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Abstract Blockchain technology uses a secure and decentralised framework for transaction management and data sharing within supply chains. This is particularly crucial in the pharmaceutical industry, where product authenticity and traceability are paramount. Blockchain plays a pivotal role in preventing product loss and counterfeiting, while simultaneously enhancing transparency and efficiency throughout the supply chain. The research introduces a step-by-step approach to implementing a proof-of-concept (PoC) for Supply Chain Risk Management (SCRM) through blockchain technology. This PoC involves simulating a supply chain process to assess feasibility and measure key performance indicators. Engaging stakeholders and gathering feedback is integral to refining the blockchain-based SCRM system. The study rigorously evaluates the performance of the SCRM blockchain across various test scenarios, featuring differing numbers of organizations and clients. Multiple fabric networks are employed to assess the system’s scalability and performance under diverse conditions. The results of these comprehensive tests inform practical deployment decisions and highlight areas for potential optimization and further development. So this research provides valuable insights into the application of blockchain in pharmaceutical supply chains, offering a roadmap for implementation and improving supply chain security, efficiency, and transparency.

Abstract The lateral motion control of an in‐wheel motor drive electric vehicle (IWMD‐EV) necessitates an accurate measurement of the vehicle states. However, these measured states are always affected by delays due to sensor measurements, communication latencies, and computation time, which results in the degradation of the controller performance. Motivated by this issue, a novel reaching law based predictive sliding mode control (NRL‐PSMC) is proposed to maintain the lateral motion control of the IWMD‐EV subjected to unknown time delay. Initially, a PSMC framework is built, in which a predictor integrating with the sliding mode control is designed to eliminate the effect of time delay and generate the virtual control signals. Further, to alleviate the chattering phenomenon, a novel‐reaching law is developed, enabling the vehicle to track the desired states effectively. Subsequently, a dynamic control allocation technique is presented to optimally allocate the virtual control input to the actual control input. The accurate estimation of the aforementioned unknown delay is realized through a delay estimator. Finally, simulation and hardware‐in‐the‐loop experiments are performed for three specific driving manoeuvres, and the results demonstrate the effectiveness of the proposed controller design.

In recent times, the continuous growth of construction and demolition (C&D) activities have resulted in increases in the utilization of natural resources as well as global C&D waste production. A major part of C&D waste produced is dumped in landfills worldwide although some countries have adopted good recycling and reuse facilities to generated C&D waste. Based on an extensive critical review of published literature on the topic including global C&D waste recycling statistics and composition of generated wastes, this paper identifies key physical, mechanical, and geotechnical characteristics of recycled C&D waste aggregates specific to use as pavement base or subbase materials. Recycled aggregates typically have sufficient CBR, abrasion resistance, compressive strength and resilient modulus in accordance with various road standard specifications, which enable their applications for pavement base and subbase layer construction. Recycled aggregates typically have higher water absorption and lower specific gravity values than virgin aggregates. Furthermore, this study evaluates the feasibility and effectiveness of recycled aggregates in pavement base and subbase layers based on the detailed laboratory investigations. Additionally, case studies involving large-volume utilization of recycled aggregates for field-scale pavement construction are presented facilitating the broader adoption of recycled materials in sustainable construction of road pavements. These studies document crucial insights into its real field performance in terms of strength, durability and longevity. Finally, authors have discussed the potential challenges, research gaps and future insights on the use of recycled aggregates in pavement construction. The use of recycled aggregates in pavement construction still has some barriers and challenges such as availability in bulk quantity especially at the field scale and absence of road standards for application, which require further research and practical developments to promote the sustainable use of these materials in the future.

The fate and stability of messenger RNA (mRNA), from transcription to degradation is regulated by a dynamic shuttle of epigenetic modifications and RNA binding proteins in maintaining healthy cellular homeostasis and disease development. While Transforming Growth Factor Beta 1 (TGFβ1) has been implicated as a key regulator for diabetic retinopathy, a microvascular complication of diabetes, the RNA binding proteins post-transcriptionally regulating its expression remain unreported in the ocular context. Further, dysfunction of TGFβ1 signalling is also strongly associated with angiogenesis, inflammatory responses and tissue fibrosis in many eye conditions leading to vision loss. In this study, computational and molecular simulations were initially carried out to identify Human Antigen R (HuR) binding sites in TGFβ1 mRNA and predict the structural stability of these RNA-protein interactions. These findings were further validated through in vitro experiments utilizing Cobalt Chloride (CoCl2) as a hypoxia mimetic agent in human retinal microvascular endothelial cells (HRMVEC). In silico analysis revealed that HuR preferentially binds to the 5′-UTR of TGFβ1 and displayed more stable interaction than the 3′UTR. Consistent with in silico analysis, RNA immunoprecipitation demonstrated a robust association between HuR and TGFβ1 mRNA specifically under hypoxic conditions. Further, silencing of HuR significantly reduced TGFβ1 protein expression upon CoCl2 treatment. Thus, for the first time in ocular pathological milieu, direct evidence of HuR- TGFβ1 mRNA interaction under conditions of hypoxia has been reported in this study providing valuable insights into RNA binding proteins as therapeutic targets for ocular diseases associated with TGFβ1 dysregulation.

