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The article discusses the application of machine learning (ML), data mining, and Internet of Things (IoT) in the development of smart and sustainable networks. It highlights the challenges faced in implementing these technologies, such as data security and privacy, data management, scalability, and interoperability. The article also provides a summary of 21 research articles that demonstrate the use of ML, AI, and IoT in various fields, including malicious domain detection, electricity load forecasting, intrusion detection, education, energy management, multimedia content delivery, voice search technology, facial emotion detection, satellite-based object detection, authentication methods, and disease prediction. The authors emphasize the importance of these technologies in improving living standards and call for further research in this area. [Extracted from the article]

Purpose: This study aims to enhance our understanding of sustainable water management in construction through a life-cycle embodied water assessment of a villa in the United Arab Emirates (UAE). It provides insights and recommendations for improving the water efficiency by identifying areas for potential embodied water saving and reduction in environmental impacts in the construction industry. Design/methodology/approach: This study uses a life-cycle assessment (LCA) approach and focuses on a UAE villa as a case study. It analyses the embodied water consumption during construction (initial embodied water) and maintenance (recurrent embodied water) using an input–output-based hybrid analysis. Additionally, it compares the embodied water observations with the operational water usage and comprehensively evaluates the water consumption in the villa's life-cycle. Findings: The initial (28%) and recurrent embodied water (42%) represent significant proportions of a building's life-cycle water demand. The structural elements, predominantly concrete and steel, contribute 40% of the initial embodied water consumption. This emphasises the importance of minimising the water usage in these materials. Similarly, internal finishes account for 47% of the recurrent embodied water. This emphasises the importance of evaluating the material service life. Practical implications: These findings indicate the efficacy of using durable materials with low embodiment and water-efficient construction methods. Additionally, collaborative research between academia, industry, and the government is recommended in conjunction with advocating for policies promoting low embodied-water materials and transparency in the construction sector through embodied water footprint reporting. Originality/value: Previous studies focused on the operational water and marginally addressed the initial embodied water. Meanwhile, this study highlights the significance of the initial and recurrent embodied water in the life-cycle water demand. It emphasises on the need for adaptable buildings with reduced embodied water and more durable materials to minimise the requirement for frequent material replacements. [ABSTRACT FROM AUTHOR]

The mechanical horizontal platform (MHP) system exhibits a rich chaotic behavior. The chaotic MHP system has applications in the earthquake and offshore industries. This article proposes a robust adaptive continuous control (RACC) algorithm. It investigates the control and synchronization of chaos in the uncertain MHP system with time-delay in the presence of unknown state-dependent and time-dependent disturbances. The closed-loop system contains most of the nonlinear terms that enhance the complexity of the dynamical system; it improves the efficiency of the closed-loop. The proposed RACC approach (a) accomplishes faster convergence of the perturbed state variables (synchronization errors) to the desired steady-state, (b) eradicates the effect of unknown state-dependent and time-dependent disturbances, and (c) suppresses undesirable chattering in the feedback control inputs. This paper describes a detailed closed-loop stability analysis based on the Lyapunov-Krasovskii functional theory and Lyapunov stability technique. It provides parameter adaptation laws that confirm the convergence of the uncertain parameters to some constant values. The computer simulation results endorse the theoretical findings and provide a comparative performance. [ABSTRACT FROM AUTHOR]

Background and Objectives: Carbapenem resistance is a growing global challenge for healthcare, and, therefore, monitoring its prevalence and patterns is crucial for implementing targeted interventions to mitigate its impact on patient outcomes and public health. This study aimed to determine the prevalence of carbapenem resistance among Escherichia coli (E. coli) strains in the largest tertiary care hospital of the capital territory of Pakistan and to characterize the isolates for the presence of antimicrobial resistance genes. Additionally, the most prevalent sequence types were analyzed. Materials and Methods: A total of 15,467 clinical samples were collected from November 2020 to May 2022, underwent antimicrobial susceptibility testing, and were analyzed for antimicrobial resistance genes through conventional PCR and sequence typing using MLST. Results: In carbapenem-resistant E. coli (CR-EC), 74.19% of isolates harbored the blaNDM gene, with blaNDM-1 (66.96%), blaNDM-5 (12.17%), and blaNDM-7 (20.87%) variants detected. Additionally, blaIMP was found in 25.81% and blaOXA-48 in 35.48% of isolates. The presence of blaCTX-M15 and blaTEM was identified in 83.87% and 73.55% of CR-EC isolates, respectively, while armA and rmtB were detected in 40% and 65.16% of isolates, respectively. Colistin and tigecycline were the most effective drugs against CR-EC isolates, with both showing an MIC50 of 0.5 µg/mL. The MIC90 for colistin was 1 µg/mL, while for tigecycline, it was 2 µg/mL. MLST analysis revealed that the CR-EC isolates belonged to ST131 (24.52%), ST2279 (23.87%), ST3499 (16.13%), ST8051 (15.48%), ST8900 (9.68%), ST3329 (7.10%), ST88 (1.94%), and ST6293 (1.29%). The ST131 complex (70.97%) was the most prevalent, harboring 95.65% of the blaNDM gene, while the ST23 complex (18.06%) harbored 62.50% of the blaIMP gene. Conclusions: Implementing large-scale surveillance studies to monitor the spread of specific pathogens, along with active infection control policies, is crucial for the effective containment and prevention of future epidemics. [ABSTRACT FROM AUTHOR]

Background: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. Materials and methods: In this research, we propose a new ViT Gradient‐Weighted Class Activation Mapping (GradCAM) based architecture named ViT‐GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. Result: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT‐GradCAM obtains better and more accurate detection and classification than other state‐of‐the‐art deep learning‐based skin lesion detection models. The architecture of ViT‐GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin‐specific pathologies. Conclusion: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models. [ABSTRACT FROM AUTHOR]

The present study aimed at converting meat industry waste, particularly waste bones and chicken feathers, into biochar to recycle valuable nutrients present in it, which ultimately become part of the municipal waste. The bone biochar (BB) and feathers biochar (FB) were prepared at 550 °C, and their potential was evaluated as an organic amendment for the growth of sunflower. The ash content (AC) and fixed carbon (FC) improved significantly in prepared biochars as compared to raw feedstock. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analyses signaled the occurrence of various functional groups viz. amide group and hydroxyapatite, porosity, and multiple nutrients. Application of BB and FB in potted soil alone as well as in composites (1:1, 1:2, 2:1) at 1%, 3%, and 5% (w/w) and synthetic fertilizer significantly increased soil pH, electrical conductivity (ECe), organic matter (OM) and water holding capacity (WHC), while reducing the bulk density (BD). The growth of plants grown in soil treated with a 2:1 composite of feathers and bone biochar at 5% application rate showed significantly greater differences in plant height, total chlorophyll content, and plant dry weight than the control but was comparable to growth with chemical fertilizer, rendering it a potential alternative to chemical-based synthetic fertilizer. [ABSTRACT FROM AUTHOR]

