0.02 g/L CDM, and 0.019 g/L PHA content. Alcaligenes sp. yielded 90.9% PHA, 0.011 g/L CDM, and 0.01 g/L PHA content. B. subtilis produced 91.6% PHA yield, 0.012 g/L CDM, 0.011 g/L PHA content; B. cereus produced 80% PHA yield, 0.015 g/L CDM, 0.012 g/L PHA content at 37 °C, pH 7. Higher concentrations of carbon sources increased the CDM and decreased the PHA yield. The maximum yield of PHA was obtained from sugarcane molasses. 24–48 h of incubation was optimal for B. subtilis and B. cereus, while for Alcaligenes and P. aeruginosa incubation time of 48–96 h was desirable for higher PHA yield. The extracted biopolymers were analyzed by Fourier transform infrared spectroscopy (FTIR), which identified the extracted biopolymers as poly-3-hydroxybutyrate P(3HB). The thermal properties of the extracted biopolymers, such as melting temperatures, were analyzed by differential scanning calorimetry (DSC), which confirmed the thermal stability.

Background and objectives: Dyslipidemia is one of the major predictors of cardiovascular disease. Cardiovascular diseases are the major cause of morbidity and mortality in developed as well as developing countries around the world. Cardiovascular diseases are becoming more prevalent in Pakistan and are the major cause of mortality in Pakistan. The number of studies previously carried out on ABO blood group association has been reported with many diseases including cancer, ulcer, diabetes, and hypertension. However, no study has previously reported an association of blood groups in apparently normal subjects. The objective of the study is to observe the association of the ABO blood group with Lipid Profile and compare the association in different ABO blood groups. Methods: This cross-sectional study includes 600 randomly selected subjects from different organizations of district Hyderabad including Universities, Colleges, Hospitals, students, faculty members, and their employees. This study was conducted from February 2021 to January 2022. Informed consent was obtained before the enrolment of volunteers. A structured questionnaire was used to obtain data related to sociodemographic characteristics. A detailed history was taken followed by a thorough clinical examination. All biochemical tests were performed using the blood analyzer and micro lab. Data obtained by structured questionnaire and anthropometric parameters were analyzed by SPSS version 20.0. Results: In this study, the Overall B blood group was found more prevalent and similarly found in both males and females. We found Cholesterol higher in the O blood group and Rh-positive subjects.Triglycerides were found higher in B and O and Rh Positive subjects. HDL was found to be higher in B and Rh-positive subjects. LDL was found higher in B and O and Rh Positive subjects. HDL was found to be higher in B and Rh-positive subjects. VLDL was found higher in both B and O AND Rh Positive subjects. Conclusion: The evidence we collected is suggestive of the association of the ABO blood group with Lipid Profile.

Intercropping with hyperaccumulators can facilitate the safe utilization of cadmium-contaminated soil. However, the effectiveness of this approach is influenced by plant species and varieties, which necessitates research on optimal plant consortia. In this study, 8 tomato varieties (3 cherry tomatoes and 5 common large-fruit tomatoes) were intercropped with Sedum alfredii in a moderately Cd-contaminated vegetable field. The results showed that the Cd concentration in the fruits of common large-fruit tomato varieties under monoculture was 1.03–1.50 mg/kg, while that in the fruits of cherry tomato varieties was 0.67–0.71 mg/kg. After intercropping with S. alfredii, the fruit Cd concentrations of Hangza 501, Hangza 503, and Hangza 108 decreased by 16.42 %, 19.72 %, and 6.76 %, respectively, while those of the other varieties significantly increased, except for those of Hangza 8. In contrast, the shoot Cd concentration of cherry tomatoes was greater than that of large-fruit tomatoes under monoculture. Furthermore, a significant increase in the shoot Cd concentration was noted in the Hangza 501, Hangza 503 and Hangza 603 plants following intercropping. Additionally, intercropping with S. alfredii increased the concentration of soluble sugars in the fruits of Hangza 8, Hangza 501, Hangza 503 and Hangza 603 by 4.66 %, 17.91 %, 10.60 % and 17.88 %, respectively. Intercropping with tomatoes resulted in a decrease in both the biomass and Cd uptake of S. alfredii. Interestingly, the inhibitory effect on S. alfredii was less pronounced when intercropped with cherry tomatoes than when intercropped with large-fruit tomatoes. Among the intercropping treatments, S. alfredii exhibited the greatest total Cd accumulation (0.06 mg/plant) when intercropped with Hangza 503. In conclusion, the cherry tomato variety Hangza 503 was the most suitable for intercropping with S. alfredii and can be used safely for vegetable production and simultaneous phytoremediation of polluted soil. Our findings suggest that strategic selection of tomato varieties can optimize the effectiveness of “phytoextraction coupled with agro-safe production” technology for managing soil Cd concentrations.

