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The persistent non-biodegradable nature of plastic highlights the urgent need for effective waste management and resource conservation, underscoring the crucial importance of recycling and upcycling within a cradle-to-cradle framework. This research introduces an eco-friendly and straightforward upcycling process for plastic waste, which produces significant quantities of reduced graphene oxide through a carefully designed 2-stage pyrolysis method. To enhance the electrochemical properties of the reduced graphene oxide, they were doped with heteroatoms (i.e. nitrogen and phosphorus) via a hydrothermal route. Also, as the nature of the electrolyte plays a significant role in electrochemical analysis, a comparative evaluation of the supercapacitive performance of the heteroatom-doped reduced graphene oxide was conducted across various aqueous electrolytes, including 1 M H2SO4, 6 M KOH, and 2 M KCl, as well as hydrogel polymer electrolytes such as 1 M H2SO4/1 M PVA, 2 M KCl/1 M PVA, and 6 M KOH/1 M PVA. Our results demonstrate that synthesized material from waste plastic exhibits excellent performance, particularly when combined with a 1 M H2SO4 electrolyte, achieving the highest specific capacitance of 407.6 F/g. In conclusion, this study presents a cost-effective and sustainable approach to promoting a circular economy by repurposing waste plastic for energy storage applications.

Abstract Major research interests on quantum key distribution (QKD) are primarily focused on increasing 1. Point-to-point transmission distance (1000 km). 2. Secure key rate (Mbps). 3. Security of quantum layer (device-independence). It is great to push the boundaries in these fronts but these isolated approaches are neither scalable nor cost-effective due to requirements of specialised hardware and different infrastructure. Current and future QKD network requires addressing different set of challenges apart from distance, key rate and quantum security. In this regard, we present ChaQra—a sub quantum network with core features as 1. Crypto agility (integration in the already deployed telecommunication fibres). 2. Software defined networking (SDN paradigm for routing different nodes). 3. reliability (addressing denial-of-service with hybrid quantum safe cryptography). 4. upgradability (modules upgradation based on scientific and technological advancements). 5. Beyond QKD (using QKD network for distributed computing, multi-party computation etc). Our results demonstrate a clear path to create and accelerate quantum secure Indian subcontinent under national quantum mission.

In today’s cashless society, the increasing threat of credit card fraud demands our attention. To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. This study dives into the realm of machine learning, evaluating the performance of various algorithms - logistic regression (LR), decision tree (DT), and random forest (RF) - in detecting credit card fraud. Taking innovation, a step further, the study introduces the integration of a genetic algorithm (GA) for feature selection and optimization alongside LR, DT, and RF models. LR achieved an accuracy of 99.89 %, DT outperformed with an accuracy of 99.936 %, and RF yielded a high accuracy of 99.932 %, whereas GA-RF (a5) achieved an accuracy of 99.98 %. Ultimately, the findings of this study fuel the development of more potent fraud detection systems within the realm of financial institutions, safeguarding the integrity of transactions and ensuring peace of mind for cardholders.

An experiment was conducted during rainy (kharif) season of 2021 at the Agricultural Farm of Banaras Hindu University, Varanasi, Uttar Pradesh for evaluating the efficacy of pre-mix herbicide combinations applied as post emergence in managing weeds in transplanted rice. We assessed 9 different weed management treatments, including sole herbicide and pre-mix combinations and hand weeding in randomized block design. Results showed that herbicide combinations such as triafamone + ethoxysulfuron and bispyribac sodium + pyrazosulfuron effectively reduced weed density and biomass, improving crop growth attributes and ultimately yield. The study highlights the importance of using herbicide combinations for effective weed management in puddle-transplanted rice cultivation.

AIM: Understanding the inhibitory role of bioactives from a novel Nocardia sp. against P. aeruginosa's Quorum-Sensing (QS) regulatory pathways and fabrication of effective foley catheters. BACKGROUND: P. aeruginosa‘s antimicrobial resistance is due to its biofilm formation, which reduces drug efficacy. Nocardia sp. has emerged as significant genus that produces bioactives with inhibitory effects on the biofilm. METHODS: Samples from a termite nest were used to isolate rare actinomycetes, which were tested to inhibit biofilm formation by the pathogen P. aeruginosa PAO1. Molecular interactions and regulatory effects were investigated using various techniques. RESULTS: The study details a new strain from the rare actinobacterial genus Nocardia sp., isolated from a termite nest known for its bioactive potential. The isolate's bioactive preparations inhibited P. aeruginosa biofilm formation by 86.4% without affecting growth. The significant compounds of the crude extract identified by GC-MS were docked against LasR, RhlR, and PQS (QS proteins), which showed high binding scores. The downregulation of QS system genes like lasA, lasB, rhlA, rhlB, and pqsA supports the idea that secondary metabolites act on the two main regulators of the QS. The bioactives also inhibited virulent factors viz., pyocyanin, pyoverdine, rhamnolipid, and proteases by 51.2, 81, 27.2 and 62.1%, respectively Finally, the bioactive preparation showed the potential to inhibit biofilm formation on catheters. CONCLUSIONS: The study indicates that Nocardia sp. produces compounds that can reduce biofilm formation in P. aeruginosa by targeting the QS-controlled pathogenicity. This research can benefit medical devices like catheters to resist contamination in clinical settings.

ABSTRACT Cytomegalovirus (CMV) reactivation is a rare complication in patients treated with immune checkpoint inhibitors (ICIs), typically occurring after immunosuppressive therapy for immune‐related adverse events (irAEs). Here, we report a unique case of severe CMV gastritis in a patient receiving cemiplimab, an anti‐PD‐1 antibody, and talimogene laherparepvec (T‐VEC), an oncolytic virus, without prior irAEs or immunosuppressive treatment. A 63‐year‐old man with advanced cutaneous squamous cell carcinoma received cemiplimab for one year and a single T‐VEC injection for recurrent disease. He presented with progressive dyspepsia, significant weight loss, and malnutrition requiring total parenteral nutrition. Endoscopy revealed extensive gastric ulceration, and biopsies confirmed CMV gastritis. Initial treatment with intravenous ganciclovir improved his viral load but provided minimal symptomatic relief. After six weeks of therapy, biopsies showed resolution of CMV infection, and the patient transitioned to oral valganciclovir for prophylaxis while resuming cancer treatment. This case highlights the potential for CMV reactivation in patients undergoing ICI therapy, even without prior immunosuppression or irAEs. Notably, the concurrent use of T‐VEC raises questions about the interplay between oncolytic viruses, ICIs, and immune modulation. Although T‐VEC is not known to directly cause CMV reactivation, its role in amplifying immune responses warrants further investigation. As ICIs and oncolytic viruses become increasingly integral in oncology, clinicians must remain vigilant for rare infectious complications like CMV reactivation. Further research is needed to elucidate mechanisms, identify risk factors, and optimize management strategies for these events.