Abstract The utilization of switched inductors, involving parallel charging and series discharging of inductors, is extensively embraced in diverse DC–DC converters for attaining high voltage gain; nevertheless, the stress on switch voltage and diode current escalates considerably with an increased count of inductors integrated into the switched inductors network. In the classical multistage switched inductor converter, the switch voltage aligns with the output voltage, and the diode experiences a high current as the number of stages increases. This research recommends a DC–DC multistage converter for energy conversion and high voltage gain with low stress. In this paper, a novel multistage switched inductor converter is introduced and designed to attain higher voltage gain while mitigating the stresses on switch voltage and diode current. The proposed circuit is created by replacing the standard multistage switched inductor converter's possible diodes with power switches. All of the switching devices are connected in such a way that the output voltage and input current are shared by all of the switches and diodes, respectively. As a consequence, the voltage stress on switches and the current stress on diodes are comparatively low, resulting in a high efficiency compared to a typical multistage switched inductor converter. It's interesting to note that the proposed converter and a typical multistage switched inductor converter both require the same amount of components. Different operation modes, analysis, a non‐ideal model, and a comparison of the suggested and recently constructed converters are discussed. The effectiveness and performance of the circuit are validated experimentally.

This paper, evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward (PDFF) controller-based direct torque controller (DTC) for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller (HCC). The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed, electromagnetic torque, and stator current. Two case studies, one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation, has been validated to show the effectiveness of the proposed control strategy. The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

High gain single switch DC-DC converter for Proton Exchange Membrane Fuel Cell (PEMFC) with Neural-Network (NN) algorithm based Maximum Power Point Tracking (MPPT) technique to drive Electric Vehicle (EV) is proposed in this paper. Apart from many maximum power extraction techniques, Neural Network (NN) based algorithm control is proposed, which uses Radial Basis Function Network (RBFN) to harvest maximum power from PEMFC under different temperature variations, which is compared with traditional Incremental Conductance (InC) method. Apart from it, a novel Single Ended Primary Inductor (SEPI) converter (boost converter) with the Coupled Inductor and Switched-Capacitor Circuit (CI + SCC) is designed to lower stress in the voltage across the switch with improved voltage gain to meet drive train (Three-Phase BLDC Motor) scenarios. Proposed PEMFC performance with RBFN-based MPPT technique integrated to drive train using a novel high gain converter is simulated using MATLAB/Simulink and implemented using dSPACE-DS1104 real-time controller. The compact sizing of the converter is proposed to integrate with PEMFC, which would be suitable for low-power EV applications.

Due to rapid population growth and urbanization, construction activities have increased worldwide resulting in generation of enormous volume of construction and demolition (C&D) waste. On one hand, C&D waste is generated during the construction, destruction, and rehabilitation of existing structures. While on the other, the transportation sector consumes large volumes of aggregates for pavement construction and maintenance. The extraction of finite natural aggregates causes potential damage to the environment. Recycled C&D waste, once converted into recycled aggregates, has the potential to be utilized in pavement layers due to its sound quality and composition; also resulting in lowering the landfill loads. This review article critically summarizes the environmental risks regarding chemical composition and leaching behavior of C&D wastes in pavements. Additionally, this review evaluates the environmental impacts of C&D waste aggregate production and application in pavements using life cycle assessment (LCA). Overall, the aim of this study is to investigate the environmental impacts and benefits of C&D waste to enable highway administrations to adopt and promote the use of C&D waste in development of sustainable road infrastructure. In this way, the review article attempts to promote a new era of sustainable road construction and achieve net zero waste goal.

Poor weather conditions, such as haze, fog, and smog, present significant challenges in capturing clear and visually appealing images. Though the existing image dehazing algorithms have achieved significant performance, they still suffer from various problems such as generalization to diverse hazy conditions, potential artifact generation, and computational complexity. Additionally, sensitivity to parameter settings, haze density variability, image content, noise, and scene-specific information remains areas of concern. To address these issues, we propose a Deep Custom Spatial and Spectral Consistency Layer-based Dehazing Network (DSSCNet) that effectively removes haze from images while preserving important spatial and spectral details. The network architecture includes a custom Haze Removal Layer (HRL), convolutional layers with ReLU activation, pooling layers, skip connections, and a custom Spatial and Spectral Consistency Layer (cSSCL). HRL estimates atmospheric light and transmission maps to generate an intermediate haze-free image. The proposed loss function combines Mean Squared Error (MSE) loss with a Consistency Loss (CL) to encourage content preservation during dehazing. Extensive experimental results demonstrate that DSSCNet outperforms competitive models in terms of various performance metrics, including contrast gain ( $c_{g}$ ), new visible edges ( $e$ ), new edge gradients ( $\bar {r}$ ), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) by average improvements of approximately 1.27%, 1.12%, 1.18%, 1.21%, and 1.24%, respectively.