Abstract Background Growing wheat under climate change scenarios challenges, scientists to develop drought and heat-tolerant genotypes. The adaptive traits should therefore be explored and engineered for this purpose. Thus, this study aimed to dissect surface traits and optimizing the leaf architecture to enhance water use efficiency (WUE) and grain yield. Twenty-six wheat genotypes were assessed for five novel leaf traits (NLTs: leaf prickle hairs, groove type, rolling, angle and wettability) under normal, drought and heat conditions following triplicated factorial randomized complete block design (RCBD). The data for NLTs, physiological traits (stomatal conductance, WUE, transpiration, and photosynthesis), and standard morphological and yield traits were recorded. Leaves were sampled at the stem elongation stage (Zadoks 34) to measure the leaf water content (%), contact angle, and to obtain pictures through scanning electron microscopy (SEM). The air moisture harvesting efficiency was evaluated for five selected genotypes. The ideotype concept was applied to evaluate the best-performing genotypes. Results The correlation analysis indicated that long leaf prickle hairs (> 100 μm), short stomatal aperture and density (40–60 mm− 2), inward to spiral leaf rolling, medium leaf indentation, low contact angle hysteresis (

Background: Ultrasound has become the standard of care for the insertion of central venous catheters (CVCs) in the intensive care unit. With the introduction of ultrasonography in CVC insertion, there has been an improvement in the success rate and a dramatic decrease in the rate of complications. Aims and Objectives: The aim of this study was to compare the safety and ease of insertion of ultrasonically guided cannulation of the internal jugular vein (IJV) with that of the supraclavicular subclavian vein (SCV) in adult patients undergoing various surgical procedures. Materials and Methods: All the patients in whom central venous cannulation was planned were assigned to two groups. Group I underwent ultrasound-guided IJV while as Group II underwent cannulation ultrasound-guided SCV cannulation. A comparison was made between the two groups, of the success rates, durations of procedure, number of attempts at needle redirections, difficulties if any during insertion of guidewires, and the complications encountered. Results: The IJV group had a higher proportion of first-attempt success (93.2% versus 62.7%). The IJV group had a lesser incidence (3.2%) of complications such as guidewire progression and needle redirections compared with SCV (15.2%). The frequency of adverse events did not differ between the two study groups with an incidence in 3.2% in IJV group and 9.6% in the subclavian group. Higher first-attempt success rates and fewer procedural complications were seen with ultrasound-guided IJV. Conclusion: IJV central venous catheterization is an easier and less invasive and less risky procedure for patients.

Background: The occurrence of delirium in patients who undergo elective surgeries is a common occurrence. Multiple studies have assessed the effect of different drugs in various elective procedures to decrease the prevalence of postoperative delirium. The effect of melatonin to decrease the prevalence of delirium and duration of neurosurgical intensive care unit (NSICU) stay in patients undergoing neurosurgery has not been studied previously. The current study aimed to assess the same. Aims and Objectives: To assess the effect of preoperative and postoperative melatonin administration on the prevalence of delirium in patients undergoing elective ventilation in NSICU and to assess the effect of preoperative and postoperative melatonin administration on the duration of stay in NSICU. Methods and Methods: In this prospective observational study, 60 patients undergoing various neurosurgical procedures and admitted to NSICU were included. The patients were divided into two groups: one group received a placebo (Group P- tablet Sugarfree containing Aspartame) and another group received a tablet of melatonin 3 mg (Group M) in the preoperative and postoperative period. The effect of melatonin on the prevalence of delirium and duration of NSICU stay in these two groups was observed. Results: A decreasing trend of delirium was noted in both groups (Group M and Group P) with no statistical significance. A decreased prevalence of delirium was observed in patients of group M (33.3%) at the end of 12 h post-extubation compared to Group P (43.3%). The mean duration of stay for the patients in NSICU in Groups M and P were 2.7 and 3.2 days, with the range (1–14 Days) in Group M and Group P (1–20 days), respectively. Conclusion: Although there was a decreased prevalence of delirium and a decrease in the mean duration of the intensive care unit stay in patients who received oral melatonin, these beneficial effects did not show any statistical significance once compared with the control group.

This research examines the natural and artificial lighting performance comparison in an architecture classroom at a university in southern Thailand and gives principles for enhancing lighting design. The sample for the study was classroom spaces. The efficacy of natural and artificial lighting was studied using static and dynamic models, and data were obtained using a 4 in 1 multi-function environmental meter at various times of the day. The current study adopted the IESNA (Illuminating Engineering Society of North America) and CIBSE (Chartered Institution of Building Services Engineers) lighting standards for the performance comparison analysis. The results showed that the natural light in the classrooms did not satisfy the standards for lighting levels. In contrast, the artificial lighting was more effective but raised some issues with uniformity and glare. The study offers recommendations for improving lighting effectiveness, maximizing natural light, reducing energy consumption, improving the uniformity of artificial illumination, addressing issues with various classroom lighting designs, and creating more comfortable and realistic learning environments.