ABSTRACT Background Inguinal hernias are encountered commonly, but there is a lack of uniformity and standardization in repair techniques. There are a variety of repair methods, from open methods to laparoscopic approaches. The available laparoscopic techniques that exist for inguinal hernia repair are transabdominal preperitoneal (TAPP) repair and totally extraperitoneal (TEP) repair. This study aims to compare the outcomes of these two laparoscopic procedures. Methods The study was initiated after receiving ethical clearance. This single‐centric retrospective study included the patients that underwent laparoscopic inguinal hernia repair from January 1, 2020, to December 31, 2023. Patient records were reviewed and assessed against the inclusion criteria of the study. Systematic random sampling was applied to select patients from the records. A structured questionnaire was prepared for the purpose of data collection. The data were refined and analyzed using the SPSS software. Results A total of 200 patients (100 in each group) who underwent laparoscopic repair of an inguinal hernia were part of this study. The median (IQR) age of patients in the TAPP group was 50 (41.25–64.75) years, and that in the TEP group was 48.5 (39.25–64.75) years. The TAPP procedure took a median (IQR) time of 81 (77–88) min, while the TEP procedure took 69 (66–73) min to finish. The patients in the TAPP group returned to their normal activities after 15 (14–16) days of surgery, and the patients in the TEP group returned after 13 (11–14) days. The pain score at 1 week was found to be better in the TEP group. No difference was observed in complications or recurrence. Conclusion TEP repair was found to be superior to TAPP repair in terms of procedural time, return to normal activities, and pain score at 1 week.

Background: Rapid urbanization and lifestyle changes have led to a surge in type 2 diabetes mellitus (T2DM) rates and its microvascular complications. Objective: : To assess the association between microvascular complications and the stages of chronic kidney disease (CKD) in patients with type 2 diabetes mellitus and to compare HbA1c levels across the different stages of CKD. Methods: It was a cross sectional descriptive study conducted from 27th October 2022 to 26th April 2023 at outpatient Department of Nephrology, Liaquat University of Medical & Health Sciences (LUMHS), Jamshoro, Sindh, Pakistan. Data was collected after approval from the Ethical Research Committee (LUMHS/REC/-243). Informed consent was obtained from 136 patients, males and females, 45 to 65 years of age, type 2 diabetics (for more than 10 years) with diagnosed CKD. The modification of diet in renal disease (MDRD) formula was used to estimate eGFR for chronic kidney disease staging. Diabetic retinopathy was diagnosed using fundoscopic examination and diabetic neuropathy was confirmed by symptoms and positive signs on examination. Data analysis was performed using SPSS version 22. A p-value of ≤ 0.05 was deemed statistically significant. Results: Out of 136 patients, 75 (55.15%) were males and 61 (44.85%) were females. Mean age, eGFR, HbA1c, and duration of diabetes were 53.27 ±6.39 years, 54.55±27.26 mL/min/1.73 m², 9.02±1.67% and 14.06±4.05 years. The analysis showed that 8.1%, 40.4%, 22.05%, 22.05%, and 7.4% of patients were in CKD stages 1, 2, 3, 4, and 5, respectively. There was a significant association between diabetic microvascular complications, retinopathy and neuropathy, and the stages of CKD (p

Introduction: The National Health Summit is a policy discussion platform that advocates the policy statement stated by National Health Policy 2019 which results in a set of declarations to address issues in the health system. This study’s objective is to monitor the extent of declaration’s implementation, its facilitators-barriers, and to formulate recommendation for future National Health Summit declarations and implementation strategies. Methods: This is a qualitative study conducted by Nepal Medical Association. in November-December 2024. Ethical approval from Nepal Health Research Council, Ethical Review Board (Reference number: 1069). The study was designed using the Realistic Evaluation Framework to look into the “context” which are the external factors impacting implementation of the declarations, the “mechanism” which are key elements facilitating or hindering policy changes, and the “outcome” which were the tangible results of NHS declarations. Results: Review and analysis of the participant responses identified policy influene and advocacy as contexts. The mechanisms identified were political and bureaucratic challenges, collaboration, finaicial hurdles, commitment, accountability, leadership, evidence based practices. The outcome discussed were health system strengthening, health workforce, governance, innovation, advocacy and digaital transformations. Conclusions: Implementation of declaration is challenging, with fragmentation as major health issues, however, continuous advocacy has had a synergistic effect in implementation and prioritization of the policy.

Introduction: Lateral epicondylitis is a common cause of elbow pain, characterized by pain on the lateral side of the elbow joint. Its multifactorial etiology may result from repetitive micro-trauma, overuse of the forearm extensor muscles, use of vibrating tools, and anatomical variables causing lateral wear. In case of increased carrying angle of elbow, the course of extensor carpi radialis brevis during movement can be altered, thus increasing the tension on the extensor carpi radialis brevis tendon and increasing the risk for lateral epicondylitis. This study aims to find out the difference in carrying angle of elbow in patients with and without lateral epicondylitis. Methods: An observational cross-section study comparing carrying angle of elbow in patient with and without lateral epicondylitis was performed after ethical approval from the Institutional Review Committee (Reference number: 080/081-019) Patients presenting who had undergone radiographic examination of the elbow were recruited in the study and divided into the lateral epicondylitis group and the normal group. The mean carrying angle of elbow in both groups was compared using the parametric test. Results: This study included 82 participants, with 41 participants in the lateral epicondylitis group and 41 participants in the normal group. The mean carrying angle of elbow among patients with lateral epicondylitis group was 14.35±1.4 degrees; and the normal group, it was 12.40±1.26 degrees (p

Introduction: The Clavien-Dindo Classification is an easy way to grade any deviation from the post-operative course that is not a normal part of the procedure and does not indicate a failure to achieve the desired cure, regardless of the physician’s age or level of competence. We aimed to conduct a this study to determine the prevalence of post-operative complications after major abdominal surgery and to grade them using the Clavien-Dindo Classification. Methods: This was an observational cross-section study conducted after approval from the Institutional Review Committee (Reference Number: 656). A retrospective data of patients admitted to surgical wards for major surgery from January to June 2022 was included in the study. The post-operative complications were divided as per Clavien-Dindo classification from grade I to grade V. Descriptive statistics was used to analyse data. Results: Out of 78 patients, 45 (57.69%; 95% CI: 52.69%-62.69%) patients had complication after major abdominal surgery. Among them, 17 (37.78%) patients belonged to age group more than 60 years and 24 (53.33%) patients were female. Amongst the morbidities, diabetes mellitus was seen in 7 (15.56%) patients, hypertension in 6 (13.33%), chronic obstructive pulmonary disease in 5 (11.11%) cases and anaemia in 3 (6.67%) cases. There were 19 (42.22%) patient with Clavien-Dindo Classification Grade II and surgical site infection was observed in 26 (57.78%) cases. Conclusions: The prevalence of post-operative complication after major abdominal surgery in our study was found to be comparatively higher as compared to similar studies done in similar settings.

Recent advances in the development of ultra-low loss silicon nitride integrated photonic circuits have heralded a new generation of integrated lasers capable of reaching fiber laser coherence. However, these devices are presently based on self-injection locking of distributed feedback laser diodes, increasing both the cost and requiring tuning of laser setpoints for their operation. In contrast, turn-key legacy laser systems use reflective semiconductor optical amplifiers (RSOAs). While this scheme has been utilized for integrated photonics-based lasers, so far, no cost-effective RSOA-based integrated lasers exist that are low noise and simultaneously feature fast, mode-hop-free, and linear frequency tuning as required for frequency modulated continuous wave (FMCW) LiDAR or for laser locking in frequency metrology. Here we overcome this challenge and demonstrate a RSOA-based, frequency agile integrated laser, that can be tuned with high speed, with high linearity at low power. This is achieved using monolithic integration of piezoelectrical actuators on ultra-low loss silicon nitride photonic integrated circuits in a Vernier filter-based laser scheme. The laser operates at 1550 nm, features a 6 mW output power and a 400 Hz intrinsic laser linewidth, and allows ultrafast wavelength switching within 7 ns rise time and 75 nW power consumption. In addition, we demonstrate the suitability for FMCW LiDAR by showing laser frequency tuning over 1.5 GHz at 100 kHz triangular chirp rate with a nonlinearity of 0.25% after linearization and use the source for measuring a target scene 10 m away with a 8.5 cm distance resolution.