Phenylalanine ammonia lyase (PAL) is a widely studied enzyme in plant biology due to its role in connecting primary metabolism to secondary phenylpropanoid metabolism, significantly influencing plant growth, development, and stress response. Although PAL genes have been extensively studied in various plant species but their exploration in cucumber has been limited. This study successfully identified 11 CsPAL genes in Cucumis sativus (cucumber). These CsPAL genes were categorized based on their conserved sequences revealing patterns through MEME analysis and multiple sequence alignment. Interestingly, cis-elements related to stress were found in the promoter regions of CsPAL genes, indicating their involvement in responding to abiotic stress. Furthermore, these gene's promoters contained components associated with light, development and hormone responsiveness. This suggests that they may have roles in hormone developmental processes. MicroRNAs were identified as a key regulators for the CsPAL genes, playing a crucial role in modulating their expression. This discovery underscores the complex regulatory network involved in the plant's response to various stress conditions. The influence of these microRNAs further highlights the complicated mechanisms that plants use to manage stress. Gene expression patterns were analyzed using RNA-seq data. The significant upregulation of CsPAL9 during HT3h (heat stress for 3 h) and the heightened upregulation of both CsPAL9 and CsPAL7 under HT6h (heat stress for 6 h) in the transcriptome study suggest a potential role for these genes in cucumber's tolerance to heat stress. This comprehensive investigation aims to enhance our understanding of the PAL gene family's versatility, offering valuable insights for advancements in cucumber genetics. [ABSTRACT FROM AUTHOR]

Zinnia elegans L. is a cut flower, to improve the growth parameters of this flower salicylic acid (SA) and calcium chloride (CaCl2) can act as growth stimulants. The purpose of this study was to assess the effects of pre-harvest SA and CaCl2 treatments in extending the vase life, quality and quantity of cut Zinnia flowers. Therefore, completely randomized design was made with SA at the rate of 0.5 mg/L, 1 mg/L and 1.5 mg/L and CaCl2 at the rate of 0.5 mg/L, 1 mg/L and 1.5 mg/L with 3 replicates in each treatment. Changes in growth morphology (such as shoot length, root length, dry weigh of root and plant, fresh weight of root and plant, total number of flowers and total number of flower cluster per plant), Physiology (chlorophyll content, PPO activity and protein) and phosphorous and potassium concentration were checked. The research study led to following conclusions that the foliar application of calcium chloride @ 1.5 mg/L + salicylic acid @ 0.5 mg/L resulted in highest growth parameters of Zinnia. The quality parameters like chlorophyll, total protein, phosphorous and catalase content also showed maximum value during the course of the study. It can be concluded in nutshell that calcium chloride in combination with salicylic acid results in better growth and quality of zinnia under the agro-climatic conditions of Lahore. These results suggest that SA and CaCl2 could be used as potential growth promoters to improve quantity and quality of Zinnia elegans L. [ABSTRACT FROM AUTHOR]

The strife for more incredible and sustainable human development is often associated with compromised environmental standards. Early growth may increase pollutant emissions that can be regulated later. Human development, the main ingredient of sustainable development, may increase emissions or reduce them through technological improvements. The emerging seven (E7) economies provide an intriguing case to test the direct and indirect impact of human development on CO2 emissions to confirm the nexus in the early stage of industrialization. The study applies the structural equation approach to investigate the relationship between the human development index (HDI), green technological innovations, and CO2 emissions in E7 economies over the period 1990–2021. Our findings indicate that HDI increases consumption-based CO2 emissions both directly and indirectly, where even the mediating impact of innovations is also positive, thereby confirming the fact that HDI and innovations both may have deteriorated implications for environmental performance at the earlier stages of development. The study findings imply that the policymakers of the transition economies need to rethink their policy choices regarding controlling pollution and safeguarding the environment. The study's conclusion has essential ramifications for reducing pollution through enacting HDI and supporting innovations. [ABSTRACT FROM AUTHOR]

From tannery effluent (TE) severely polluted with heavy metals (HMs viz., Cr, Cu, Cd, and Pb), hydrophytic phytoextraction remains a challenge as transplanted plants succumb to death on facing acclimatization shock. Current study was aimed at diluting TE with harvested rainwater (HR) for improving HM phytoextraction potential of Phragmites australis (a hydrophyte) assisted with phycoremediation of coupled algae (viz., Oedogonium sp. and Pithophora sp.). The TE:HR dilutions (TEDs) 0, 25, 50, 75, and 100% (v/v) included three sets: set-1 included algae only, set-2 included P. australis only and set-3 included P. australis coupled with combined algal inoculum. Results showed that P. australis assisted with HR dilution and combined algal inoculum showed significantly greater uptake of HMs from each of the TEDs than respective control treatments. Combined algal application in the TEDs proved phycoremediation assistants based on their bioaccumulation factor (BF). The dry biomass of P. australis in TEDs applied with phycoremediation assistants remained greater than uninoculated ones. Overall, HM translocation factor (TF) of P. australis for Cr, Cu, Cd, and Pb remained ≥ 1. The study concludes that HM phytoextraction is substantially increased when concentrated TE is diluted with HR and assisted with phycoremediation of HM tolerant algae. [ABSTRACT FROM AUTHOR]

This paper has a twofold aim. On one side, it aims to show a paramount application of an innovative approach to automatic partial discharge (PD) monitoring and analysis, referring to types of cable defects that can trigger internal and surface discharges. The type of defect generating PD is automatically identified, which is of fundamental importance to develop condition maintenance tools of any electrical asset component. On the other side, the way to create artificial defects, namely using a tool to test any PD measurement approach, is illustrated and discussed, relying upon partial discharge inception field and voltage modelling. Also, the so-called three-leg approach, which is capable of achieving the optimal and reliable design of electrical insulation systems, was applied and successfully validated for simple test objects and real electrical assets such as cable with artificially developed defects. [ABSTRACT FROM AUTHOR]

Background and Objectives: Urinary tract infections (UTIs), one of the most prevalent bacterial infections, are facing limited treatment options due to escalating concern of antibiotic resistance. Urine cultures significantly help in identification of etiological agents responsible for these infections. Assessment of antibiotic susceptibility patterns of these bacteria aids in tackling the emerging concern of antibiotic resistance and establishment of empirical therapy guidelines. Our aim was to determine various agents responsible for urinary tract infections and to assess their antibiotic susceptibility patterns. Materials and Methods: This cross-sectional study was performed over a period of six months from January 2023 to July 2023 in Department of Microbiology of Pakistan Institute of Medical Sciences (PIMS). Results: Out of 2957 positive samples, Gram negative bacteria were the most prevalent in 1939 (65.6%) samples followed by Gram positive bacteria in 418 (14.1%) and Candida spp. in 269 (9.1%) samples. In gram negative bacteria, Escherichia coli (E. coli) was the most prevalent bacteria isolated from 1070 samples (55.2%) followed by Klebsiella pneumoniae in 397 samples (20.5%). In Gram positive bacteria, Enterococcus spp. was the most common bacteria in 213 samples (51%) followed by Staphylococcus aureus in 120 samples (28.7%). Amikacin was the most sensitive drug (91%) for Gram negative bacteria. Gram positive bacteria were most susceptible to linezolid (97%-100%). Conclusion: The generation of a hospital tailored antibiogram is essential for the effective management of infections and countering antibiotic resistance. By adopting antimicrobial stewardship strategies by deeper understanding of sensitivity patterns, we can effectively combat antibiotic resistance. [ABSTRACT FROM AUTHOR]

