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Businesses of all kinds are breaching into Industry 5.0, and they require pursuing digital transformation (DT) with great aggression as it provides them the leverage in getting a competitive advantage, more numbers on top line as revenue, and much deeper relationships from a customer standpoint. DT is a far-reaching organizational change necessitating the integration of technologies such as blockchain, IoT, AI, and Cloud Computing. The article aims to emphasize the importance of theoretical frameworks to guide successful DT in organizations. It critiques the role of these frameworks in addressing common challenges seen in DT programs. The study reviews and analyses a range of theoretical frameworks such as Technology Acceptance Model (TAM), Task-Technology fit, and the Unified Theory of acceptance and use technology (UTAUT) extensively. This article reviews existing models and synthesizes their findings from thorough research in a wide range of organizational environments to understand how they can be applied and have been used within DT projects. The proposed article asserts that the main reason behind most of the failure of DT initiatives is planning which does not take into account many strategic factors and one of those factors are flexible organization and cultural integration. This concludes how the ever-ongoing problem of adopting technology is no longer unexplored by stating that TAM, TTF, and UTAUT can be successfully used to let organizations overcome these hurdles. The article underscores the need for strategic, culturally appropriate approaches to DT and highlights that theoretical underpinnings are integral in this effort. By embedding TAM, TTF, and UTAUT into a typical DT strategy, the way businesses navigate their complex environment in time is more likely to acknowledge where technological development should align with strategic goals as well as cultural values.

Through a detailed case study, this work brings out another facet at the inter-section of drone technology with computational methodologies: demonstrating code and machine learning-driven approaches toward supreme flying abilities. The article provides a systematic review of the integration between drones and computational frameworks with applications in areas like agriculture, logistics, and surveillance. The proposed approach uses a systematic process that integrates data collection, algorithm design, and real-life trials to develop novel algorithms that enhance drone capabilities, including autonomous navigation, adaptive processing, and decision-making. The results indicate that machine learning models significantly improve predictive maintenance, data analytics, and decision-making, leading to better operational efficiency, particularly in obstacle avoidance and flight path optimization. This study highlights the importance of computational algorithms in advancing drone electronics and provides insight into how they could transform various industries. These results offer a new dimension to the growing body of work in autonomous systems and their utilization, illustrating how innovation in drone capabilities can have wide-ranging impacts on advancing technology within various industries.

Background: The digitization of healthcare data has made significant progress in medical research and personalized medicine. Nonetheless, conventional centralized data-sharing structures present primary obstacles to information privacy and security due to laws like the Health Insurance Portability and Accountability Act (HIPAA). Federated Learning (FL) has been proposed as a potential solution that can enable collaborative learning with decentralized datasets without the requirement of data centralization. Objective: This study examines how well Federated Learning performs in meeting both the requirements of secure data exchange between healthcare organizations and achieving high model accuracy without violating any privacy compliance regulations. Methods: A Federated Learning framework was implemented with a neural network model using federated learning on an electronic health records (EHR) database collected from multiple hospitals. Its accuracy was compared to a traditional centralized model across various states, while the people also consider its convergence speed and data leakage risks. We incorporated differential privacy mechanisms in order to improve the security of data sets as well as prevent malicious attacks. Results: The FL model achieved accuracy comparable to the centralized model, with only a marginal reduction. Furthermore, the integration of differential privacy significantly reduced the risk of data breaches, providing robust protection against adversarial attacks. Conclusion: The FL model reported a performance difference which was only slightly reduced as compared with the centralized model. Moreover, this privacy compliance overcomes the risk of data breaches by integrating with differential privacy & securing against adversarial attacks.

Abstract In plants, the BAX inhibitor-1 (BI-1) gene plays a crucial part in controlling cell death under stress conditions. This mechanism of Programmed Cell Death (PCD) is genetically regulated and is crucial for the elimination of unwanted or damaged cells in a controlled manner, which is essential for normal development and tissue maintenance. A study on cucumber identified and characterized five BI-1 genes: CsBI1, CsBI2, CsBI3, CsBI4, and CsBI5. These genes share conserved domains, indicating common evolutionary history and function. Physicochemical analysis revealed their molecular weights and isoelectric points, while subcellular localization showed their presence in different cellular compartments. The phylogenetic analysis highlighted evolutionary relationships with related crops. Chromosomal distribution and synteny analysis suggested segmental or tandem duplications within the gene family. Protein-protein interaction analysis revealed extensive interactions with other cucumber proteins. Cis-regulatory elements in the promoter regions provided insights into potential functions and transcriptional regulation. miRNAs showed diverse regulatory mechanisms, including mRNA cleavage and translational inhibition. The CsBI3, CsBI4 and CsBI5 genes exhibit elevated expression levels during cold stress, suggesting their vital involvement in cucumber plant defense mechanisms. The application of chitosan oligosaccharides externally confirms their distinct expression patterns. The qRT-PCR confirms the upregulation of CsBI genes in ToLCNDV-infected plants, indicating their potential to mitigate biotic and abiotic stresses. The comprehensive genome-wide exploration provides opportunities for the development of cold-tolerant and virus-resistant cucumber variants by traditional breeding or gene.

Ferroptosis, a form of regulated cell death, has emerged as a crucial process in diverse pathophysiological states, encompassing cancer, neurodegenerative ailments, and ischemia-reperfusion injury. The glutathione (GSH)-dependent lipid peroxidation pathway, chiefly governed by glutathione peroxidase 4 (GPX4), assumes an essential part in driving ferroptosis. GPX4, as the principal orchestrator of ferroptosis, has garnered significant attention across cancer, cardiovascular, and neuroscience domains over the past decade. Noteworthy investigations have elucidated the indispensable functions of ferroptosis in numerous diseases, including tumorigenesis, wherein robust ferroptosis within cells can impede tumor advancement. Recent research has underscored the complex regulatory role of non-coding RNAs (ncRNAs) in regulating the GSH-GPX4 network, thus influencing cellular susceptibility to ferroptosis. This exhaustive review endeavors to probe into the multifaceted processes by which ncRNAs control the GSH-GPX4 network in ferroptosis. Specifically, we delve into the functions of miRNAs, lncRNAs, and circRNAs in regulating GPX4 expression and impacting cellular susceptibility to ferroptosis. Moreover, we discuss the clinical implications of dysregulated interactions between ncRNAs and GPX4 in several conditions, underscoring their capacity as viable targets for therapeutic intervention. Additionally, the review explores emerging strategies aimed at targeting ncRNAs to modulate the GSH-GPX4 pathway and manipulate ferroptosis for therapeutic advantage. A comprehensive understanding of these intricate regulatory networks furnishes insights into innovative therapeutic avenues for diseases associated with perturbed ferroptosis, thereby laying the groundwork for therapeutic interventions targeting ncRNAs in ferroptosis-related pathological conditions.

One of the main causes of death worldwide is lung cancer, which is largely caused by cigarette smoking. The crucial transcription factor NF-κB, which controls inflammatory responses and various cellular processes, is a constitutively present cytoplasmic protein strictly regulated by inhibitors like IκB proteins. Upon activation by external stimuli, it undergoes phosphorylation, translocates into the nucleus, and modulates the expression of specific genes. The incontrovertible association between pulmonary malignancy and tobacco consumption underscores and highlights a public health concern. Polycyclic aromatic hydrocarbons and nitrosamines, potent carcinogenic compounds present in the aerosol emitted from combusted tobacco, elicit profound deleterious effects upon inhalation, resulting in severe perturbation of pulmonary tissue integrity. The pathogenesis of smoking-induced lung cancer encompasses an intricate process wherein NF-κB activation plays a pivotal role, triggered by exposure to cigarette smoke through diverse signaling pathways, including those associated with oxidative stress and pro-inflammatory cytokines. Unraveling the participation of NF-κB in smoking-induced lung cancer provides pivotal insights into molecular processes, wherein intricate crosstalk between NF-κB and pathways such as MAPK and PI3K-Akt amplifies the inflammatory response, fostering an environment conducive to the formation of lung cancer. This study reviews the critical function of NF-κB in the complex molecular pathways linked to the initiation and advancement of lung carcinogenesis as well as potential treatment targets.

