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Abstract In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

In this article, we study the convergence behavior of G-nonexpansive mappings for three-step iterations in convex metric space along with the directed graph. We demonstrate the Δ-convergence as well as the strong convergence results for these mappings. For supporting our main results we presented a numerical example and convergence comparison of modified Thianwan iteration with SP iteration scheme and Noor iteration scheme.

Abstract Nanotechnology is the art and science of dealing with nanoscale particles. This has transformed contemporary dental practices through myriad contributions to biomaterial science. Titanium dioxide nanoparticles procured from Bacillus subtilis, an eco-friendly and biogenic source, can significantly magnify the physiochemical attributes of dental materials. However, postoperative sensitivity is a major drawback of composite restorations. The incorporation of these nanoparticles into dental adhesives can greatly benefit clinical dentistry by resolving this issue. This trial aimed to evaluate the effectiveness of a novel titanium dioxide nanofortified adhesive on the postoperative sensitivity of composite restorations. Methods This triple-blind, parallel-group randomized controlled trial was conducted at the Department of Operative Dentistry and Endodontics, School of Dentistry, Islamabad, from May 15, 2023, to November 25, 2023. Participants (n = 60) with Class I and II primary carious lesions with a minimum cavity depth of 3–5 mm were randomly assigned to two groups (n = 30). After obtaining informed consent, the restorative procedure was accomplished using a minimally invasive approach and etch-and-rinse adhesive strategy. In group A, a nanofortified adhesive was used for composite restoration, whereas in group B, an adhesive without nanoparticles was used. Postoperative sensitivity was evaluated using the Visual Analog Scale (VAS) score at follow-up periods: of one day, one week, two weeks and one month. A Chi-square test was used to compare postoperative sensitivity between the two groups. The level of significance was set at p

Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30%, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.

Aeromonas salmonicida causes furunculosis in the Oncorhynchus mykiss (rainbow trout) and the effective control over this infection requires knowledge of the genetic variability and epidemiology of A. salmonicida. Pathogenic strains were isolated from kidneys, muscles, liver and spleen of rainbow trout from the provinces, Punjab, Khyber Pakhtunkhwa and Gilgit Baltistan, Pakistan. On the base of 16S rRNA sequencing, physiological and biochemical characterization, isolated bacterial strains were identified as A. salmonicida (NCBI Ref. ArS-Pak-19 [MW307221], ArS-Pak- GB1-19 [MW720959], ArS-Pak-MRE-19 [MW720960], ArS-Pak-SW2-19 [MW720961], ArS-SW1-Pak-19 [MW720962]). Isolated strains were resistant to antibiotics like sulfamethoxazole, penicillin, vanomycine, rifampicin and bacitracin but were extremely sensitive to spectinomycin, ciprofloxacin, ofloxacin, levofloxacin and nalidixic acid. To check out the pathogenicity, rainbow trout were experimentally infected with isolated strains. Experimental fishes showed the same symptoms as were recorded in naturally infected fish including jaw bleeding, intra-abdominal fluid, intestinal bleeding and gill filament anemia. After ten days of post-challenge study, histopathological analysis revealed that there were severe alterations in the spleen, liver and kidney of the infected fish. The present study provides further research foundation and for upcoming research on A. salmonicida disease, its control and epidemiology.

The removal of organic pollutants from aquatic environments is a pressing global concern due to their adverse effects on ecosystems and human health. Rhodamine B (RhB), a widely used dye, poses a significant threat to water quality. This study investigates the adsorption of RhB dye onto 3-((1-(4-((1H-benzo[d]imidazol-2-yl)amino)phenyl)ethylidene)amino)phenol (BIAPEHB)/ P(AA-co-AM) composite as a potential solution to this environmental challenge. The adsorbent was synthesized by free-radical polymerization method and characterized using various techniques, confirming successful synthesis of composite. The efficiency of RhB dye removal from water highly depend on contact time (1–300 min), dye concentration (50–1000 mg/L), solution temperature (10 °C to 40 °C), pH (2−10), dose of BIAPEHB (0.08 g to 0.16 g) and BIAPEHB/P(AA-co-AM) composite (0.01 g to 0.1 g). Optimized parameters include contact time of 120 min at pH 10 using 0.12 g of BIAPEHB loading to prepare composite. Lower temperature favors dye adsorption with 0.1 g of composite dose where 99.53 % RhB dye removal observed. Analysis revealed pseudo second model and Langmuir model as best fitted models. Thermodynamically, studied process was feasible and spontaneous. The composite's effectiveness in treating actual industrial wastewater containing RhB and other dyes demonstrates its potential for practical applications in wastewater treatment.

Abstract Hemodialysis is crucial for patients with end‐stage renal disease, yet evaluating its operating parameters often requires complex mathematical models. To simplify this process, user‐friendly modules have been developed to accurately assess key parameters with minimal inputs, enabling users to track disease prognosis. These modules incorporate governing equations and allow straightforward analysis. Validation against experimental data from polymer membrane studies demonstrated that at a blood flow rate of 300 mL min−1, the model predicted a clearance of 262 mL min−1, showing 7% difference from the actual value of 281 mL min−1. At a dialysate flow of 400 mL min−1, the model's predicted clearance was 286.47 mL min−1, with only a 1% difference compared to previous model. The module also showed 40% higher clearance in counter‐current flow compared to co‐current, with a 47% difference at 400 mL min−1 dialysate flow. Increasing the hollow fibre length from 27 to 50 cm led to a 4% clearance increase. Additionally, increasing residual renal clearance by 0.5 mL min−1 doubled the standard Kt V−1 Kt/V, and similar effects were seen by increasing weekly hemodialysis sessions. The app allows simulations, plots, and comparisons with minimal inputs and can be integrated into MATLAB or other platforms, benefiting both patients and researchers in prognosis and treatment analysis.