Mycobacterium orygis , a subspecies of the Mycobacterium tuberculosis complex (MTBC), has emerged as a significant concern in the context of One Health, with implications for zoonosis or zooanthroponosis or both. MTBC strains are characterized by the unique insertion element IS 6110 , which is widely used as a diagnostic marker. IS 6110 transposition drives genetic modifications in MTBC, imparting genome plasticity and profound biological consequences. While IS 6110 insertions are customarily found in the MTBC genomes, the evolutionary trajectory of strains seems to correlate with the number of IS 6110 copies, indicating enhanced adaptability with increasing copy numbers. Here, we present a comprehensive analysis of IS 6110 insertions in the M. orygis genome, utilizing ISMapper, and elucidate their genetic consequences in promoting successful host adaptation. Our study encompasses a panel of 67 paired-end reads, comprising 11 isolates from our laboratory and 56 sequences downloaded from public databases. Among these sequences, 91% exhibited high-copy, 4.5% low-copy, and 4.5% lacked IS 6110 insertions. We identified 255 insertion loci, including 141 intragenic and 114 intergenic insertions. Most of these loci were either unique or shared among a limited number of isolates, potentially influencing strain behavior. Furthermore, we conducted gene ontology and pathway analysis, using eggNOG-mapper 5.0, on the protein sequences disrupted by IS 6110 insertions, revealing 63 genes involved in diverse functions of Gene Ontology and 45 genes participating in various KEGG pathways. Our findings offer novel insights into IS 6110 insertions, their preferential insertion regions, and their impact on metabolic processes and pathways, providing valuable knowledge on the genetic changes underpinning IS 6110 transposition in M. orygis .

The current study placed an intense emphasis on the excess discharge of agro-based industrial effluent and the use of plant extract antimicrobials to inhibit the growth of pathogens in crop plants. An effluent (treated and untreated) from the marigold flower processing industry has been identified for the presence of volatile and semi-volatile organic compounds, and a total of 18 in treated effluent and 23 in untreated effluent were found using gas chromatography–mass spectrometry. A total of 13 classes were identified, which include carboxylic acid, phenols, esters, alkanes, alkenes, alcohols, cyanide, heterocyclic, flavonoids, aldehydes, polycyclic aromatic, cycloalkanes, and cycloalkenes. A principal component analysis with varimax rotation was applied to discern the abundance of identified compounds under each class. An in vitro antifungal bioassay was conducted using effluents at three different concentrations against plant pathogens (Alternaria alter nata, Sclerotium rolfsii, Rhizoctonia solani, Pythium aphanidermata, Fusarium oxysporum, and Colletotrichum gloeosporioides). The study proved that treated and untreated effluents clearly inhibited the growth of fungal pathogens by 10 to 32% and 37 to 92%, respectively. The findings suggest that marigold flower effluent can be a promising resource for developing new plant protection methods that are effective against pathogenic fungi.

Lung cancer is one of the common cancers globally with high mortality and poor prognosis. Most cases of lung cancer are diagnosed at an advanced stage due to limited diagnostic resources. Screening modalities, such as sputum cytology and annual chest radiographs, have not proved sensitive enough to impact mortality. In recent years, annual low-dose computed tomography has emerged as a potential screening tool for early lung cancer detection, but it may not be a feasible option for developing countries. In this context, exhaled breath condensate (EBC) analysis has been evaluated recently as a noninvasive tool for lung cancer diagnosis. The breath biomarkers also have the advantage of differentiating various types and stages of lung cancer. Recent studies have focused more on microRNAs (miRNAs) as they play a key role in tumourigenesis by regulating the cell cycle, metastasis and angiogenesis. In this review, we have consolidated the current published literature suggesting the utility of miRNAs in EBC for the detection of lung cancer.

AbstractThe photochemical process of photosynthesis is significantly influenced by the availability of nutrients. The purpose of this research is to ascertain how photosynthetic pigment function is affected by nutrient elemental changes caused by severe drought stress. Using elemental analysis, we looked at the changes in mineral nutrient composition in eastern white pine (Pinus strobus L) seedlings 32 days after drought treatment. According to our findings, severe drought resulted in a significant and non-significant decrease in the contents of Chl “b and a”, respectively. The elemental composition of iron (Fe), zinc (Zn), magnesium (Mg), potassium (K), phosphorus (P) and nitrogen (N) was measured. After severe drought treatment, leaf nutrient status showed a significant decline in total N (control-1.57 ± 0.1; drought-0.65 ± 0.07), P (control-959.4 ± 17; drought-645 ± 46), Mg (control-1030.4 ± 33; drought-750.7 ± 76), and K (control-3062.5 ± 32; drought-1853.3 ± 198), with a non-significant decrease in leaf Fe (control-120.3 ± 20; drought-98.9 ± 28) and increase in leaf Zn (control-33.49 ± 2; drought-39.05 ± 4). A positive correlation was found between the content of Fe, P, Mg, K, and N in leaf Chl “b”, but only a positive correlation was found between the content of Zn in leaf Chl “a” during severe drought. During severe drought, nutrient reallocation has a significant impact on leaf chlorophyll levels, as evidenced by this correlation.