OBJECTIVE: To study the frequency and antifungal susceptibility of the Candida albicans and non-albican species in diabetic foot infections from samples collected in a tertiary care hospital of district Peshawar. METHODOLOGY: This cross-sectional study was conducted from March 2020 to February 2021 in Khyber Teaching Hospital and Hayatabad Medical Complex, Peshawar. Specimen for culture swabs or tissue from diabetic foot ulcers was taken using aseptic methods. If pus was absent in the wound, ulcer scraping was collected. One tissue sample was soaked in 10% KOH for microscopy, while the second sample was used for fungal culture and sensitivity using Sabouraud dextrose agar. RESULTS: Of the 600 samples, 200 patients had diabetic foot ulcers with positive fungal culture from Males 102(51%) and Females 98(49%). The age range was 40-78 years. The frequency of C. albicans, C. dubliniensis, C. famata, C. glabrata, C. parapsilosis, and C. lusitaniae was 23(11.5%), 27(13.5%), 19(9.5%), 19(9.5%), 88(44%) and 24(12%). C. albicans was the most common fungal species. Antifungal susceptibility testing was done, and resistance to drugs like Amphotericin, Caspofungin, Fluconazole, Flucytosine, Itraconazole, Micafungin, Voriconazole which was 6.5%, 16%, 25.5%, 5.5%, 3%, 22.5%, 21% respectively. Resistance to Fluconazole, Micafungin and Voriconazole was the highest among all commonly used antifungal drugs. CONCLUSION: Fungal infection in diabetic foot ulcers usually does not respond to antibiotics. Candia albicans and non-albicans spp are also associated with diabetic foot ulcer infection and inflammation, and these fungi have the highest resistance to commonly used antifungal agents.

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. These applications can help improve the quality, safety, and nutritional value of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learning algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying time led to an increase in carbohydrates, sweetness, and CIE-L*a*b levels, while it led to a decrease in the moisture content in dried banana slices. Therefore, there is a direct relationship between CIE-L*a*b levels and sweetness. On the other hand, the RF and SVM algorithms gave the highest prediction accuracy of 86% and 0.8 on the Kappa measure. While the other algorithms (CART, LDA, KNN) gave a prediction accuracy of 80% and 0.7 on the Kappa measure. In terms of testing statistical significance, the null hypothesis (H0) was accepted because there is no relationship between the metric distributions of the algorithms used.

Summary: HIV-1 reservoirs display a heterogeneous nature, lodging both intact and defective proviruses. To deepen our understanding of such heterogeneous HIV-1 reservoirs and their functional implications, we integrated basic concepts of graph theory to characterize the composition of HIV-1 reservoirs. Our analysis revealed noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, played a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthened the accuracy of classification between elite controllers and ART-treated patients. Markov chain modeling for the simulation of different graph networks demonstrated the presence of an intrinsic barrier between elite controllers and non-elite controllers. Overall, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV-1 reservoirs.

Chilli, one of the most popular vegetables in the world is infested by many insect-pests and diseases. Fipronil, a phenylpyrazole class insecticide is used to manage insect-pests on chilli. The present study aimed to analyze the dissipation patterns and residual concentrations of the fipronil and its metabolites in chilli fruits during the Kharif season of 2020–21, in semi-arid environment. In the study, fipronil was applied to the plants twice, with a 10-day gap between treatments, using a 5 % suspension concentrate. The applications were undertaken using two separate concentrations i.e. the lower dosage of 40 g a.i. ha−1 and higher dosage of 80 g a.i. ha−1. There were four sets of data for each concentration. The chilli crop was systematically sampled at predetermined intervals after the application of the second spray. The procedure encompassed utilizing the modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) technique for extraction and purification, followed by analyzing the resulting residues using Gas Chromatography Electron Capture Detector. Gas Chromatography- Mass Spectrometry was subsequently conducted for the confirmation of the findings. The research revealed that the mean initial deposit of fipronil and its metabolites (desulfinyl, sulphide, and sulfone) at the authorized dosage was determined to be 0.574, 0.123, 0.031, and 0.180 mg kg−1, respectively. In contrast, when administered at twice the prescribed dosage, the mean first deposit was 1.204, 0.230, 0.067, and 0.382 mg kg−1. The half-life values of these residues exhibited a range of 1.2–4.1 days for both dosages. A prudent waiting duration was determined for the doses, leading to the conclusion that an average interval of 7 days is deemed safe for harvesting chilli peppers. The significance of this discovery is related to the maximum residue limits of 0.001 mg kg−1 for fipronil in green chilli, as established by the Food Safety and Standards Authority of India. This study provides significant insights into fipronil's persistence and proper management in chilli plant cultivation, emphasizing the importance of following prescribed dosages and designated waiting intervals to ensure the safety of food products.

IntroductionIndividuals experiencing mental health disorders encounter numerous challenges while accessing mental healthcare services. Despite the inclusion of mental health in the primary healthcare system, screening and managing mental health disorders remain significantly overlooked. Therefore, there is a need to understand the perceptions of healthcare providers in primary care settings, identify the challenges faced, and gather suggestions for effective mental healthcare delivery.MethodsThe present qualitative study was conducted in 13 primary healthcare facilities in the Jodhpur district of Rajasthan, India, from 2023 to 2024 among 25 primary healthcare providers. Semi-structured interview guides were developed for each category of primary healthcare provider, including Medical Officers, Community Health Officers, and General Nurse Midwives, Auxiliary Nurse Midwives and in-depth interviews were recorded, transcribed, and thematically analyzed using codes and sub-codes.ResultsResults are summarized under the themes: (i) Navigating Mental Healthcare in Primary Healthcare Facilities, (ii) Challenges and Barriers in Mental Healthcare Delivery, (iii) Recommendation for Enhancing Mental Healthcare Services, and (iv) Approaches for Comprehensive Capacity Building Training and Module and further findings of each theme are presented under various sub-themes.ConclusionThe findings suggest that despite a perceived competence in providing mental health services, there were substantial levels of negative attitudes towards mental health disorders among participants and provide insights for policymakers and healthcare professionals to develop targeted interventions and improve mental healthcare delivery at primary care facilities.

