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Introduction: Subarachnoid Block (SAB) is a commonly used technique for lower abdominal surgery. Dexamethasone has been proven effective as an additive when used with bupivacaine. However, there has been no study comparing the analgesic efficacy of dexamethasone with levobupivacaine under SAB. Aim: To compare the analgesic efficacy of levobupivacaine with or without dexamethasone in patients scheduled for lower abdominal surgeries. Materials and Methods: A pilot randomised double-blind controlled trial was undertaken in the Department of Anaesthesiology, University College of Medical Sciences Guru Tagh Bahudur (UCMS-GTB) Hospital, New Delhi, India on 60 patients with Americian Soceity of Anaesthesiolgy (ASA) grade I or II, aged 18-65 years, undergoing lower abdominal surgeries under SAB from January 2021 to August 2022. In Group LS, 12.5 mg of 0.5% isobaric levobupivacaine (2.5 mL) with normal saline (1 mL) was administered, and in Group LD, 12.5 mg of 0.5% isobaric levobupivacaine (2.5 mL) with 4 mg dexamethasone (1 mL) was given. Patients were assessed for sensory and motor block, haemodynamic parameters, pain score, and side-effects. Unpaired t-tests/Mann-Whitney U tests were used to determine significance. A p-value of less than 0.05 was considered significant. Results: The two groups were comparable in terms of demographic profile. The age in the LS group was 37.80±9.98 years, and in the LD group, it was 38.63±12.37 years. The male-to-female ratio in the LS and LD groups was 17:13 and 21:9, respectively. The weight range in both groups was between 50 to 67 kg. The Basal Metabolic Index (BMI) was 22.40±2.43 kg/m2 and 21.70±2.21 kg/m2 in the LD and LS groups, respectively. All these parameters, including height, had a p-value of more than 0.005, except for the duration of analgesia. The duration of effective analgesia was also longer in the LD group (247.00±36.69 min) than in the LS group (138.50±22.52 min) with a p-value of

Abstract Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system.

In Ayurveda, every individual is believed to possess a unique entity known as Prakriti, which distinguishes them from others physically, physiologically, and psychologically. This entity also determines an individual's response to a particular stimulus, and it is believed that such responses are not solely determined by genetics. The present research aims to validate the Ayurvedic concept of Prakriti from a modern molecular perspective to strengthen the personalized and precise treatment approach. A study was conducted to investigate the role of the KCNJ11gene in the susceptibility of individuals to type 2 diabetes mellitus (T2DM) with their metabolic status. The research involved allele mining on three major Prakriti groups - Vata, Pitta, and Kapha - in 112 patients with T2DM and 112 healthy individuals. The KCNJ11 gene, responsible for insulin secretion membrane pore formation, was analyzed to determine the susceptibility of different Prakriti types to T2DM. The MutPred tool predicted the molecular cause of disease-related amino acid substitution. According to the study, only Pitta and Kapha Prakriti were diagnosed with diabetes, while all three Prakriti types were present in the control group of healthy individuals. A protein model was prepared, and the changes resulting from mutations were observed for each group in their protein sequence, both as synonymous and non-synonymous mutations. Ultimately, these changes contributed to the manifestation of T2DM. Based on the findings, it appears that Prakriti groups may experience changes in protein function due to nonsynonymous mutations and differences in amino acids at the protein level.

Abstract This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.

Abstract The judiciary is a foundation of democracy, upholding the rule of law and protecting rights. Efficient court administration is vital for public trust, justice, and timely proceedings. Currently, courts face challenges such as inconsistencies, adjournments, and absence of transparency, undermining the justice system. Traditional manual processes and paper-based documentation cause significant backlogs, slow resolutions, and limited public access. As case volumes and complexities rise, modernizing court administration through digital transformation is progressively critical. Currently, many countries are integrating technologies in the courts for its administration and other activities. In recent years, courts and judges have been subjected to pressure to improve performance, uplifting judicial effectiveness to a top priority. Subsequently, several countries have integrated simplification and digitization strategies in judicial initiatives to enhance court efficiency. Switzerland’s Justitia 4.0 and Brazil’s PJE are notable initiatives that focused to strengthen court administration through digitalization. These aspects motivated this study to examine the detailed integration of industry 4.0 technologies such as the Internet of things, cloud computing, blockchain, big data analytics, robotics, drones, Metaverse, digital twins, and artificial intelligence for court administration with digitalized infrastructure. According to the study, integrating these technologies in less complex cases helps minimize expenditures and save time, making to resolve cases conveniently, efficiently, and superiorly. The study also identified the challenges and issues associated with industry 4.0 technologies such as evidence gathering, evidence preservation, robot judges for pre-judgment analysis, and judgment delivery, which future studies need to be explored.

Abstract With an aging population and an increase in chronic sickness, healthcare systems are under increasing pressure. Internet of Things (IoT) technology has recently garnered a lot of interest for its ability to alleviate this burden. The IoT is a network of interconnected wireless digital devices that can gather, transmit, and store data automatically, without the need for human or computer intervention. In order to better anticipate health problems, diagnose, treat, and monitor patients in and out of the hospital, the IoT offers numerous advantages that could streamline and improve health care delivery. As a matter of urgency, decision-makers and heads of state around the world are enacting policies to provide healthcare services through the use of technology. Thanks to recent advancements in the IoT, healthcare has been able to evolve at a rapid pace. Research on Internet IoT uses in healthcare and newly developed emergency equipment are summarized in this article.