Abstract A multiport converter (MPC) with a non‐isolated high gain bidirectional port is proposed for the grid integration of solar photovoltaic array (SPA) fed telecommunication load. The SPA along with a battery energy storage (BES) meets the power demand of the telecom DC load and the excess/deficit power is exchanged with AC grid. The MPC feeds the DC link of a voltage source converter for bidirectional operation with the AC grid. The small signal analysis of the converter shows that its operation is stable. The SPA, BES, and telecommunication load are rated for lower voltages, consecutively reducing the complexity with series‐connected SPA. The proposed MPC possesses the merits of high voltage gain, reduced inductor size, and reduced number of components. Moreover, a power flow management algorithm is devised for the proposed converter that regulates the DC voltage at the telecom load and ensures smooth power flow control among various ports. The MPC is able to operate at various modes by controlling the ports independently. The converter performance during steady state and dynamic operating conditions under various modes are analyzed with detailed simulation studies. An experimental prototype is developed and test results are demonstrated to prove the viability of the designed converter.

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

Waterlogging induces oxidative damage by accumulation of reactive oxygen species due to stomatal closure. Plants alter their physiological and molecular mechanisms to reduce and adapt to oxidative stress. This mechanism of adaptation to stress, known as hardening, can support future stress tolerance. Pinus densiflora seedlings were grown under waterlogging treatment for three years and then transplanted to another site to identify the waterlogging hardening effect on transplanting. Transcriptome analysis was conducted before and after transplanting, and physiological factors were measured after transplanting. After transplanting, wounding stress is the main cause of transplant stress, and 13 genes related to phenylpropanoid were upregulated for the recovery of wounded roots in waterlogged hardened seedlings. The leaf starch and soluble sugar content of the waterlogged hardened seedlings were 50.3% and 40.5% lower due to the formation of cell walls. However, auxin-related genes were downregulated in waterlogging hardened seedlings, resulting in a lower tendency for height growth in hardened waterlogged seedlings. Waterlogging hardening mitigated transplant stress by wounding more than non-hardening, whereas waterlogging hardening may negatively affect seedling height. Our study provides evidence for the hardening effect of long-term waterlogging on transplanted P. densiflora seedlings.

Stabilization of flexible pavement layers using geogrids to improve the mechanical response of pavement layers is gaining importance over conventional stabilization techniques due to their low cost and superior performance. However, the lack of experimental data on quantifying the design input parameters of stabilized subgrades or granular layers limits the extensive use of geogrids in the field. Evaluation of design input parameters such as modulus improvement factor (MIF) or layer coefficient ratio (LCR) would promote the use of geogrids in the pavement, reducing the consumption of natural aggregates and the overall project cost. This study attempts to evaluate MIF and LCR due to geogrid stabilized soft subgrades considering different scenarios. All possible combinations of stabilization of pavement layers using biaxial and triaxial geogrids were considered. This involved stabilization of (a) the subgrade layer alone, (b) base layer alone, and (c) subgrade, subbase, and base layers. Accordingly, an extensive, systematic experimental program consisting of eighteen large-scale model pavement experiments (LSMPE) were conducted in five categories (designated as Series I through V). The stabilization of subgrade and granular layers was carried out using commercially available biaxial (BX1 and BX2) and triaxial (TX1) geogrids overlying soft and moderate subgrades with California bearing ratio (CBR) equal to 2.5 and 4%. Test results showed that stabilized subgrade prepared with existing and prepared subgrade material in conjunction with geogrid improved the effective CBR to as high as 10.9% from effective CBR = 7% corresponding to existing and prepared subgrade material without geogrid. The design inputs of geogrids (BX1/BX2/TX1) stabilized granular layers resulted in the MIF and LCR values ranging from 1.9 to 2.8 and 1.31 to 1.63, respectively, for the tested configuration considered in the study. Based on the findings of the study, inputs on resilient modulus of pavement layers were recommended for similar reinforcement and subgrade conditions considered.

Background: COVID-19 pandemic can lead to burnout among health workers. The study aimed to know the prevalence of burnout among resident doctors and its relation to resilience. Methodology: An online anonymous cross-sectional study was done at the sole COVID nodal center of Telangana, India, using a three-part questionnaire E-mailed to all the resident doctors. It included a consent form, sociodemographic data, the Copenhagen Burnout Inventory to measure burnout, and the Brief Resilience Coping Scale to measure resilience. Data were analyzed using SPSS statistical software version 22.0 (IBM). P value was considered statistically significant below 0.05 and all tests were two-tailed. Results: Personal burnout (53.6%) was highest followed by work-related burnout (46.4%) and client-related burnout (40.8%). All three domains of burnout showed a significant negative correlation with scores of resilience (personal burnout [r = −0.240), work burnout (r = −0.203), and client burnout [r = −0.212]; P ≤ 0.001). Conclusion: Client-related burnout has increased when compared to nonpandemic times. Burnout was inversely associated with resilience, suggesting a role for resilience as a protective factor.

Due to safety considerations and the challenges involved in tracking the maximum output of series-connected cells, solar photovoltaic (PV) arrays are generally operated at lower voltage levels. A multiport converter can be used to interface telecom DC loads, typically rated at 48 V and powered by PV arrays and battery energy storage system (BESS). The grid integration of the system improves reliability while lowering the BESS rating. This work proposes a sliding mode control-based power flow management controller that maintains the load voltage of a telecom DC load, allows maximum power extraction from the PV module, and facilitates power sharing with AC grid. A voltage source converter and a high-gain bidirectional converter exchange power with the AC grid. A second-order generalized integral algorithm-based voltage source converter control is provided to inject/absorb active power, reactive power, and eliminate the harmonics of the telecom AC load. Detailed simulation studies employing MATLAB software are performed to validate the functionality of the converter as well as the power flow management control. Moreover, the system’s performance is evaluated using a laboratory-developed experimental prototype.