Background Children and young people (CYP) with primary brain tumour (PBT) are at high risk for developing late effects, potentially affecting long-term quality of life (QoL). In low-income and middle-income countries, QoL has not been studied in depth in CYP. In the present study, CYP treated for PBTs in Pakistan were evaluated regarding (A) mean change in QoL scores pretreatment and 12 months post-treatment and (B) predictors of change in QoL scores 12 months post-treatment.Methods A prospective cohort study was conducted from November 2020 to July 2023. CYP aged 5–21 years, with newly diagnosed PBTs, were recruited from tertiary care centres in Karachi, Pakistan. QoL was assessed using the Paediatric Quality of Life Inventory 4 generic and brain tumour module, pretreatment and at 12 months post-treatment, by a trained psychologist.Results A total of 48 patients diagnosed with PBTs were enrolled in the study. At the 12-month post-treatment, 25 (52%) of the patients were reassessed, while 23 (48%) were lost to follow-up. There was no significant difference in mean global QoL scores of patients at 12 months post-treatment. On multivariable analysis, there was a statistically significant improvement in mean global QoL scores among those who underwent total surgical tumour resection (beta 7.7; 95% CI 0.9, 14.5) and maximum safe surgical tumour resection (beta 10.6; 95% CI 4.7, 16.6). However, there was a significant decline in mean global QoL scores among those who had hydrocephalous at diagnosis managed with a shunt and/or external ventricular drain (EVD) (beta −10.0; 95% CI −14.5, –5.5).Conclusion This study found a decline in mean global QoL scores among those with hydrocephalous at diagnosis who were managed with a shunt and/or EVD but an improvement in those who underwent total or maximum safe surgical tumour resection. Larger-scale studies are needed to comprehensively evaluate and validate these outcomes.

Agricultural finance plays a pivotal role in facilitating the modernization and commercialization of farming practices and bolstering global food security. However, the provision of sustainable financial services in remote regions poses a significant challenge within the developing and underdeveloped countries. Consequently, farmers often grapple with inadequate access to formal credit sources and essential productive assets due to diverse constraints. Although, several studies have investigated constraints to agriculture finance; the literature is divergent and a holistic framework of constraints to agriculture finance is missing. Therefore, this research explores the constraints to agriculture finance in 31 developing and underdeveloped countries using systematic review approach, and synthesizes a holistic framework of the constraints using Qualitative Evidence Synthesis (QES) method that can be used as dependable framework for conceptualization and policy formation. Moreover, it describes the interplay and mutual reinforcement between the challenges faced by different stakeholders to elaborate how they influence each other in complex ways. Additionally, this study suggests a collective and multi-faceted approach for overcoming barriers to agricultural financing, and highlights the limitations and unintended consequences of agricultural credit and its implications for agricultural sustainability.

Abstract Background There are limited data available, particularly in low- and middle-income countries (LMICs), on the long-term quality of life (QoL) and family functioning of primary caregivers of children and young people (CYPs) affected by primary brain tumors (PBTs). This study aimed to assess the factors associated with the mean change in QoL and family functioning scores of primary caregivers of CYP patients with PBTs 12 months posttreatment. Methods This prospective cohort study enrolled CYPs aged 5–21 years with newly diagnosed PBTs and their primary caregivers. The study was carried out between November 2020 and July 2023. The primary caregivers of CYPs were recruited from two major tertiary care centers in Karachi, Pakistan. The primary caregivers QoL were assessed by the Pediatric Quality of Life Inventory (PedsQL) Family Impact Module. The assessment was undertaken by a psychologist at the time of diagnosis and 12 months posttreatment. The data were analyzed with STATA version 12. Results Forty-eight CYPs with newly diagnosed PBTs and their primary caregivers (46 mothers and 2 fathers) were enrolled. At 12 months posttreatment, 25 (52%) CYPs and their primary caregivers (mothers) were reassessed, and 23 (48%) were lost to follow-up. On multivariable analysis, a significant decrease in mothers’ mean 12-month posttreatment QoL and family functioning scores was associated with CYP having posttreatment seizures (beta= -10.2; 95% CI: -18.4 to -2.0) and with the financial burden associated with the CYP’s illness (beta= -0.3; 95% CI: -0.4 to -0.1). However, in those cases where CYP had higher posttreatment quality of life scores (beta = 0.4; 95% CI = 0.1, 0.6) and posttreatment higher verbal intelligence scores (beta = 0.1; 95% CI = 0.01, 0.3), the mothers’ QoL and family functioning scores were significantly greater. Conclusion We found a significant decrease in QoL of mothers who had a high financial burden and whose CYP had posttreatment seizures. However, those whose CYPs had higher posttreatment verbal intelligence scores and quality of life scores had significantly greater QoL scores. Identification of the factors that influence primary caregivers QoL has the potential to aid in the development of targeted strategies to alleviate stressors and improve the overall quality of life for primary caregivers and their children who are at high risk.

The prediction of liquefaction-induced lateral spreading/displacement (Dh) is a challenging task for civil/geotechnical engineers. In this study, a new approach is proposed to predict Dh using gene expression programming (GEP). Based on statistical reasoning, individual models were developed for two topographies: free-face and gently sloping ground. Along with a comparison with conventional approaches for predicting the Dh, four additional regression-based soft computing models, i.e. Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimization regression (SMOR), and M5-tree, were developed and compared with the GEP model. The results indicate that the GEP models predict Dh with less bias, as evidenced by the root mean square error (RMSE) and mean absolute error (MAE) for training (i.e. 1.092 and 0.815; and 0.643 and 0.526) and for testing (i.e. 0.89 and 0.705; and 0.773 and 0.573) in free-face and gently sloping ground topographies, respectively. The overall performance for the free-face topology was ranked as follows: GEP > RVM > M5-tree > GPR > SMOR, with a total score of 40, 32, 24, 15, and 10, respectively. For the gently sloping condition, the performance was ranked as follows: GEP > RVM > GPR > M5-tree > SMOR with a total score of 40, 32, 21, 19, and 8, respectively. Finally, the results of the sensitivity analysis showed that for both free-face and gently sloping ground, the liquefiable layer thickness (T15) was the major parameter with percentage deterioration (%D) value of 99.15 and 90.72, respectively.