Climate change-driven diverse abiotic stresses continue to negatively affect plant growth and development, ultimately altering sustainable agricultural production and food security. Multi-omics approaches have revolutionized how plant biologists explore stress-responsive, adaptation, and tolerance mechanisms and pathways, driven by improvements in scientific practices. Therefore, this special issue was designed to feature the latest advancements in omics studies to understand and improve the stress acclimation and tolerance mechanisms in diverse plant species.

Facial emotion detection is crucial in affective computing, with applications in human-computer interaction, psychological research, and sentiment analysis. This study explores how deep convolutional neural networks (DCNNs) can enhance the accuracy and reliability of facial emotion detection by focusing on the extraction of detailed facial features and robust training techniques. Our proposed DCNN architecture uses its multi-layered design to automatically extract detailed facial features. By combining convolutional and pooling layers, the model effectively captures both subtle facial details and higher-level emotional patterns. Extensive testing on the benchmark Fer2013Plus dataset shows that our DCNN model outperforms traditional methods, achieving high accuracy in recognizing a variety of emotions. Additionally, we explore transfer learning techniques, showing that pre-trained DCNNs can effectively handle specific emotion recognition tasks even with limited labeled data.Our research focuses on improving the accuracy of emotion detection, demonstrating the model’s capability to capture emotion-related facial cues through detailed feature extraction. Ultimately, this work advances facial emotion detection, with significant applications in various human-centric technological fields.

BackgroundThe World Health Organization defined adolescent age group as a life span between 10 and 19 years. Adolescence is a phase of transition from childhood to adulthood, it is a unique stage for human development and a very sensitive period for laying the foundation of good health. Investment in this life-stage yields triple dividend. To promote health and well-being of adolescents, Government of India has launched various programs. An institutionalized evaluation of documenting achievement would strengthen these government led programs.MethodsA cross-sectional survey will be conducted among adolescents enrolled in government schools in the age group of 13–17 years. Two research instruments for students, principals, and for data collectors a checklist will be developed. Standard measures will be followed for data quality assurance. Data from students and principals will be collected in self-administration mode. Bivariate and multiple logistic regression will be employed for data analysis.ResultsThe study protocol for the school-based adolescent health survey will be implemented in Himachal Pradesh, India. The survey results will be disseminate both at state and national level.ConclusionThe study findings will provide policymakers and program planners with scientific evidence on key thematic areas essential for adolescent development and health outcomes. These insights will help identify and prioritize specific focus areas within the school health program, guiding more effective implementation strategies to address the unique needs of adolescents.

Introduction The evolving landscape of inflammatory bowel disease (IBD) necessitates refining colonoscopic surveillance guidelines. This study outlines methodology adopted by the British Society of Gastroenterology (BSG) Guideline Development Group (GDG) for updating IBD colorectal surveillance guidelines.Methods and analysis The ‘Grading of Recommendations, Assessment, Development and Evaluation’ (GRADE) approach, as outlined in the GRADE handbook, was employed. Thematic questions were formulated using either the ‘patient, intervention, comparison and outcome’ format or the ‘current state of knowledge, area of interest, potential impact and suggestions from experts in the field’ format. The evidence review process included systematic reviews assessed using appropriate appraisal tools. An extensive list of potential outcomes was compiled from literature and expert consultations and then ranked by GDG members. The top outcomes were identified for evidence synthesis in three key areas: utility of surveillance in IBD, quality of bowel preparation and use of advanced imaging techniques in colonoscopy for IBD. Risk thresholding exercises determined specific risk levels for different surveillance strategies and intervals. This approach enabled the GDG to establish precise thresholds for interventions based on relative and absolute risk assessments, directly informing the stratification of surveillance recommendations. Significance of effect sizes (small, moderate, large) will guide the final GRADE assessment of the evidence.Ethics and dissemination Ethics approval is not applicable. By integrating clinical expertise, patient experiences and innovative methodologies like risk thresholding, we aim to deliver actionable recommendations for IBD colorectal surveillance. This protocol, complementing the main guidelines, offers GDGs, clinical trialists and practitioners a framework to inform future research and enhance patient care and outcomes.

Due to global warming and adverse climatic conditions, researchers have focused on the survival of plant species. Certain plant species are nearly extinct and thus must be saved. However, this requires a comprehensive understanding of the species, which involves expertise and time. Many researchers have expressed their interest in developing automated plant species recognition techniques by analyzing various issues, such as expertise, complex venation structure, interclass and intraclass similarity, occluded leaves, problems with feature extraction of premature and dried leaves, etc. Classifiers and other machine learning and deep learning algorithms that can accurately categorize and extract data can be further developed. In this article, we presented a systematic review of effective machine learning methods and image processing and deep learning algorithms for plant species recognition that have been used by researchers. The most common research problems, recommendation techniques, methodological analysis, and datasets presented in these selected studies were examined. The review and analysis indicated that there is scope for improvement in the case of multi-feature fusion, such as concatenation of shape, size, margin, texture, venation contour, etc. The extraction of characteristics of leaves in different stages, such as seedlings, tiny, mature, and dried, can be considered for feature extraction. Hybridization of machine intelligence approaches can also be implemented for multifeatured fusion extraction which might increase the performance of the system. We also investigated evaluation metrics, challenges, trends, and the most interesting ideas for future studies.

Dopants are frequently used to improve graphitic carbon nitride (gCN) photoactivity. As a doping source, phosphomolybdic acid (PMA) can activate doping sites inside the gCN lattice, resulting in 2D Mo:P‐gCN porous material. However, the gradual loading of the PMA fraction has no systematic improvement in the Mo:P‐gCN photoactivity. For improving the optoelectronic properties of Mo:P‐gCN, its textural geometry is a controllable parameter that can provide enhanced photonic properties, achievable by shaping its morphology through a crystalline template structure, namely, photonic crystals (PCs). Herein, a doped PC material is made of Mo:P‐gCN and PCs and labeled as Mo:P‐gCN/PCs. The impact of PCs is highlighted in the structural, electronic, and optical performances of Mo:P‐gCN. A well‐defined 3D crystalline network is evidenced by microscopic measurements (scanning electron microscopy, AFM, focused ion beam). Mo:P‐gCN/PCs shows a hydrogen production rate (750 μmol g−1 h−1) one time higher than Mo:P‐gCN and 6 times higher than pure gCN. The synthesis strategy proposed in this work leads simultaneously to the Mo:P codoping effect provided by PMA and the slow photon effect due to the PC structure, offering a novel strategy to improve the gCN photoactivity by simultaneously applying polyoxometalates as modifiers and polystyrene opals as templates.