Abstract During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

This study investigates the impact of digital capability (DC) on digital business transformation (DBT), and its effect on small business performance (BP). This study adopts a quantitative approach. The theoretical framework uses knowledge-based view (KBV) and dynamic capability theories. This study empirically examines the direct relationship between DT and DBT in small business performance (financial, flexibility, and quality performance). This study involved 330 small businesses in East Java, Indonesia, using a convenience sampling technique. This hypothesis was tested using structural equation modeling. The findings showed a significant effect of DC on DBT and BP, DBT, and BP. Thus, the results provide evidence that DBT plays a mediating role between DC and BP. This study makes three contributions to the literature. First, empirical evidence supports the KBV and dynamic capability theories that knowledge is the most valuable asset in the digital age. Second, this study significantly extends the relevant literature by highlighting the relationship between DC, DBT, and BP. Finally, this study proposes a multiple-dimension small business performance model that uses three dimensions (financial, flexibility, and quality) as the determinant factors of DC and DBT in the SMEs sector.

In a ground breaking endeavor, we investigate a fundamental quantum property of fermions, specifically electrons, within a non-uniform quantized magnetic field, unveiling novel insights into shear Alfve’n waves in the relativistic realm of quantum plasma, particularly within the context of a degenerate Fermi gas. Employing the quantum magnetohydrodynamic model, we delve into the profound implications of the Landau quantization effect. This quantum phenomenon holds paramount importance across diverse fields such as condensed matter physics, astrophysics, and quantum information. Our study focuses on a plasma composed of degenerate relativistic electrons coexisting with cold fluid ions, which are non-degenerate particles. The findings of this research reveal substantial modifications in the thermodynamic, kinetic, and dispersion relation characteristics of shear Alfve’n waves when subjected to a non-uniform quantized magnetic field. This exploration offers a pioneering perspective on the intricate interplay between quantum physics and plasma dynamics, with implications for a wide array of scientific disciplines.

Plant extract-mediated fabrication of metal nanocomposites is used in cell proliferation inhibition and topical wound treatment, demonstrating significant effectiveness. Salvia hispanica L. (chia) seed extract (CE) is used as the reaction medium for the green fabrication of ecofriendly ZnO(CE) nanoparticles (NPs) and Ag/Ag2O(CE) and ZnO/Ag/Ag2O(CE) nanocomposites. The resultant nanoparticles and nanocomposite materials were characterized using UV–visible, Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive X-ray (EDX) techniques. In the context of antioxidant studies, ZnO/Ag/Ag2O(CE) exhibited 57% reducing power and 86% 2,2, diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging. All three materials showed strong antibacterial activity against Staphylococcus aureus (S. aureus), Escherichia coli (E.coli), and Bacillus subtilis (B. subtilis) bacterial strains. Additionally, ZnO(CE), Ag/Ag2O(CE), and ZnO/Ag/Ag2O(CE) also revealed 64.47%, 42.56%, and 75.27% in vitro Michigan Cancer Foundation-7 (MCF7) cancer cell line inhibition, respectively, at a concentration of 100 μg/mL. Selectively, the most effective composite material, ZnO/Ag/Ag2O(CE), was used to evaluate in vivo wound healing potential in rat models. The study revealed 96% wound closure in 10 days, which was quite rapid healing compared to wound healing using clinically available ointment. Therefore, in conclusion, the ZnO/Ag/Ag2O(CE) nanocomposite material could be considered for further testing and formulation as a good anticancer and wound healing agent.