Abstract The study addressed the limitations of classical statistical methods when dealing with ambiguous data, emphasizing the importance of adopting neutrosophic statistics as a more effective alternative. Classical methods falter in managing uncertainty inherent in such data, necessitating a shift towards methodologies like neutrosophic statistics. To address this gap, the research introduced a novel sampling approach called “neutrosophic median ranked set sampling” and incorporated neutrosophic estimators tailored for estimating the population mean in the presence of ambiguity. This modification aims to address the inherent challenges associated with estimating the population mean when dealing with neutrosophic data. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Additionally, neutrosophic estimators were developed to leverage auxiliary information within the framework of median-ranked set sampling, enhancing the accuracy of population mean estimation under uncertain conditions. The methods employed involved modifying traditional ranked set sampling techniques to accommodate neutrosophic data characteristics. Bias and mean squared error equations for the suggested estimators were provided, offering insights into their theoretical underpinnings. To illustrate the effectiveness and practical applications of the proposed methodology and estimators, a numerical demonstration and simulation study have been conducted using the R programming language. The key results highlighted the superior performance of the proposed estimators compared to existing alternatives, as demonstrated through comprehensive evaluations based on mean squared error and percentage relative efficiency criteria. The conclusions drawn underscored the effectiveness of the neutrosophic median ranked set sampling approach and suggested estimators in estimating the population mean under conditions of uncertainty, particularly when utilizing neutrosophic auxiliary information and validated real-life applicability. The methodology and estimators presented in the study were shown to yield interval-based results, providing a more realistic representation of uncertainty associated with population parameters. This interval estimation, coupled with minimum mean squared error considerations, enhanced the efficacy of the estimators in determining population mean values. The novelty of the work lies in its introduction of a tailored sampling approach and estimators designed specifically for neutrosophic data, filling a significant gap in the literature. By extending classical statistics to accommodate ambiguity, the study offers a substantial advancement in statistical methodology, particularly in domains where precise data is scarce and uncertainty is prevalent. Furthermore, the empirical validation through numerical demonstrations and simulation studies using the R programming language adds robustness to the proposed methodology and contributes to its practical applicability.

Abstract Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative $${{\ell}}_{1}$$ ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.

Abstract Background Treatment with regorafenib, a multiple-kinase inhibitor, to manage metastatic colorectal cancers (mCRCs) shows a modest improvement in overall survival but is associated with severe toxicities. Thus, to reduce regorafenib-induced toxicity, we used regorafenib at low concentration along with a dual JAK/HDAC small-molecule inhibitor (JAK/HDACi) to leverage the advantages of both JAK and HDAC inhibition to enhance antitumor activity. The therapeutic efficacy and safety of the combination treatment was evaluated with CRC models. Methods The cytotoxicity of JAK/HDACi, regorafenib, and their combination were tested with normal colonic and CRC cells exhibiting various genetic backgrounds. Kinomic, ATAC-seq, RNA-seq, cell cycle, and apoptosis analyses were performed to evaluate the cellular functions/molecular alterations affected by the combination. Efficacy of the combination was assessed using patient-derived xenograft (PDX) and experimental metastasis models of CRC. To evaluate the interplay between tumor, its microenvironment, and modulation of immune response, MC38 syngeneic mice were utilized. Results The combination therapy decreased cell viability; phosphorylation of JAKs, STAT3, EGFR, and other key kinases; and inhibited deacetylation of histone H3K9, H4K8, and alpha tubulin proteins. It induced cell cycle arrest at G0-G1 phase and apoptosis of CRC cells. Whole transcriptomic analysis showed that combination treatment modulated molecules involved in apoptosis, extracellular matrix-receptor interaction, and focal adhesion pathways. It synergistically reduces PDX tumor growth and experimental metastasis, and, in a syngeneic mouse model, the treatment enhances the antitumor immune response as evidenced by higher infiltration of CD45 and cytotoxic cells. Pharmacokinetic studies showed that combination increased the bioavailability of regorafenib. Conclusions The combination treatment was more effective than with regorafenib or JAK/HDACi alone, and had minimal toxicity. A clinical trial to evaluate this combination for treatment of mCRCs is warranted.

Abstract The field of synthetic active matter has, thus far, been led by efforts to create point-like, isolated (yet interacting) self-propelled objects (e.g. colloids, droplets, microrobots) and understanding their collective dynamics. The design of flexible, freely jointed active assemblies from autonomously powered sub-components remains a challenge. Here, we report freely-jointed active polymers created using self-propelled droplets as monomeric units. Our experiments reveal that the self-shaping chemo-hydrodynamic interactions between the monomeric droplets give rise to an emergent rigidity (the acquisition of a stereotypical asymmetric C-shape) and associated ballistic propulsion of the active polymers. The rigidity and propulsion of the chains vary systematically with their lengths. Using simulations of a minimal model, we establish that the emergent polymer dynamics are a generic consequence of quasi two-dimensional confinement and auto-repulsive trail-mediated chemical interactions between the freely jointed active droplets. Finally, we tune the interplay between the chemical and hydrodynamic fields to experimentally demonstrate oscillatory dynamics of the rigid polymer propulsion. Altogether, our work highlights the possible first steps towards synthetic self-morphic active matter.

The Nuclear Factor Y (NF–Y) is one of the widely explored transcription factors (TFs) family for its potential role in regulating molecular mechanisms related to stress response and developmental processes.Finger millet (Eleusine coracana (L.) Gaertn) is a hardy and stress-tolerant crop where partial efforts have been made to characterize a few transcription factors. However, the NF–Y TF is still poorly explored and not well documented. The present study aims to identify and characterize NF–Y genes of finger millet using a bioinformatics approach. Genome mining revealed 57 EcNF-Y (Eleusine coracana Nuclear Factor-Y) genes in finger millet, comprising 18 NF-YA, 23 NF-YB, and 16 NF-YC genes. The gene organization, conserved motif, cis-regulatory elements, miRNA target sites, and three-dimensional structures of these NF-Ys were analyzed. The nucleotide substitution rate and gene duplication analysis showed the presence of 7 EcNF-YA, 10 EcNF-YB, and 8 EcNF-YC paralogous genes and revealed the possibilities of synonymous substitution and stabilizing selection during evolution. The role of NF-Ys of finger millet in abiotic stress tolerance was evident by the presence of relevant cis-elements such as ABRE (abscisic acid-responsive elements), DRE (dehydration-responsive element), MYB (myeloblastosis) or MYC (myelocytomatosis). Twenty-three isoforms of miR169, mainly targeting a single NF–Y gene, i.e., the EcNF-YA13 gene, were observed. This interaction could be targeted for finger millet improvement against Magnaporthe oryzae (blast fungus). Therefore, by this study, the putative functions related to biotic and abiotic stress tolerance for many of the EcNF-Y genes could be explored in finger millet.