Pesticides are widely used to control pests, but their widespread use raises concerns regarding potential health risks for humans. There are several routes through which pesticides can be ingested, inhaled, and absorbed, resulting in acute and long-term health consequences. This systematic review synthesizes the available evidence regarding the health risks and long-term effects of pesticide exposure, with a particular focus on epidemiological and toxicological studies. A systematic review was conducted by searching scientific databases i.e. Scopus, and Web of Science for peer-reviewed articles published between 2000 and 2024. Studies were selected based on their focus on pesticide exposure, health risks, and long-term effects. Meta-analysis was conducted where sufficient homogeneity of outcomes allowed. This review identified consistent associations between chronic pesticide exposure and non-communicable diseases, including cancer, neurological disorders, and endocrine disruptions. An increased incidence of respiratory issues and neurodegenerative diseases was often associated with occupational exposure to pesticides. People exposed for a prolonged or high intensity time period, particularly agricultural workers, were more likely to experience long-term health effects. There are a number of factors that influences the ability to draw definitive conclusions, including variations in pesticide types, exposure levels, and health outcomes. Chronic exposure to pesticides presents significant health risks, particularly for individuals in high-exposure environments like agriculture. While evidence indicates strong associations with several long-term health conditions, additional research is necessary to elucidate dose-response relationships and mechanisms of action. This review underscores the necessity for enhanced regulatory measures and improved safety protocols to mitigate pesticide-related health risks.

Summary: Brain waste clearance from the interstitial fluid environment is challenging to measure, which has contributed to controversy regarding the significance of glymphatic transport impairment for neurodegenerative processes. Dynamic contrast enhanced MRI (DCE-MRI) with cerebrospinal fluid administration of Gd-tagged tracers is often used to assess glymphatic system function. We previously quantified glymphatic transport from DCE-MRI data utilizing regularized optimal mass transport (rOMT) analysis, however, information specific to glymphatic clearance was not directly derived. To fill this knowledge gap, we here implemented unbalanced rOMT analysis which allows for assessment of both influx and clearance. Dynamic influx/clearance brain maps were derived from rTg-DI rats with cerebral amyloid angiopathy (CAA) and TgSD-AD rats with Alzheimer’s disease (AD). The rTg-DI rats with severe CAA disease exhibited abnormal influx/clearance kinetics, while TgSD-AD rats with a moderate Aβ plaque load exhibited normal transport suggesting that different Aβ lesions and their overall burden differentially impact glymphatic system function.

BackgroundThe sinoatrial node (SN) generates the heart rate (HR). Its spontaneous activity is regulated by a complex interplay between the modulation by the autonomic nervous system (ANS) and intrinsic factors including ion channels in SN cells. However, the systemic and intrinsic regulatory mechanisms are still poorly understood. This study aimed to elucidate the sex-specific differences in heart morphology and SN function, particularly focusing on basal HR, expression and function of hyperpolarization-activated HCN4 and HCN1 channels and mRNA abundance of ion channels and mRNA abundance of ion channels contributing to diastolic depolarization (DD) and spontaneous action potentials (APs).MethodsBody weight, heart weight and tibia length of 2- to 3-month-old male and female mice were measured. Conscious in-vivo HR of male and female mice was recorded via electrocardiography (ECG). Unconscious ex-vivo HR, stroke volume (SV) and ejection fraction (EF) were recorded via echocardiography. Ex-vivo HR was measured via Langendorff apparatus. Volume of atria, ventricles and whole hearts were measured from the ex-vivo hearts by microcomputed tomography (micro-CT). Immunohistochemistry targeting HCN4 and HCN1 was conducted in the SN and RA tissues from both male and female hearts. The funny current (If) of SN cells in 1 nM and following wash-on of 1 μM isoproterenol (ISO) were recorded via whole cell patch clamp. The APs of SN tissue were recorded via sharp microelectrode and optical mapping of membrane voltage. The relative abundance of mRNAs was measured in male and female mice by qPCR.ResultsHeart weight to tibia length ratio and heart volume of females were significantly smaller than males. Unconscious in-vivo HR in male mice was higher than that in females. Conscious in-vivo HR, ex-vivo HR, SV, and EF showed no notable difference between male and female mice. Immunohistochemistry revealed HCN4, HCN1, and the sum of HCN4 and HCN1, expression in the SN was notably elevated compared with the RA in both male and females, but there was no sex difference in these channels expression. There were also no significant sex differences in the V0.5 of If in SN cells in the presence of 1 nM ISO, however wash-on 1 μM ISO in the same cells induced a significantly increased shift of V0.5 to more positive voltages in males than in females. The expression of mRNA coding for adrenergic receptor beta-1 (Adrb1) and cholinergic receptors muscarinic 2 (chrm2) in male mice was higher compared with that in female mice. Early diastolic depolarization (EDD) rate in APs from peripheral SN (pSN) from male mice were higher than these in female mice. Mice of both sexes showed equivalent frequency of SN APs and spatial localization of the leading site in control, and similar significant response to ISO 100 nM superfusion.ConclusionMales display faster in-vivo HR, but not ex-vivo HR, than females associated with increased expression of Adrb1 in male versus female. This suggests a possible difference in the β-adrenergic modulation in males and females, possibly related to the greater ISO response of If observed in cells from males. The role of hormonal influences or differential expression of other ion channels may explain these sex-specific variations in HR dynamics. Further investigations are necessary to pinpoint the precise molecular substrates responsible for these differences.

Introduction: Traumatic Brain Inury (TBI) is a neurodegenerative disorder. Oleanolic acid (OA) is a pentacyclic triterpenoid showed a large number of neuroprotective effects such as anti-alziermer, anti-ischemic etc. Objectives: In the present investigation an effort has been made to evaluate the neuroprotective effect of OA and molecular mechanism is involved in TBI. Methods: Weight drop model has been utilized to induce moderate TBI and assess the neuroprotective effect of OA at the doses 50 mg/kg and 100 mg/kg intraperitoneally (i.p.) in mice. After 30 min of injury, OA was administered. The neuroprotective effect of OA was observed after 24 h and 21 days of drug administration. Neurological behaviour (neurological score, open field test, beam walk and morris water maze test) were assessed before sacrifice the animal at different time interval. Oxidative stress (MDA, CAT, GSH, SOD and Nitrite), neuro-inflammatory cytokines (NF-ĸB, TNF-α, IL-6 and IL-1β and mitochondrial dysfunction were evaluated. Results: OA at both the doses significantly improved the neurological behaviours as compared to vehicle treated group. OA also showed significant anti-oxidant and anti-inflammatory effect via regulated the oxidative stress (MDA, CAT, and GSH) and inflammatory cytokines (TNF-α, IL-6, NF-ĸB and IL-1β) on 24 h and 21st day of injury. OA significantly reduced the mitochondrial dysfunction by regulated various complex enzyme activity. Conclusion: OA can be potentially considered as a neuroprotective compound for therapeutic management of TBI.

The primary goal of the current investigation is to explore the molecular interactions between aspirin (2-Acetoxybenzoic acid) and normal saline (0.9% w/v aqueous NaCl) at various concentrations (0.001–0.010) m and temperature (T= 300.15–315.15) K. To understand the various possible molecular interactions, volumetric, acoustic, conductometric and viscometric parameters were evaluated using experimental measured values of densities (ρ), ultrasonic velocities (u), specific conductance (κ), and viscosities (η). The results derived from these parameters have been examined in terms of drug–solvent and drug-drug interactions. The results of physicochemical studies indicate that aspirin exhibits strong interactional and structure–forming behaviour in normal saline. These results were well supported by UV-visible spectral studies which indicate the existence of intense aspirin–normal saline interactions in the form of modification in absorption maximum and absorption wavelength. The cyclic voltammetric studies were also performed to explore the oxidation/reduction behaviour and effect of aspirin on hemolysis. The experimental analysis of these studies provides valuable insights for better drug design, drug delivery, compatibility, potential safety, and biological relevance for the pharmaceutical and health industries.