This work presents the development of a novel antifungal ternary approach that is eco-friendly, biocompatible, and cost-effective. It consists of Bryum argenteum extract, silver nanoparticles, and healing clay smectite. The research focuses on the green production of silver nanoparticles using an aqueous extract of Bryum argenteum as a stabilizing and reducing agent, as well as the synthesis of clay dispersion and plant extract. The silver nanoparticles were characterized by FTIR, SEM, UV–Vis spectroscopy, and XRD. Using a standard phytochemical screening procedure, the aqueous, ethanolic, and acetonic extracts of Bryum argenteum were subjected to phytochemical screening. Silver nanoparticles and plant extract were evaluated for their antioxidant properties using DPPH scavenging activity at 517 nm. Using the agar well diffusion method, a number of investigations have been conducted on the antifungal potential of the proposed innovative ternary approach against Rhizopus oryzae (R. oryzae) and Aspergillus Niger (A. Niger). The color shift validated the silver nanoparticle generation, and the maximum absorbance at 432.11 nm was observed employing UV–Vis spectroscopy. Several functional groups responsible for the synthesis of AgNPs were identified by Fourier transform infrared spectroscopy. The particles are spherical in shape, with the range of 10 nm to 50 nm in size. Both ethanolic and aqueous extracts of Bryum argenteum contain sterols, flavonoids, cardiac glycosides, and terpenoids. AgNPs have stronger antioxidant activity in comparison to the aqueous extract of Bryum argenteum. Out of all the individual components, AgNPs demonstrated the strongest antifungal activity, with clay and plant extract coming in second and third, respectively. Additionally, their binary composition exhibits a synergistic effect with each combination. However, the ternary combination consisting of smectite clay, Bryum argenteum mediated silver nanoparticles, and Bryum argenteum extract showed exceptional antifungal efficiency against the mentioned fungus species. In respect to the zone of inhibition, the maximal antifungal activity was correlated with concentration and pH. Conclusion: It is concluded here that the proposed ternary system has significant antifungal potential. Future technological and medicinal studies will find this new ternary system to be an invaluable tool.

Objective: To compare the frequency of anastomotic leakage in interrupted versus continuous, single layer extramucosal anastomotic techniques in jejunum. Study Design: Quasi-experimental study. Place and Duration of Study: Combined Military Hospital, Multan Pakistan, from Feb 2020 to Feb 2021. Methodology: Patients aged 15-60 years, from the indoor surgical department of the hospital who required jejunal anastomosis were selected. Patients were categorized as Group-A (interrupted single layer extra mucosal anastomosis) or Group-B (continuous single layer extra mucosal anastomosis). Results: The mean age was 43±9.5 years in Group-A and 41.6±10.1 years in Group-B. Eighteen patients (56.3%) in Group-A and 21(65.6%) in Group-B were male while 14 patients (43.67%) in Group-A and 11(34.4%) in Group-B were females. Frequency of anastomotic leakage was recorded as one patient (3.13%) in Group-A and 3(9.38%) in Group-B while rest of the patients in both groups had no anastomotic leakage (p=0.302). Chi square test was applied, which showed statistically insignificant difference between the two groups (p=0.32) Conclusion: Comparatively higher frequency of anastomotic leakage was recorded in patients treated with continuous as compared to interrupted suturing but the results were statistically insignificant.

This study explores the integration of sustainable practices in the textile industry through the use of naturally colored cotton, which offers significant environmental advantages by eliminating the need for chemical bleaching and dyeing, thereby conserving water, chemicals, and energy. We examined the impact of yarn count and twist multiplier (TM) on the quality parameters of knitted fabrics tailored for winter cotton sweaters, utilizing a flatbed knitting machine to create a rib-cum-interlock structure. Our analysis aimed at optimizing thermal resistance, pilling resistance, and bursting strength, while reducing air permeability and fabric shrinkage. The findings indicated that thermal resistance peaked at 2.33 × 10 −3 m 2 K/W for yarns with a 20/2 Ne count and a 4.5 TM. Furthermore, coarser yarns demonstrated enhanced bursting strength, with the highest at 276.8 kPa for a 16/2 Ne count and 4.25 TM, and an increase in yarn count improved pilling resistance. Air permeability was lowest at 103 mm/s for 16/2 Ne yarns. Interestingly, a rise in yarn count led to negative lengthwise shrinkage but positive widthwise shrinkage. The 20/2 Ne yarn count with a 4.50 twist multiplier emerged as optimal across considered properties, underscoring the potential of naturally colored cotton as a sustainable alternative in textile manufacturing. These findings provide valuable insights into the potential of naturally colored sustainable cotton as a viable alternative to conventional cotton in the textile industry.

Aerobic granular sludge (AGS) biotechnology has recently obtained considerable interest as a viable alternative to the activated sludge process (ASP) technique. This is because AGS can improve the performance of wastewater treatment and its significant capability for attaining sustainable growth. One of the main challenging issues for the aerobic granular sludge is the long startup time in wastewater treatment. This review presents a comprehensive analysis of AGS biotechnology, aiming specifically at its global adoption, startup duration, and granule stability. Aerobic granular sludge (AGS) is becoming more widespread globally. To ensure the successful implementation of AGS biotechnology, it is crucial to thoroughly identify the development of dense and stable granules, which is vital for the proper operation of wastewater treatment plants (WWTPs). Additionally, it offers a comprehensive summary of the latest advancements in the inoculum and polymer additives and their respective contributions to accelerate various processes through distinct mechanisms. In addition, this paper reviews the prevailing research patterns in the prompt initiation of rapid startup of AGS technology and outlines specific issues for future investigations.