With the widespread use of lithium-ion batteries in a wide range of consumer electronics products, the CE industry has undergone a dramatic shift. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved over time to meet the demands of automobiles. International research groups and the performance of production electric vehicles are used to discuss and inform vehicle-driven battery targets. There is still a lot of room for improvement in terms of energy, life expectancy, cost, safety, and fast-charging capabilities for LIBs suited for the automotive sector. In this study, a review of lithium-ion battery applications in electric vehicles is presented.

Summary: Background: The COVID-19 pandemic showcased the power of genomic sequencing to tackle the emergence and spread of infectious diseases. However, metagenomic sequencing of total microbial RNAs in wastewater has the potential to assess multiple infectious diseases simultaneously and has yet to be explored. Methods: A retrospective RNA-Seq epidemiological survey of 140 untreated composite wastewater samples was performed across urban (n = 112) and rural (n = 28) areas of Nagpur, Central India. Composite wastewater samples were prepared by pooling 422 individual grab samples collected prospectively from sewer lines of urban municipality zones and open drains of rural areas from 3rd February to 3rd April 2021, during the second COVID-19 wave in India. Samples were pre-processed and total RNA was extracted prior to genomic sequencing. Findings: This is the first study that has utilised culture and/or probe-independent unbiased RNA-Seq to examine Indian wastewater samples. Our findings reveal the detection of zoonotic viruses including chikungunya, Jingmen tick and rabies viruses, which have not previously been reported in wastewater. SARS-CoV-2 was detectable in 83 locations (59%), with stark abundance variations observed between sampling sites. Hepatitis C virus was the most frequently detected infectious virus, identified in 113 locations and co-occurring 77 times with SARS-CoV-2; and both were more abundantly detected in rural areas than urban zones. Concurrent identification of segmented virus genomic fragments of influenza A virus, norovirus, and rotavirus was observed. Geographical differences were also observed for astrovirus, saffold virus, husavirus, and aichi virus that were more prevalent in urban samples, while the zoonotic viruses chikungunya and rabies, were more abundant in rural environments. Interpretation: RNA-Seq can effectively detect multiple infectious diseases simultaneously, facilitating geographical and epidemiological surveys of endemic viruses that could help direct healthcare interventions against emergent and pre-existent infectious diseases as well as cost-effectively and qualitatively characterising the health status of the population over time. Funding: UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) grant number H54810, as supported by Research England.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the ‘black box’ challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI’s capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

BackgroundHNF1A is an essential component of the transcription factor network that controls pancreatic β-cell differentiation, maintenance, and glucose stimulated insulin secretion (GSIS). A continuum of protein malfunction is caused by variations in the HNF1A gene, from severe loss-of-function (LOF) variants that cause the highly penetrant Maturity Onset Diabetes of the Young (MODY) to milder LOF variants that are far less penetrant but impart a population-wide risk of type 2 diabetes that is up to five times higher. Before classifying and reporting the discovered variations as relevant in clinical diagnosis, a critical review is required. Functional investigations offer substantial support for classifying a variant as pathogenic, or otherwise as advised by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) ACMG/AMP criteria for variant interpretation.ObjectiveTo determine the molecular basis for the variations in the HNF1A gene found in patients with monogenic diabetes in India.MethodsWe performed functional protein analyses such as transactivation, protein expression, DNA binding, nuclear localization, and glucose stimulated insulin secretion (GSIS) assay, along with structural prediction analysis for 14 HNF1A variants found in 20 patients with monogenic diabetes.ResultsOf the 14 variants, 4 (28.6%) were interpreted as pathogenic, 6 (42.8%) as likely pathogenic, 3 (21.4%) as variants of uncertain significance, and 1 (7.14%) as benign. Patients harboring the pathogenic/likely pathogenic variants were able to successfully switch from insulin to sulfonylureas (SU) making these variants clinically actionable.ConclusionOur findings are the first to show the need of using additive scores during molecular characterization for accurate pathogenicity evaluations of HNF1A variants in precision medicine.

Background: The objective of this study is to find novel antineoplastic agents that display greater toxicity to malignant cells than to neoplasms. In addition, the mechanisms of action of representative compounds are sought. This report describes the cytotoxicity of a number of dimers of 3,5-bis(benzylidene)-4-piperidones against human malignant cells (promyelocytic leukemia HL-60 and squamous cell carcinoma HSC-2, HSC-3, and HSC-4). Methods: Tumor specificity was evaluated by the selectivity index (SI), that is the ratio of the mean CC50 for human non-malignant oral cells (gingival fibroblasts, pulp cells, periodontal ligament fibroblasts) to that for malignant cells. Results: The compounds were highly toxic to human malignant cells. On the other hand, these molecules were less toxic to human non-malignant cells. In particular, a potent lead molecule, 3b, was identified. A QSAR study revealed that the placement of electron-releasing and hydrophilic substituents into the aryl rings led to increases in cytotoxic potencies. The modes of action of a lead compound discovered in this study designated 3b were the activation of caspases-3 and -7, as well as causing PARP1 cleavage and G2 arrest, followed by sub-G1 accumulation in the cell cycle. This compound also depolarized the mitochondrial membrane and generated reactive oxygen species in human colon carcinoma HCT116 cells. In conclusion, this study has revealed that, in general, the compounds described in this report are tumor-selective cytotoxins.