Irregular and complex behavior in the financial system can disrupt stability and smooth economic growth. It causes randomness within the system, generating chaos; hindering synchronization behaviour. Achieving smooth and rapid synchronization between two coupled hyperchaotic finance (HF) systems with lessened fluctuation of input and output signals is vital for continuing financial stability and fostering economic growth, a challenge addressed in this article. The paper proposes a novel time-efficient nonlinear control (TENLC) technique and investigates HF systems synchronization using the drive-response system (DRS) arrangement. The proposed TENLC strategy realizes fast and smooth synchronization behaviour between two coupled HF systems, reducing closed-loop state-variable trajectory oscillations. The controller is designed to retain the nonlinear components within the closed-loop system and does not depend on the system's parameters, simplifying the design and analysis process. The Lyapunov stability technique confirms the closed-loop's global stability at the origin. Proofs of mathematical analysis and computer-based simulation results validate the theoretical findings, showing that the presented TENLC strategy converges the state error trajectories to zero in a short transient time with lessened fluctuations for all signals. The comparative computer-based simulation analysis confirms that the presented TENLC approach outperforms other synchronization control techniques. [ABSTRACT FROM AUTHOR]

The coffee industry holds importance, providing livelihoods for millions of farmers globally and playing a vital role in the economies of coffee-producing countries. Environmental conditions such as drought and temperature fluctuations can adversely affect the quality and yield of coffee crops.Carotenoid cleavage oxygenases (CCO) enzymes are essential for coffee plants as they help break down carotenoids contributing to growth and stress resistance. However, knowledge about the CCO gene family in Coffee arabica was limited. In this study identified 21 CCO genes in Coffee arabica (C. arabica) revealing two subfamilies carotenoid cleavage dioxygenases (CCDs) and 9-cis-epoxy carotenoid dioxygenases (NCED) through phylogenic analysis. These subfamilies exhibited distribution patterns in terms of gene structure, domains, and motifs. The 21 CaCCO genes, comprising 5 NCED and 16 CCD genes were found across chromosomes. Promoter sequencing analysis revealed cis-elements that likely interact with plant stress-responsive, growth-related, and phytohormones, like auxin and abscisic acid. A comprehensive genome-wide comparison, between C. arabica and A. thaliana was conducted to understand the characteristics of CCO genes. RTqPCR data indicated that CaNCED5, CaNCED6, CaNCED12, and CaNCED20 are target genes involved in the growth of drought coffee plants leading to increased crop yield, in a conditions, with limited water availability. This reveals the role of coffee CCOs in responding to abiotic stress and identifies potential genes useful for breeding stress-resistant coffee varieties. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a scale for measuring housing quality in the context of Pakistan. Design/methodology/approach: The inductive and deductive approaches for item generation have been combined, and items have been purified using multistage expert review. Data was collected from a sizeable purposive sample of 445 respondents, and exploratory and confirmatory factor approaches used for assessing psychometric properties of the scale. Findings: The result is a 21-item scale covering five dimensions, namely, Design and Construction Quality, Neighborhood Quality, Adequacy of Space, Quality of Institutional Services and Proximity of Basic Amenities. Originality/value: This study contributes to housing quality literature by deepening our understanding of the concept of housing quality in the context of Pakistan, the world's fifth most populous country. The findings of the study have important implications for both theory and practice. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease remains a significant global health concern, with high low-density lipoprotein cholesterol (LDL-C) levels contributing to an increased risk. Familial hypercholesterolemia (FH) further complicates its management, necessitating additional lipid-lowering therapies. Evinacumab, an angiopoietin-like protein 3 monoclonal antibody, has emerged as a potential treatment, particularly for patients with FH, by effectively reducing LDL-C and triglyceride levels. This meta-analysis aimed to evaluate the efficacy and safety of evinacumab across diverse patient populations. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, relevant randomized controlled trials (RCTs) were systematically retrieved from multiple databases until November 24, 2023. The inclusion criteria were studies comparing evinacumab (at doses of 5 and 15 mg) to placebo, with outcomes focusing on lipid levels and adverse events. Standardized protocols were employed for data extraction and quality assessment, and statistical analysis was conducted using RevMan software. Results: Four RCTs, involving 270 patients, were included in the analysis. The analysis revealed significant reductions in lipid markers, particularly with the 15-mg dose of evinacumab, including triacylglycerols (standard mean difference [SMD] = −6.09, 95% confidence interval [CI] − 14.53 to 2.36, P = 0.16), total cholesterol (SMD = − 6.20, 95% CI − 11.53 to − 0.88, P = 0.02), high-density lipoprotein cholesterol (SMD = − 0.79, 95% CI − 1.27 to − 0.31, P = 0.001), LDL-C (SMD = − 4.58, 95% CI − 9.13 to − 0.03, P = 0.05), apolipoprotein (Apo) B (SMD = − 4.01, 95% CI − 7.53 to − 0.46, P = 0.03), and Apo C3 (SMD = − 7.67, 95% CI − 12.94 to − 2.41, P = 0.004). Adverse event analysis revealed no significant association, indicating good tolerability. Conclusion: High-dose evinacumab (15 mg) consistently demonstrated efficacy in reducing cholesterol and other lipid markers, with favorable tolerability. Further research is warranted to comprehensively assess its safety and clinical effectiveness, emphasizing the need for additional data to support its use in managing cardiovascular disease. [ABSTRACT FROM AUTHOR]

The environmental challenges associated with high-power, high-voltage electrified aircraft require a targeted approach with regard to the development of next-generation aerospace electrical insulation. This study reports findings on polyphenylsulfone (PPSU) as a matrix material based on its unique thermal, mechanical, and dielectric properties, filled with hexagonal boron nitride (h-BN) with micron- and nanoscale particulates. The inorganic ceramic filler was selected for its thermally conductive and electrically insulating performance in extreme environments. The main goal was to investigate the dielectric strength and electrical resistance (endurance) to partial discharges (PDs). Since PDs are a leading accelerated degradation phenomenon causing premature failure in organic electrical insulation, the capability of an insulating material to endure PD-induced degradation for the whole (or part of) its design life is of paramount importance. It was observed that incorporation of h-BN micro fillers can significantly improve the PD resistance, even in comparison with insulating materials typically used for electrified transportation, such as corona-resistant Kapton. It was also observed that a suitable combination of micro and nano fillers can also be used as a viable solution to increase the electrical performance and reliability of the avionics insulation components. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinase 2 (CDK2) regulates cell cycle checkpoints in the synthesis and mitosis phases and plays a pivotal role in cancerous cell proliferation. The activation of CDK2, influenced by various protein signaling pathways, initiates the phosphorylation process. Due to its crucial role in carcinogenesis, CDK2 is a druggable hotspot target to suppress cancer cell proliferation. In this context, several studies have identified spirooxindoles as an effective class of CDK2 inhibitors. In the present study, three spirooxindoles (SOI1, SOI2, and SOI3) were studied to understand their inhibitory mechanism against CDK2 through a structure-based approach. Molecular docking and molecular dynamics (MD) simulations were performed to explore their interactions with CDK2 at the molecular level. The calculated binding free energy for the spirooxindole-based CDK2 inhibitors aligned well with experimental results regarding CDK2 inhibition. Energy decomposition (ED) analysis identified key binding residues, including I10, G11, T14, R36, F82, K89, L134, P155, T158, Y159, and T160, in the CDK2 active site and T-loop phosphorylation. Molecular mechanics (MM) energy was identified as the primary contributor to stabilizing inhibitor binding in the CDK2 protein structure. Furthermore, the analysis of binding affinity revealed that the inhibitor SOI1 binds more strongly to CDK2 compared to the other inhibitors under investigation. It demonstrated a robust interaction with the crucial residue T160 in the T-loop phosphorylation site, responsible for kinase activation. These insights into the inhibitory mechanism are anticipated to contribute to the development of potential CDK2 inhibitors using the spirooxindole scaffold. [ABSTRACT FROM AUTHOR]