Introduction: Daidzein, a secondary metabolite found in Soybean (Glycine max L.) and Pueraria lobata (Willd.), plays a pivotal role in various therapeutic applications, including its potential as an anticancer agent, particularly in estrogen-dependent breast cancer, along with anti-allergic, anti-inflammatory, cardiovascular, antidiabetic, antioxidative, and neurogenerative effects. Daidzein's ability to traverse the blood-brain barrier enhances cognition, reduces aggression, and promotes locomotor activity. Its structural resemblance to estrogens positions it as a potential remedy for menopausal osteoporosis. Methodology: The methodology for the comprehensive review on Daidzein entailed a literature search utilising keywords such as ''Daidzein'' and various therapeutic activities, including “Antioxidant,” “Anti-inflammatory,” “Anticancer,” “Antiviral,” “Antiobesity,” “Antidiabetic,” “Nephrotoxicity,” “Cardiovascular,” “Neuroprotective,” “Osteoporosis,” “Menopausal Symptoms,” “Aging,” “Cognitive Activities”. Academic databases such as PubMed, Google Scholar, Scopus, and Web of Science were employed for the search, considering articles published within the last 20 years to capture recent research while encompassing significant historical studies. Results: The results provide insights into the diverse effects of daidzein across various health domains, including its antioxidant, anti-inflammatory, anticancer, antiviral, antiobesity, antidiabetic, nephrotoxicity, cardiovascular, neuroprotective, osteoporosis, menopausal symptoms, ageing, and cognitive activities. Discussion: : The discussion section critically analyses the collective evidence gathered from the studies. It explores the mechanisms underlying daidzein's therapeutic effects and discusses the implications of these findings for both Traditional Chinese Medicine and modern healthcare practices. However, challenges related to its low bioavailability and toxicity necessitate further research, providing a foundation for future studies on daidzein's potential applications.

Cancer poses intricate challenges to treatment due to its complexity and diversity. Ferroptosis and circular RNAs (circRNAs) are emerging as innovative therapeutic avenues amid the evolving landscape of cancer therapy. Extensive investigations into circRNAs reveal their diverse roles, ranging from molecular regulators to pivotal influencers of ferroptosis in cancer cell lines. The results underscore the significance of circRNAs in modulating molecular pathways that impact crucial aspects of cancer development, including cell survival, proliferation, and metastasis. A detailed analysis delineates these pathways, shedding light on the molecular mechanisms through which circRNAs influence ferroptosis. Building upon recent experimental findings, the study evaluates the therapeutic potential of targeting circRNAs to induce ferroptosis. By identifying specific circRNAs associated with the etiology of cancer, this analysis paves the way for the development of targeted therapeutics that exploit vulnerabilities in cancer cells. This review consolidates the existing understanding of ferroptosis and circRNAs, emphasizing their role in cancer therapy and providing impetus for ongoing research in this dynamic field.

Modern agricultural machinery such as harvesting robotic arms are necessary to optimize the semi-automated agricultural processes to meet the ever-growing food needs of the population of the world. Production of low cost and highly efficient robotic arms are needed to meet the needs of the small-scale growers of underdeveloped countries. In this regard, mechanical design, sensor placement, and object identification are essential to optimize the performance of agricultural robots. This research is conducted to design a fully automated robotic arm for fruit picking in hydroponics system and research farm using machine vision to help the smallholders. For this purpose, a robotic arm with 1.3 m in height is designed using aluminum (18-gauge) and mild steel (MS-16) as fabrication material for the mechanical structure. The prototype design is tested for stress estimation at various payloads using SolidWorks simulations. The proposed robotic arm is based on a four degrees of freedom (4-DoF) design with the gripper having an opening range of 5.5 to 12 cm depending upon the task to handle various objects. The robotic arm is installed on an expandable vertical mobile platform that enables an expanded operational workspace with lower energy consumption. The total weight of the robotic arm is 60 kgs housing a payload capacity ranging between 8 and 10 kgs in accordance with industry-standard load-bearing specifications. The inverse kinematic algorithm using DH-table is computed with an accuracy of up to 95 % and target height of gripper is cross checked by mounting the ultrasonic sensor at the base of robotic arm. The YOLOv8 algorithm is used for object detection using the depth sense camera having angle range of 0.3 m to 3 m with the precision of up to 96 %. The time needed for recognition of fruits and pitching was about 15 s, with a success rate of up to 90 %. This research produces a low-cost and efficient solution for stallholders by developing a fully automated robotic arm for fruit picking. It optimizes agricultural productivity through improved mechanical design, sensor accuracy, and object detection which reduces labor costs by increasing efficiency of semi-automated farming systems. Further research is required to optimize the overall weight of the system and automation of the movement of robotic arm system between the consecutive rows to build a fully autonomous fruit picking operation in hydroponics.

The evolution of the steep unstable slopes is influenced by landslides as a major geomorphological feature in the Kashmir region. The NW-SE–oriented landslide has affected the village of Birpani and the main Sudhan Gali Road. The Lower Sudhan Gali (LSG) landslide was studied using a combined use of ground-based remote sensing (Electrical Resistivity Tomography (ERT)) and space-borne remote sensing (Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)) measurements, geotechnical and field surveys. PSInSAR data highlighted the parts of the landslide that are currently active as well as the immediate surrounding area experiencing uplift and subsidence phenomenon. ERT imaging has delineated the geometry and subsurface structural features of the LSGL. Geotechnical results indicated that the landslide material had a low to medium plasticity index, with a higher clay fraction that may have reduced its shear strength. Finally, the characteristics of this mass movement were observed during the geological field by analyzing the geomorphological features and the lithological units. The interplay among rainfall and close proximity of a major fault may have contributed to the landslide kinematics. The results provided important on- and near-ground surface information for effective planning interventions that can help with hazard mitigation.

Traditionally constructed wetlands face significant limitations in treating tailwater from wastewater treatment plants, especially those associated with sugar mills. However, the advent of novel modified surface flow constructed wetlands offer a promising solution. This study aimed to assess the microbial community composition and compare the efficiencies of contaminant removal across different treatment wetlands: CW1 (Brick rubble, lignite, and Lemna minor L.), CW2 (Brick rubble and lignite), and CW3 (Lemna minor L.). The study also examined the impact of substrate and vegetation on the wetland systems. For a hydraulic retention time of 7 days, CW1 successfully removed more pollutants than CW2 and CW3. CW1 demonstrated removal rates of 72.19% for biochemical oxygen demand (BOD), 74.82% for chemical oxygen demand (COD), 79.62% for NH4+-N, 77.84% for NO3−-N, 87.73% for ortho phosphorous (OP), 78% for total dissolved solids (TDS), 74.1% for total nitrogen (TN), 81.07% for total phosphorous (TP), and 72.90% for total suspended solids (TSS). Furthermore, high-throughput sequencing analysis of the 16S rRNA gene revealed that CW1 exhibited elevated Chao1, Shannon, and Simpson indices, with values of 1324.46, 8.8172, and 0.9941, respectively. The most common bacterial species in the wetland system were Proteobacteria, Spirochaetota, Bacteroidota, Desulfobacterota, and Chloroflexi. The denitrifying bacterial class Rhodobacteriaceae also had the highest content ratio within the wetland system. These results confirm that CW1 significantly improves the performance of water filtration. Therefore, this research provides valuable insights for wastewater treatment facilities aiming to incorporate surface flow-constructed wetland tailwater enhancement initiatives.