With the growing demand for delay-sensitive voice, video, and data-based applications, the challenge of efficient spectrum utilization is becoming more critical. To address the challenges of spectrum scarcity, the Federal Communications Commission (FCC) has authorized the sharing of the federally held Citizen Broadband Radio Service (CBRS) band, also known as the 3.5 GHz spectrum band, with commercial use. The 3.5 GHz spectrum band controlled by the spectrum access system (SAS) is shared with three types of users i.e., Incumbent users, Primary access licensee (PAL) users, and general authorized access (GAA) users as opportunistic users. In this paper we proposed the SAS-based channel allocation (SAS-CA) algorithm to optimize spectrum utilization and system performance, considering the potential risks of adding opportunistic GAA users. The proposed algorithm ensures low outage probability for PAL users and provides an optimized channel allocation strategy for GAA users when PAL users are inactive in PAL reserved channels. Additionally, a security module integrated with SAS enhances spectrum utilization and reduces interference levels. The algorithm’s efficiency is validated through multiple test sets, showing rapid convergence within 100 iterations. The integration of the security module results in a median spectrum utilization increase of 16 points and a notable reduction in interference levels, enhancing execution efficiency by approximately 37% compared to traditional methods. These findings demonstrate the substantial benefits of advanced allocation algorithms and robust security mechanisms in ensuring equitable access, stabilizing system performance, and optimizing spectrum utilization within the CBRS band.

Abstract Excess salinity can affect the growth and development of all plants. Salinization jeopardizes agroecosystems, induces oxidative reactions in most cultivated plants and reduces biomass which affects crop yield. Some plants are affected more than others, depending upon their ability to endure the effects of salt stress. Cotton is moderately tolerant to salt stress among cultivated crops. The fundamental tenet of plant breeding is genetic heterogeneity in available germplasm for acquired characteristics. Variation for salinity tolerance enhancing parameters (morphological, physiological and biochemical) is a pre-requisite for the development of salt tolerant cotton germplasm followed by indirect selection or hybridization programs. There has been a limited success in the development of salt tolerant genotypes because this trait depends on several factors, and these factors as well as their interactions are not completely understood. However, advances in biochemical and molecular techniques have made it possible to explore the complexity of salt tolerance through transcriptomic profiling. The focus of this article is to discuss the issue of salt stress in crop plants, how it alters the physiology and morphology of the cotton crop, and breeding strategies for the development of salinity tolerance in cotton germplasm.

Ethereum Blockchain technology introduced a competitive environment in the financial sector. Consequently, new technologies emerged, such as Smart Contracts (SCs), which preclude code corrections due to their immutable nature. However, the incorrect and faulty uploaded SCs led to uninvited penetrations into SCs’ accounts, resulting in considerable customer losses. This SC’s drawback requires tools to test the SCs and paves the way for research on vulnerability detection techniques. Our survey paper comprehensively reviews 41 SC tools and presents the vulnerability detection techniques (VDTs) of several previously invented tools by dividing them into general and specific classes. Finally, we also perform a classification of detection techniques to standardize the approaches. Thus, our study will help SC developers and security analysts to streamline the security of SCs and reduce the chances of malicious monetary transfers.

Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The Whale Optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitation-oriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced Whale Optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wilcoxon Signed-rank test.

Smart Contracts (SCs) communicate with each other using external calls. Their interactions can be malicious, resulting in the loss of Ether. One can blame the reentrancy attack for this exploitation. Several previous endeavors detected the reentrancy vulnerability by creating testing tools using static analysis like Remix. However, these approaches do not execute the programs. Hence, we cannot confirm their actual results. In this paper, we present TechyTech that detects both reentrancy and ${\mathtt { tx.origin}}$ vulnerabilities using a novel dynamic analysis approach of involuntary transfer (i.e., unintended transfer). Henceforth, we use a tree-based categorization string to distinguish the two vulnerabilities and their variations. Further, our research discusses multiple SC-related issues like the hijackedStack, deployed owner, and non-generation of transaction receipts in connection with reentrant calls, which we could not find in previous work. Through an example, we demonstrate how the actual Ether transfer is greater than the intended due to reentrancy.

In optical communication systems, the Q-factor is an important performance metric to evaluate the performance of an optical link. In this paper, a deep learning-based eye diagram analyzer is proposed to estimate the Q-factor. CNN architectures such as LeNet, Wide ResNet, and Inception-v4 are used for ON-Off Keying (OOK) and Pulse Amplitude Modulation (PAM) formats’ eye diagrams. The performance of these architectures is evaluated in terms of accuracy, Mean Squared Error (MSE), and error tolerance. This work shows that Wide ResNet demonstrates better performance in both OOK and PAM4 transmission schemes, achieving MSE values of 0.00188 and 0.00036, respectively. Additionally, it attains a high R-squared (R2) value of 0.9998. This deep learning-based eye diagram analyzer may be a promising approach for analyzing and optimizing optical communication systems without extensive human intervention.