Limited research in Saudi Arabia has devolved into the prevalence and genetic diversity of begomoviruses. Utilizing Illumina MiSeq sequencing, we obtained 21 full-length begomovirus sequences (2.7–2.8 kb) from eight cucumber plants grown in fields and greenhouses. We found that two complete begomovirus genomes were variants of the Boushehr strain of tomato yellow leaf curl virus (TYLCV) with nucleotide (nt) sequence identities of 94.7-95.9%. Another full-length genome was a variant of TYLCV-Iran with 94.6% identity. Five full-length sequences closely matched the DNA-A of watermelon chlorotic stunt virus (WmCSV) isolates with 97.9-98.7% nt sequence identities, while five sequences had their highest nt sequence identities (95.8-96.3%) with the DNA-B of WmCSV isolates. Simultaneously, four sequences were 99.1-99.6% identical to the DNA-A of tomato leaf curl Palampur virus (ToLCPalV). Four sequences matched the DNA-B of ToLCPalV reported from Iran and Saudi Arabia with identities ranging from 96.2-100%. Four plants showed a mixed infection of these begomoviruses. Most ORFs showed evidence of negative selection pressure, suggesting that purifying selection plays a crucial role in shaping the diversity of these begomoviruses. Additionally, potential intra- and interspecies recombination events were detected in the TYLCV and WmCSV DNA-B genomic regions. The ToLCPalV isolates identified in this study formed a cluster with the other ToLCPalV isolates reported from Saudi Arabia, Iran and Iraq, representing a unique lineage distinct from ToLCPalV reported from Southeast Asia. High mutation rate and robust selection facilitated the independent evolution of ToLCPalV without recombination. Overall, this study offers valuable insights into the diversity and evolutionary dynamics of begomoviruses infecting cucumber crops in Al-Ahsa, Saudi Arabia.

In this paper, Randomized Response technique (RRT) is used to propose some separate and combined variance estimators for a sensitive variable using stratified random sampling. The performances of the proposed estimators are examined using a unifed measure of respondent privacy and estimator efficiency.

Abstract The swift advancement of sustainable energy technologies, coupled with the urgent need to address environmental challenges, has generated considerable interest in the multifaceted applications of biochar materials to promote energy, water, and environmental sustainability. This comprehensive review examines recent advancements in the production and applications of functionalized biochar materials, emphasizing their pivotal roles in energy conversion and storage, wastewater treatment, CO2 reduction, soil amelioration, and the promotion of carbon neutrality within a circular economy framework. The functionalization of biochar materials involves surface chemistry and porosity modifications, achieved through techniques like templating, chemical activation, metal impregnation, or heteroatom doping. These modifications substantially enhance the catalytic activity, energy storage capacity, and cycling stability of biochar materials, making them particularly effective in diverse energy applications such as water splitting, fuel cells, and supercapacitors. Additionally, functionalized biochar materials demonstrate remarkable efficacy as catalysts and adsorbents in wastewater treatment, proficiently removing pollutants like heavy metals, organic contaminants, and nutrients, thereby facilitating resource recovery from wastewater. The review also underscores the potential of functionalized biochar materials in CO2 capture and conversion, exploring innovative strategies to augment their CO2 adsorption capacity and state-of-the-art catalytic processes for transforming captured CO2 into valuable fuels and chemicals. In summary, this review offers valuable insights into the recent advancements in biochar research, underscoring its substantial commercial potential as a versatile material contributing to a cleaner and more sustainable future. Article Highlights The current status of biochar research is comprehensively reviewed. The potential of biochar in energy, water, and environmental fields is critically examined. Technology readiness levels (TRLs) of various biochar-based technologies are evaluated. Graphical Abstract

OBJECTIVE: To determine the Frequency of Germinal Center B (GCB) type Diffuse Large B Cell Lymphoma (DLBCL) in Karachi, Pakistan, and to screen the DLBCL patients for EZH2 mutation. METHODOLOGY: A cross-sectional study was conducted on DLBCL patients, and IHC analysis, PCR, and sequencing of the EZH2 gene were used. This study was conducted from August 2016 to July 2020. Surgical specimens received in the histopathology section of DDRRL centrally and from different collection units throughout Sindh having been diagnosed with DLBCL were collected. Three markers (CD10, BCL6, and MUM1) were used for subtyping. DNA was manually extracted from the FFPE Blocks of DLBCL cases using phenol: chloroform: isopropanol alcohol. Polymerase chain reaction (PCR) was performed, and samples were sent from Dow University to Korea (Macrogen) for sequencing. The amplified product was 189bp. Primers were used for EZH2 to flank the region containing the tyrosine domain on exon 16 and exon 18. RESULTS: Out of 116 cases, 60.3% and 39.7% were male and female, respectively. The mean age was 49.73±16.38 years. The mean survival duration of cases diagnosed with GCB was 8.21±7.39 months. Polymorphism of EZH2 mutation (Tyr 701 Asn) was found to be positive in GCB-DLBCL for three samples (16.6%). CONCLUSION: In the current study, Sanger sequencing was used to detect any mutation in the coding region of EZH2, and a unique mutation was reported (Tyr701Asn).

BACKGROUND: Charcot foot deformity, a severe complication of diabetes, involves neuropathy and abnormal peak plantar pressure in the midfoot and forefoot. However, orthotic interventions and shoe modifications are used to address the sequelae of Charcot neuroarthropathy, offering different approaches to managing abnormal peak plantar pressure. OBJECTIVE: To compare the effects of three types of therapeutic offloading diabetic shoes; prefabricated, relasting, and double rocker-modified shoes on peak plantar pressure in the midfoot and forefoot of nonulcerated chronic Charcot foot during walking. METHODOLOGY: A repeated measure design involved 15 participants (40% males and 60% females) with a mean age of 60.73 years (SD=10.50), with Charcot neuropathy. Participants were provided with three types of shoes; prefabricated, relasting, and double rocker-modified shoes, each equipped with the same custom-made insole (CMI). Plantar pressure was recorded while walking on level ground, focusing on the forefoot, midfoot, and hindfoot. The study also investigated additional variables affecting plantar pressure distribution, including the pressure-time integral and contact area. FINDINGS: The type of shoe had distinct effects on the distribution of plantar pressure. The double rocker-modified shoe particularly impacted forefoot pressure during the terminal stance phase of the gait cycle. Peak plantar pressure at the forefoot increased by 5.37% with double rocker-modified shoes compared to relasting shoes. Both double rocker-modified and prefabricated shoes reduced midfoot peak plantar pressure by 8.73% and 11.97%, respectively. Similar trends were observed at the hindfoot, with reductions in peak plantar pressure. However, there were no significant differences in regional peak plantar pressure between the types of shoes except for the central forefoot (F (1.61, 22.5) = 5.69, p = 0.014). CONCLUSION: There were no significant differences in the effectiveness of prefabricated, relasting, and double rocker-modified shoes in reducing and redistributing peak plantar pressure in high-risk areas of chronic Charcot foot.