Future viability depends on ensuring a sustainable society because green energy methods may efficiently reduce greenhouse gas emissions. Nevertheless, stakeholders, consumers, and developers continue to be notably ignorant of the financial incentives connected to green technology. Moreover, there is still a dearth of studies on the range of financial incentives offered by different authorities in India. Monetary incentives, such as tax breaks, indirect tax exemptions, and refunds, are crucial in encouraging the use of green technology in the modern world. This study explores the importance of financial incentives for green building technologies in India, which also looks at the wide range of incentives provided by federal, state, and local governments. Furthermore, the study highlights various state government programs such as goods subsidies, exemptions from local taxes, and fee waivers. Notably, several incentives aimed at consumers, developers, and other stakeholders have been implemented by the Indian Green Building Council (IGBC). This review study emphasizes the effectiveness of financial incentives in green construction projects and draws attention to a clear knowledge gap regarding the adoption of green technology. This study also provides insights into potential future directions. Studies and research results emphasize the importance of spreading the word about financial incentives as a key factor in determining the adoption of green technologies. Many parties, including governmental organizations, municipal governments, developers, and clients engaged in green building technology projects, stand to gain increased awareness.

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.

Future viability depends on ensuring a sustainable society because green energy methods may efficiently reduce greenhouse gas emissions. Nevertheless, stakeholders, consumers, and developers continue to be notably ignorant of the financial incentives connected to green technology. Moreover, there is still a dearth of studies on the range of financial incentives offered by different authorities in India. Monetary incentives, such as tax breaks, indirect tax exemptions, and refunds, are crucial in encouraging the use of green technology in the modern world. This study explores the importance of financial incentives for green building technologies in India, which also looks at the wide range of incentives provided by federal, state, and local governments. Furthermore, the study highlights various state government programs such as goods subsidies, exemptions from local taxes, and fee waivers. Notably, several incentives aimed at consumers, developers, and other stakeholders have been implemented by the Indian Green Building Council (IGBC). This review study emphasizes the effectiveness of financial incentives in green construction projects and draws attention to a clear knowledge gap regarding the adoption of green technology. This study also provides insights into potential future directions. Studies and research results emphasize the importance of spreading the word about financial incentives as a key factor in determining the adoption of green technologies. Many parties, including governmental organizations, municipal governments, developers, and clients engaged in green building technology projects, stand to gain increased awareness.

Shilajit is an ancient herbal medicine from India with many uses. It is a viscous material prominently present in the Himalayan and Hindukush rocks of the Indian region. The formation due to plant decompostion with time. Herbs assosciated to the stones reveals about composition of Shilajit. Primary objective is to create GCMS/MS and LC-analysis techniques for separating the phytochemicals and complete profiling of volatile and non-volatile metabolites found in Shilajit. Accordingly, an attempt was undertaken to develop a method for standardisation and, subsequently, certification inorganic content and purity of shilajit in various indian regions S-01, S-02, S-03, S-04, and S-05. Thus, an effort was made to create a fundamental technique for standardization and, consequently, authentication of shilajit that was acquired from various geographical locations.Trace metal concentrations were measured using ICP OES in mg Kg−1 for each sample. These elements included As, Pb, Cd, Ag, Al, B, Ba, Cr, Cu, Fe, Mn, Ni, Sb, Se, Zn, P, Ca, K, Mg, and Na. ICP OES analysis observed the significant concentration of Ca and K, in S-01 (102631 and 26779), S-02 (13529.25 and 36866.35), S-03 (8170.84 and 37989.10), S-04 (9616.37 and 18843.77), and S-05 (4992.10 and 2120.64), appropriately. By measuring the DPPH radical scavenging activity were assessed for their antioxidant activities of the S-01, S-02, S-03, S-04, and S-05 samples: 12.316 ± 0.561 %, 13.144 ± 2.884 %, 15.105 ± 1.421 %, 19.683 ± 2.013 %, and 0.0724 ± 1.045 %, respectively. Concurrently, TPC studies on Shilajit found between 10 and 150 mg g−1 wt Similarly, shilajit’s TFC was analysed with geographically. TFC were found in mg QCE/g of wt of S-01 12.56, S-02 5.56, S-03.48, S-04 49.23, and S-05 5809 each respectively. The phytochemicals protocatechuic acid and hippuric were tested for in silico anti-inflammatory and antihyperglycemic activity of acid, which need to be explored in-vivo applications.

Prefabricated construction, increasingly recognized as a sustainable method, enhances productivity while mitigating the drawbacks of traditional approaches. Lean construction, pivotal for sustainability, targets waste reduction and cost efficiency while delivering value to customers. In India's prefabrication sector, numerous barriers impede the implementation of lean principles, necessitating their identification and resolution to advance lean practices. This study aims to identify and analyze primary barriers to implementing lean principles within India's prefabrication industry, focusing on professionals' perceptions. Employing exploratory factor analysis, it examines these barriers' interconnections and causal relationships, providing actionable recommendations for enhanced lean construction effectiveness. Through a review of the literature, 26 significant barriers were identified and primary data was obtained with the help of a questionnaire. 25 barriers were discerned after pre-exploratory factor analysis, culminating in ten common components. Notably, the study highlights a primary barrier: understanding of lean construction. Drawing from expert insights, substantial recommendations are provided, intending to guide the prefabricated building sector in overcoming barriers to on-site lean construction. These findings and recommendations offer valuable direction for industry stakeholders.