A novel self-propelled smart herbicide applicator (1600 mm × 500 mm × 1100 mm) was developed using machine vision to target weeds and apply herbicides variably in wide row-spaced crops. The applicator integrates a weed detection system, smart sprayer, mechanical components, and power transmission system. The weed detection system utilizes a webcam based image sensor, with images processing performed through MATLAB's image processing toolbox. The system processed images using an RGB-based algorithm (R > G + 5, & R > B + 5) to separate weeds from soil. Weed density, calculated as the green area percentage (ratio of green pixels to total pixels), was sent to a microcontroller-operated smart sprayer via a serial port. The microcontroller regulates herbicide flow by controlling the flow control valve with a servo motor, following an algorithm based on green area percentage. Field tests demonstrated a 40 % reduction in herbicide use, 91.26 % weeding efficiency, and 63.4 % field efficiency. The smart herbicide applicator's unique features include real-time measurement, portability, and precise herbicide application for wide-row crops, even at night. It also reduced environmental hazards and drudgery among farmers.

Plants encounter various biotic and abiotic stresses throughout their lifecycle, which can adversely impact their productivity and lead to yield loss. This ultimately results in a significant decline in food production with global pressure on declining agriculture. The agrochemical pesticides have been proven effective in controlling various diseases in major crops, their excessive use poses significant risks to the environment and human health and negatively impacts non-target host microbiota. Novel approaches in the form of host plant microbiome as an alternative strategy to overcome these stresses have recently gained increasing attention. Emerging evidence suggests that plants subjected to biotic and abiotic stress experience diverse microbial local (diffusible) and distant (volatile organic compounds) metabolites and respond with the expression of defense genes (transcriptomic variations) that result in changing leaf and root exudates and alter resident microbial community structure. The relative abundance of symbiotic microorganisms under stress may favor plant survival and enhance adaptation during stress. Thus, to counter the negative effects, introducing an efficient microbiome into the host crop is highly desirable due to the attractive functional potential of the phytomicrobiome. Here, we summarize more effective and reproducible strategies to use microbial activities in crop production and protection to promote a suitable agricultural environment. In addition, detailed molecular mechanisms of phytomicrobiome in stimulating plant immune system, the interaction between microbiome and plant defense system, and defense strategies of microbiome against biotic and abiotic factors have been described. Finally, we explored the plant-based strategies for microbiome engineering and bioinformatics methods to predict microbiome functions.

Background and objective: Graft versus host disease (GVHD) is the common complication seen after allogeneic hematopoietic stem cell transplantation (HSCT) and a pleomorphic syndrome that resembles autoimmune and other immunologic disorders, leading to profound immune dysregulation and organ dysfunction. The most common targets of GVHD are skin, gastrointestinal tract and liver. GVHD is classified as acute graft versus host disease (aGvHD) if it occurs within the first 100 days after HSCT and chronic graft versus host disease(cGVHD) if it occurs after day 100. The skin is most frequently and earliest affected by aGvHD, followed by the gastrointestinal tract and liver. An ideal biomarker would predict the onset and severity of clinical acute GVHD and help to direct management, and this is an area of active research regarding the use of biomarkers for diagnosis and prognosis of acute GVHD. Recently, elafin has been identified as a potential plasma biomarker for aGVHD.Method: We searched the databases PubMed, Cochrane library, and medRxiv for all studies investigating the Diagnostic or prognostic role of elafin in GVHD. We set the search strategy incorporating the search terms, ‘elafin’, ‘graft versus host’, and ‘GVHD’, and operated using the Boolean operators ‘AND’, and ‘OR’. Thus, retrieved articles were then exported on an Excel® sheet, and duplicates were removed. The systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After selecting the study based on inclusion criteria, data on study characteristics and biomarker description was extracted on a pre-determined data extraction table on the Microsoft Excel version. The quality assessment of the included studies was determined using the QUIPS tool.Result: The search revealed 547 studies and 6 studies that met the eligibility criteria of this review have been included. The major finding of our study is the significant elevation of elafin in skin aGVHD.Conclusion: Elafin is a significant biomarker for diagnosis and prognosis of skin aGVHD and should be assessed within 2 weeks of the onset of the disease.

ABSTRACTFloods pose devastating effects on the resiliency of human and natural systems. flood risk management challenges are typically complicated in the transboundary river basin due to conflicting objectives between multiple countries, lack of systematic approaches to data monitoring and sharing, and limited collaboration in developing a unified system for hazard prediction and communication. An open-source, low-cost modeling framework that integrates open-source data and models can help improve our understanding of flood susceptibility and inform the design of equitable risk management strategies. This study integrates open-source datasets and machine -learning techniques to quantify flood susceptibility across the data-scare transboundary basin. The analysis focuses on the transboundary Gandak River Basin, spanning China, Nepal, and India, where damaging and recurring floods pose serious concern. flood susceptibility is assessed using four widely used machine learning techniques: Long-Short-Term-Memory, Random Forest, Artificial Neural Network, and Support Vector Machine. Our results exhibit the improved performance of Artificial Neural Network and Support Vector Machine in predicting flood susceptibility maps, revealing higher vulnerability in the southern plains. This study demonstrates that remote sensing and machine learning can improve flood prediction, hazard mapping, and susceptibility analysis in a data-scare environment.

In this study, we concern with the improved estimation of sensitive variable when there is non-response and measurement error on sensitive variable but the auxiliary variable is non sensitive in nature. For the purpose, we propose an improved estimator in the presence of non-response and measurement error using Optional randomized response technique (ORRT) under simple random sampling without replacement (SRSWOR). The properties of the estimator have been studied and the efficiency conditions are obtained in comparison to the mean estimator, ratio estimator and Zhang’s estimator. Simulation study based on hypothetical populations has been carried out to demonstrate the performance of the proposed estimator at its optimum among others. It has been observed that the proposed estimator is more efficient than other considered estimators in term of having higher Percent Relative Efficiency (PRE).

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/

In this paper, a modified method is used to approximate the solution to the time-fractional n-dimensional Navier–Stokes equation. The modified method is the Variational Iteration Transform Method, which is implemented in the equation whose fractional order derivative is described in the Caputo sense. The proposed method's findings are presented and examined using figures. It is demonstrated that the proposed method is efficient, dependable, and simple to apply to various science and engineering applications.

This study aimed to evaluate the safety and efficacy of oral administration of Ashwagandha root extract (ARE) in cats subjected to different types of stress. A randomized, double-blind, placebo-controlled veterinary clinical trial was conducted with sixteen healthy pet cats. Each cat was randomly assigned to receive ARE (15 mg/kg body weight) or a placebo and were subjected to four types of stress over a period of one month. No detrimental changes were observed in ALP, ALT, AST, glucose, creatinine, and BUN levels on day 30 with ARE with all values within the physiological range. Additionally, in the ARE-treated group, the protein (p

ABSTRACTThe objective of this work is to study the effects of different coating materials (black paint, thermoplastic elastomers, black vinyl tape and uncoated surfaces) in Laser Shock Peening (LSP) processing of laser powder bed fusion (L-PBF) printed thin AlSi10Mg tubes. The investigations were carried out with pre-identified optimal LSP parameters for the AlSi10Mg material. The study was performed using an analysis of surface residual stresses, where a maximum compressive stress of −85 MPa has been reached, while the depth of the compressive regime stays till 2 mm. The microstructural study reveals the multifold dislocation of the grains and formation of new sub-grains, thus changing the grain boundaries in all experiments due to lattice strain development by LSP. Microhardness has also shown alteration after LSP and accounted for a 5–15% increase in it after LSP. Surface properties, such as roughness and volumetric parameters, have also shown changes after LSP processing.