Torrefied biomass is a vital green energy source with applications in circular economies, addressing agricultural residue and rising energy demands. In this study, ML models were used to predict durability (%) and mass loss (%). Firstly, data was collected and preprocessed, and its distribution and correlation were analyzed. Gaussian Process Regression (GPR) and Ensemble Learning Trees (ELT) were then trained and tested on 80 % and 20 % of the data, respectively. Both machine learning models underwent optimization through Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for feature selection and hyperparameter tuning. GPR-PSO demonstrates excellent accuracy in predicting durability (%), achieving a training R2 score of 0.9469 and an RMSE value of 0.0785. GPR-GA exhibits exceptional performance in predicting mass loss (%), achieving a training R2 value of 1 and an RMSE value of 9.7373e-05. The temperature and duration during torrefaction are crucial variables that are in line with the conclusions drawn from previous studies. GPR and ELT models effectively predict and optimize torrefied biomass quality, leading to enhanced energy density, mechanical properties, grindability, and storage stability. Additionally, they contribute to sustainable agriculture by reducing carbon emissions, improving cost-effectiveness, and aiding in the design and development of pelletizers. This optimization not only increases energy density and grindability but also enhances nutrient delivery efficiency, water retention, and reduces the carbon footprint. Consequently, these outcomes support biodiversity and promote sustainable agricultural, ecosystem, and environmental practices.

Objective: To evaluate the frequency of depression among adolescents aged 13 to 19 years who visit medicine outpatient departments (OPD) for asthma. Methods: This prospective, cross-sectional study was conducted in Outpatient Departments of Medicine, of Indus Medical College TM Khan. All the cases between the ages of 13 and 19 who visited the medicine departments and have been diagnosed as patients of asthma for at least one year were included. Patients with a history of major life events, such as the death of a family member or any other relevant severe trauma within the past 3 months, patients already on antidepressant treatment for the past 3 months, those with other diseases such as diabetes, tuberculosis, or epilepsy, and those with a history of substance abuse or dependence were not incorporated. Depression diagnosis was based on DSM-IV criteria, requiring the presence of at least five symptoms during the same two-week period, representing a change from previous functioning. Data was entered and analyzed using SPSS version 26. Results: A total of 262 asthma patients were included, with a mean age of 16.63+4.8 years and aver- age duration of asthma was 5.6 years. Among the participants, 169 (64.5%) were female. The family history of asthma was reported in 30 (11.4%) of the cases. Overall frequency of depression was found to be at 14.1% (n=37). In terms of gender, a significant difference was observed, with a higher occurrence of depression in males (78.4%) and patients with poor and middle socio-economic status (SES) (p = 0.001). Higher rates of depression were also among those who were illiterate compared to educated patients (p = 0.001). Conclusion: Study revealed that depression is common among teenagers with asthma. Both asthma and depression are highly prevalent in teenagers as well as in adults, and emerging evidence indicates that these conditions often occur together

Objective: To compare the efficacy of Platelet Rich Plasma versus Steroids among patients managed for Tennis elbow at our Orthopaedic unit. Study Design: Quasi-experimental study. Place and Duration of Study: Orthopaedic Department, Combined Military Hospital, Rawalpindi Pakistan, from Jan 2021 to Jul 2022. Methodology: Patients with Tennis Elbow diagnosed by a consultant orthopaedic surgeon were included in the study. They were randomly allocated treatment with platelet-rich plasma and steroid injections. The team assessed them after three months of treatment using the Disabilities of Arm, Shoulder, and Hand score. Different factors, including mode of treatment, were associated with significant improvement in these patients at the end of three months. Results: Out of 250 patients with tennis elbow included in the final analysis, 193(77.2%) were male, and 57(22.3%) were female. Patients were divided into two groups for comparison, and Group-1 had significant improvement, while Group-2 had no improvement. Out of the total, 117(46.8%) patients received platelet-rich plasma, while 133(53.2%) patients were managed with corticosteroids. The presence of comorbid illnesses and the use of platelet-rich plasma was statistically significantly found in a group with significant improvement in Disabilities of Arm, Shoulder, and Hand score (p-value

Introduction: Isatin, a heterocycle scaffold, is the backbone of many anticancer drugs and has previously been reported to engage multiple cellular targets and mechanisms, including angiogenesis, cell cycle, checkpoint pathways and multiple kinases. Here, we report that a novel isatin derivative, 5i, degrades estrogen receptor alpha (ERα) in estrogen-dependent breast cancer cells. This effect of the isatin nucleus has not been previously reported. Tamoxifen and fulvestrant represent standard therapy options in estrogen-mediated disease but have their own limitations. Isatin-based triple angiokinase inhibitor BIBF1120 (Nintedanib) and multikinase inhibitor Sunitinib (Sutent) have been approved by the FDA.Methods: Keeping this in view, we synthesized a series of N'-(1-benzyl-2-oxo-1, 2-dihydro-3H-indol-3-ylidene) hydrazide derivatives and evaluated them in vitro for antiproliferative activities in MCF-7 (ER+) cell line. We further investigated the effect of the most potent compound (5i) on the Erα through Western Blot Analysis. We used in silico pharmacokinetics prediction tools, particularly pkCSM tool, to assess the activity profiles of the compounds.Results and discussion: Compound 5i showed the best antiproliferative activity (IC50 value; 9.29 ± 0.97 µM) in these cells. Furthermore, 5i downregulated ERα protein levels in a dose-dependent manner in MCF-7. A multifaceted analysis of physicochemical properties through Data Warrior software revealed some prominent drug-like features of the synthesized compounds. The docking studies predicted the binding of ligands (compounds) with the target protein (ERα). Finally, molecular dynamics (MD) simulations indicated stable behavior of the protein-ligand complex between ERα and its ligand 5i. Overall, these results suggest that the new isatin derivative 5i holds promise as a new ERα degrader.

Background. Middle East Respiratory Syndrome Coronavirus (MERS-CoV), associated with severe respiratory illness, originates from the Middle East region. The virus is transmitted from animals to humans, with the dromedary camel serving as a significant reservoir. The virus's high fatality rate has spurred research into vaccine development and therapeutics. Objective. This study aimed to employ an in silico approach to design a potential vaccine candidate against MERS-CoV, focusing on the M protein as an antigen. Methods. The FASTA sequence of M protein was used to predict B cell and major histocompatibility complex class I and class II epitopes. The best epitopes were selected from these predicted epitopes. The vaccine candi­date's construct consisted of epitopes, linkers, and a tag. The sequence of the vaccine candidate's construct, consisting of 390 amino acids, was back-translated, optimized, and then inserted into a plasmid for cloning and expression using SnapGene. The 3D structure of the vaccine candidate is docked with TLR-4 receptor. Molecular dynamics simulation was run for this docked complex using GROMACS gmx, version 2021.4. Results. Through computational modeling and analysis, we developed a novel vaccine candidate with pro­mising structural and functional properties. Our results suggest that the designed vaccine candidate has the potential to induce a robust immune response. Conclusions. This in silico approach presents a promising MERS-CoV vaccine candidate designed to trigger both humoral and cellular immune responses. This candidate holds the potential to provide broad-spectrum protection against MERS-CoV.