Environmental safety is one of the key issues that are directly related to a country's prosperity. One of the most fundamental aspects of a sustainable economy is waste management and recycling. Better recycling safety and efficiency may be achieved via the use of intelligent devices rather than manual effort. In this research, we describe a machine learning-based architecture for smart trash collection and sorting using the Internet of Things and wireless sensor networks. The goal of this study was to develop an autonomous method for producing an efficient and intelligent waste parameter monitoring system for a novel waste management system, using the Internet of Things (IoT) and Long Range (LoRa) technologies. Several possibilities are explored, all of which may be applied to the development of the three nodes. The number of trash cans, garbage stench, air quality, weight, smoke levels, and waste categories are all tracked in real-time via the Internet of Things and the Thing Speak Cloud Platform, which can be set up in numerous places. In the end, a fog layer-deployed intelligent waste classification framework consists mostly of four layers: input, feature, classification, and output. Using the Thrash Box dataset, the proposed system develops a categorization method into trash classes such as household, medical, and electronic garbage, in addition to object identification. Traditional machine learning methods, such as the multi-kernel support vector machine (SVM) and the Adaboost ensemble classifier, are employed in the classification layer, while the Resnet-101 deep convolutional neural network model is used in the feature layer. Experiments were conducted to evaluate the suggested method's ability to classify garbage and provide accurate predictions about their respective categories. Compared to other state-of-the-art models, the suggested method's performance was shown to be superior in the presented trials.

Abstract According to the United Nations, Sustainable Development Goals (SDG) goal 6 and goal 14 seeks to ensure the sustainable management of water present over the earth for all. Urban cities saw a lot of expansion in terms of population and the number of industries established there. Water quality analysis becomes a huge requirement of today’s scenario due to the impurities present in water which harms the livelihood of society. Some of the hazardous impurities like heavy metals destruct the surroundings. In this study, the removal of heavy metals from wastewater with an efficient technique i.e. Bioremediation is represented with the analysis and evaluation of results recorded in the laboratory testing. Two samples were taken from two different sites which are being analyzed with the implementation methodology represented in the present article. The inclusion of the ANOVA model for the optimization of the outcome generated is evaluated and received the P-value and F-crit value. Two algae Chlorella Minutissima & Chlorella Singularis were evaluated based on their impurity removal efficiency as well an analysis of the biological treatment method over other chemical methods has been examined. The results were analyzed and represented in the form of a table as well and the variation in the value of WQP is shown in the form of graphs. The observation shows that the variation in WQP after the integration of algae lies under the permissible limit. Alkalinity is estimated in the range of 20–40 mg/l, Hardness lies in the range of 0–60mg/l, and pH comes approximately in a range of 6.5–8. The results of the ANOVA model is also depicted in graphical form highlighting the P-value and F-crit value of different result generated. Finally, the summary of the proposed work is illustrated with the challenges faced and future recommendations have been provided. Based on the evaluation, the framework is generated for the efficient technique used for heavy metal removal i.e. Bioremediation which provides a great advancement in the efficacy of removal of heavy metals.

Intrusion detection systems (IDS) play a critical role in ensuring the security and integrity of computer networks. There is a constant demand for the development of powerful, novel, and generalized methods for IDS that can accurately detect and classify intrusions. In this study, we aim to evaluate the benefits of linear classifiers (LC) and nonlinear classifiers (NLC) in IDS. We employed ten machine learning (ML) classifiers, consisting of five LC and five NLC. These classifiers underwent cross-validation for performance evaluation, unseen analysis, statistical tests, and power analysis on measuring the minimum sample size. Four hypotheses were formulated and validated on five processed intrusion attack datasets. NLC outperformed LC, with a mean accuracy (ACC)/area-under-the-curve (AUC) increase of 22.26%/20.3% on the WUSTL-EHMS dataset, with improvements of ACC/AUC by 5.5%/2.3% on the UNSW-NB15 dataset. In the unseen analysis, NLC achieved an ACC/AUC increase of 21.9%/21.8% when trained on WUSTL-EHMS and tested on UNSW-NB15. Lastly, when using a mixed dataset of WUSTL-EHMS and UNSW-NB15, NLC demonstrated an ACC/AUC increase of 11.67%/5.5%. The model performed well in cross-validation protocols, and the statistical tests yielded significant p-values. NLC provides generalized and robust solutions to detect intrusion attacks, ensuring the integrity and security of computer networks.

This comprehensive review investigates the dynamic landscape of trace elemental analysis methodologies applied to plants and food-based extracts. The exploration spans from the inception of techniques to the latest procedures, contributing to heightened precision and sensitivity in elemental detection. According to the WHO, herbal plants and medicine from varied soil compositions serve as crucial therapeutic agents for 70–80 % of the world's population. Yet, their susceptibility to trace element toxicity poses a significant risk to human health. Rising population and increased food demands have led to environmental pollution, contaminating the food chain through unintended activities like industrialization, mining, and pesticide production. The elemental composition of plants and derived extracts is central to comprehending nutritional profiles, evaluating product quality, and ensuring food safety. Methodological advancements, progressing from manual procedures to sophisticated technologies such as X-ray fluorescence (XRF), neutron activation analysis (NAA), inductively coupled plasma optical emission spectroscopy (ICP OES), and inductively coupled plasma mass spectrometry (ICP-MS), are delineated. Recent strides in paper-based electrochemical sensors are highlighted for their distinctive capabilities and elucidation of associated advantages and limitations.Moreover, the review delves into innovative sample preparation methodologies, encompassing microwave-assisted digestion and solid-phase microextraction, to amplify the efficiency of elemental extraction and subsequent analysis. Integrating data analytics and machine learning in elucidating complex elementary datasets is explored, underscoring the potential for heightened accuracy and automation in trace elemental analysis. This review compiles literature data, summarizing sample preparation methods for various herbal parts (roots, soil, stems, bark, fruits, food). Standard protocols from WHO, United States Pharmacopeia-National Formulary (USP-FR), Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy (AYUSH) are considered. Lead, Cadmium, Mercury, and Arsenic are the primary toxic elements of concern in herbal medicines. This review furnishes valuable insights tailored for researchers, analysts, and policymakers actively involved in advancing the domain of trace elemental analysis in plants and food-based extract.