Cotton, a key source of income for Pakistan, has suffered significantly by cotton leaf curl disease (CLCuD) since 1990. This disease is caused by a complex of phylogenetically-related begomovirus (genus Begomovirus, family Geminiviridae) species and a specific betasatellite (genus Betasatellite, family Tolecusatellitidae), cotton leaf curl Multan betasatellite. Additionally, another DNA satellite called alphasatellite (family Alphasatellitidae), is also frequently associated. All these virus components are vectored by a single species of whitefly (Bemisia tabaci). While many factors affect cotton productivity, including cotton variety, sowing time, and environmental cues such as temperature, humidity, and rainfall, CLCuD is a major biotic constraint. Although the understanding of begomoviruses transmission by whiteflies has advanced significantly over the past three decades, however, the in-field seasonal dynamics of the viruses in the insect vector remained an enigma. This study aimed to assess the levels of virus and betasatellite in whiteflies collected from cotton plants throughout the cotton growing season from 2014 to 2016. Notably, begomovirus levels showed no consistent pattern, with minimal variations, ranging from 0.0017 to 0.0074 ng.μg-1 of the genomic DNA in 2014, 0.0356 to 0.113 ng.μg-1 of the genomic DNA in 2015, and 0.0517 to 0.0791 ng.μg-1 of the genomic DNA in 2016. However, betasatellite levels exhibited a distinct pattern. During 2014 and 2015, it steadily increased throughout the sampling period (May to September). While 2016 showed a similar trend from the start of sampling (July) to September but a decline in October (end of sampling). Such a study has not been conducted previously, and could potentially provide valuable insights about the epidemiology of the virus complex causing CLCuD and possible means of controlling losses due to it. [ABSTRACT FROM AUTHOR]

The purpose of this article was to examine the role of social networking sites in fostering electronic word-of-mouth and enhancing brand loyalty in Pakistan. The study initially aimed for a sample size of 384 but received 370 completed questionnaires. After eliminating 15 responses due to aberrant findings, 355 questionnaire answers were analyzed, resulting in a response rate of 92.44 percent. Data were gathered by using an electronic survey questionnaire and analyzed with SPSS to test the proposed model for normality, reliability, frequency tables, standard deviation, mean, and correlation. The findings indicated that social networking sites, along with social networking site rewards and self-image, significantly boosted brand loyalty by facilitating electronic word of mouth and brand commitment. This research provides valuable insights into the effectiveness of social networking sites in strengthening customer loyalty through enhanced online engagement and communication. These findings are particularly significant for marketers and businesses aiming to leverage social media platforms to build stronger, more loyal customer bases. [ABSTRACT FROM AUTHOR]

The contamination of seafood by heavy metals is a rising health issue in the context of pollution caused by increasing industrialization and urbanization. Crustaceans are particularly susceptible to heavy metal pollution in aquatic ecosystems due to their benthic and sedimentary lifestyle. Here we review crustaceans contamination by heavy metals with a focus on metal sources and dynamics, interaction of metals with other pollutants, metal analysis, bioconcentration and bioaccumulation, toxicity, and strategies to control metals. We observed that crustaceans tend to accumulate more heavy metals than other aquatic animals. Consequently, in certain regions of the world, consuming crustaceans as food may potentially threaten human health. The bioavailability, transport, and interaction of heavy metals with other pollutants depend on various factors, potentially leading to differential toxicity. Heavy metals induce multiple toxic effects on crustaceans, including metabolic dysfunction, genotoxic effects, respiratory impairments, DNA damage, sperm mobility, and quantity, and these poisonous effects will intensify with prolonged exposure time and increasing concentration. The concentration of heavy metals in crustacean samples is usually determined by inductively coupled plasma optical emission spectrometry and mass spectrometry. Approaches to reducing this potential threat include proper industrial wastewater treatment and using low-cost adsorbent materials in aquaculture. [ABSTRACT FROM AUTHOR]

Carotenoid cleavage oxygenases (CCOs) enzymes play a vital role in plant growth and development through the synthesis of apocarotenoids and their derivative. These chemicals are necessary for flower and fruit coloration, as well as the manufacture of plant hormones such as abscisic acid (ABA) and strigolactones, which control a variety of physiological processes. The CCOs gene family has not been characterized in Arachis hypogaea. Genome mining of A. hypogaea identifies 24 AhCCO gene members. The AhCCO gene family was divided into two subgroups based on the recent study of the Arabidopsis thaliana CCO gene family classification system. Twenty-three AhCCO genes, constituting 95.8% of the total, were regulated by 29 miRNAs, underscoring the significance of microRNAs (miRNAs) in governing gene expression in peanuts. AhCCD19 is the only gene that lacks a miRNA target site. The physicochemical characteristics of CCO genes and their molecular weights and isoelectric points were studied further. The genes were then characterized regarding chromosomal distribution, structure, and promoter cis-elements. Light, stress development, drought stress, and hormone responsiveness were discovered to be associated with AhCCO genes, which can be utilized in developing more resilient crops. The investigation also showed the cellular location of the encoded proteins and discovered that the peanut carotenoid oxygenase gene family's expansion was most likely the result of tandem, segmental, and whole-genome duplication events. The localization expresses the abundance of genes mostly in the cytoplasm and chloroplast. Expression analysis shows that AhCCD7 and AhCCD14 genes show the maximum expression in the apical meristem, lateral leaf, and pentafoliate leaf development, while AhNCED9 and AhNCED13 express in response to Aspergillus flavus resistance. This knowledge throws light on the evolutionary history of the AhCCO gene family and may help researchers better understand the molecular processes behind gene duplication occurrences in plants. An integrated synteny study was used to find orthologous carotenoid oxygenase genes in A. hypogaea, whereas Arabidopsis thaliana and Beta vulgaris were used as references for the functional characterization of peanut CCO genes. These studies provide a foundation for future research on the regulation and functions of this gene family. This information provides valuable insights into the genetic regulation of AhCCO genes. This technology could create molecular markers for breeding programs to develop new peanut lines. [ABSTRACT FROM AUTHOR]