Objective: To assess the efficacy of Intra-operative Intravitreal Bevacizumab in treating Diabetic Macular Edema in patients undergoing Phacoemulsification with Foldable intraocular lens (IOL) Implantation, in comparison to a control group. Methodology: This experimental study was performed in department of Ophthalmology, Madinah Teaching Hospital, Faisalabad from 5th October to 2022 – 5th March 2023. Sixty patients (30 in each group) were enrolled in the study. Patients in group “A” received Intravitreal Bevacizumab immediately after phacoemulsification with foldable IOL implantation.Whereas Intravitreal Bevacizumab was not given to patients in group B (control group). Best Corrected Visual Acuity (BCVA) and macular thickness using optical coherence tomography (OCT) were recorded within a timeframe of 7-30 days prior to surgery, as well as at 1 and 3 months after the surgery for comparison between the two study groups. Results: The study comprised 60 patients, with a mean age of 56.43 ± 5.90 years, representing both genders. Among the participants, males were predominant, accounting for 32 (53.3%) individuals, while females constituted 28 (46.7%) of the sample. The baseline best-corrected visual acuity (BCVA) measurements between group A and group B were 0.22 ± 0.68 and 0.21 ± 0.07, respectively. There was no statistically significant difference observed between the two groups (P = 0.677). However, significant disparities in BCVA were observed after one- and three-months post-surgery, favoring group A.Similarly, mean optical coherence tomography (OCT) at baseline was 271.09 ± 26.87 in group A and 307.57 ± 32.81 in group B, with no significant difference (P = 0.771). Nonetheless, significant differences in OCT were noted after one- and three-months post-surgery, with group A demonstrating superior results. These findings underscore the beneficial impact of intra-operative Bevacizumab in improving both BCVA and OCT outcomes post-surgery. Conclusion: The administration of intravitreal bevacizumab during phacoemulsification has been shown to yield notable improvements in Best Corrected Visual Acuity (BVCA) and Optical Coherence Tomography (OCT) among those with diabetes with macular edema who are undergoing cataract surgery.

The present study was carried out at the Plant Pathology Hafizabad Research Station, the University of Layyah, during the crop seasons 2021–2022 and 2022–2023 to evaluate the response of various wheat genotypes against leaf rust severity (%), environmental conditions favourable for disease development and grain yield. Except for minimum temperature and minimum relative humidity, which had a negative association with disease development, there was a significant correlation between leaf rust severity (%) and all environmental conditions such as maximum temperature, maximum relative humidity, rainfall, and wind speed. All epidemiological variables such as maximum temperature, minimum temperature, minimum relative humidity, rainfall and wind speed significantly affect the disease progression. The disease predictive model accounted for 48–69 % variability in leaf rust severity. The model performance was evaluated using the coefficient of determination (R2 = 0.69) and RMSE, both demonstrated acceptable predictive results for leaf rust severity (%) management. Leaf rust severity (%) increased with an increase in maximum temperature (17.8–30 °C), maximum relative humidity (76.3–85 %), rainfall (2.2–10.85 mm) and wind speed 1.1–2.7 km/h and decreased with the increase of minimum temperature (7.91–16.71 °C) minimum relative humidity (47.15–56.45 %) during both rating seasons 2021–2022 and 2022–2023. The single and two applications of fungicides at the Zadok's scale 3, ZS 4.3, and ZS 5.4 stages led to a significant reduction in grain yield losses caused by leaf rust severity (%) in both the 2021–2022 and 2022–2023 crop seasons. Single and two sprays of prothioconazole, were found to be the first choice among all treatments to reduce the disease severity and increase grain production and maximum gross revenue (513.1–777.8$/ha), as compared to followed by single and two sprays of propiconazole (Progress), tebuconazole + trifloxystrobin, tebuconazole, bixafen + tebuconazole, and propiconazole (Tilt), respectively. These findings recommend the involvement of genotype resistance and weather predictors in wheat leaf rust development, along with fungicide application studies, to improve the predictability of host resistance to disease, future models, and the sustainability of disease control methods.

Background & Objective: Benign prostatic hyperplasia (BPH) is a prevalent condition in older males, primarily responsible for lower urinary tract symptoms (LUTS). Transurethral resection of the prostate (TURP), a frequently utilized surgical procedure for BPH management, has mixed effects on erectile function (EF). The objective of this study is to evaluate the changes in EF following TURP in patients with BPH. Methodology: This descriptive study was conducted at Bilawal Medical College for Boys, LUMHS Jamshoro, involving 261 male subjects between the ages of 50 and 70 who underwent TURP. Evaluation of EF was carried out before and after the surgical procedure using the International Index of Erectile Function (IIEF) scoring system. RESULTS: The average age of the participants was 60.47 years, and the mean duration of BPH prior to surgery was 15.83 months. The mean IIEF score prior to surgery was 16.72, which notably decreased to 11.50 after surgery. This decline was statistically significant (p < 0.001), and this trend remained consistent across variables such as age, duration of BPH, and prostate volume. Conclusion: The study concludes that TURP significantly correlates with a decline in EF among patients with BPH. These outcomes highlight the necessity for comprehensive preoperative counseling about the potential impacts on sexual health for patients considering TURP.

Alopecia Areata is an autoimmune disorder where the body’s immune system attacks normal cells instead of intruders, leading to hair loss. If not detected early, it can progress to complete scalp baldness (Alopecia Totalis) or total body hair loss (Alopecia Universalis). Therefore, early detection of Alopecia Areata is crucial. Computer vision and deep learning techniques have been used for the last few years in the field of dermatology to detect different relevant diseases. We proposed a robust feature fusion technique, named AlopeciaDet for the timely detection of Alopecia Areata using camera images instead of dermoscopic images that require specialized equipment. AlopeciaDet combines Corner Rhombus Shape HOG (CRSHOG) features with those extracted from the ResNet-50 pre-trained model to detect Alopecia Areata with high accuracy by using the Dermenet dataset. The geometric properties of rhombus shapes make them useful for recognizing patterns in an image. HOG captures local object appearance and shape by computing the distribution of intensity gradients in localized portions of the image. We combined these characteristics into CRSHOG. Alopecia Areata, on the other hand, is characterized by distinctive patterns and shapes of hair loss, with the most common feature being round or oval-shaped patches. These patches can vary in size and usually have well-defined, sharp edges. Consequently, our proposed CRSHOG significantly improves the extraction of local information from images of affected areas. It achieves this by integrating sign and magnitude data, thereby enhancing discrimination capabilities for texture classification tasks. Finally, the magnitudes and directions of these pixel values are calculated. We achieved an accuracy of 99.45% with an error rate of 0.55% using Artificial Neural Network. These results surpass the accuracy of current state-of-the-art techniques in this field.

Healthcare management is a major application of future Internet of Things (IoT)-based smart cities. The healthcare applications rely on the use of Internet of Things (IoT) devices, which consist of sensor networks with diverse communication infrastructure. This makes it more vulnerable and increases the malicious attacks resulting in compromised Quality of Service (QoS) and anomalies in data transmission resulting in transmission delay of the legitimate nodes in the network. This may cause life threats in the healthcare system as critically ill patients’ data needs to be delivered to the centralized healthcare centre of the smart cities. In this work, a Trust-based Improved QoS for Health Care System (TIHCS) is proposed. TIHCS offers a Time Division Multiple Access (TDMA) based slots allocation mechanism for legitimate nodes by introducing a malicious detection mechanism. In addition, it proposes a mechanism based on the Intelligent Reflecting Surfaces (IRS) to restrict the communication of the specific area where a malicious node is present and allow legitimate nodes of the affected area through relay node selection by proposing a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The simulation results show that TOPSIS-based relay selection in TIHCS offers a better secrecy sum rate as compared to the other three methods. Furthermore, the proposed trust-supported TDMA-based Media Access Control (MAC) protocol offers improved data transmission of the most sensitive data traffic, by allowing more highly sensitive data nodes to transmit their data as compared to the well-established standards such as First Come First Serve, Round Robin, Shortest Job First, and Longest Job First.