The functional significance of the HIV-1 Antisense Protein (ASP) has been a paradox since its discovery. The expression of this protein in HIV-1-infected cells and its involvement in autophagy, transcriptional regulation, and viral latency have sporadically been reported in various studies. Yet, the definite role of this protein in HIV-1 infection remains unclear. Deciphering the 3D structure of HIV-1 ASP would throw light on its potential role in HIV lifecycle and host-virus interaction. Hence, using extensive molecular modeling and dynamics simulation for 200 ns, we predicted the plausible 3D-structures of ASP from two reference strains of HIV-1 namely, Indie-C1 (subtype-C) and NL4-3 (subtype-B) so as to derive its functional implication through structural domain analysis. In spite of sequence and structural differences in subtype B and C ASP, both structures appear to share common domains like the Von Willebrand Factor Domain-A (VWFA), Integrin subunit alpha-X (ITGSX), and ETV6-Transcriptional repressor, thereby reiterating the potential role of HIV-1 ASP in transcriptional repression and autophagy, as reported in earlier studies. Gromos-based cluster analysis of the centroid structures also reassured the accuracy of the prediction. This is the first study to elucidate a highly plausible structure for HIV-1 ASP which could serve as a feeder for further experimental validation studies.

Due to the challenges and restrictions posed by COVID-19 pandemic, technology and digital solutions played an important role in the rendering of necessary healthcare services, notably in medical education and clinical care. The aim of this scoping review was to analyze and sum up the most recent developments in Virtual Reality (VR) use for therapeutic care and medical education, with a focus on training medical students and patients. We identified 3743 studies, of which 28 were ultimately selected for the review. The search strategy followed the most recent Preferred Reporting Items for Systematic Reviews and Meta-Analysis for scoping review (PRISMA-ScR) guidelines. 11 studies (39.3%) in the field of medical education assessed different domains, such as knowledge, skills, attitudes, confidence, self-efficacy, and empathy. 17 studies (60.7%) focused on clinical care, particularly in the areas of mental health, and rehabilitation. Among these, 13 studies also investigated user experiences and feasibility in addition to clinical outcomes. Overall, the findings of our review reported considerable improvements in terms of medical education and clinical care. VR systems were also found to be safe, engaging, and beneficial by the studies’ participants. There were huge variations in studies with respect to the study designs, VR contents, devices, evaluation methods, and treatment periods. In the future, studies may focus on creating definitive guidelines that can help in improving patient care further. Hence, there is an urgent need for researchers to collaborate with the VR industry and healthcare professionals to foster a better understanding of contents and simulation development.

Introduction: Face masks prevent acute respiratory infections. Healthcare workers are prone to infections owing to their direct and constant exposure to infected patients. This pioneering study in Bangalore city, analyzed the awareness and attitude of healthcare staff toward using a face mask. Methods: This observational study was conducted among 290 subjects (mean age, 33.89 6 7.88 years; male:female ratio, 1:2.67). The study included all the healthcare workers from various departments. Data on basic demographic characteristics, attitude, awareness, and observations on the use of face mask were collected. Results: A large proportion of the sample were from the nursing department (n = 161, 55.51%). Most of the healthcare workers believed that making wearing a mask mandatory in public was very effective (80%) and 79% believed that wearing a mask could prevent the spread of coronavirus. Most of the healthcare workers said they would wear masks even when not mandatory. Most of them used an N95 mask or an N95 mask along with a surgical mask. Almost all were conscious of wearing the mask above the nose, covering the mouth and chin (289, 99.65%). A significantly higher number of doctors (medical postgraduates) (17, 45.95%, p = 0.01) used two masks when compared with others. Significantly fewer doctors (4, 10.81%) and other healthcare staff (8, 22.22%) reused masks (p = 0.002). Most of them used the mask only once, and those who reused it, cleaned it on alternate days (< 0.001). Sun-drying or air-drying, preceded by washing with detergent or soap, were the cleaning methods used, and most used paper or plastic bags to store the masks when not in use. Most of them did not use hand sanitizer before wearing the mask but followed all other precautions and measures to prevent infection. Conclusions: Most of the healthcare workers believed the use of masks could prevent virus transmission. A significantly higher number of doctors used two masks when compared with other healthcare staff. Significantly fewer doctors and other healthcare staff reused masks when compared with nurses and technicians.