Memristors have several chaotic dynamic models and have been used successfully in various fields, including secure communication systems, information storage, and artificial neural networks. The memristor-based four-dimensional chaotic (FDMC) systems generate unpredictable and intricate time domain signals. Parameter fluctuations in the FDMC system may give birth to chaos, making it difficult to suppress. Stabilizing chaos in the FDMC system improves the circuit's performance. This paper synthesizes a novel time-efficient chatter-free nonlinear robust adaptive control (NLRAC) technique that stabilizes chaos in the FDMC system affected by time-varying unknown bounded exogenous disturbances and model uncertainties. The proposed NLRAC strategy decimates the time-varying unknown bounded exogenous disturbances and model uncertainties effects; it establishes a faster, smoother state-variable trajectories convergence to the zero vicinity. The theoretical analysis and mathematical proofs are based on the Lyapunov stability technique. Computer simulation results show that the proposed NLRAC technique effectively brings the FDMC system's state-variable trajectories to zero with reduced fluctuations for control input signals and state-variable trajectories. This feedback controller's attribute enhances closed-loop stability performance, improves precision, and reduces risk overshoot. The paper includes comparative computer simulation results to endorse the proposed controller performance. [ABSTRACT FROM AUTHOR]

Silver nanoparticles (AgNPs) are appealing due to their cost-effectiveness and remarkable capabilities in sensing and antibacterial applications. Herein, we report a facile near room-temperature synthesis of AgNPs capped with a cationic surfactant using a single-step recipe. The AgNPs were synthesized by reducing silver ions with ascorbic acid in a basic reaction medium, and in the presence of cetyltrimethylammonium bromide (CTAB). By varying the concentration of CTAB and the pH of the reaction medium, the yield of the AgNPs can be tuned. The essential experimental conditions supporting the high yield of uniform AgNPs are 10 mM concentration of CTAB and a strong basic pH. The impact of aging on AgNPs was also investigated by examining changes in their ultraviolet–visible (UV–Vis) spectra over time. The AgNPs, purified by centrifugation and redispersed in CTAB solution, showed reliable shelf-life under both light and dark storage conditions. Furthermore, the synthesized AgNPs showed excellent surface-enhanced Raman scattering (SERS)-based trace detection toward anionic dye, methyl orange (MO). The AgNPs allowed detecting MO down to 10–10 M. Finally, the AgNPs also exhibited good antibacterial activity against two gram-positive bacteria, namely Enterococcus spps. and Staphylococcus aureus. [ABSTRACT FROM AUTHOR]

Citrus fruits, revered for their nutritional value, face significant threats from diseases like citrus canker, particularly impacting global citrus cultivation, notably in Pakistan. This study delves into the critical role of NPR1-like genes, the true receptors for salicylic acid (SA), in the defense mechanisms of citrus against Xanthomonas axonopodis pv. citri (Xcc). By conducting a comprehensive genome-wide analysis and phylogenetic study, the evolutionary dynamics of Citrus limon genes across diverse citrus cultivars are elucidated. Structural predictions unveil conserved domains, such as the BTB domain and ankyrin repeat domains, crucial for the defense mechanism. Motif analysis reveals essential conserved patterns, while cis-regulatory elements indicate their involvement in transcription, growth, response to phytohormones, and stress. The predominantly cytoplasmic and nuclear localization of NPR1-like genes underscores their pivotal role in conferring resistance to various citrus species. Analysis of the Ks/Ka ratio indicates a purifying selection of NPR1-like genes, emphasizing their importance in different species. Synteny and chromosomal mapping provide insights into duplication events and orthologous links among citrus species. Notably, Xac infection stimulates the expression of NPR1-like genes, revealing their responsiveness to pathogenic challenges. Interestingly, qRT-PCR profiling post-Xac infection reveals cultivar-specific alterations in expression within susceptible and resistant citrus varieties. Beyond genetic factors, physiological parameters like peroxidase, total soluble protein, and secondary metabolites respond to SA-dependent PR genes, influencing plant characteristics. Examining the impact of defense genes (NPR1) and plant characteristics on disease resistance in citrus, this study marks the inaugural investigation into the correlation between NPR1-associated genes and various plant traits in both susceptible and resistant citrus varieties to citrus bacterial canker. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and data mining are used in various fields such as data analysis, prediction, image processing and especially in healthcare. Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results. Using ML algorithms, researchers have developed applications for decision support, analyzed clinical aspects, extracted informative information from historical data, predicted the outcomes and categorized diseases which help physicians make better decisions. It is observed that there is a huge difference between women depending on the region and their social lives. Due to these differences, scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected child. In this study, the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section (C-Section) or normal delivery. A voting ensemble model for the classification of a birth dataset was made by using a Random Forest (RF), Gradient Boosting Classifier, Extra Trees Classifier and Bagging Classifier as base learners. It is observed that the voting ensemble modal of proposed classifiers provides the best accuracy, i.e. 94.78%, as compared to the individual classifiers. ML algorithms are more accurate due to ensemble models, which reduce variance and classification errors. It is reported that when a suitable classification model has been developed for birth classification, decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the datasets. Developing such a system will not only allow health organizations to improve maternal health assessment processes, but also open doors for interdisciplinary research in two different fields in the region. [ABSTRACT FROM AUTHOR]

Vitamin C, also known as ascorbic acid, is an essential nutrient that plays a critical role in many physiological processes in plants and animals. In humans, vitamin C is an antioxidant, reducing agent, and cofactor in diverse chemical processes. The established role of vitamin C as an antioxidant in plants is well recognized. It neutralizes reactive oxygen species (ROS) that can cause damage to cells. Also, it plays an important role in recycling other antioxidants, such as vitamin E, which helps maintain the overall balance of the plant's antioxidant system. However, unlike plants, humans cannot synthesize ascorbic acid or vitamin C in their bodies due to the absence of an enzyme called gulonolactone oxidase. This is why humans need to obtain vitamin C through their diet. Different fruits and vegetables contain varying levels of vitamin C. The biosynthesis of vitamin C in plants occurs primarily in the chloroplasts and the endoplasmic reticulum (ER). The biosynthesis of vitamin C is a complex process regulated by various factors such as light, temperature, and plant hormones. Recent research has identified several key genes that regulate vitamin C biosynthesis, including the GLDH and GLDH genes. The expression of these genes is known to be regulated by various factors such as light, temperature, and plant hormones. Recent studies highlight vitamin C's crucial role in regulating plant stress response pathways, encompassing drought, high salinity, and oxidative stress. The key enzymes in vitamin C biosynthesis are L-galactose dehydrogenase (GLDH) and L-galactono-1, 4-lactone dehydrogenase (GLDH). Genetic studies reveal key genes like GLDH and GLDH in Vitamin C biosynthesis, offering potential for crop improvement. Genetic variations influence nutritional content through their impact on vitamin C levels. Investigating the roles of genes in stress responses provides insights for developing resilient techniques in crop growth. Some fruits and vegetables, such as oranges, lemons, and grapefruits, along with strawberries and kiwi, are rich in vitamin C. Guava. Papaya provides a boost of vitamin C and dietary fiber. At the same time, red and yellow bell peppers, broccoli, pineapple, mangoes, and kale are additional sources of this essential nutrient, promoting overall health. In this review, we will discuss a brief history of Vitamin C and its signaling and biosynthesis pathway and summarize the regulation of its content in various fruits and vegetables. [ABSTRACT FROM AUTHOR]