Multiprocessor System-on-Chips (MPSoCs) computing architectures are gaining popularity due to their high-performance capabilities and exceptional Quality-of-Service (QoS), making them a particularly well-suited computing platform for computationally intensive workloads and applications. Nonetheless, The scheduling and allocation of a single task set with precedence restrictions on MPSoCs have presented a persistent research challenge in acquiring energy-efficient solutions. The complexity of this scheduling problem escalates when subject to conditional precedence constraints between the tasks, creating what is known as a Conditional Task Graph (CTG). Scheduling sets of Periodic Conditional Task Graphs (PCTGs) on MPSoC platforms poses even more challenges. This paper focuses on tackling the scheduling challenge for a group of PCTGs on MPSoCs equipped with shared memory. The primary goal is to minimize the overall anticipated energy usage, considering two distinct power models: dynamic and static power models. To address this challenge, this paper introduces an innovative scheduling method named Energy Efficient Successor Tree Consistent Earliest Deadline First (EESEDF). The EESEDF approach is primarily designed to maximize the worst-case processor utilization. Once the tasks are assigned to processors, it leverages the earliest successor tree consistent deadline-first strategy to arrange tasks on each processor. To minimize the overall expected energy consumption, EESEDF solves a convex Non-Linear Program (NLP) to determine the optimal speed for each task. Additionally, the paper presents a highly efficient online Dynamic Voltage Scaling (DVS) heuristic, which operates in O(1) time complexity and dynamically adjusts the task speeds in real-time. We achieved the average improvement, maximum improvement, and minimum improvement of EESEDF+Online-DVS 15%, 17%, and 12%, respectively compared to EESEDF alone. Furthermore, in the second set of experiments, we compared EESEDF against state-of-the-art techniques LESA and NCM. The results showed that EESEDF+Online-DVS outperformed these existing approaches, achieving notable energy efficiency improvements of 25% and 20% over LESA and NCM, respectively. Our proposed scheduler, EESEDF+Online-DVS, also achieves significant energy efficiency gains compared to existing methods. It outperforms IOETCS-Heuristic by approximately 13% while surpassing BESS and CAP-Online by impressive margins of 25% and 35%, respectively.

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.

Introduction: Ventricular septal defect (VSD) is a severe complication following acute myocardial infarction (MI) resulting from mechanical disruption of the interventricular septum due to extensive myocardial necrosis. Despite advances in management, the mortality rate approaches 50%. We report a case of a 58-year-old male with VSD following MI who was successfully treated with a delayed surgical approach after haemodynamic support using Impella. Case description: A 58-year-old man with type 2 diabetes mellitus and hypertension presented with three days of chest pain. Testing revealed late presenting acute anterior ischaemic infarction and left-to-right shunt in the apical ventricular septum. Urgent cardiac catheterisation showed near-total occlusion of the left anterior descending artery. An Impella CP® was placed before angioplasty with a drug-eluting stent to optimise haemodynamics. After a multidisciplinary discussion, the Impella CP® was upgraded to Impella 5.5®, and surgery was delayed allowing for scar formation. The patient remained in the intensive care unit, where he underwent physical therapy, showing improvements in exercise tolerance by the time of surgery. He underwent a left ventriculotomy with a successful repair via an endocardial patch 28 days after initial presentation. Post-operative recovery was uneventful, with the patient discharged five days later, reporting no physical limitations one month post-discharge. Conclusion: The successful management of VSD post-MI relies on interdisciplinary collaboration, careful timing of surgical intervention and the strategic use of mechanical support devices such as the Impella. This case highlights the potential for favourable outcomes when tailored treatment approaches are employed.

This study aimed to identify efficient Trichoderma isolate(s) for the management of Fusarium wilt in peas. Four different pea germplasms (Sarsabz, Pea-09, Meteor and Supreme) were evaluated for resistance against Fusarium oxysporum in pot assay. Resistant germplasm exhibits a varying range of disease severity (23%) and percent disease index (21%), whereas susceptible and highly susceptible germplasm exhibit maximum disease severity (44–79%) and percent disease index (47–82%). The susceptible germplasm Meteor was selected for in vivo experiment. Five different Trichoderma spp. (Trichoderma koningii, T. hamatum, T. longibrachiatum, T. viride, and T. harzianum) were screened for the production of hydrolytic extracellular enzymes under in vitro. In-vitro biocontrol potential of Trichoderma spp. was assayed by percentage inhibition of dry mass of Fusarium oxysporum pisi (FOP) with Trichoderma spp. metabolite filtrate concentrations. Maximum growth inhibition was observed by T. harzianum (50–89%). T. harzianum metabolites in filtrate conc. (40%, 50%, and 60%) exhibited maximum reduction in biomass and were thus used for in vivo management of the disease. The pot experiment for in-vivo management also confirmed the maximum inhibition of FOP by T. harzianum metabolites filtrate at 60% by reducing disease parameters and enhancing growth, yield, and physiochemical and stress markers. Trichoderma strains led to an increase in chlorophyll and carotenoids (34-26%), Total phenolic 55%, Total protein content 60%, Total Flavonoid content 36%, and the increasing order of enzyme activities were as follows: CAT > POX > PPO > PAL in all treatments. These strains demonstrate excellent bio-control of Fusarium wilt in pea via induction of defense-related enzymes. The present work will help use Trichoderma species in disease management programme as an effective biocontrol agent against plant pathogens.

Concrete is utilized in a wide range of civil engineering applications specifically in infrastructure projects. In general, as with any construction material, it may be subjected to deterioration over time because of various reasons such as excessive loading and so on. In this research, two reinforced concrete beams on a large scale (length 2400 mm, depth 350 mm, and width 250 mm) are cast and tested under static load using the ultrasonic pulse velocity (UPV) technique consisting of three pairs of transducers (54 kHz, 150 kHz, and 250 kHz). During the loading, the signals are sent and captured through the used transducers at selected loading steps. Two new proposed procedures based on signal peaks in time and frequency domains are used to monitor the crack progress induced in concrete beams under concentrated load. The findings of this study revealed the suitability of the proposed two approaches to detect the propagation of cracks to evaluate damage induced in concrete beams.

This paper proposes a supervised learning based cell selection/reselection prediction technique incorporating ensembles, and is reinforced by readily available lasso and ridge regression for regularization. This data-driven approach replaces event-driven cell selection/reselection requests and helps in solving complex calculations for generalizing the loss function. The proposed work uses the term “cell selection for affluence'' to refer to both cell selection and reselection. In technical terms, handovers are reserved for calls, and cell selections are for data services. This study highlights that by exploiting advance information about the success or failure of cell selection request, how a data-driven cell selection can improve the cell selection success rate of future mobile networks. Our results demonstrate, the proposed algorithm can improve cell selection success rate by up to 4% and quickens model response time by up to 602 ms.

In spite of achieving human-level efficacy in gland and nuclei segmentation, modern deep learning-driven techniques still face challenges related to the loss of regional context information and disregard the importance of long-range semantic information during the optimization. Furthermore, one typical issue with CNN-based approaches is also their lack of clinical explainability. In such context, we propose an explainable multitask learning network (EMTL-Net) to enhance the segmentation quality of gland and nuclei segmentation via histopathology images. Initially, to obtain better feature maps, boost the network growth, and reduce the computational cost in the encoding path, we introduce the tweaked residual module (TRM) in which the diagnosis information of histopathology images structure will not be overlooked. After that, we design the two modules to address the challenges of regional context information, such as the complementary feature detection module (CFDM) for the intermediate layers and the cascade feature module (CFM) for the deeper layer of the encoder block. We also design a competitive decoder by casting an efficient pyramid split attention (EPSA) unit that preserves long-range semantic information, stimulates robust features, and dynamically recalibrates spatial and channel-wise features. Extensive experimental findings on real-world datasets, including Glas, MoNuSeg and TNBC, reveal that our proposed model has achieved a Dice Coefficient and Intersection over Union of 0.923 and 0.869 on Glas, 0.855 and 0.741 on TNBC, and 0.829 and 0.713 on MoNuSeg when compared with the SOTA approaches for gland and nuclei segmentation. In addition, the suggested EMTL-Net is also examined for paying full attention on the regions of the gland and nuclei to meet the needs of clinical scenarios.