Since its inception more than a decade ago, Internet of Things (IoT) technology has been guiding people in the development of a world full of smart solutions in which all devices and physical objects, represented as “things,” are interlinked with sensors using the Internet. In some areas, the delivery of medications to patients or receivers at their destinations remains highly outdated and informal. In smart medicine delivery, the medicine needs to maintain its original state while facing multiple environmental factors, such as temperature fluctuations, humidity, etc. This paper presents an effective implementation of IoT (Internet of Things) for monitoring the transportation of medicines and vaccines, along with temperature control facilitated through mobile applications and sensor networks. The system employs mobile applications as the user interface, utilizes Arduino, MQTT (Message Queuing Telemetry Transport) for communication, incorporates a temperature sensor, and employs a mini portable cooling box. Designed for the generalized delivery of medicines/vaccines from sender to receiver, the system also suggests CRC-32 as an optimal algorithm for error detection instead of complex hash functions such as MD5 and SHA, ensuring better performance, smooth operation, and data integrity. In addition, elliptic curve-based shared keys are used for protected data transmission. The accuracy of the proposed system is 89.88%, and the value of the F1-score is 0.686, which is greater than the threshold of 0.5, hence conclusively validating the authenticity and reliability of the proposed system.

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head selection where detection of such attacks is challenging. The dependency of WSN on batteries elevates the concern posed by these threats, making detection and isolation crucial, especially within the framework of energy-efficient clustering protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). Various approaches have been proposed in prior research to deal with such attacks. However, the use of memory-efficient data structures has yet to be effectively addressed. In this article, considering the limitations of WSN, we utilize memory-efficient data structures named Bloom filters, count-min (CM) sketch, and cellular automata (CA) to address abnormal energy drainage. A CA-based trust model is used to choose the legitimate node as the cluster head. CM sketch is used to control the frequency of a node selected as a cluster head, achieving fairness in the cluster head selection process, and Bloom filters maintain the record of malicious nodes blocked from participating in the communication or cluster head selection process. CA and trust functions collectively keep a record of neighbors’ energy and their trust in the network. Grayhole, blackhole, and scheduling attacks are three well-known threats that lead to abnormal energy drainage in legitimate nodes. The proposed solution effectively detects and addresses abnormal energy drainage in WSN. Its impact is simulated and observed using ns2 IEEE 802.15.4 medium access control (MAC) and LEACH clustering protocols, specifically in the context of the mentioned attacks. The effectiveness of the proposed model was rigorously analysed, and it was observed that it reduces the energy consumption of WSN by approximately 16.66%, 48.33%, and 43.33% in the cases of grayhole, blackhole, and scheduling attacks, respectively. In terms of space/time complexity, its growth is linear O(n). The proposed solution also consumes 0.08-0.10 J more energy compared to the original LEACH as a cost of the solution, which is not more than 2% of the total initial energy. The trade-off of implementing heightened security is worthwhile, as the proposed approach outperforms the original LEACH and related methods, effectively mitigating abnormal energy drainage in WSN and extending network lifetime, especially in challenging environments with persistent battery recharging challenges.

The domain of Mobile Edge Computing (MEC) has seen rapid growth, making consumer behavior research an essential element in many applications. Nevertheless, in MEC systems that are decentralized and have limited resources, challenges arise in ensuring both data integrity and access control. This paper introduces a new technique called Quantum Hash-Based Attribute-Based Encryption (QH-ABE) to address these issues. Previously, there were several methods available to guarantee data integrity and access control in MEC. However, these systems had limits in managing large and intricate datasets, lacked a standardized protocol for revocation, and incurred significant computational costs. We were prompted by these restrictions to suggest a more efficient technique, which included using the QH-ABE method in our study. The suggested solution integrates hash functions with quantum computing concepts to enhance security and control access in MEC-enabled consumer behavior research. The suggested technique provides several benefits compared to traditional hash algorithms by using hash functions. In this technique, we introduce a Recursive Non-Linear Polynomial graph-centered Integrity Algorithm (RNLPIA). RNLPIA enhances security by thwarting covert alterations to data and guaranteeing tamper-evident measures via the generation of unique hash values derived from the content of the data. The suggested method’s efficacy and efficiency are proved by a comprehensive experimental assessment, highlighting its capability to fulfill the data integrity and access control needs of MEC situations. The performance of our technology showcases its potential and paves the way for using quantum computing technologies for accessing control and data security. This work contributes to the progress of privacy-preserving, secure consumer behavior analysis in the dynamic MEC environment.