Aims: Heavy metals are significant environmental pollutants and toxic to life and chromium (Cr) (VI) is one of them being discharged in the environment due to many human activities. The leather industry uses Cr(VI) salt in the tanning process, which is discharged untreated and becomes a source of many diseases. The use of microbes to remove metals is a cost-effective and clean method. The present study aims to isolate local and native microbes for Cr(VI) removal from tannery wastewater and enhance their capacity to bioremediate the tannery wastewater. Further, efficiency in free and immobilized forms was also checked. Methodology and results: Microbes were isolated from a local tannery wastewater outlet and after many rounds of minimum inhibitory concentration, and concentration of 500 µg/mL was found to be that concentration at which microbes could survive, above which they died. The sequencing of 16S rRNA and its analysis showed that it was closely related to Staphylococcus saprophyticus and in the given study, it was named B6. At 37 °C, pH 7.5 and 120 h of incubation, it removed 77% Cr(VI) from the reaction mixture. B6 was exposed to UV to obtain mutant. Exposure of 15 min to a UV lamp gave mutant MB6, which showed a removal capacity of 77% after 72 h only. Cr(VI) removal capacity of the mutant was then analyzed in the free and attached form where coal and sodium alginate were used as solid surfaces. Mutants immobilized on coal showed 91% Cr(VI) removal after 96 h, while sodium alginate showed 58% Cr(VI) removal in 120 h, thus showing coal as a more effective surface for adsorption. Conclusion, significance and impact of study: Our present study shows the use of cheap and environmentally friendly methods to remediate tannery wastewater, which is a big problem in a country like Pakistan. Pakistan is the second largest producer of leather but lacks a wastewater treatment facility. So, this method offers in-situ wastewater treatment, which can be further enhanced in different ways. [ABSTRACT FROM AUTHOR]

This study aims to explore the empirical relationship between climate policy uncertainty (CPU) and regional innovation performance. The empirical analysis was conducted on 36 years of available data over the period 1987 to 2022 in the United States (US). By using an autoregressive distributed lag (ARDL) model for regression analysis, we examine the impact of CPU on two crucial indicators of innovation, namely, research and development (R&D) expenditures and patent applications (PTA). The empirical analysis reveals a significant negative effect of CPU on both R&D expenditures and PTA, suggesting that higher levels of climate uncertainty may hinder innovation activities in the regional context. A high CPU introduces hesitancy and risk aversion, impacting the allocation of resources, confidence in markets, and the willingness of businesses to pursue innovative endeavors. The negative effect remains consistent even after the inclusion of several control variables and robustness checks. The findings underscore the significance of a stable and supportive policy environment and economic conditions in promoting innovation. Overall, this research provides valuable empirical evidence on the interplay between CPU and innovation outcomes, offering insights for policymakers, businesses, and researchers seeking to navigate the complexities of climate‐related challenges and their impact on innovation. We did not find any study exploring the comparable impact of CPU on innovation. [ABSTRACT FROM AUTHOR]

Antibiotics are the drugs that are used for the management of microbial diseases. However, these conventional synthetic drugs can harmfully affect the human health. Since phytochemicals are extracted from natural sources and, are hence relatively safer for human health, they are the enticing alternatives in this regard. Cinnamon is also one of those plants which is being employed as herbal medication for centuries against certain microbial infections due its significant therapeutic effectiveness. A well-known pathogenic bacterium called H. pylori causes a wide range of illnesses in human body. This pathogen's pathogenicity is determined by certain virulent proteins. In this study, some of such proteins, which included virB4, virB8, and virB9 were selected to evaluate the therapeutic efficiency of cinnamon compounds. These proteins were identified in different isolates of H. pylori. The structural modelling of all these proteins were performed initially in order to proceed them for molecular docking analysis. While, the docking studies illustrated that one of the cinnamon compounds, cinnamyl acetate, showed significant binding interactions with virB4 and virB9. However, benzyl benzoate which is another cinnamon compound, docked well with virB8. Afterwards, the MD simulations were incorporated to explore the interaction motions and structural stability of all the docked complexes. In this regard, the resultant maps of Bfactor, eigenvalues and elastic network model, among other factors ensured the structural stabilities of all the respective complexes. After these crucial estimations, benzyl benzoate and cinnamyl acetate underwent the ADMET investigation to assess their pharmacokinetic characteristics. SwissADME and ADMETLab 2.0 server were employed for this investigation. The compiled findings these servers revealed that both, benzyl benzoate and cinnamyl acetate, exhibited a significant level of pharmacokinetic and drug-likeness conformity. [ABSTRACT FROM AUTHOR]

This study set out to determine how Emotional Intelligence (EI) impacts the success of construction projects. This study showed that organisational culture has a role in moderating the relationship between team cohesion and project success, and its significance in this regard was also examined. Workers on several construction projects in Pakistan were contacted and provided information for this study. The data was analysed utilising the regression analysis in SPSS and partial least squares in SMART PLS, and it has been found that emotional intelligence significantly influences project success. Relationships between emotional intelligence and project success are both favourable and statistically significant. Emotionally astute colleagues can help teams to build stronger bonds with one another. Furthermore, the organisational culture positively and significantly impacts the relationship between a successful project and a cohesive team. [ABSTRACT FROM AUTHOR]

YABBY gene family is a plant-specific transcription factor with DNA binding domain involved in various functions i.e. regulation of style, length of flowers, and polarity development of lateral organs in flowering plants. Computational methods were utilized to identify members of the YABBY gene family, with Carrot (Daucus carota) 's genome as a foundational reference. The structure of genes, location of the chromosomes, protein motifs and phylogenetic investigation, syntony and transcriptomic analysis, and miRNA targets were analyzed to unmask the hidden structural and functional characteristics YABBY gene family in Carrots. In the following research, it has been concluded that 11 specific YABBY genes irregularly dispersed on all 9 chromosomes and proteins assembled into five subgroups i.e. AtINO, AtCRC, AtYAB5, AtAFO, and AtYAB2, which were created on the well-known classification of Arabidopsis. The wide ranges of YABBY genes in carrots were dispersed due to segmental duplication, which was detected as prevalent when equated to tandem duplication. Transcriptomic analysis showed that one of the DcYABBY genes was highly expressed during anthocyanin pigmentation in carrot taproots. The cis-regulatory elements (CREs) analysis unveiled elements that particularly respond to light, cell cycle regulation, drought induce ability, ABA hormone, seed, and meristem expression. Furthermore, a relative study among Carrot and Arabidopsis genes of the YABBY family indicated 5 sub-families sharing common characteristics. The comprehensive evaluation of YABBY genes in the genome provides a direction for the cloning and understanding of their functional properties in carrots. Our investigations revealed genome-wide distribution and role of YABBY genes in the carrots with best-fit comparison to Arabidopsis thaliana. [ABSTRACT FROM AUTHOR]

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables. [ABSTRACT FROM AUTHOR]