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.

The current methods, while in use, continue to grapple with accuracy and effectiveness concerns. It is imperative to establish dependable solutions capable of distinguishing and categorizing people according to their assistive devices to tackle these issues. People with disabilities, such as those experiencing paralysis, limb deficiencies, or amputations, may encounter issues related to discrimination and inadequate support. Hence, this research was undertaken to detect and track people with conditions like paralysis, limb deficiency (Amelia), or amputation among the differently-abled population. Earlier investigations have predominantly focused on recognizing people and their mobility aids, utilizing a variety of methods such as Fast R-CNN, Faster R-CNN, RGB or RGB-D cameras, Kalman filters, and hidden Markov models. Modern deep learning models, including YOLO (You Only Look Once) and its variations, have gained substantial acceptance in current applications owing to their distinctive architectural designs and performance attributes. In this study, a substantial dataset comprising 4,300 images and 8,447 labels spanning five distinct categories is employed to assess the efficacy of YOLOv8, YOLOv5, and YOLOv7 models in the identification of people with disabilities. The evaluation findings show that YOLOv8, which achieved an overall precision of 0.907, performs better than both YOLOv5 (precision: 0.885) and YOLOv7 (precision: 0.906). Notably, YOLOv8 has the best wheelchair detection precision (0.998). Furthermore, YOLOv8 outperforms YOLOv5 (recall: 0.887) and YOLOv7 (recall: 0.925) in terms of recall performance (recall: 0.943). YOLOv8 achieves the greatest mean average accuracy (mAP@.5) value of 0.951, followed by YOLOv5 (mAP@.5: 0.942), and YOLOv7 (mAP@.5: 0.954). In a similar vein, of the three models, YOLOv8 has the best performance (mAP@.5:.95: 0.713). The analysis of detection time also shows that YOLOv8 performs best, processing 5,597 frames in just 5.9 milliseconds and achieving a remarkable frame rate of 169.49 frames per second.

Environmental tax rate (ETR) is an environmental cost charged on enterprises on pollution emissions while corporate statutory tax rate (CTR) is the rate payable by enterprises on net earnings. Both factors have an explicit role in determining the environmental performance. Given that, the current study attempts to explore an appealing nexus between ETR, CTR, and pollution emissions. The empirical analysis was conducted on a sample of 20-years data ranging from 2000 to 2019 of 10 industrialized economies of the world. To check the regression, we employ the generalized least square and strengthen the regression through the system generalized method of moment’s technique. The empirical analysis infers the negative and statistically significant effect of ETR while a positive and statistically significant impact of CTR on pollution emissions. High ETR discourages the pollution-intensive activities of enterprises and limits the pollution volume. However, CTR augments pollution emissions as high CTR impedes the financial capability of enterprises to explore green activities. The important policy layout of the current study is that the ETR can be utilized as a policy tool to abate pollution. The analysis offers the cutting-edge evidence on relevant role of both ETR and CTR in determining pollution emissions even in the presence of other control variables.

The authors would like to make the following corrections to this published paper [...]

The demand for probiotic-based functional food is increasing globally owing to its health-endorsing attributes. There are various driving forces behind probiotic therapy. However, Intestinal dysbiosis in humans is the prime driving force behind this increasing trend in the consumption of probiotic-based functional food. Probiotics have numerous health potentials, however, their target delivery and stability is a great challenge for food manufacturer. Microencapsulation with various types of coating materials is trending for the target and stable delivery of potential probiotics. There are various encapsulation techniques with pros and cons. The type of probiotic bacteria, encapsulation methods, and coating materials are considered crucial factors to prolong the viability of probiotics under hostile conditions. The current review addresses the opportunities, challenges, and future trends surrounding matrix materials used in probiotic encapsulation. The review also describes the current studies and their findings on the various types of encapsulation materials. This comprehensive review could be a way forward in the selection of efficient and effective wall material for the target delivery of sensitive ingredients.

Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-quality output voltage with minimal harmonic distortion. However, their intricate architecture presents notable challenges for fault diagnosis, particularly concerning open switch faults. In this study, we propose a deep learning-based approach for diagnosing open switch faults in CHB-5LIs. We present a simulation model of the CHB-5LI with open switch faults and generate a dataset comprising voltage waveforms for various fault scenarios. Leveraging this dataset, we train a Convolutional-1D Neural Network (CNN-1D) featuring a multi-layer architecture comprising convolutional and fully connected layers, culminating in the Softmax function for classification. Our method achieves an impressive classification accuracy exceeding 98 percent on previously unseen fault scenarios, underscoring the efficacy of our approach for CHB-5LI fault diagnosis. Additionally, we conducted a thorough analysis of CNN-1D performance and compared it with traditional and other deep learning models for fault diagnosis techniques. The accuracy of other deep learning models on the generated dataset is as follows: RNN is 88.9 percent, 1D-ResNet is 88.8 percent, and Time Inception model is 89.4 percent. Simulation results showcase that our proposed CNN-1D based approach surpasses other methods in terms of accuracy and robustness, elucidating the potential of deep learning for fault diagnosis in intricate power electronics systems. The fault diagnosis time for the proposed method as a fault diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet.

The present study examined the mental health of international students through cultural communication strategy in the metaverse with the moderating effects of cyberchondria and healthcare disparity. The relevance of this research study can be made evident with the ongoing trends of today’s world. In the present time, the concept of virtual reality in the metaverse has become one of the most important and debatable topics all around the globe. The concept of metaverse has led to the evolution of the Internet as a whole which can create direct impacts on the real world too. The target country of the current research is China. In order to gather information for the study, the researchers employed a questionnaire technique. A total of 335 surveys were sent out to Chinese medical students; 320 were deemed relevant for the study. In order to measure the variables using measurement scales, questionnaires are created using various items from the variables. The results obtained after analyzing the questionnaires indicated a significant relationship between cultural communication in the metaverse and the mental health of internal Chinese students from medical universities. The moderating impact of healthcare disparity was found to be significant and the cyberchondria entails an insignificant impact on the cultural communication in the metaverse and the mental health of students. The present study is found to be beneficial in promoting cultural communication between international students and local students for promoting the better exchange of cultural values. The 3D virtual environment used for conducting the present study has also helped in improving communication as well as interactions. However, from the present research study, it was concluded that cultural communication is essential for promoting the mental health of international students as this could be beneficial in obtaining skillful professionals for various businesses. The current study added novelty by focusing on cultural communication in the metaverse but it has prevented the study to focus on many essential aspects which are experienced by international students in the real world.

Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using blockchain and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a blockchain prevention module and an ML detection module. The blockchain prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes the Light Gradient Boosting Machine (LightGBM) algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.

In this article, we offered two ln-type estimators for the population mean estimation of a sensitive study variable by using the auxiliary information under the design of basic probability sampling. The Taylor and log series were used to derive the expressions of mean square error and bias up to the first order. Improved classes of proposed estimators are obtained by using conventional parameters associated with the supplementary variable to obtained precise estimates. Mathematical comparisons of the estimators have been made with the usual mean and ratio estimators using theoretical equations of mean square error. A simulation study is conducted for the evaluation of proposed estimator's implementation using four artificial populations generated through R-software with different choices of mean vectors and variance-covariance matrices. The demonstration of proposed ln-type estimators was implemented through the real data application.

Recently, the Covid-19 pandemic has caused tremendous trauma over the world, leading to psychological and behavioral harm in addition to social and economic instabilities. Even though the pandemic’s statistical analysis is still in progress, it is essential to fit Covid-19 data using statistical models to prevent further harm. In order to model Covid-19 data, the study suggests a novel family of distributions called the exponentiated log-logistic family. The basic Pareto distribution is transformed as a special case, and certain properties of the proposed distribution are discussed. To estimate the model parameters, the maximum likelihood estimation approach is used. Moreover, a simulation study is conducted to ensure the consistency of parameter estimates. Three real-world datasets relevant to the Covid-19 pandemic are examined to demonstrate the applicability of the suggested approach. The proposed model is shown to be more flexible and provides an improved fit to describe the Covid-19 data when compared to various alternative forms of Pareto distribution.

This study used multi-platform remote sensing datasets to assess the impacts of land use and land cover (LULC) change on forest biomass density across the Three Gorges Reservoir (TGR) region along the Yangtze River. This study maps land use and land cover (LULC) change and forest aboveground biomass estimation (AGB) using Sentinel-2A and GEDI-L4B data based on observations between 2019 and 2022. The data analysis results of monitoring revealed a strong association frequency between land-use change and forest land cover since the significant relationship with forest aboveground biomass density has also been explored. The total deforestation decreased significantly in 2022. The accuracy assessment results show a classification accuracy of between 90.4% and 94.1%, with a kappa coefficient of 82.4%–92.1%. For the TGR region forest, the minimum to maximum mean above-ground biomass values are from 15.81 to 373.66 Mgha-1, respectively. Moreover, the fitting performance of the statistical analysis of aboveground biomass density of forest mean for the forest was quite decent with training (R2: 0.92, RMSE: 12.30 Mg/ha) and testing (R2: 0.79, RMSE: 18.07 Mg/ha) datasets. The results highlight LULC change impacts, particularly forest land class over forest AGB stands, and the effectiveness of the GEDI-L4B product in regional biomass estimation in the TGR region.

Settlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers. Hence, in this paper, a novel hybrid artificial intelligence (AI)-based model was developed by the combination of artificial neural network (ANN) and Harris hawks’ optimisation (HHO), that is, ANN-HHO, to predict the settlement of the GRS abutments. Five other robust intelligent models such as support vector regression (SVR), Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimisation regression (SMOR), and least-median square regression (LMSR) were constructed and compared to the ANN-HHO model. The predictive strength, relalibility and robustness of the model were evaluated based on rigorous statistical testing, ranking criteria, multi-criteria approach, uncertainity analysis and sensitivity analysis (SA). Moreover, the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature. The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models. Therefore, it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments. Finally, the model has been converted into a simple mathematical formulation for easy hand calculations, and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

The aim of the present study was to elucidate the effect of different parts (fruit, leaves, and seed) of papaya on thrombocytopenia. For the Purpose, the papaya fruit, leaves, and seeds were procured from Botanical Garden of University of Agriculture Faisalabad. Afterwards, the papaya fruit, leaves, and seeds were converted to powder followed by extraction of polyphenol with various solvents (aqueous ethanol and methanol). The obtained extracts were characterized for total flavonoid contents, polyphenols as well as anti,nutritional compounds, such as tannins and saponins. Moreover, efficacy trials were carried out to explore the effect of papaya parts (fruit, leaves, and seed) on the platelets count of potassium bromate-induced (PBI) rats. The PBI rats were fed with extract enriched diet for a time period of 56 days. Results showed that maximum extraction was obtained with ethanol. In addition, a higher bioactive profile (polyphenols) was observed in papaya leaves extract followed by fruit and seed extracts. Papaya leaves extract (PLE) treatment showed a maximum increase in platelets count from 169.9 ± 2.1 × 109/L to 231.1 ± 6.1 × 109/L in PBI rats as well as WBCs and RBCs 521 ± 16.8 × 107/L to 594.5 ± 11.2 × 107/L and 469.2 ± 13.2 × 107/L to 562.2 ± 14.2 × 107/L. But for hemoglobin, the papaya fruit extract treatment showed a higher value of 6.4 ± 0.1 to 7.4 ± 0.2 (mg/dL). Conclusively, papaya leaves should be included in dietary regimens for health promotion to combat thrombocytopenia and related diseases.