Energy is of paramount importance for the world, and it is a fundamental driver of economic growth and development. Industries, businesses, and households rely on energy for even a small task. Due to its high demand, a significant portion of the global population still lacks access to reliable and affordable energy sources. Many industries and sectors continue to waste significant amounts of energy through inefficiencies. While energy is essential, the production and consumption of energy can have significant environmental consequences. Predicting power usage can help to significantly improve energy efficiency, reduce costs, enhance grid reliability, and minimize the environmental impact of energy consumption. In this work, a novel model named, the Optimized Multi-level Multi-type Ensemble (OMME) Forecasting Model is presented to estimate the power consumption. The proposed model was applied to the data set of total power consumption recorded in Austria at each hour after the pre-processing. The model applied bootstrapping at level 1 and a hybrid ensemble at level 2. Both ensemble methods utilized different algorithms for time series forecasting including ARIMA, Exponential smoothing, LSTM, GRU, and MLP. The parameters were tuned using Bayesian and Tabu search optimization. Different experiments were conducted for day and night usage separately. The proposed model was able to estimate the power usage with an error of 22%. This work also learned the most suitable technique for power consumption time series. GRU performed very well in different experiments and gave the forecast with 12% error. Distribution graphs of the OMME prediction further validate the integrity of the results.

After the coronavirus disease 2019 (COVID-19) outbreak, the viral infection known as monkeypox gained significant attention, and the World Health Organization (WHO) classified it as a global public health emergency. Given the similarities between monkeypox and other pox viruses, conventional classification methods encounter difficulties in accurately identifying the disease. Furthermore, sharing sensitive medical data gives rise to concerns about security and privacy. Integrating deep neural networks with federated learning (FL) presents a promising avenue for addressing the challenges of medical data categorization. In light of this, we propose an FL-based framework using deep learning models to classify monkeypox and other pox viruses securely. The proposed framework has three major components: (a) a cycle-consistent generative adversarial network to augment data samples for training; (b) deep learning-based models such as MobileNetV2, Vision Transformer (ViT), and ResNet50 for the classification; and (c) a flower-federated learning environment for security. The experiments are performed using publicly available datasets. In the experiments, the ViT-B32 model yields an impressive accuracy rate of 97.90%, emphasizing the robustness of the proposed framework and its potential for secure and accurate categorization of monkeypox disease.

Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm Optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

Abstract Background Malnutrition causes nutrient deficiencies that have both physical and clinical consequences in severe acute malnutrition children. Globally, there were 47 million wasted children under the age of five in 2019. One in four were located in sub-Saharan Africa, with half being in South Asia. This study aims to apply the Boruta algorithm to identify the determinants of undernutrition among children under five living in Dera Ghazi Khan, one of the marginalized districts of densely populated Punjab Province in Pakistan. Methods A multicenter cross-sectional study design was used to collect data from 185 children with severe acute malnutrition aged under five years visiting the OTPs centers located in Dera Ghazi Khan, Punjab, Pakistan. A purposive sampling technique was used to collect data using a pretested structured questionnaire from parents/caregivers regarding family sociodemographic characteristics, child nutrition, and biological and healthcare characteristics. Anthropometric measurements, including height, weight, and mid-upper arm circumference, were collected. The Boruta models were used to incorporate the children’s anthropometric, nutritional, and household factors to determine the important predictive variables for undernutrition using the Boruta package in R studio. Results This study included 185 children, with a mean age of 15.36 ± 10.23 months and an MUAC of 10.19 ± 0.96 cm. The Boruta analysis identifies age, mid-upper arm circumference, weaning practices, and immunization status as important predictors of undernutrition. Income per month, exclusive breastfeeding, and immunization status were found to be key factors of undernutrition in children under the age of five. Conclusion This study highlights age, mid-upper arm circumference, weaning practices, and immunization status as key determinants of weight-for-height and weight-for-age in children under five years. It also suggests that economic context may influence undernutrition. The findings can guide targeted strategies for combating undernutrition.