Objective: We developed and evaluated a training program for Accredited Social Health Activists (ASHAs), female community health workers (CHWs) in India, on non-communicable diseases (NCDs). Methods: A 5-day training program, developed using government-approved manuals, was tested in a randomised controlled trial in the Tehri-Garhwal district. Quantitative comparisons were undertaken using Student's t-test and two-way ANOVA. ASHAs in the intervention group were asked questions about new skills learnt. Results: Thirty-six ASHAs (20 intervention, 16 controls) participated (response rate 75.0%). Mean pre-test knowledge score was 43.3/100 points (95% CI 36.7–49.9) for the intervention group and 44.4 (38.9–49.9) for controls. The mean post-test knowledge score increased more in the intervention group (48.5-point increase; P

AbstractThis study explores the potential benefits of integrating Industry 4.0 technologies into educational settings, focusing on Artificial Intelligence/Machine Learning, Augmented Reality, Big Data, Blockchain, Cloud Computing, Internet of Things, Metaverse, Robotics, and Virtual Reality. The aim is to develop learning environments that are more responsive to the diverse needs of students and better equipped to meet those requirements. In today’s educational system, diversity manifests in various forms, including differences in learning styles, talents, interests, and cultural backgrounds. To effectively accommodate students with diverse profiles, educators must adapt and adjust their learning and teaching approaches accordingly. This study proposes that the incorporation of Industry 4.0 technologies into educational settings may offer adaptable solutions to meet the varying needs of students. It highlights the potential advantages of these technologies in fostering inclusive learning environments for modern education. By utilizing the power of technology, educators can create personalized learning experiences that cater to the unique needs of each student, thereby promoting inclusivity and equity in education. In conclusion, this study underscores the importance of embracing a more inclusive model of education that harnesses the potential of technology to enhance learning outcomes and support the diverse needs of students in today’s educational landscape.

The bromodomain and extraterminal (BET) family of proteins show altered expression across various cancers. The members of the bromodomain (BRD) family contain epigenetic reader domains that bind to acetylated lysine residues in both histone and non-histone proteins. Since BRD proteins are involved in cancer initiation and progression, therapeutic targeting of these proteins has recently been an area of interest. In experimental settings, JQ1, a commonly used BRD inhibitor, is the first known inhibitor to target BRD-containing protein 4 (BRD4), a ubiquitously expressed BRD and extraterminal family protein. BRD4 is necessary for a normal cell cycle, and its aberrant expression activates pro-inflammatory cytokines, leading to tumor initiation and progression. Various BRD4 inhibitors have been developed recently and tested in preclinical settings and are now in clinical trials. However, as with many targeted therapies, BRD inhibitor treatment can lead to resistance to treatment. Here, we investigated the kinases up-regulated on JQ1 treatment that may serve as target for combination therapy along with BRD inhibitors. To identify kinase targets, we performed a comparative analysis of gene expression data using RNA from BRD inhibitor-treated cells or BRD-modulated cells and identified overexpression of several kinases, including FYN, NEK9, and ADCK5. We further validated, by immunoblotting, the overexpression of FYN tyrosine kinase; NEK9 serine/threonine kinase and ADCK5, an atypical kinase, to confirm their overexpression after BRD inhibitor treatment. Importantly, our studies show that targeting FYN or NEK9 along with BRD inhibitor effectively reduces proliferation of cancer cells. Therefore, our research emphasizes a potential approach of utilizing inhibitors targeting some of the overexpressed kinases in conjunction with BRD inhibitors to enhance therapeutic effectiveness.

Summary: Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Background and noveltyWhen RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.MethodologyAnnotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland–Altman, and (iv) Correlation plots.ResultsAmong the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann–Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p

Controlling the translational and rotational motion of microparticles is a topic of current interest in soft matter and statistical mechanics. Roll rotation of a microparticle in a hydro-thermophoretic trap – which is a device that uses the flow of fluids and thermophoresis to trap and manipulate microparticles – was recently reported (Nalupurackal et al., 2023). Here, we study the coupled translational and rotational motion of a microparticle in a hydro-thermophoretic trap. We demonstrate, using experiments and theory, that the dynamical system describing the motion of the microparticle undergoes bifurcations when the power of lasers used in a hydro-thermophoretic trap is changed. We show that the advective fluid flow from laser heating and thermophoresis depend on the power of the laser through two distinct exponents. We obtain these exponents from experimental data. Using these exponents and a linear stability analysis of the equations of motion, we show that locations at which a microparticle can be trapped (stable fixed points of the dynamical system) can be tuned by changing the power of the lasers at the two hotspots. At higher power values, we find that there are no stable fixed points; hence, the microparticle can no longer be trapped. Our experimental findings support these theoretical results.

Introduction: Neurological diseases are significant challenges to global healthcare systems, demanding innovative and effective therapeutic approaches. Traditional Chinese medicine has emerged as a promising adjunct or alternative in the treatment of various neurological disorders. Methods: Relevant articles were searched using electronic search platforms such as PubMed, Web of Science Google, Scholar, and Scopus” from August 2023 to October 2023. Research and review articles were searched using the keywords such as “Depression”, “Brain injury”, “Brain-related disorder”, “Neurological disorder”, ”Insomnia” “Ischemic Stroke”, “Alzheimer's Disease”, “Parkinson's disease”, Cerebral Ischemia” paired with “Traditional Chinese Medicine” for the last fifteen years. Critical analysis was performed to identify the appropriate articles after that information was extracted and retrieved. Results: In recent years, scientific research has been activated to unravel the neuroprotective and therapeutic mechanisms underlying Traditional Chinese Medicine interventions. Herbal plants, such as Huperzia serrata, and Ginkgo biloba demonstrated potential in enhancing cognitive function and mitigating symptoms in conditions like Alzheimer's disease, and Parkinson's disease. Acupuncture, through its influence on neural pathways and neurochemical signaling, has shown promise in alleviating pain associated with neurological disorders and improving motor function in conditions like Parkinson's disease. Furthermore, Traditional Chinese Medicine shows therapeutic potential in the treatment of various patients suffering from brain-related diseases. Discussion: Traditional Chinese Medicines play a significant role in the treatment of neurological diseases. An approach to neurological treatment that is more thorough and patient-centered may be accomplished by incorporating the holistic principles of Traditional Chinese Medicine and integrating them with contemporary medical procedures. In the field of neurological diseases, further investigation and cooperation between the Western and traditional medicine communities could bring new therapeutic innovations.