The efficacy of antibiotics and other antimicrobial agents in combating bacterial infections faces a grave peril in the form of antimicrobial resistance (AMR), an exceedingly pressing global health issue. The emergence and dissemination of drug-resistant bacteria can be attributed to the rampant overuse and misuse of antibiotics, leading to dire consequences such as organ failure and sepsis. Beyond the realm of individual health, the pervasive specter of AMR casts its ominous shadow upon the economy and society at large, resulting in protracted hospital stays, elevated medical expenditures, and diminished productivity, with particularly dire consequences for vulnerable populations. It is abundantly clear that addressing this ominous threat necessitates a concerted international endeavor encompassing the optimization of antibiotic deployment, the pursuit of novel antimicrobial compounds and therapeutic strategies, the enhancement of surveillance and monitoring of resistant bacterial strains, and the assurance of universal access to efficacious treatments. In the ongoing struggle against this encroaching menace, phage-based therapies, strategically tailored to combat AMR, offer a formidable line of defense. Furthermore, an alluring pathway forward for the development of vaccines lies in the utilization of virus-like particles (VLPs), which have demonstrated their remarkable capacity to elicit a robust immune response against bacterial infections. VLP-based vaccinations, characterized by their absence of genetic material and non-infectious nature, present a markedly safer and more stable alternative to conventional immunization protocols. Encouragingly, preclinical investigations have yielded promising results in the development of VLP vaccines targeting pivotal bacteria implicated in the AMR crisis, including Salmonella, Escherichia coli, and Clostridium difficile. Notwithstanding the undeniable potential of VLP vaccines, formidable challenges persist, including the identification of suitable bacterial markers for vaccination and the formidable prospect of bacterial pathogens evolving mechanisms to thwart the immune response. Nonetheless, the prospect of VLP-based vaccines holds great promise in the relentless fight against AMR, underscoring the need for sustained research and development endeavors. In the quest to marshal more potent defenses against AMR and to pave the way for visionary innovations, cutting-edge techniques that incorporate RNA interference, nanomedicine, and the integration of artificial intelligence are currently under rigorous scrutiny.

With rising worldwide concerns about climate change and environmental sustainability, it is crucial to understand the link between energy usage, technological innovation, and CO2 emissions. Therefore, this study examines the complex link between energy consumption, technological innovation, and CO2 emissions in the BRICS economies over the time period from 1990 to 2022. To understand the BRICS dynamics and environmental sustainability, the Panel Mean Group Autoregressive Distributed Lag (PMG-ARDL) model is used. The results indicate a significant positive relationship between energy consumption and CO2 emissions, implying that BRICS economies experience a rise in CO2 output due to increased energy usage. Simultaneously, the research reveals an inverse connection between technological innovation and CO2 emissions. In this regard, technological advances are crucial for sustainable growth and environmental protection in the region. This research provides useful insights into the intricate interaction of factors that influence CO2 emissions in developing nations.

Ginger (Zingiber officinale Roscoe) is a perishable commodity that requires proper processing to maintain its bioactivity. This study evaluated the effect of different air-drying temperatures (50 °C, 60 °C, and 70 °C) on ginger’s drying kinetics and quality attributes. For an enhanced understanding of the drying kinetics, we employed a detailed approach by combining an existing drying model (namely, Midilli) with the Arrhenius model. This combined model facilitates a thorough analysis of how temperature and time concurrently affect the moisture ratio, offering more profound insights into the drying mechanism. A higher drying rate was achieved at 70 °C, yet elevated drying temperatures could compromise the quality attributes of ginger slices. Ginger slices dried at 50 °C displayed improved physicochemical properties and less color browning. The evaluation of the bioactivity profile of resultant ginger extracts also revealed higher total phenolic contents (1875.87 ± 31.40 mg GAE/100 g) and DPPH radical scavenging activity (18.2 ± 0.9 mg TE/kg) in 50 °C treated ginger samples. Meanwhile, the hydroethanolic mixture (70% ethanol) was also reorganized with better extraction efficiency than water and MWF (a ternary blend of methanol, water, and formic acid) solution. The promising outcomes of this study endorse the influence of drying temperature on the quality characteristics and bioactive profile of ginger and the selection of suitable extraction solvents to acquire phenolic-rich extract.

This study introduces a novel application of gene expression programming (GEP) for the reliability analysis (RA) of reinforced soil foundations (RSFs) based on settlement criteria, addressing a critical gap in sustainable construction practices. Based on the principles of probability and statistics, the soil uncertainties were mapped using the first-order second-moment (FOSM) approach. The historical data generated via a parametric study on a validated finite element numerical model were used to train and validate the GEP models. Among the ten developed GEP frameworks, the best-performing model, abbreviated as GEP-M9 (R2 = 0.961 and RMSE = 0.049), in the testing phase was used to perform the RA of an RSF. This model’s effectiveness in RA was affirmed through a comprehensive evaluation, including parametric sensitivity analysis and validation against two independent case studies. The reliability index (β) and probability of failure (Pf) were determined across various coefficient of variation (COV) configurations, underscoring the model’s potential in civil engineering risk analysis. The newly developed GEP model has shown considerable potential for analyzing civil engineering construction risk, as shown by the experimental results of varying settlement values.

Abstract Background A group of genetically altered cells that have not transformed into a clinical or histologically identifiable state of malignancy but contains a higher risk of transforming into one is known as the field of cancerization. Numerous molecules are being investigated for their significance in the development of this phenomenon. One such protein of this family is Kaiso also known as ZBTB33 (Zinc Finger and BTB Domain containing 33). This protein belongs to the POZ-ZF family of transcription factors and may have functional tasks similar to its other siblings such as the growth and development of vertebrates and the pathogenesis of neoplastic diseases. Nevertheless, its role in the pathogenesis, progression, epithelial mesenchyal transition and field cancerization in case of oral cancer still needs exploration. Hence, this study was designed to explore the expressional differences between the mucosa of controls and those diagnosed with oral squamous cell carcinoma (OSCC). Methods Soft tissue samples were obtained from the main tumor, tumor periphery and opposite buccal mucosa of 50 oral cancer patients, whereas normal mucosa was taken from 50 volunteers undergoing elective tooth removal. The acquired samples were subjected to Immunohistochemical exploration for expression of Kaiso and E-Cadherin. The expression was measured using Image-J IHC profiler and summed as Optical density. The Optical density values were then subjected to statistical analysis. Results Results revealed a significant differential expression of Kaiso between the mucosal tissues taken from oral cancer patients and controls (p-value:

Introduction MicroRNAs are a noncoding RNA involved in affecting several transcription and translation pathways. Their use has been discussed as potential predictors of several tumors. Their use as potential biomarker in glioma patients is still controversial. The purpose of this meta-analysis is to explore the possible role of such microRNAs in glioma patients. Methods After an extensive literature search done on PubMed and Embase, 20 studies were chosen for our analyses with the 9 discussing 11 tumor promoting microRNAs and 11 studies discussing 11 tumor suppressing microRNAs. The data needed was extracted from these studies including the hazard ratio that was used as the effect size for the purpose of our analysis. The needed analysis was performed using Stata and Excel. Results The pooled hazard ratio for our analysis with patients having a lower microRNA expression for tumor promoting microRNAs came to be 2.63 (p