Iron (Fe) malnutrition in humans is a global concern which can be revised by improved Fe density in staple crops. A field experiment was performed to evaluate the effect of chelated iron on growth, yield and iron concentration in bread wheat (cv. Moomal) at Tando Jam Pakistan. The treatments included, Control (No Fe-EDTA), Soil supplement of Fe-EDTA (@ 2 kg Fe ha-1), Soil + foliar supplement of Fe-EDTA (@ 2 kg ha-1 and 0.2% Fe at booting, flowering, and milky stage), and Foliar supplement of Fe-EDTA (@ 0.2% Fe at booting, flowering and milky stages). The defined growth and yield traits of wheat were increased with Fe-EDTA applications over control treatment. Among different Fe-EDTA application methods, there was no significant difference for most of the growth and yield parameters (excluding spike length, number of spikelets spike-1, and 1000 grain weight). The amount of Fe in wheat grains was significantly higher in all Fe-EDTA treatments over control, with maximum value (86.54 ± 5.57 mg kg-1) in the treatment where Fe-EDTA was applied in soil + foliar. Similarly, a high Fe build up in surface soil was obtained with treatment of Fe-EDTA in soil + foliar. Overall, with various Fe-EDTA treatments, an increase of 21.2 to 29.1% in grain yield and 1.9 to 4.3 times in Fe concentration of wheat grains was achieved in current study. It is suggested that the Fe should be included in wheat production technology to attain better yield and Fe concentration in grains.

South-facing collectors are the optimum choice for solar applications in the northern hemisphere. However, obstacles may limit the feasibility of this orientation. Therefore, altering the orientation of the collector impacts solar insolation. In this study, the Perez model is utilized to evaluate incoming solar radiation on tilted surfaces for solar collectors in four climatic zones across Pakistan. The results are presented in contour plots to analyze the optimal tilt and orientation for solar applications. The findings of the study indicate substantial energy gains when collectors are placed at optimum angles. More specifically, Quetta leads with a 14.54% increase, followed by Karachi and Multan at 9.81% and 9.3%, respectively, compared to horizontally placed collectors. Analysis of vertical surfaces reveals a notable decrease in monthly solar radiation, especially in Peshawar (37.22%). Monthly adjustments in tilt angles outperform fixed positions, enhancing solar energy intensity. When comparing yearly adjustments with monthly adjustments, Quetta shows the maximum increase of 5.92%, followed by Karachi (4.86%), Multan (4.01%), and Peshawar (3.65%). It is also observed that ±15° azimuth angle change from the south ensures receiving up to 98% of insolation, regardless of the climatic region. Lastly, the validation against the NASA SSE database further highlights the reliability of our simulation model. Overall, the outcomes of the study will contribute to informed solar energy planning in the studied regions.

The aim of this study was to examine the best electrode design for the electrocoagulation process with the best removal performance, reduced passivation on the electrode surface, and lower energy consumption requirements for removing Chromium (Cr) and Lead (Pb) from leather tannery effluent. Three different electrodes were compared: non-rotating disk electrode (NRDE), rotating disk electrode (RDE) and rotating disk mesh electrode (RDME). All electrodes were used to observe a reduction in passivation on the electrode surface and its effects on the removal performance of Cr and Pb. The material used for the electrodes was iron. The maximum removal efficiency obtained was Cr = 87.9% and Pb = 97.5% under the following operating conditions: pH = 7, treatment time = 90 min, current density = 6.57 mA/cm², and RPM = 80. The results show that the electrical energy requirement for treating chromium and lead using RDME was 4.5 kWh/m³, which was found to be lower than the energy requirement observed in various other studies for treating tannery effluent. According to the results, RDME shows the highest removal performance with lower specific energy consumption compared to NRDE and RDE. RDME can be efficiently employed at a larger scale for treating leather tannery effluent.

The demand for polymer composites with improved mechanical and electrical properties is crucial for advanced aerospace, electronics, and energy storage applications. Single-walled carbon nanotubes (SWCNTs) and iron oxide (Fe3O4) nanoparticles are key fillers that enhance these properties, yet challenges like orientation, uniform dispersion, and agglomeration must be addressed to realize their full potential. This study focuses on developing SWCNTs/Fe3O4 epoxy composites by keeping the SWCNT concentration constant at 0.03 Vol.% and varying with Fe3O4 concentrations at 0.1, 0.5, and 1 Vol.% for two different configurations: randomly orientated (R-) and magnetic field-assisted horizontally aligned (A-) SWCNTs/Fe3O4 epoxy composites, and investigates the effects of filler concentration, dispersion, and magnetic alignment on the mechanical and electrical properties. The research reveals that both composite configurations achieve an optimal mechanical performance at 0.5 Vol.% Fe3O4, while A- SWCNTs/Fe3O4 epoxy composites outperformed at all concentrations. However, at 1 Vol.% Fe3O4, mechanical properties decline due to nanoparticle agglomeration, which disrupts stress distribution. In contrast, electrical conductivity peaks at 1 Vol.% Fe3O4, indicating that the higher density of Fe3O4 nanoparticles enhances the conductive network despite the mechanical losses. This study highlights the need for precise control over filler content and alignment to optimize mechanical strength and electrical conductivity in SWCNTs/Fe3O4 epoxy nanocomposites.