AbstractThis study aims to validate a model on the green entrepreneurial intention (GEI), attitude, subjective norms (SN), perceived behavioral control (PBC) and green entrepreneurial behavior (GEB) among students pursuing higher education in Delhi &NCR. This study also captures the mediating role of GEI between attitude, SN, PBC and GEB. About 327 samples were used for data analysis purposes using Smart-PLS. The result of the study suggests that attitude, SN and PBC have a significant positive relationship with GEB. Whereas, GEI as a mediator has no effect between attitude and GEB, while GEI as a mediator has a significant effect between SN, PBC and GEB. GEB among young professionals has been found a changing paradigm in the area of entrepreneurial practices in India. It has a significant impact on the sustainability aspects of society as well as the organization. Millennials get ignited toward such orientation which improved global environmental dynamics. The result of the study provides a significant source to the policymakers to promote GEBs among professionals.

Tumour is a very serious and dangerous health risk, which is defined as a mass or lump of tissue formalized by the aggregation of abnormal cells. In this paper, we have presented a comparative and chaotic investigation of tumour and effector cells using non-integer order tumour immune dynamical model. Also, we have looked at the interactions between different tumour cell populations and immunological composition using a model of a real-world medical research problem. Additionally, stability analysis of the proposed model is established with the help of fixed-point iteration. Further bifurcation diagrams, as well as phase portraits, have been used to study the proposed system numerically and to analyse its behaviour. For numerical simulations, we have used the Hermite wavelet operational matrix and Taufik-Atangana methods using Caputo fractional derivatives. models. The main objective of the paper is to convert nonlinear fractional systems into algebraic equations. Our findings will be valuable to biologists in cancer treatment.

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human and natural ecosystems in the Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing and machine learning techniques is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). Vulnerability is particularly pronounced in low-elevation and low-slope southern downstream areas (Central part of Lao PDR). The results reveal that 36%–53% of the basin’s total area is highly susceptible to flooding, emphasizing the dire need for coordinated floodplain management strategies. This research uses freely accessible remote sensing data, addresses data scarcity in flood studies, and provides valuable insights for disaster risk management and sustainable planning in Lao PDR.

The increased utilization of opioids in low– and middle-income countries (LMICs) presents a growing threat of opioid-related abuse, misuse and diversion. Pharmacists, as integral members of healthcare teams, are responsible for dispensing and monitoring opioid use and hold a pivotal role in opioid stewardship within LMICs. This commentary describes the potential and multifaceted roles of pharmacists in opioid stewardship in resource-constrained settings and highlights appropriate strategies for promoting responsible opioid utilization.Opioid stewardship involves prioritising evidence-based prescribing, management and practices for pain management. It includes measures such as incorporating prescription drug monitoring programmes for appropriate opioid prescription, implementing safe disposal through drug take-back programmes, promoting non-opioid pain management, addressing the opioid addiction stigma, tapering opioid dose, educating patients and caregivers, establishing drug information centers, providing rehabilitative services and integrating collaboration with communities and experts.The combined difficulties of restricted access to healthcare resources and services coupled with low levels of literacy worsen the susceptibility to opioid abuse, misuse, and diversion in LMICs. Early detection, assessment and implementation of interventions to optimise opioid use are imperative for ensuring safe and effective opioid utilization, thereby mitigating the risks of overdose and addiction. The involvement of pharmacists in promoting safe and effective opioid utilization through education, monitoring, collaboration, and policy advocacy serves as a critical component in bridging existing gaps in opioid stewardship within LMICs.

In this research, we aim to propose a non-integer order mathematical model that illustrates the detrimental impact of illegal logging on forest biomass. The model is based on the idea that soil nutrients play a vital role in the growth of forestry biomass. However, the growth of forestry biomass is hindered by the harvesting of trees for industrial expansion. To develop the model, we use a fractional order approach, which allows us to explore the impact of conservation efforts on the extension of industries. If the amount of forestry biomass that is illegally harvested exceeds a certain threshold, the system becomes unstable. To investigate the proposed model's solution, we use fixed-point theory, which is highly effective in determining the existence and uniqueness of the solution. We also use Ulam-Hyers stability to analyse the stability of the proposed model. We have also employed new versions of fractional numerical techniques using power law and exponential decay. Our proposed model provides more details through numerical calculations with fractional orders different from integer ones. Moreover, we have addressed all significant solution behaviours in the fractal-fractional system and parameter values. The graphical representation offers a better understanding of the forestry biomass model, allowing us to identify the direction and stability of solutions. From various perspectives, we analysed the features of solutions to this fractal-fractional model for stopping illegal logging.

Phytoconstituents are being emphasized as chemotherapeutic agents, as well as probable anticancer agents. The purpose of the present research was to assess the antioxidant, anti-inflammatory, and anticancer properties of crude ethanolic extracts and bioactive fractions of Adiantum venustum D. Don collected from three sites in Himachal Pradesh, India. The bioactive phytoconstituents were examined qualitatively and quantitatively in crude ethanolic extracts and fractions of all sites, and biological activities were analyzed. The potential presence of terpenoids, saponins, tannins, phenols, and flavonoids was examined, and it came to light that BF-II [n-butanol fraction of Site-II (2000 m altitude)] had a significantly higher phytochemical concentration. Additionally, the crude ethanolic extracts and fractions were examined for antioxidant and anti-inflammatory properties, and BF-II was found to have potential antioxidant and anti-inflammatory activity, as verified by the IC50 values (lowest half inhibitory concentration) in the DPPH (57.86 ± 0.72 μg/mL), reducing power assay (45.20 ± 0.72 μg/mL), FRAP (73.12 ± 0.23 μM Fe equivalent), BSA (56.76 ± 0.25 μg/mL), and EAA (EAA59.84 ± 0.13 μg/mL). Following that, the anticancer potential was investigated further using the MTT assay on the HeLa cell line, MTT assay revealed that BF-II possesses significantly higher anticancer potential. Furthermore, the dominant crude ethanolic extract and fraction were further investigated using gas chromatography-mass spectroscopy, and high-performance thin layer chromatography techniques from the selected plant. Overall, the study suggests that the n-butanol fraction of A. venustum possesses significantly higher therapeutic value but is still not explored to the extent of its potential.

Melanoma is a type of skin cancer that poses significant health risks and requires early detection for effective treatment. This study proposing a novel approach that integrates a transformer-based model with hand-crafted texture features and Gray Wolf Optimization, aiming to enhance efficiency of melanoma classification. Preprocessing involves standardizing image dimensions and enhancing image quality through median filtering techniques. Texture features, including GLCM and LBP, are extracted to capture spatial patterns indicative of melanoma. The GWO algorithm is applied to select the most discriminative features. A transformer-based decoder is then employed for classification, leveraging attention mechanisms to capture contextual dependencies. The experimental validation on the HAM10000 dataset and ISIC2019 dataset showcases the effectiveness of the proposed methodology. The transformer-based model, integrated with hand-crafted texture features and guided by Gray Wolf Optimization, achieves outstanding results. The results showed that the proposed method performed well in melanoma detection tasks, achieving an accuracy and F1-score of 99.54% and 99.11% on the HAM10000 dataset, and an accuracy of 99.47%, and F1-score of 99.25% on the ISIC2019 dataset.• We use the concepts of LBP and GLCM to extract features from the skin lesion images.• The Gray Wolf Optimization (GWO) algorithm is employed for feature selection.• A decoder based on Transformers is utilized for melanoma classification.