For smart cities, the successful large-scale implementation of solar PV technology, Quality Certification and Standards are mandatory. The International Electrotechnical Commission (IEC) is a global organization for standardization consisting of all IEC national committees. The IEC PV standards comprise IEC technical committee 82 solar PV Energy System (IEC TC82) which develops and adopts all Photovoltaic related standards. There are nearly 80 standards applicable to photovoltaic and five working groups in IEC TC82. For necessary safety requirements ‘Quality and Standards’ technologically need to be revised and up to date. This paper presents PV standards developed by various technical committees worldwide, mainly focusing on various IEC PV standards, gaps identified by them and the recommendations provided by the committee in recent times. The breakup costs of the various sub-systems of a PV installation with an illustrative example for India is also discussed. It is intended to provide a guideline for consumers and investors, opting for solar PV installations.

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype–environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia–North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype–environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which–won–where pattern of genotype plus genotype–environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

There is a need for novel antiepileptic agents whose modes of action differ from those of current antiepileptic drugs. The objective of this study was to determine whether 1-diethylamino-3-phenylprop-2-en-1-one (2) could prevent or at least diminish convulsions caused by different mechanisms. This amide afforded protection in the maximal electroshock and subcutaneous pentylenetetrazole screens when given intraperitoneally to both mice and rats. A number of specialized tests in mice were conducted and are explained in the text. They revealed (2) to have efficacy in the 6 Hz psychomotor seizure test, the corneal kindling model, the mouse temporal epilepsy screen and a peripheral neuronal transmission test using formalin. Three screens in rats were undertaken, which revealed that (2) blocked chloride channels, inhibited peripheral neuronal transmission (tested using sciatic ligation and von Frey fibres) and afforded protection in the lamotrigine-resistant kindled rat model. The biodata generated reveal that (2) is an important lead molecule in the quest for novel structures to combat epilepsy.

The paper presents an innovative approach to control the voltage of an LCL-T type converter at the output side against variation at input and load ports, utilizing a fixed-frequency phase-shift control scheme. The examination of the converter is performed employing a Fourier series method that takes into account the effect of n-harmonics. To assure high-frequency switches with a zero-voltage switching (ZVS) technique, the lagging pf mode is utilized. PSIM simulations were used to investigate the performance of a 300 W converter. With the minimal input voltage, all switches turn on with ZVS for all loading conditions, whereas the ZVS strategy loses by two switches when the voltage at the input is highest. The power loss calculations of each component are performed and presented in a pie chart. The findings of the experiments are presented and verified with theoretical and simulation results. It is demonstrated that for both input voltage and load fluctuations, a minor adjustment in pulse width is sufficient to keep the output voltage constant.

Background: Clinical profiling helps to identify the common psychiatric problems that would result in psychiatric referrals and admissions and helps to develop protocols to counteract common psychiatric problems. Aim: The study aims to identify the profile of forensic ward patients in terms of sociodemographic, clinical, and criminal profiles; evaluate the challenges faced while treating them; and to come up with certain recommendations to the concerned prison authorities to how to deal with them. Materials and Methods: Retrospective chart reviews of 90 male forensic ward inpatients were done and the details as per the semi-structured intake pro forma were taken, and the data were analyzed using descriptive statistical methods. Results: Majority of the patients were single, uneducated, unemployed, and belonged to low socioeconomic status. Referral letters from the concerned prison medical officer were absent in half of the cases. Majority of the patients had personality disorders as the main diagnosis, and antisocial personality disorder was seen in majority (34.4%), followed by substance disorders (32.2%), psychosis (30%), and mood disorders (22.2%). 12.2% of convicted murder individuals diagnosed as schizophrenia. Most common crime committed by the individuals with Anti-social personality disorder was theft (15.6%). Conclusion: In many patients, the information about the reasons for referral and behavioral observation reports was lacking. This lack of information makes it difficult for the psychiatrist to accurately diagnose and treat. Sensitization and creating awareness of prison authorities are of paramount importance to deal with these challenges.

Background: COVID-19, caused by the coronavirus, was declared a global pandemic by the WHO on March 11, 2020. The COVID-19 pandemic hit India, similar to the world, reporting its first case in March 2020. After the first wave, the second wave in India started on March 2021. The impact of the second wave was huge on all tertiary care centers, especially psychiatric care centers, compared to the first wave of COVID-19. Aim: The aim of this study is to study the impact of COVID-19 on psychiatric patients at a tertiary psychiatric hospital in Telangana state of South India. Methodology: A retrospective chart review of all patients who got infected with COVID-19 during the second wave was done. We screened all the inpatient cases who were under admission during the second wave. For the study, a semi-structured intake pro forma was used. Sociodemographic variables, clinical variables, treatment variables, and outcomes of COVID-19-infected psychiatric patients were studied. Results: The current article compiles the management of the COVID-19 outbreak in the Institute of Mental Health in inpatient care and the challenges and experiences during the management of psychiatric inpatients with COVID-19 in a tertiary care center in Telangana.