The rising awareness of cybersecurity among governments and the public underscores the importance of effectively managing security incidents, especially zero-day attacks that exploit previously unknown software vulnerabilities. These zero-day attacks are particularly challenging because they exploit flaws that neither the public nor developers are aware of. In our study, we focused on dynamic application security testing (DAST) to investigate cross-site scripting (XSS) attacks. We closely examined 23 popular WordPress plugins, especially those requiring user or admin interactions, as these are frequent targets for XSS attacks. Our testing uncovered previously unknown zero-day vulnerabilities in three of these plugins. Through controlled environment testing, we accurately identified and thoroughly analyzed these XSS vulnerabilities, revealing their mechanisms, potential impacts, and the conditions under which they could be exploited. One of the most concerning findings was the potential for admin-side attacks, which could lead to multi-site insider threats. Specifically, we found vulnerabilities that allow for the insertion of malicious scripts, creating backdoors that unauthorized users can exploit. We demonstrated the severity of these vulnerabilities by employing a keylogger-based attack vector capable of silently capturing and extracting user data from the compromised plugins. Additionally, we tested a zero-click download strategy, allowing malware to be delivered without any user interaction, further highlighting the risks posed by these vulnerabilities. The National Institute of Standards and Technology (NIST) recognized these vulnerabilities and assigned them CVE numbers: CVE-2023-5119 for the Forminator plugin, CVE-2023-5228 for user registration and contact form issues, and CVE-2023-5955 for another critical plugin flaw. Our study emphasizes the critical importance of proactive security measures, such as rigorous input validation, regular security testing, and timely updates, to mitigate the risks posed by zero-day vulnerabilities. It also highlights the need for developers and administrators to stay vigilant and adopt strong security practices to defend against evolving threats.

Objective: To determine in vitro efficacy of Imipenem/Relebactam against multi-drug resistant strains. Study Design: Cross-sectional study. Place and Duration of Study: Department of Microbiology, Fauji Foundation Hospital (FFH) Rawalpindi, Pakistan from Jul to Dec 2022. Methodology: All specimens from indoor and outdoor patients with infection due to Gram-negative organisms showing resistance to Imipenem disc (10μg) were included. Cultures were inoculated on Blood and MacConkey’s agar. Imipenemresistant Gram-negative species identification was made by Gram stain, Oxidase test and commercially available identification strips Analytical profile index 10S (API-10S) (from bioMerieux, Inc.).Imipenem-resistant species were identified, showing a zone diameter of less than 23mm against10µg Imipenem disc for Enterobacterale and Non-Enterobacterales, and for Pseudomonas species, zone size is ≤19mm. These resistant organisms were tested for sensitivity against Imipenem/Relebactam by E-strip (MTSTM –MIC Test Strip) (Liofilchem, Inc., Waltham, MA). Results: Out of 160 Carbapenem-resistant Enterobacterale and Non-Enterobacterales specimens, only 14 isolates (including 02 Pseudomonas spp.) showed susceptibility to Imipenem/Relebactam combination of drug. US-FDA Imipenem/Relebactam susceptibility interpretive criteria (STIC) was used. Conclusion: In our setup, only 8.75% isolates showed sensitivity to this newer drug combination Imipenem/Relebactum. This highlights the importance of evaluating newer drugs in our population before using them empirically.

Vegetables are important agricultural products with numerous health benefits. Excessive chemical fertilization to meet the food challenge has resulted in environmental and soil health hazards. Considering this aspect, the current study was conducted with the aim to introduce biostimulants as an alternative to chemical fertilizers to improve soil quality, crop quality, and yield. In the first experiment, the response of radish was noted against the application of glycine (GLY), aspartic acid (ASP), lysine (LYS), vitamin B complex (VBC), and chemical fertilizers (CF). The biostimulants were sourced from Sigma Aldrich and Martin Dow Market Ltd. The results indicated that ASP has significantly improved the phenolic contents in shoot (1.01%) and root (12.23%) compared with CF. Additionally, total protein was significantly increased in shoot with GLY (251.81%) and in root with ASP (57.06%). Shoot ascorbic acid contents were significantly improved with ASP (179.90%), VBC (159.91%), and LYS (139.92%). Plant fresh and dry weight was improved with VBC (478.31%) and ASP (364.73%). The N and P concentrations in radish root were higher in VBC (25.93%) and LYS (100%). Soil organic matter was improved ASP (61.51%), followed by VBC (60.13%). Soil available P concentration was also enhanced with LYS (40.43%), ASP (31.20%), and VBC (23.19%). The second experiment was focused on identifying the response of turnip crop against the following treatments: chemical fertilizers (CF), Isabion® (ISA), 25% CF + LYS + GLY (CLG), 25% CF + ASP + GLY (CAG), and 25% CF + ASP + LYS (CAL). The biostimulants were sourced from Sigma Aldrich Syngenta, Pakistan. The results denoted that CAL and ISA significantly improved the phenolic contents in turnip shoot and root. The ascorbic acid in turnip shoot was improved with CAL (19.27%), CAG (18.13%), ISA (17.68%), and in root with CLG (26.96%). The P concentration in turnip shoot was significantly higher in ISA (19.25%), CLG (16.85%), and CAG (12.26%). Soil total N was improved in all treatments. ISA improved the available P concentration, whereas CF (67.87 mg kg−1) followed by ISA (65.93 mg kg−1) improved the soil available K. Both studies conclude that biostimulants capable of improving vegetable quality.

Background Knowledge about multisystem inflammatory syndrome in children (MIS-C) is evolving, and evidence-based standardised diagnostic and management protocols are lacking. Our review aims to summarise the clinical and diagnostic features, management strategies and outcomes of MIS-C and evaluate the variances in disease parameters and outcomes between high-income countries (HIC) and middle-income countries (MIC).Methods We searched four databases from December 2019 to March 2023. Observational studies with a sample size of 10 or more patients were included. Mean and prevalence ratios for various variables were pooled by random effects model using R. A mixed generalised linear model was employed to account for the heterogeneity, and publication bias was assessed via funnel and Doi plots. The primary outcome was pooled mean mortality among patients with MIS-C. Subgroup analysis was conducted based on the income status of the country of study.Results A total of 120 studies (20 881 cases) were included in the review. The most common clinical presentations were fever (99%; 95% CI 99.6% to 100%), gastrointestinal symptoms (76.7%; 95% CI 73.1% to 79.9%) and dermatological symptoms (63.3%; 95% CI 58.7% to 67.7%). Laboratory investigations suggested raised inflammatory, coagulation and cardiac markers. The most common management strategies were intravenous immunoglobulins (87.5%; 95% CI 82.9% to 91%) and steroids (74.7%; 95% CI 68.7% to 79.9%). Around 53.1% (95% CI 47.3% to 58.9%) required paediatric intensive care unit admissions, and overall mortality was 3.9% (95% CI 2.7% to 5.6%). Patients in MIC were younger, had a higher frequency of respiratory distress and evidence of cardiac dysfunction, with a longer hospital and intensive care unit stay and had a higher mortality rate than patients in HIC.Conclusion MIS-C is a severe multisystem disease with better mortality outcomes in HIC as compared with MIC. The findings emphasise the need for standardised protocols and further research to optimise patient care and address disparities between HIC and MIC.PROSPERO registration number CRD42020195823.