Abstract Background The gut microbiota plays a crucial role in regulating the host’s immune responses during aging, which was characterized by a different abundance of bacteria in several age groups. Main body Gut microbiota dysbiosis is associated with aging, antibiotic exposure, underlying diseases, infections, hormonal variations, circadian rhythm, and malnutrition, either singularly or in combination. The appropriate use of prebiotics and probiotics may be able to prevent or reduce this disruption. Conclusion The current review focuses on the gut microbiota composition across the life cycle, factors affecting gut microbiota changes with aging, and interventions to modulate gut microbiota.

Abstract Background High-grade serous ovarian cancer (OvCa) is the most common type of epithelial OvCa. It is usually diagnosed in advanced stages, leaving a woman’s chance of survival below 50%. Despite traditional chemotherapeutic therapies, there is often a high recurrence rate following initial treatments. Hence, a targeted drug delivery system is needed to attack the cancer cells and induce apoptosis, overcome acquired drug resistance, and protect normal cells from cytotoxicity. The present study shows that targeting folate receptor alpha (FRα) through planetary ball milling (PBM) nanoparticles (NPs) induces apoptosis in OvCa cells. Results Human tissue microarrays (TMAs) show overexpression of FRα in Stage IV OvCa tissues compared to matched normal tissues. They provide a focus for a targeted delivery system. We formulated PBM nanoparticles encapsulated with paclitaxel (PTX) or fisetin (Fis) and conjugated with folic acid (FA). The cytotoxic effect of these PBM NPs reduced the concentration of the toxic chemotherapy drug PTX by five-fold. The combined treatment of PTX-FA NPs and Fis-FA NPs inhibited cell proliferation and induced apoptosis more extensively than the individual drugs alone. Apoptosis of OvCa cells, determined by flow cytometry, showed an increase from 14.4 to 80.4% (OVCAR3 cells) and from 2.69 to 90.0% (CAOV3 cells) in the number of apoptotic cells. Also, expressions of the pro-apoptotic markers, BAK and active caspase-3, were increased after PTX-FA + Fis-FA PBM NP treatment. In addition to looking at targeted treatment effects on apoptosis, drug resistance was investigated. Drug resistance in OvCa cells was reversed by ABCG2, an ABC-transporter marker. Conclusions Our study shows that PTX-FA and Fis-FA PBM NPs directly target platinum-resistant OvCa cells, induce cytotoxic/apoptotic effects, and reverse multi-drug resistance (MDR). These findings allow us to create new clinical applications using PTX-FA and Fis-FA combination nanoparticles to treat drug-resistant cancers.

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Abstract Human activities have degraded lakes in Uttarakhand, endangering their vital role in urban sustainability, which includes providing essential services like water supply, flood mitigation, agriculture support, and biodiversity conservation in the Himalayan region. This study focused on Nainital district lakes, utilizing remote sensing and GIS techniques to assess their condition. Time series Landsat 8 satellite imageries acquire by USGS earth explorer from 2017 and 2022 were captured, pre-processed, and subjected to spectral-based classification algorithms in ArcGIS software to calculate Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) indices to assess changes in vegetation, water bodies, and build-up area in and around the lakes respectively. The results indicate a decrease in built-up areas for Nainital district lakes from 2017 to 2022: Naini Lake (1.42%), Bhimtal Lake (1.83%), Naukuchiatal Lake (1.45%), Sattal Lake (2.18%), Khurpatal Lake (2.25%), and Sariyatal Lake (1.3%). Additionally, Bhimtal, Naukuchiatal, and Khurpatal lakes exhibited reductions in shrub and grass vegetation by approximately 12%, 16%, and 0% over the five-year period. Notably, Sattal and Khurapatal lakes demonstrated significant decreases in built-up areas, likely attributed to restoration efforts or landslides. Findings emphasize the need for conservation, sustainable land-use practices, and effective management to protect lake ecosystems.

Aluminum composite materials have gained immense popularity in the construction and aerospace industries as these materials are lightweight, durable, and exhibit a high strength-to-weight ratio. The present study aims to develop dissimilar AA2024 and AA2014-based composite materials using nano-TiO2 reinforcement particles via Friction Stir Processing (FSP) techniques. The FSP technique is a solid-state processing method that is used to modify the properties of metal alloys without altering their chemical composition. The samples were prepared by placing a nano-TiO2over AA2024 and AA2014 aluminum alloys sheet and subjected to FSP process parameters. The microstructure, hardness, and tensile properties of the composite samples were assessed using scanning electron microscopy (SEM), Vickers hardness tester, and tensile testing machine respectively. The results showed that the FSP technique led to the formation of a homogeneous and fine-grained microstructure in the composite samples. The nano-TiO2 reinforcement also provided additional strengthening to the AA2024 and AA2014 aluminum alloys matrix resulting in improved hardness and tensile properties of the composites. The resulting composites exhibited enhanced wear resistance properties. The FSP method presented in this study has implications for extending the properties of other metal alloys, opening up new avenues for research in the field of materials science.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Endometriosis, an incurable gynecological disease that causes abnormal growth of uterine-like tissue outside the uterine cavity, leads to pelvic pain and infertility in millions of individuals. Endometriosis can be treated with medicine and surgery, but recurrence and comorbidities impair quality of life. In recent years, nanoparticle (NP)-based therapy has drawn global attention, notably in medicine. Studies have shown that NPs could revolutionize conventional therapeutics and imaging. Researchers aim to enhance the prognosis of endometriosis patients with less invasive and more effective NP-based treatments. This study evaluates this potential paradigm shift in endometriosis management, exploring NP-based systems for improved treatments and diagnostics. Insights into nanotechnology applications, including gene therapy, photothermal therapy, immunotherapy, and magnetic hyperthermia, offering a theoretical reference for the clinical use of nanotechnology in endometriosis treatment, are discussed in this review.