Background: COVID 19 lockdown has an impact on the mental health of the general population, COVID patients, and health professionals. However, knowledge about its impact on psychiatric patients is limited. Objectives: To assess the mental health outcomes among psychiatric patients due to disruption of access to psychiatric services caused by the COVID 19 lockdown. Materials and Methods: It was a single-center, cross-sectional, observational, pragmatic study conducted at a tertiary care psychiatric hospital. All consecutive psychiatric patients presented to review in the outpatient department for 3 weeks following the relaxation of lockdown were screened, and a total of 305 eligible patients were enrolled. A semi-structured questionnaire was applied to capture the mental health status of the patients. Results: More than three-fourths (78.3%) of patients were off medications during the lockdown. During lockdown, more than half (64.3%) of patients' mental health conditions worsened, and significantly more in those who were not on medications. More than half of patients who were doing well before lockdown showed re-emergence of symptoms, and it was more commonly seen in mood disorder patients, followed by schizophrenia and other psychotic disorder patients. Thirty-one (10.1%) patients reported self-harm ideas; six (1.9%) claimed to have made self-harm attempts during lockdown. Conclusions: COVID-19 lockdown has worsened the mental health status of psychiatric patients. Strengthening community-based psychiatric care in tie-up with telepsychiatry services and adopting new innovative measures may help face such eventualities in the future.

Introduction: Alcohol use disorders (AUDs) have adverse effects on physical and psychosocial health and results in marital problems with reduced quality of life in partners. Aims and Objectives: The aims and objectives of the study are to investigate the relationship between domestic violence (DV), marital satisfaction, and well-being in partners of AUD patients. Materials and Methods: This was a cross-sectional study on patients with AUD and their partners. We administered the Severity of Alcohol Dependence Questionnaire to the participants and DV questionnaire, marital satisfaction scale, coping questionnaire, and the World Health Organization (WHO-5) well-being index scales to the partners. Statistical Analysis Used: Descriptive statistics were depicted using frequency tables, and linear regression analysis was performed. Results: DV was reported by 36.98% of the partners, and the marital satisfaction rate was 62.58%. The WHO-5 well-being was 33.88%, engaged coping was 38.60%, tolerant coping was 23.60%, withdrawal coping being 6.18%, and total coping was 72.76%. The severity of alcohol dependence syndrome (ADS) negatively correlated with well-being (r = 0.24) and marital satisfaction (r = 0.17). Furthermore, DV showed a negative correlation with the marital satisfaction (r = 0.32; P = 0.02) and well-being (r = 0.50; P = 0.0001) with statistical significance. Conclusions: The majority of the partners experienced DV, marital dissatisfaction, and poor well-being.

The main aim of this paper is to introduce a framework for the design and modelling of a photovoltaic (PV)-wind hybrid system and its control strategies. The purpose of these control techniques is to regulate continuous changes in the operational requirements of the hybrid system;currently, in power system networks, the distribution of energy plays a major role in maintaining power reliability in distribution systems. In this study, the proposed hybrid system was incorporated with a combined PV and wind energy system. Maximum power point tracking (MPPT) methods have been proposed to achieve maximum efficiency from the designed system. In addition, this study focused on improving the stability of the hybrid system. To improve the power quality and transient stability of the proposed system, we introduce a novel control strategy called the distributed power flow controller (DPFC) implementation with an optimization technique called the lion optimization algorithm(LOA)technique. This LOA control technique was developed for the first time in the application of a DPFC controller in a grid-connected system. The control technique was developed using signals from the system parameters, that is, voltage and current. To tune these parameters, this study used fuzzy logic and lion optimization techniques. The proposed system with controllers was tested in MATLAB/Simulink and the results were compared.

Android malware has been rising alongside the popularity of the Android operating system. Attackers are developing malicious malware that undermines the ability of malware detecting systems and circumvents such systems by obfuscating their disposition. Several machine learning and deep learning techniques have been proposed to retaliate to such problems; nevertheless, they demand high computational power and are not energy efficient. Hence, this article presents an approach to distinguish between benign and malicious malware, which is robust, cost-efficient, and energy-saving by characterizing CNN-based architectures such as the traditional CNN, AlexNet, ResNet, and LeNet-5 and using transfer learning to determine the most efficient framework. The OAT (of-ahead time) files created during the installation of an application on Android are examined and transformed into images to train the datasets. The Hilbert space-filling curve is then used to transfer instructions into pixel locations of the 2-D image. To determine the most ideal model, we have performed several experiments on Android applications containing several benign and malicious samples. We used distinct datasets to test the performance of the models against distinct study questions. We have compared the performance of the aforementioned CNN-based architectures and found that the transfer learning model was the most efficacious and computationally inexpensive one. The proposed framework when used with a transfer learning approach provides better results in comparison to other state-of-the-art techniques.

Introduction: Quantitation of microRNAs secreted by lung cells can provide valuable information regarding lung health. Exhaled breath condensate (EBC) offers a non-invasive way to sample the secreted microRNAs, and could be used as diagnostic tools for lung cancer. Materials & Methods: EBC samples from twenty treatment-naïve patients with pathologically confirmed lung cancer and twenty healthy subjects were profiled for miRNAs expression. Selected microRNAs were further validated, using quantitative-PCR, in an independent set of 10 subjects from both groups. Results: A total of 78 miRNAs were found to be significantly upregulated in the EBC of lung cancer patients compared to the control group. Six of these 78 miRNAs were shortlisted for validation. Of these, miR-31–3p, let7i, and miR-449c were significantly upregulated, exhibited good discriminatory power. Discussion: Differential expression of miRNAs secreted by lung cells could be quantitated in EBC samples, and could be used as a potential non-invasive tool for early diagnosis of lung cancer.