In this study, a series of platinum (Pt)-doped strontium iron oxide (SrFe2O4) catalysts with varying particle sizes were synthesized through the four different catalysis synthesis methods such as solution combustion synthesis (SCS), co-precipitation (Co-PPT), oxalic acid assisted sol-gel (OXA) and, hydrothermal (HT). The objective was to investigate the impact of particle size on the catalytic activity and long-term stability of these four catalysts. The XRD and Raman results confirmed the formation of the SrFe2O4 perovskite structure. HRTEM, SEM, and other characterizations revealed a clear correlation between the synthesis conditions and the resulting particle sizes. The highest%CH4 conversion was around 95 % for the catalyst prepared through Solution combustion synthesis and the catalyst was found to be thermally stable up to. 100 h at 800 °C with a negligible variation of conversion while maintaining the H2/CO ratio at 2.0. To gain insight into catalytic activity, stability, and selectivity of catalysts we have performed Temperature-programmed surface reaction (TPSR) at a controlled temperature ramping program. This study also includes the study of coke deposition on the spent catalysts through different characterization techniques. Furthermore, we have performed a kinetic study to find the initial rate of the reaction and the activation energy of the Pt-doped SrFe2O4 catalyst and it has been found that activation energy was 35 KJ/mol for the catalyst Pt/SrFe2O4 synthesis through the solution combustion method.

The human gut microbiota is a complex community comprising hundreds of species, with a few present in high abundance and the vast majority in low abundance. The biological functions and effects of these low-abundant species on their hosts are not yet fully understood. In this study, we assembled a bacterial consortium (SC-4) consisting of B. paravirosa, C. comes, M. indica, and A. butyriciproducens, which are low-abundant, short-chain fatty acid (SCFA)-producing bacteria isolated from healthy human gut, and tested its effect on host health using germ-free and human microbiota-associated colitis mouse models. The selection also favored these four bacteria being reduced in abundance in either Ulcerative Colitis (UC) or Crohn’s disease (CD) metagenome samples. Our findings demonstrate that SC-4 can colonize germ-free (GF) mice, increasing mucin thickness by activating MUC-1 and MUC-2 genes, thereby protecting GF mice from Dextran Sodium Sulfate (DSS)-induced colitis. Moreover, SC-4 aided in the recovery of human microbiota-associated mice from DSS-induced colitis, and intriguingly, its administration enhanced the alpha diversity of the gut microbiome, shifting the community composition closer to control levels. The results showed enhanced phenotypes across all measures when the mice were supplemented with inulin as a dietary fiber source alongside SC-4 administration. We also showed a functional redundancy existing in the gut microbiome, resulting in the low abundant SCFA producers acting as a form of insurance, which in turn accelerates recovery from the dysbiotic state upon the administration of SC-4. SC-4 colonization also upregulated iNOS gene expression, further supporting its ability to produce an increasing number of goblet cells. Collectively, our results provide evidence that low-abundant SCFA-producing species in the gut may offer a novel therapeutic approach to IBD.

Micro drilling using Electrical Discharge Machining (MD-EDM) has emerged as a prominent technique in precision manufacturing, offering the capability to create small and intricate holes. This study focuses on investigating the influence of input process parameters on nickel-based superalloy (Ni3 (Ti, Al)) material, while the electrode materials studied are copper, aluminum and tungsten, with varying electrode diameters of 0.4 mm, 0.5 mm and 0.6 mm. The research encompasses an L9-orthogonal array (L9-OA) in which the Machining Voltage, Peak Current (IP), Pulse off time (Poff) and Servo Standard Voltage (SVR) are varied. Three levels of Machining Voltage (4 volts, 6 volts and 8 volts) and IP are selected. Moreover, scanning electron microscope (SEM) imaging is employed to observe and analyze the morphological characteristics of the drilled holes. The experimental results reveal that the choice of electrode material and diameter significantly influences the SR of the drilled holes. Copper electrodes demonstrate the lowest SR, followed by tungsten and aluminum electrodes. Smaller electrode diameters result in improved surface finish due to reduced discharge energy concentration. Additionally, increasing the Machining Voltage leads to larger discharge energy and heat, resulting in increased SR. SEM imaging demonstrates distinct differences in hole morphology based on the electrode material and diameter, with copper electrodes producing cleaner and more precise holes with minimal recast layer formation compared to aluminum and tungsten electrodes. The influence of Machining Voltage is observed through the presence of craters and debris in higher voltage conditions.

Traditional weed management often involves blanket herbicide spraying, resulting in substantial herbicide wastage, environmental concerns, and herbicide resistant issues. Smart spraying systems utilizing robotics and sensors technologies can minimize herbicide usage and provide a sustainable solution for site-specific weed management. A machine vision-based spraying system was designed and developed for weed identification and precise spray application onto the target weeds. The sprayer platform utilizes a deep learning YOLOv4 model to accurately recognize multiple weed species, facilitating targeted spray application. The platform is equipped with an FLIR RGB camera for real-time image acquisition and Nvidia Jetson AGX Orin edge device for deploying weed detection deep-learning model. The GPIO pins of Nvidia Jetson were utilized to activate relay, providing precise on/off control over the TeeJet solenoid valves for spot spraying. Both indoor and field experiments were conducted to evaluate and compare the performance of vision-based sprayer system for weed identification and precise spraying onto the target weeds. In the indoor experiment, the sprayer system showed the average effective spraying rate of 93.33 %, with the precision of 100 % and recall of 92.8 %. Conversely, the field experiment resulted in a slightly lower average effective spraying rate of 90.6 %, while maintaining a precision of 95.5 % and a recall of 89.47 %. The reduced accuracy of the spraying system in field experiment was due to varying outdoor conditions such as lighting, shadows, and wind velocity. Overall, the result of this study demonstrates the spraying system's potential for targeted herbicide application onto the grid cells containing weeds, effectively reducing herbicide usage and overall weed management costs.

p-Nitrophenol (PNP) is a nitroaromatic compound that poses a significant threat to human health and the environment due to its carcinogenic, mutagenic, cytotoxic, and embryotoxic properties at low concentrations. Therefore, the selective and sensitive detection of PNP is crucial for both human health and environmental monitoring. Boron (B) and Nitrogen (N) doped quantum dots (B,NQDs) have been found to be effective as blue-green luminescent materials for this purpose. These B,NQDs were synthesized using a one-step hydrothermal method, resulting in the formation of highly stable quantum dot. The addition of trace amounts of PNP, the luminescence of the B,NQDs was significantly quenched, which was found to be linearly dependent on the PNP concentration in the range of 100pM to 6 μM. Further analysis of steady-state absorption and emission, along with photoluminescence decay dynamics, revealed the formation of both static and dynamic quenching complexes. Our simple fluorimetry-based sensor demonstrated an impressive limit of detection (LOD) of 9.08 nM, making it highly selective and sensitive for the detection of PNP. Additionally, the B,NQDs exhibited exceptional stability with respect to pH, UV exposure, salinity, and storage conditions. Finally, we successfully demonstrated the detection of PNP in real water systems and pesticides.