Abstract The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

Natural fibre reinforced composites are being used in a variety of industries. Epoxy matrix composites reinforced with woven bamboo-cotton fibre has been fabricated via., hand lay-up technique and compression moulding technique. In the fabrication of the composite panels, the number of layers varied from 8 to 14 according to the weight of the fabric. The tensile, bending, impact, compression, water-absorption, and ILS properties for the developed composites have been tested or examined. SEM characterization was being used to examine the fractured morphology and assess interface-strength of the developed composites. It was found that increasing the number of fibre layers improved the composites' physico-mechanical properties. Due to the micro-voids, detachment, and hydrophilic nature of natural fibres, the cotton/bamboo twill fabric reinforced composites absorb the most water. In addition, the results have demonstrated that the fibre matrix debonding, which was actually the result of a weak-bonding amid the fibre and matrix, was the cause of the escalated water absorption behavior. Hence, the fabricated composites are suitable for the applications of moderate load bearing, including automobile body parts and structural components that require sufficient strength.

A printed ultrawideband (UWB) monopole antenna with triple-rectangular band rejection characteristics at WiMAX, WLAN, and satellite band is presented in this paper. The frequency selectivity of each rejection band is improved by employing the mutual coupling of two spiral-shaped electromagnetic band-gap (EBG) structures. The proposed design consists of the modified rectangular patch with a compact size of 23.5 × 23 mm2 having a partial ground plane and three pairs of spiral EBGs. To mitigate the interference from the WiMAX band, one pair of the spiral is coupled with the microstrip line, which exhibits band rejection of 3.3–3.7 GHz (11.42%). WLAN band from 5.1 to 5.9 GHz (14.5%) is notched by another pair of spiral EBGs designed on the opposite side of the radiating patch, and for satellite band rejection, two spiral EBGs are embedded on the opposite side of the microstrip line which ranges from 7.25 to 7.8 GHz (7.3%). Moreover, peak gain, radiation pattern, and time domain behavior of the proposed design are also investigated in Ansys Electronics Desktop 17.0 to find its suitability in UWB applications.

In the realm of food safety, dried foods have traditionally been perceived as having a lower risk profile. However, recent reports have highlighted concerning levels of mycotoxin contamination in dried food products, such as dried fruits, vegetables, and spices, exceeding permissible limits. This contamination not only compromises shelf life but also poses a significant threat to consumers, while depleting the bioactive components of these products. The food industry grapples with considerable challenges arising from the substantial risk of mycotoxin contamination in dried foods. To meet the demands of a disc; erning food market, there is an imperative need for sustainable mycotoxin-degrading technologies. These technologies must be non-thermal and chemical-free, minimizing thermal damage to treated food to preserve essential nutrients, ultimately enhancing food quality and safety. Developing non-thermal decontamination technologies for dried foods holds enormous economic and public health significance.This review centers its focus on the decontamination of dried foods, proposing a green approach through the utilization of Cold Plasma Technology (CPT). We delve into the working principles and applications of CPT in dried foods, highlight the influential factors affecting its efficacy, and acknowledge the limitations of this innovative technique. Furthermore, we pinpoint and explore emerging research opportunities in the decontamination of dried foods, particularly in conjunction with other complementary methods. CPT, an evolving and environmentally friendly technology, exhibits promising capabilities in controlling pathogens in dried food products and extending their shelf life. Critical determinants encompass the parameters of cold plasma, operational conditions, food-related variables, and microbiological factors, which collectively influence the efficacy of CPT. We advocate for future research dedicated to assessing the impact of CPT on various mycotoxins, food composition, and its interaction with food-related factors. In this vein, it is imperative to emphasize the scaling up of this technology for practical commercial applications, addressing the growing need for enhanced food safety in dried products.

Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10−15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis.

Abstract Aim: Exploitation of nano-based materials has increased the disposal of nanoparticles into the environment. Toxic nanomaterials pose their adverse effects and severely impact the human and environmental health. The aim of this study was to explore the toxic effects of chemically-synthesized copper oxide nanoparticles (CuO-NPs) on Chlorella vulgaris using an algal growth inhibition assay. Materials and Methods: Nanoparticles were synthesized using cupric sulfate and sodium hydroxide. Synthesized nanoparticles were characterized by Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible (UV-vis) spectroscopy, X-ray diffraction (XRD), and transmission electron microscopy. C. vulgaris culture was exposed to various concentrations of CuO-NPs at intervals of 3, 6, 12, and 24 h. Growth inhibition assay of algal culture was estimated via the spectroscopic method and cell enumeration was done by Neubauer hemocytometry. Results: The average diameter of CuO-NPs was ~35.85 nm. UV-Vis spectroscopy was used to confirm the configuration of CuO-NPs. Therefore, FTIR results has indicated high purity of synthesized CuO-NPs. Size of the CuO-NPs was determined by XRD. Results showed that growth of C. vulgaris microalgae was notably affected by the exposure of 2 mg/L CuO-NPs concentration exposure for 6 h. Conclusion: The current work showed that CuO-NPs have significant toxic to Chlorella sp. Data analysis showed NPs have broad effects on growth of C. vulgaris and there is a correlation between nanoparticles concentration as well as their toxicity on the microalgae.

In present investigation, the impact of nanoparticle concentration on the machining accomplishment of Hastelloy C-276 has been examined in turning operation. The outputs like temperature, surface roughness, chip reduction coefficient (CRC), tool wear, and friction coefficient along with angle of shear have been estimated. The graphene nanoparticles (GnP) have been blended into soybean oil in distinct weight/volume ratio of 0.5, 1 and 1.5%. The experimental observations revealed that higher concentration of nanoparticles has enhanced the heat carrying capacity of amalgamation by 12.28%, surface roughness (27.88%), Temperature (16.8%), tool wear (22.5%), CRC (17.5%), coefficient of friction (46.36%) and shear angle (15%). Scanning electron microscopy identified nose wear, abrasion, adhesion and loss of tool coating. Further, lower tool wear has been noticed at 1.5% concentration, while the complete failure of insert has been reported during 116 m/min, 0.246 mm/rev having 0.5% concentration. ANOVA results exhibited that surface roughness is highly influenced by speed rate (41.66%) trailed by feed rate (28.16%) and then after concentration (13.68%). Temperature is dominated by cutting speed (69.31%), concentration (14.53%) and feed rate (13.25%). Likewise, tool wear was majorly altered by cutting speed (67.2%) accompanied by feed rate (23.90%) and thirdly concentration of GnP (5.03%).