Thin film technology has gained significant attention in the industry due to its superior properties in comparison to bulk materials. Specifically, iron nitride-based thin films are a fascinating area of research in semiconductor technologies. The crystal structure and optical properties of these films are highly dependent on the growth method, deposition technique, gas flow, pressure, and several other factors. In this work, we prepared polycrystalline thin films of iron nitride on silicon (100) substrates by using the active screen low temperature plasma-assisted method. These films are deposited under the varying atmosphere of nitrogen and hydrogen in four proportions: 80% N2 + 15% H2; 60% N2 + 35% H2; 40% N2 + 55% H2; and 20% N2 + 75% H2, along with 5% argon to investigate its influence on the structural, morphological, and optical properties. The structural properties are determined by x-ray diffraction and revealed a mixture of ζ-Fe2N, ε-Fe3N, and γ′-Fe4N phases. Surface morphology is analyzed by scanning electron microscopy, which confirms the formation of a thin layer on the substrate. In addition, UV–Vis spectrophotometry is used to assess the optical response and bandgap energy of the samples. The result of UV–Vis optical diffuse reflectance spectra shows a decrease in the bandgap from 3.17 to 3.06 eV of substrate. For the S4 sample, we obtained a pure Fe4N phase, showing a significant decrease in the energy bandgap. [ABSTRACT FROM AUTHOR]

Multiplexing techniques will be employed based on duration, polarization, and frequency to achieve the expanding demand for broadcast bandwidth. Adding time as an additional aspect to transmission networks has been put out as a flexible way to handle potential bandwidth problems. For interaction programs such as space imaging, optical fiber setup, submerged portable visual hyperlinks, onboard interconnects, information centers indoor relations, radio signals, and auditory interactions, we examine the possibilities of utilizing time as a further level of independence. Beginning in 2010, space-divisional multiplexer (SDM) studies gained popularity, partly because they had been suggested to increase the data-carrying ability of fiber optics while also boosting effectiveness through collaboration on assets. The proposed SDM transfer technologies spectrum includes identical single-mode fibers using shared amplifiers pumping laser light and the complete spatial arrangement of transceiver equipment, signal analysis, and amplifiers near a fiber with more than 100 geographical networks composed of several threads, all executing various forms. In this study, we discuss SDM study's advancements. The major categories and characteristics of innovative SDM fibers, like multiple-core fibers (MCFs), multiple-mode fibers, fewer-mode MCFs, and coupled-core MCFs, are initially described. While examining present tendencies and speculating on potential advances and uses outside optical data transfer, we provide analyses between various fiber kinds. [ABSTRACT FROM AUTHOR]

This study sheds light on strategies for retaining skilled pharmacists in Pakistan's pharmaceutical sector, offering valuable insights for both academia and industry stakeholders by investigating the impact of human resource management practices, including training and development, compensation and rewards, job performance, and job satisfaction, on employee retention. It also examines the moderating role of career growth in this context. Theoretical foundations are grounded in international migration theories and social exchange theory, providing a comprehensive framework for the study. A cross‐sectional survey design was used to collect data from 254 pharmacists in southern Punjab, Pakistan. Structural equation modeling (SEM) was employed for data analysis. Findings reveal that training and development, compensation and rewards, job performance, and job satisfaction all have significant direct effects on employee retention. Additionally, career growth acts as a crucial moderator, strengthening the relationship between training and development and job performance, as well as compensation and rewards and job satisfaction in relation to employee retention. Theoretical implications emphasize the importance of tailored training programs, fair compensation practices, and career growth opportunities. Practical recommendations include creating a positive work environment, rewarding high‐performing employees, and fostering career development. Future research directions encompass longitudinal studies, the influence of organizational culture and technology, regional variations, and additional factors affecting retention. [ABSTRACT FROM AUTHOR]

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) diagnostics test is a reliable solution for estimating insulation health conditions in power system components. During laboratory PD diagnostics, the effect of harmonic components in the voltage waveform generated by variable frequency drives (VFD) fed electric motors (EMs) is often overlooked. As VFD‐fed EMs operate at low speeds, the harmonic concentration in the voltage increases. It has been found that PD activity in VFD‐fed EM is significantly affected by the addition of harmonic components in the voltage, thus making it necessary to consider their impact when performing true PD diagnostics. This paper investigates the influence of voltage harmonic distortion produced in VFD‐fed EM on PD severity and the subsequent insulation degradation. Eight VFD‐fed EM are used to study the PD behaviour under different levels of harmonic pollution. The PD characteristics, such as inception voltage, pulse repetition rate, accumulated apparent charge, average discharge current, discharge power, and quadratic rate, are determined. Using probability distribution functions, PD activity and severity at various voltage harmonic levels are estimated. The proposed technique can be used to plan the maintenance activities that ensure the reliability for industrial applications dealing with the voltage harmonic distortion. [ABSTRACT FROM AUTHOR]

Headwater in the Indus River in Pakistan is largely dependent on the glaciers located in the northern part of the country, along with other sources such as direct precipitation. Glaciers are a major source of freshwater that provides agriculture and livelihood to millions of people. The hydro-climatic variations in the Gilgit watershed of the Upper Indus basin are poorly investigated scientifically due to high topographical differences, geography, remoteness of the region, and larger variations in climatic conditions. These glaciers are continuously changing due to melting as a consequence of global warming or accumulation due to snowfall/precipitation at higher altitude regions. The study is carried out using remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an area of more than 5 square kilometers were selected, namely Phakor, Karamber, East Gammu, Bhort, and Bad-e-Swat glaciers. These glaciers were monitored for changes in their sizes through a cloud-free continuous series of Landsat satellite imagery. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970–2000, utilized for assessment of meteorological condition by analyzing the variations of minimum and maximum temperature, solar radiation, and precipitation. The temporal variations in five glaciers in the Gilgit watershed are found to be minimal and, thus, are rather stable and show no sign of rapid melting or diminishing. The little variability of glaciers' extent may be attributed to their geographic condition, altitude, topography, and orientation. The mapped glacier classes have been validated to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated as 84%, which shows a good agreement. The study makes a critical input towards understanding the dynamics of the glacier in the upper Indus catchment's Gilgit watershed. [ABSTRACT FROM AUTHOR]

The China-Pakistan Economic Corridor (CPEC) between China and Pakistan passes through the environmentally fragile region of the Himalayas−Karakorum−Hindukush (HKH). Surging human influence in the form of transportation and tourism may pose a potential threat to the socio-environmental sustainability especially, the water resources of the region. Therefore, it is important to evaluate the status and hydrogeochemistry of freshwater in mountainous catchments. For this purpose, water samples were collected from the Hunza, Gilgit, and Indus River Basins from 2019 to 2020. The physicochemical and biological characteristics of surface water were investigated using the water quality index (WQI), multivariate statistics, Piper diagram, and Gibbs plots. The results revealed high seasonal variability in the hydrochemistry of water samples in all basins. Heavy metals (HM) i.e., arsenic (As), and lead (Pb) were detected in more than 50% of total water samples, whereas cadmium (Cd) and mercury (Hg) were detected in less than 15% of total water samples. 82% of water samples collected during summer and 48% in winter were contaminated with bacterial contamination (Escherichia coli (E. coli) and total coliform (T. coli)). Piper diagram and Gibbs plot suggested that rock weathering and dissolution of carbonate rocks are the dominating factors controlling the chemistry of glacial streams and river water. The results of this study have important implications for policymakers, regulators, and practitioners involved in water and environmental management across CPEC. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN) for classification. We tested ourmodel on the IDRiD dataset and achieved an accuracy of 98.06%, a sensitivity of 83.67%, and a specificity of 100% for DR detection and evaluation tests, respectively. Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection. [ABSTRACT FROM AUTHOR]