The study aimed to develop a silkworm pupae protein-based film for enhancing the lipid oxidative and microbial stability of cheddar cheese. The bioactive properties were imparted to the silkworm pupae protein-based film using an optimum level (2.0%) of Catharanthus roseus leaf extract-based nanoparticles (Cat-Ros-NPs) synthesised following a green method. The cheese samples were packaged within the treated film (T2, containing 2.0% Cat-Ros-NPs) and compared with control samples [control (cheese samples without any film), T0 (cheese samples within the films without any bioactive agent) and T1 {cheese samples within the films containing 2.0% C. roseus leaf extract (Cat-Ros-Ext)}] during 90 days trial (4±1 °C). The addition of bioactive agents (Cat-Ros-Ext or Cat-Ros-NPs) increased the thickness (µm) as well as density (g/ml) of the film, thereby decreasing the transmittance (%), solubility (%), moisture content (%), and water-vapour transmission rate (mg/mt2). Both the bioactive agents increased the redness (a*) and yellowness (b*) whereas decreased the brightness (L*) of the film. The films enhanced the antioxidant and antimicrobial properties of the enclosed cheese samples during storage and the highest values were recorded for the samples packed within T2 films. The cheese samples packaged within T2 and T1 films showed significantly lower values for lipid oxidation and microbial counts. This positive effect of the films (T2 and T1) was also recorded on protein oxidation (total-carbonyl content) after day 30 and sensory quality after day 60. Our results indicate the successful use of silkworm pupae protein for the development of bioactive packaging for cheddar cheese.

The implementation of a machine-vision system for real-time precision weed management is a crucial step towards the development of smart spraying robotic vehicles. The intelligent machine-vision system, constructed using deep learning object detection models, has the potential to accurately detect weeds and crops from images. Both data-centric and model-centric approaches of deep learning model development offer advantages depending on environment and non-environment factors. To assess the performance of weed detection in real-field conditions for eight crop species, the Yolov8, Yolov9, and customized Yolov9 deep learning models were trained and evaluated using RGB images from four locations (Casselton, Fargo, and Carrington) over a two-year period in the Great Plains region of the U.S. The experiment encompassed eight crop species—dry bean, canola, corn, field pea, flax, lentil, soybean, and sugar beet—and five weed species—horseweed, kochia, redroot pigweed, common ragweed, and water hemp. Six Yolov8 and eight Yolov9 model variants were trained using annotated weed and crop images gathered from four different sites including combined dataset from four sites. The Yolov8 and Yolov9 models’ performance in weed detection were assessed based on mean average precision (mAP50) metrics for five datasets, eight crop species, and five weed species. The results of the weed and crop detection evaluation showed high mAP50 values of 86.2 %. The mAP50 values for individual weed and crop species detection ranged from 80.8 % to 98 %. The results demonstrated that the performance of the model varied by model type (model-centric), location due to environment (data-centric), data size (data-centric), data quality (data-centric), and object size in the image (data-centric). Nevertheless, the Yolov9 customized lightweight model has the potential to play a significant role in building a real time machine-vision-based precision weed management system.

Nowadays, nanotechnology is extensively employed in the medical profession. In this study, we used the aqueous extract of the bark of Mitragyna parvifolia to synthesize silver nanoparticles (AgNPs) by microwave-assisted green synthesis. The synthesis of AgNPs was confirmed by visual color change to brown color and characteristic surface plasmon resonance peak at 432 nm. The hydrodynamic diameter of the AgNPs was 171.81 nm having a zeta potential of −24.14 mV; Fourier Transmission Infrared Spectroscopy confirmed the presence of different functional groups on the NP surface; Scanning Electron Microscope and High-Resolution Transmission Electron Microscopy indicated predominantly circular shape of nanoparticle; Selected Area Electron Diffraction and X-ray Diffraction analyses determined the crystalline structure of AgNPs. Energy-Dispersive X-ray indicated the elemental composition and formation of AgNPs. The AgNPs were screened at different concentrations for antioxidant activity, antimicrobial, and anticancer potential in breast cancer cells (MCF-7 and MDA-MB-231). The AgNPs exhibited remarkable antioxidant, antimicrobial, and anticancer activities. The sedative and antinociceptive activities were also tested on Swiss albino mice, which showed mild sedative and very potent antinociceptive activity. However, detailed mechanistic studies are warranted in the future for clinical application of the AgNPs as a biologically active agent as well as a carrier for drug delivery.

In this paper, we analyse the unique characteristics of hyperparasites and discharge incidents in the initial stage of parasite populations. If hyperparasites are effectively developed in a population, they can play a crucial role in controlling host–parasite interactions. First, we expanded the integer-order host–parasite–hyperparasite model by using different fractional-order operators. We then examined the uniqueness and existence of the solutions of the non-integer order host–parasite–hyperparasite model. We inspected the stability of the model using the Ulam–Hyers stability theorem and employed a numerical technique to find a solution to the host–parasite–hyperparasite model using the fractional operator. Finally, we discussed the graphical results with the help of the Toufik–Atangana (T–A) numerical scheme. To examine the host–parasite–hyperparasite models and approximate results, we employed the Caputo fractional operator and the Atangana–Baleanu (A–B) fractional operator. The numerical results demonstrate that every important aspect of the model is preserved by the anticipated numerical method, which also provides an understandable solution for the fractional problem. The findings of this study bear significant implications for the theory and methodology of biological control of plant diseases associated with hyperparasites. It sheds light on the ecology of biological invasions, which is a subject of great importance in today's world. This research provides valuable insights that can design effective strategies to combat plant diseases and prevent the spread of invasive species.

This research has sought to determine how the crude oil price volatility (COPV) relates to economic growth (EG) using the case of the OECD countries between 2000 and 2022. Therefore, this article employs various panel data estimation strategies: random effect regression, fixed effect regression, dynamic panel data estimation, one-step system GMM and dynamic panel data estimation; two-step system GMM. The chosen time domain consists of oil-producing and consuming major countries within the OECD. The key findings of the study suggest that COPV has adverse effects on the growth of OECD economies. This would consequently mean that the knowledge of how volatility in crude oil prices (COPs) could affect very influential economic performance is of paramount importance and might bring out certain vital challenges that could be brought up by oil market volatility. The article ends with a few policy suggestions that may assist in mitigating the adverse impact of exogenous, unpredictable fluctuations in oil prices on the EG of the countries of the OECD.

Incorporating natural industrial waste as a reinforcement for the development of sustainable composite is now being extensively practiced to eliminate harmful synthetic fiber. This study exhaustively evaluated the potential of tasar silk fiber waste (TSFW) as a reinforcing agent in a polylactic acid (PLA) matrix to optimize the physical, mechanical, and wear properties. Therefore, PLA-based biocomposites with varying TSFW proportions (0, 2, 4, 6, 8, and 10 by weight) are manufactured and then evaluated for physical (density, voids, water absorption), mechanical (tensile, flexural, impact, and hardness), and sliding wear properties. Results suggested that water uptake of PLA composites increases with the addition of TSFW and becomes saturated after nine days of immersion. The TSFW reinforcement at 8 wt% in PLA yields 32 % and 22 % improvement in tensile strength (66.2 MPa) and flexural strength (103.2 MPa), respectively. A remarkable improvement was observed in the impact strength at similar reinforcement (8 wt%) of TSFW with the observed value of 27.90 kJ/m2. A continuous increase in hardness was obtained by including TSFW in PLA, with the highest value of 86 Shore D recorded for 10 wt% TSFW added biocomposite. The specific wear rate of all samples increased, but the incorporation of TSFW significantly reduced the same. Microscopic examination suggests that microploughing, microcutting and groove formations were the main mechanism of material removal. After examining the results, it is recommended that TSFW be used in bidirectional mat form for further enhancement of mechanical properties and improved bonding between fiber and matrix.