The current study explores the effectiveness of coconut husk for crystal violet dye sequestration employing a batch experimental setup. Characterization of adsorbent was carried out via FTIR, and SEM techniques and results confirmed the involvement of OMe, COC and hydroxyl functional groups in dye uptake, and the rough, porous nature of adsorbent and after adsorption dye molecules colonized these holes resulting in dye exclusion. Effects of various adsorption parameters such as pH, adsorbent dose, contact time, initial dye concentration, and temperature of solution were studied. Crystal violet adsorption on coconut husk was highly pH-dependent, with maximum removal occurring at basic pH. Maximum removal of dye, i.e., 81%, takes place at optimized conditions. Kinetic data was analyzed by pseudo-first, pseudo-second order and an intra-particle diffusion model. Results showed that the pseudo-second order kinetic model best described adsorption of crystal violet onto coconut husk. Langmuir, Freundlich, and D-R adsorption isotherms were also used to test their appropriateness to experimental data and the Freundlich isotherm fits best to data. Thermodynamic parameters showed that the current process was spontaneous, endothermic in nature with continuous decrease in entropy. Established practice is 79% applicable to tap water and in acidic medium nearly 80% of adsorbent was recovered, confirming the effectiveness and appropriateness of coconut husk for crystal violet dye exclusion from wastewater. HIGHLIGHTS Adsorption is an effective and economical process for dye removal from wastewater.; Coconut husk is a potential adsorbent for crystal violet exclusion.; Adsorbent characterization, operational parameters, and adsorbent recovery was studied.; Kinetics, isotherm models, and thermodynamics were evaluated.; The potential efficiency of adsorbent will also be checked for actual textile wastewater in future.;

Custom made insole (CMI) effectively reduces the incidence of foot ulceration and re-ulceration in the diabetic foot with neuropathy. Several factors influence the effectiveness of CMI, which can be studied using the finite element analysis (FEA). This study investigated the effect of varying CMI thickness and length on the peak plantar pressure using FEA. A subjectspecific 3D foot model was reconstructed with the CMI. The top surface of tibia and fibula were fixed and a displacement of 3 mm was exerted from the ground along with the upward Achilles tendon force. The peak contact pressure was reduced by 17.52% and 9.46% with an overall thickness of CMI of 9mm and 6mm compared to 3mm, respectively. Full-length CMI redistributed contact pressure at the midfoot better than half-length CMI. In conclusion, FEA provides more insight for the intervention of CMI on plantar pressure in the diabetic foot with neuropathy.

Citrus viroid infection is emerging as a serious threat because of its efficient systemic movement within the host plant and its quick spread due to contaminated pruning tools. A survey was conducted to investigate the primary distribution and molecular characterization of Citrus bent leaf viroid (CBLVd) and its variants in different citrus cultivars. A total of 154 symptomatic citrus samples were collected and detected by RT‒PCR with newly designed specific primers with the incidence of 36.33%. During biological indexing study on Etrog citron, expressions of reduced leaf size, yellowing with a light green pattern, and bending were observed. Amplified products were sequenced and analyzed using a nucleotide BLAST search, which showed 98% homology with other CBLVd isolates. The results of the phylogenetic tree analysis showed the presence of two main groups (A and B), with the predominant variants of CBLVd, i.e., CVd-I-LSS (Citrus viroid Low Sequence Similarity) sequences, clustering in subgroup A1 along with newly detected CVd-I-LSS from Palestinian sweet lime (Citrus limettioides), which has been identified as a new host of CVd-I-LSS in Pakistan. Further analysis of the sequences in subgroup A1 showed that the variant of CVd-I-LSS infecting citrus cultivars had a close relationship with isolates reported from China, Japan, and Iran, which may have resulted from the exchange of planting material. This study also unveiled the variability in nucleotide sequences of CBLVd, which made it unable to be detected by old primers. The results of this study indicate that the widespread presence of divergent variants of CBLVd is a major concern for the citrus industry in Pakistan and other countries where virulent isolates of CBLVd are prevalent. These findings suggest the need for future research on effective management and quarantine measures to stop the spread of CBLVd.

In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates of the proposed estimator. The mathematical expressions for bias and mean square error of proposed the estimator are derived under large sample approximation. Many other generalized ratio and product-type estimators are obtained from the proposed estimator using different choices of scalar constants. Some special cases are also discussed in which the proposed generalized estimator reduces to the usual mean, classical ratio, product, and regression type estimators. Mathematical conditions are obtained for which the proposed estimator will perform more precisely than the challenging estimators mentioned in this article. The efficiency of the proposed estimator is evaluated using four populations. Results showed that the proposed estimator is efficient and useful for survey sampling in comparison to the other existing estimators.

The present study suggested memory-type ratio and product estimators for variance estimation in the presence of measurement errors. We applied the exponentially weighted moving averages statistic which simultaneously utilizes the current and prior information for better estimation in surveys based on the time-scale. The expressions of approximate mean square errors of memory-type estimators are derived using Taylor series up to first order. Mathematical conditions are also obtained for which the suggested memory-type ratio and product estimators perform better than the conventional ratio and product estimators. The efficiency of the proposed estimators is observed using an extensive simulation study in the presence of measurement errors. A real data application is also carried out to support the mathematical expressions. From the results, it is shown that the use of prior sample information significantly increased the efficiency of the proposed estimators.

Ginger is an herbaceous and flowering plant renowned for its rhizome, which is widely employed as both a spice and an herb. Since ancient times, ginger has been consumed in folk medicine and traditional cuisines for its favorable health effects. Different in vitro and in vivo studies have disclosed the advantageous physiological aspects of ginger, primarily due to its antioxidant, anti-inflammatory, antimicrobial, and anti-carcinogenic properties. These health-promoting features are linked to the variety of bioactive compounds that are present in ginger. Following the advancement in consumer awareness and the industrial demand for organic antioxidants and functional ingredients, the application of ginger and its derivatives has been broadly investigated in a wide range of food products. The prominent features transmitted by ginger into different food areas are antioxidant and nutraceutical values (bakery); flavor, acceptability, and techno-functional characteristics (dairy); hedonic and antimicrobial properties (beverages); oxidative stability, tenderization, and sensorial attributes (meat); and shelf life and sensorial properties (film, coating, and packaging). This review is focused on providing a comprehensive overview of the tendencies in the application of ginger and its derivatives in the food industry and concurrently briefly discusses the beneficial aspects and processing of ginger.