Background and Objectives: Antimicrobial resistance has emerged as a significant global health threat. Infections caused by Multi Drug-Resistant (MDR) bacteria pose formidable challenges in terms of treatment options and patient outcomes. Pus cultures serve as crucial diagnostic tools in identifying the agents responsible for various infections, and their antimicrobial susceptibility patterns which help in establishment of empirical therapy guidelines. This study was conducted to determine the pathogen and its susceptibility pattern from pus cultures and to generate antibiogram in our tertiary care setting. Materials and Methods: It was a cross-sectional study, conducted for a period of six months, from July 2022 to December 2022, in the Pathology Department of Pakistan Institute of Medical Sciences (PIMS). Results: Out of total 2507 samples received, 1242 (49.5%) showed positive culture. Among the 1242 positive samples, 364 were Gram positive cocci (GPCs) and 878 were Gram negative rods (GNRs). Methicillin resistant Staphylococcus aureus (MRSA) was the most common isolate (23%) followed by Klebsiella pneumoniae (22.6%), Pseudomonas aeruginosa (16.9%), Enterobacter spp. (15.5%) and Escherichia coli (14.2%). Vancomycin was found to be highly effective (100%) against MRSA. GPCs were highly susceptible to linezolid (98%) while GNRs showed high level of sensitivity to colistin (96%) and tigecycline (92%). Conclusion: The generation of a local antibiogram specific to the hospital setting is essential to effectively manage infections empirically and preserve the efficacy of existing antibiotics. By implementing antimicrobial stewardship practices based on a better understanding of antibiotic susceptibility patterns, we can contribute to the mitigation of antibiotic resistance and improve patient outcomes. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in diagnosing diabetic retinopathy, a major contributor to global vision impairment, is becoming increasingly pronounced. Notably, the detection of vision-threatening diabetic retinopathy (VTDR) has been significantly fortified through automated techniques. Traditionally, the reliance on manual analysis of retinal images, albeit slow and error-prone, constituted the conventional approach. Addressing this, our study introduces a novel methodology that amplifies the robustness and precision of the detection process. This is complemented by the groundbreaking Hierarchical Block Attention (HBA) and HBA-U-Net architecture, which notably propel attention mechanisms in image segmentation. This innovative model refines image processing without imposing excessive computational demands by honing in on individual pixel intricacies, spatial relationships, and channel-specific attention. Building upon this innovation, our proposed method employs a multi-stage strategy encompassing data pre-processing, feature extraction via a hybrid CNN-SVD model, and classification employing an amalgamation of Improved Support Vector Machine-Radial Basis Function (ISVM-RBF), DT, and KNN techniques. Rigorously tested on the IDRiD dataset classified into five severity tiers, the hybrid model yields remarkable performance, achieving a 99.18% accuracy, 98.15% sensitivity, and 100% specificity in VTDR detection, thus surpassing existing methods. These results underscore a more potent avenue for diagnosing and addressing this crucial ocular condition while underscoring AI's transformative potential in medical care, particularly in ophthalmology. [ABSTRACT FROM AUTHOR]

Employee psychological well-being is crucial in today's complex and ever-changing corporate environment. This is because technological advances, international competition, and changing consumer expectations have changed the labour market. This study aims to examine the role of transformational leadership in improving employees' psychological well-being. This research is a descriptive qualitative study that explores previous studies related to transformational leadership and employee psychological well-being. First, relevant studies were collected related to transformational leadership and employee psychological well-being and the possible impact of both constructs. After that, the author summarises the relevant studies and discusses the concepts of transformational leadership and employee psychological well-being and how they can be used in organisations. The next step is to express the author's views on the potential of transformational leadership and employee psychological well-being in organisations. Study results show that transformational leadership improves employee psychological well-being. Transformational leaders inspire and motivate their people, making them feel valued and invested in the company's objective. This boosts workplace motivation, self-esteem, and growth. [ABSTRACT FROM AUTHOR]

Grid-based approaches render an efficient framework for data clustering in the presence of incomplete, inexplicit, and uncertain data. This paper proposes an entropy-based grid approach (EGO) for outlier detection in clustered data. The given hard clusters obtained from a hard clustering algorithm, EGO uses entropy on the dataset as a whole or on an individual cluster to detect outliers. EGO works in two steps: explicit outlier detection and implicit outlier detection. Explicit outlier detection is concerned with those data points that are isolated in the grid cells. They are either far from the dense region or maybe a nearby isolated data point and therefore declared as an explicit outlier. Implicit outlier detection is associated with the detection of outliers that are perplexedly deviated from the normal pattern. The determination of such outliers is achieved using entropy change of the dataset or a specific cluster for each deviation. The elbow based on the trade-off between entropy and object geometries optimizes the outlier detection process. Experimental results on CHAMELEON datasets and other similar datasets suggested that the proposed approach(es) detect the outliers more precisely and extend the capability of outliers detection to an additional 4.5% to 8.6%. Moreover, the resultant clusters became more precise and compact when the entropy-based gridding approach is applied on top of hard clustering algorithms. The performance of the proposed algorithms is compared with well-known outlier detection algorithms, including DBSCAN, HDBSCAN, RE3WC, LOF, LoOP, ABOD, CBLOF and HBOS. Finally, a case study for detecting outliers in environmental data has been carried out using the proposed approach and results are generated on our synthetically prepared datasets. The performance shows that the proposed approach may be an industrial-oriented solution to outlier detection in environmental monitoring data. [ABSTRACT FROM AUTHOR]

Carotenoid cleavage oxygenase (CCO) is an enzyme capable of converting carotenoids into volatile, aromatic compounds and it plays an important role in the production of two significant plant hormones, i.e., abscisic acid (ABA) and strigolactone (SL). The cucumber plant genome has not been mined for genomewide identification of the CCO gene family. In the present study, we conducted a comprehensive genome-wide analysis to identify and thoroughly examine the CCO gene family within the genomic sequence of Cucumis sativus L. A Total of 10 CCO genes were identified and mostly localized in the cytoplasm and chloroplast. The CCO gene is divided into seven subfamilies i.e. 3 NCED, 3 CCD, and 1 CCD-like (CCDL) subfamily according to phylogenetic analysis. Cis-regulatory elements (CREs) analysis revealed the elements associated with growth and development as well as reactions to phytohormonal, biotic, and abiotic stress conditions. CCOs were involved in a variety of physiological and metabolic processes, according to Gene Ontology annotation. Additionally, 10 CCO genes were regulated by 84 miRNA. The CsCCO genes had substantial purifying selection acting upon them, according to the synteny block. In addition, RNAseq analysis indicated that CsCCO genes were expressed in response to phloem transportation and treatment of chitosan oligosaccharides. CsCCD7 and CsNCED2 showed the highest gene expression in response to the exogenous application of chitosan oligosaccharides to improve cold stress in cucumbers. We also found that these genes CsCCD4a and CsCCDL-a showed the highest expression in different plant organs with respect to phloem content. The cucumber CCO gene family was the subject of the first genome-wide report in this study, which may help us better understand cucumber CCO proteins and lay the groundwork for the gene family's future cloning and functional investigations. [ABSTRACT FROM AUTHOR]