This study investigates the significance of self-management in academic staff stress management related to social networking sites (SNS). It emphasizes particularly on reducing social media exhaustion and increasing job effectiveness. The research applies the stressor-strain-outcome theory and the Smart PLS (partial least squares) analytical approach to examine data from 391 respondents. The study’s goal is to provide empirical data on the efficacy of self-control management in reducing SNS stress and its effects on academic staff’s psychological wellbeing and job performance. Data is collected by survey using online email platforms among academic employees, and the collected data is examined utilizing the Smart PLS approach. This approach allows for an investigation of the proposed links and their statistical importance. This research’s ramifications are important for academic institutions since its results can help academic personnel effectively cope with SNS-related stress. Academic employees can better limit their SNS usage and avoid social media tiredness by promoting self-control management practices. As a result, academic employees’ job performance and overall wellbeing may increase. The study’s findings help to comprehend how self-management might reduce SNS stress and improve staff performance in the academic sector.

Background: Proper information about the anatomy of the maxillary sinus is required to avoid any unexpected complications that may arise due to the close relation between the maxillary sinus and posterior superior alveolar artery. Objective: To the current study used cone beam computed tomography CBCT imaging to assess the position of the posterior superior alveolar artery (PSAA) in relationship to the alveolar ridge and the flour of maxillary sinus. Patients and Methods: A number of 95 Iraqi patients participated in this prospective study (53 females, 42 males; age range 20-49 years). From January 2021 to February 2022, attended a 2nd specialised dentistry institution in Baquba city for CBCT scanning for numerous diagnostic and management purposes. Results: According to our study, we detected the presences of artery in (83.68%) of the sample. females showed higher PSAA prevalence than men on both sides, and the difference was significant overall, Also, the existence of artery for each side and total in the intramembranous locating in females is greater than that in males, which may demonstrate that the probability of Bleeding and other side effects is higher in males, since the existence of artery in the intramembranous area in females will reduce the likelihood of traumatic injury throughout any surgical treatment. Conclusion: This research used CBCT to determine the precise location of PSAA in the Iraqi population. This data could assist in decreasing the likelihood of hemorrhage as well as other complications which may happen throughout any surgical treatment, such as dental implant placement, ridge expansion.As well as other surgical interventions in this region.

Heat sinks are a type of heat exchanger that is utilized to cool electronic devices. The study was focused on improving the hydrothermal performance of multi-mini-channel (MMCHS) heat sinks by tackling the issue of flow maldistribution through the amelioration of flow distribution. A numerical simulation was conducted to examine the hydrothermal performance of innovative heat sink arrangements, which included square fins and square pin fins. The finite element method was used to solve partial differential equations for laminar flow simulations. The investigation was executed in an environment of a single-phase flow, and water was employed as a coolant. The Stationary study was used when variables do not change over time. The mass flow rate spanned from 0.0021 to 0.0045 kg s−1. The data obtained from the experiment demonstrates that the altered channels exhibit enhanced heat transfer in contrast to the conventional rectangular channels. Increasing the mass flow rate leads to an increase in the average Nusselt number, pressure drop, and performance index. While reducing the average thermal resistance. Out of all the novel configurations tested, the heat sink with square fins and square pin fins multi-mini-channel (SSPMMCHS) demonstrated the most significant improvement in the Nusselt number, more than double in comparison with the conventional design. The average thermal resistance of the SSPMMCHS design was 0.1052, compared to 0.2008 for the conventional rectangular channel. The MMCHS achieved the best performance index, with a value of 2.21, which was accomplished by the SSPMMCHS.

Background Acute kidney injury (AKI) and chronic kidney disease CKD cause different GFR declines (AKI). A gradual, irreversible decline in GFR is a CKD symptom. Diseases impair the kidneys’ ability to concentrate or dilute tubular filtrate, eliminate nitrogenous waste, and maintain acid-base balance. Acute kidney failure (AKF) is a sudden, reversible renal function loss requiring RRT. Chronic kid- ney disease has a 13.4% global prevalence and 1.2 million annual deaths. The Aim Serum creatinine, urea, potassium, chloride, calcium, sodium, and serum uric acid in CKD patients and healthy controls, and how these indicators vary with CKD development. Determination of some trace elements such as copper, zinc, and iron in the sera of patients. Materials and Methods The study included (30 healthy participants) and (60) patients with CKD (control). The participants in this study were people who traveled to Basrah Teaching Hospital aged 25 to 65. From Octo- ber 2020 to February 2021, specialized physicians examined each participant in this study at Basrah Teaching Hospital. Serum potassium, sodium, chloride, calcium, phosphorus, urea, uric acid, and serum creatinine were measured using kits. Results The current study results have shown a signif- icant increase (P < 0.01) in the following biomarkers in CKD patients: serum creatinine, urea, phosphorus, chlo- ride, uric acid, and Cystatin-C. The results also showed a significant decrease in levels following biomarkers in CKD patients: serum calcium and sodium. Conclusion Chronic renal disease raises serum creati- nine, urea, potassium, chloride, and uric acid. Chronic renal disease patients had lower calcium and sodium levels.

Article accepted. The article will be published here soon. Pending final quality checks.

One of the most widespread metabolic diseases, Type-2 Diabetes Mellitus (T2DM) is defined by high blood sugar levels brought on by decreased insulin secretion, reduced insulin action, or both. Due to its cost-effectiveness and eco-friendliness, plant-mediated green synthesis of nanomaterials has become more and more popular. The aim of the study is to synthesize AgNPs, their characterizations and further in-vitro and in-vivo studies. Several methods were used to morphologically characterise the AgNPs. The AgNPs were crystalline, spherical, and clustered, with sizes ranging from 20 to 50 nm. AgNPs were found to contain various functional groups using Fourier transform infrared spectroscopy. This study focuses on the green-synthesis of AgNPs from Fagonia cretica (F. cretica) leaves extract to evaluate their synthesized AgNPs for in-vitro and in-vivo anti-diabetic function. For the in-vivo tests, 20 male Balb/C albino-mice were split up into four different groups. Anti-diabetic in-vivo studies showed significant weight gain and a decrease in all biochemical markers (pancreas panel, liver function panel, renal function panel, and lipid profile) in Streptozotocin (STZ)-induced diabetic mice. In vitro anti-diabetic investigations were also conducted on AgNPs, comprising α-amylase, α-glucosidase inhibitions, and antioxidant assays. AgNPs showed antioxidant activity in both the DPPH and ABTS assays. The research showed that the isolated nanoparticles have powerful antioxidant and enzyme inhibitory properties, especially against the main enzymes involved in T2DM.

Abstract Understanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies.

The present experiment was conducted to evaluate the palatability, growth and economics of the silkworm pupae meal (SWPM) feeding, replacing fish meal (FM) in cattle calves. Crossbred cattle calves (15) around one-month of age were selected for the study and were divided into three equal groups (n=5) viz., T0 (100% FM), T1 (75% FM: 25% SWPM) and T2 (50% FM: 50% SWPM). To estimate the growth parameters fortnightly, a growth trial of 90 days was conducted, and a digestibility trial of 7 days was conducted at the end of experiment. The results revealed non-significant differences between the experimental groups in DMI, OMI, periodical body weight, body weight gain, average daily gain (ADG) (g/d), feed conversion ratio (FCR), feed conversion efficiency (FCE) (%), digestibility coefficients of dry matter (DM), organic matter (OM) and crude protein (CP). Significant differences were observed in digestibility coefficients of crude fibre (CF), ether extract (EE) and nitrogen free extract (NFE) between the groups. The digestible nutrients intake revealed significant difference only in CPI (g/d) with higher values in T0 followed by T1 and T2, but non-significant differences were observed in DDMI, DCPI and TDNI. The economics of feeding showed a decrease in the feed cost and feeding cost per kg body weight gain with increased inclusion of SWPM. Thus, it is concluded that costly FM can be replaced upto 50% level by SWPM without any adverse effect on feed intake, growth and nutrient utilization of crossbred cattle calves.

Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to adversarial attacks, which can make the DL techniques perform poorly. Developing such attacks and their countermeasures is the prerequisite for making artificial intelligence techniques robust, secure, and deployable. Previous survey papers only focused on one or two techniques and are outdated. They do not discuss application domains, datasets, and testbeds in detail. There is also a need to discuss the commonalities and differences among DL techniques. In this paper, we comprehensively discussed the attacks and defenses in four popular DL models, including DNN, DRL, FL, and TL. We also highlighted the application domains, datasets, metrics, and testbeds in these fields. One of our key contributions is to discuss the commonalities and differences among these DL techniques. Insights, lessons, and future research directions are also highlighted in detail.

Abstract In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.

Abstract Background: Worldwide, esophageal cancer (EC) is a common cancer in term of incidence and mortality and is the 4th common cancer in Afghanistan. Current study aimed to evaluate the profile of risk factors for EC among patients diagnosed at tertiary level in Afghanistan. Methodology: A descriptive cross-sectional study was carried out between January 2019 up to February 2021 including all esophageal cancers diagnosed at pathology department of French Medical Institute for Mothers and Children, Afghanistan. Result: 240 diagnosed cases were analyzed, in which 59.40% of squamous cell carcinoma and 41.07% Adenocarcinoma. Both histopathological type of were predominantly diagnosed in males. The majority of the patients were residents of rural areas. More than 80% of the patients were illiterate with only less than 2% completing higher education. Majority of the patients were laborers and farmers while less than 10% were employed. According to income assessment, more than 80% were from low-income household, the rest from middle-income and none from high-income family. Oral snuff consumption was noted in 33.9% of squamous cell carcinoma patients and 40% adenocarcinoma patients whereas, family history of esophageal cancer was observed in 37.8% and 36.7% in both types of carcinomas, respectively. More than 60% of both types of carcinomas patients were hot tea drinkers. Conclusion: Current study demonstrated that most patients diagnosed with esophageal cancers were male, uneducated, belongs to low-income groups, lives in rural areas. These findings suggest distribution of esophageal cancer in specific socioeconomic groups, clearly demonstrating the need further analytical study.

Abstract Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.

ABSTRACT The oomycete Pythium oligandrum is a potential biocontrol agent to control a wide range of fungal and oomycete-caused diseases, such as Pythium myriotylum-caused rhizome rot in ginger, leading to reduced yields and compromised quality. Previously, P. oligandrum has been studied for its plant growth-promoting potential by auxin production and induction of disease resistance by elicitors such as oligandrin. Volatile organic compounds (VOCs) play beneficial roles in sustainable agriculture by enhancing plant growth and resistance. We investigated the contribution of P. oligandrum-produced VOCs on plant growth and disease suppression by initially using Nicotiana benthamiana plants for screening. P. oligandrum VOCs significantly enhanced tobacco seedling and plant biomass contents. Screening of the individual VOCs showed that 3-octanone and hexadecane promoted the growth of tobacco seedlings. The total VOCs from P. oligandrum also enhanced the shoot and root growth of ginger plants. Transcriptomic analysis showed a higher expression of genes related to plant growth hormones and stress responses in the leaves of ginger plants exposed to P. oligandrum VOCs. The concentrations of plant growth hormones such as auxin, zeatin, and gibberellic acid were higher in the leaves of ginger plants exposed to P. oligandrum VOCs. In a ginger disease biocontrol assay, the VOC-exposed ginger plants infected with P. myriotylum had lower levels of disease severity. We conclude that this study contributes to understanding the growth-promoting mechanisms of P. oligandrum on ginger and tobacco, priming of ginger plants against various stresses, and the mechanisms of action of P. oligandrum as a biocontrol agent. IMPORTANCE Plant growth promotion plays a vital role in enhancing production of agricultural crops, and Pythium oligandrum is known for its plant growth-promoting potential through production of auxins and induction of resistance by elicitors. This study highlights the significance of P. oligandrum-produced VOCs in plant growth promotion and disease resistance. Transcriptomic analyses of leaves of ginger plants exposed to P. oligandrum VOCs revealed the upregulation of genes involved in plant growth hormone signaling and stress responses. Moreover, the concentration of growth hormones significantly increased in P. oligandrum VOC-exposed ginger plants. Additionally, the disease severity was reduced in P. myriotylum-infected ginger plants exposed to P. oligandrum VOCs. In ginger, P. myriotylum-caused rhizome rot disease results in severe losses, and biocontrol has a role as part of an integrated pest management strategy for rhizome rot disease. Overall, growth enhancement and disease reduction in plants exposed to P. oligandrum-produced VOCs contribute to its role as a biocontrol agent.

Therapeutic moieties derived from medicinal plants as well as plants-based ecofriendly processes for producing selenium nanoparticles have shown great promise in the management of type 2 diabetes mellitus (T2DM). The current study was aimed to assess the anti-diabetic potentials of Fagonia cretica mediated biogenic selenium nanoparticles (FcSeNPs) using in-vitro and in-vivo approaches. The bio-synthesized FcSeNPs were characterized using various techniques including UV–VIS spectrophotometry and FTIR analysis. The in-vitro efficacy of FcSeNPs were assessed against α-glucosidase, α-amylase enzymes as well as the anti-radical studies were performed using DPPH and ABTS free radicals scavenging assays. For in-vivo studies, 20 Male Balb/C albino-mice were randomly divided into 4 groups (n = 5) including normal group, disease group (Diabetic group with no treatment), control group and treatment group (Diabetic group treated with FcSeNPs). Further, biochemistry markers including pancreas, liver, kidney and lipid profile were assessed for all treatment groups. The FcSeNPs exhibited a dose-dependent inhibition against α-amylase and α-glucosidase at 62–1000 µg mL−1 concentration with IC50 values of 92 and 100 µg mL−1 respectively. In antioxidant experiments, the FcSeNPs demonstrated significant radicals scavenging effect against DPPH and ABTS radicals. In STZ-induced diabetic mice, a considerable decline in blood glucose level was observed after treatment with FcSeNPs. Anti-hyperglycemic effect of FcSeNPs treated animals were high (105 ± 3.22**) as compared to standard drug (128.6 ± 2.73** mg dL−1). Biochemical investigations revealed that all biochemical parameters for pancreas, liver function, renal function panel and lipid profile were significantly lowered in FcSeNPs treated animals. Our findings indicate a preliminary multi-target efficacy for FcSeNPs against type-2 diabetes and thus warrant further detailed studies.

Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises such as the global financial crisis of 2007–2009 or the recent crisis caused by COVID-19. To examine the cross-correlation of overall international monetary policies with stock markets, we employ the daily shadow short rate (SSR), which has the advantage of allowing comparison across unconventional and conventional regimes. The analysis is made through a multifractal context using multifractal detrended cross correlation analysis (MF-DXA), considering daily data from 1st January 2000 to 31st March 2022 and country specific SSR and the stock markets of eight developed economies. The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. Lastly, the findings of this study have several important implications for central banks and stock market participants.