Introduction: Suicide is a global phenomenon. Psychiatric disorders are estimated to contribute to a large proportion of suicides; however, impulsive suicide is also common. Among the ages of 15–29 years, suicide was the second leading cause of death worldwide and has been hypothesized to have reduced response inhibition and decision-making abilities as contributing factors, which directly relate to impaired executive functioning and suicidal behavior. Aim: This study aimed to study sociodemography, method, and pattern of suicide, and response inhibition in suicide survivors. Material and Methods: A case–control study was designed in a tertiary medical center in Madhya Pradesh using convenient sampling of suicide survivors and controls from the general population over 3 months. Data collection was semi-structured pro forma, and response inhibition was assessed using the Stroop test. Statistical analysis was performed using IBM Statistical Package for the Social Sciences (SPSS) Windows version 25.0. Independent-samples Student's t-test, Chi-square test, and Mann–Whitney U-test were used for analyzing means of continuous data, nominal data, and ordinal data, respectively. The level of significance was at P < 0.05. Result: A total of 126 suicide survivors and 99 controls were assessed after the end of the study period. Age (mean age for cases 25.4 ± 9.16; controls 23 ± 7.9), gender (F ~ M), and socioeconomic status were used to match cases and controls. 85.7% of suicide survivors had no history of suicide, but the majority had precipitating factors. Anomic suicide was the most common type, and poisoning was the most common method of suicide. Response inhibition between cases and control was significantly different. Conclusion: The majority of suicide attempts were impulsive in youth, with no gender discrimination. Response inhibition seemed to play a role in a suicide attempt.

Background: In Asia, there are approximately 2.3 billion internet users. Addiction to internet gaming takes a multifaceted toll on an individual's physical and mental well-being, casting a long shadow over their daily endeavors and also their sleep cycle. Aim: To study the prevalence of internet gaming addiction and how it affects sleep quality in medical students. Materials and Methods: A cross-sectional study was performed with a sample size (n = 112) in the Government Medical College, Datia (M.P.), and data were collected using a semi-structured proforma including the Internet Gaming Disorder Scale and Mini Sleep Questionnaire in the study population. Results: In the study population with age group 18–28 years (mean age: 21 ± 1.7 years), the majority of them belong to the Hindu religion (91.1%), nuclear family (66.1%), and urban community (75.9%). Most students (n = 74) had mild to moderate sleep difficulty related to internet gaming addiction, that is, 65.1%. The most common response was “sometimes” on the Internet Gaming Disorder Scale. A significant association was observed between gaming disorder and parameters of sleep mainly in waking up too early, daytime sleepiness, snoring, feeling tired, and headache upon waking up with gaming disorder. Conclusion: Among medical students, gaming addiction is significantly associated with poor sleep quality. Steps need to be taken to promote healthy internet use to improve sleep quality and mitigate negative effects to avoid long-term health impacts.

Background: Stress is a common phenomenon. Everyone faces stress, albeit of different intensity and due to different reasons. Stress in students can be due to many reasons—non-academic and academic. Academic stress is a major deciding factor in the fluctuation in academic achievements. It has also been implicated in mental illnesses like depression, anxiety, sleep disorders, and even suicide, and physical illnesses like hypertension, obesity, etc., Additionally, post-COVID-19 pandemic in 2020, there have been adverse effects on the education sector due to the offline–online mode of classes. Aim: To assess academic stress in high school students, to compare stress between male and female students, and to find the correlation of academic stress with the 10th standard's result, i.e. percentage. Material and Methods: A retrospective study was designed by the Department of Psychiatry, of a medical college of Madhya Pradesh in schools of Khandwa and nearby areas from June 2022 to August 2022. A total of 162 high school students were selected as per inclusion and exclusion criteria. Perception of Academic Stress (PAS) scale and Scale for Assessing Academic Stress (SAAS) were used to assess academic stress. Statistical analysis was done by SPSS version 25 using the Student t-test and Pearson correlation tests. Results: The mean age of the study participants was 15.82 ± 0.81 years. There was a significant difference in the mean percentage of the 9th and 10th standard. Both genders differed significantly in class percentages, but not the mean SAAS score. Most students had moderate stress. Only the cognitive indicator of SAAS was found to be significantly correlated with 10th standard percentages. Conclusion: Academic stress is ubiquitous. External factors like the transition from online to offline exams in addition to anticipation of the exams themselves can add to the stress. Prompt identification of stress can alleviate the emergence of future psychiatric illnesses.

Background and Aims: Pre-eclamptic parturients may have an exaggerated response to vasopressors. This study compares the efficacy of a 50 μg fixed bolus of phenylephrine for treatment of post-spinal hypotension in pre-eclamptic versus normotensive parturients. Material and Methods: After written informed consent and ethics committee approval, 30 normotensive and 30 pre-eclamptic parturients between 18 and 40 years with singleton term pregnancy about to undergo cesarean section (CS) under spinal anesthesia were included. Post-spinal hypotension was treated with a 50 μg fixed bolus of phenylephrine. The cumulative dose of phenylephrine, the number of boluses, and the median dose required to treat the first hypotensive episode, total number of hypotensive episodes, maternal side effects, neonatal appearance, pulse, grimace, activity, and respiration (APGAR) scores, and umbilical arterial cord blood pH were noted. Statistical analysis was done using Student's t-test, Mann–Whitney U-test, Chi-square test/Fisher's exact test as appropriate. A P

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal; nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model