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Introduction: Uterine carcinosarcoma is an aggressive gynecologic malignancy that accounts for 5% of all gynecological malignancies. There is a disproportion in its incidence and mortality among different races. This study describes demographic and clinicopathological factors and racial disparities affecting the survival of patients with uterine carcinosarcoma. Methods: Data on uterine carcinosarcoma patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2020. Results: Of the 11,338 patients identified, the median age at diagnosis was 68 years, and the five-year cause-specific survival (CSS) rate was 38.7%. for all races. Compared with Asian patients (39.5%, 95% CI, 36.0–43.4%), Hispanic patients (39.4%, 95% CI, 36.5–42.5%), and White patients (37.9%, 95% CI, 36.7–39.2%), Black patients accounted for 21% of the patients and had a significantly lower 5-year CSS (95% CI, 27.2–31.2%). The CSS rates were 84.4% (95% CI, 83.3–85.6%) for localized tumors, 68.5% (95% CI, 66.9–70.1%) for regional tumors, and 39.0% (95% CI, 36.9–41.2%) for distant tumors. Multimodal treatment involving chemotherapy, surgery, and radiation improved the overall one- and five-year survival rates by 88.2% (95% CI, 87.0–89.5%) and 52.8% (95% CI, 50.7–55.1%), respectively, across all disease stages. Multivariate analysis identified age >60 years, Black race, tumor size >4 cm, and distant metastases as independent risk factors for mortality (p < 0.0001). Conclusions: This large database study presents the most up-to-date epidemiological information regarding cases of uterine carcinosarcoma. The findings suggest that a combination of surgery, chemotherapy, and radiation may be most efficacious in treating this malignancy, especially in patients with distant disease.

Abstract Metal-organic frameworks (MOFs) play a pivotal role in modern material science, offering unique properties such as flexibility, substantial pore space, distinctive structure, and large surface area. Recently, zinc-based MOFs have attracted significant attention, particularly in the biomedical arena, owing to their versatile applications in drug delivery, biosensing, and cancer imaging. However, there remains a crucial need to explore and understand the structural properties of zinc silicate-based MOFs to fully exploit their potential in various applications. The objective of this study is to address this need by employing topological modeling techniques to characterize zinc silicate networks. Utilizing connection number concept of chemical graph theory and novel AL molecular descriptors, we aim to investigate the structural intricacies of these MOFs. More precisely, zinc silicate-based MOF networks are topologically modeled via novel AL topological indices, and derived mathematical closed form formulae for them. By comparing experimental and calculated values and constructing linear regression models, the predictive capabilities of the proposed descriptors are evaluated. Specifically, the performance of derived topological indices against the physico-chemical properties of octane isomers is assessed, which provide valuable insights into their predictive potential. The findings of this study demonstrated the potential of novel AL indices in predicting a wide range of important physico-chemical properties, further enhancing their practicality in materials science and beyond.

Abstract For successful treatment of diseases, sufficient therapeutics must be provided to the body. Microneedle applications in therapeutic delivery and analytics sampling are restricted because of various issues, including smaller area for drug loading and analytics sampling. To achieve sufficient drug loading and analytics sampling and improve drug penetration while maintaining painless administration, patch-type microneedle arrays were designed and fabricated using polymer casting from a conical cavity mold. Microcavities were formed on a carbon plate via micromechanical machining. A porous polymer layer was coated on a microneedle patch (MNP). The pores of the porous polymer layer provided space and channels for drug delivery. A pH-sensitive polymer layer was employed to cap the porous polymer layer, which prevented drug leakage during storage and provided a stimulus drug release in response to body pH conditions. The drug can be delivered through holes connected to both sides of the patch. The drug release of the MNP was investigated in vitro and in vivo and showed conceptual proof that these MNs have the potential to enhance treatment protocols for various diseases with the flexibility of coating and therapeutic materials and offer significant scope for further variations and advancement.

The study investigates the impact of incorporating Ni and Cu into the lattice of ZnO nanoparticles (NPs) to enhance their anticancer and antioxidant properties. Characterization techniques including pXRD, FTIR, UV–visible absorption spectroscopy, FESEM, and EDAX confirm the successful synthesis and structural modifications of Ni/Cu-ZnO NPs. Anticancer activity against breast cancer (MDA) and normal skin (BHK-21) cells reveals dose-dependent cytotoxicity, with Ni/Cu-ZnO NPs exhibiting higher efficacy against MDA cells while being less harmful to BHK-21 cells. Morphological studies corroborate these findings. Additionally, antioxidant assays using TAC, FRAP, and DPPH assay demonstrate the superior antioxidant activity of Ni/Cu-ZnO NPs matched to pure ZnO. Overall, the synergistic effect of Ni and Cu incorporation leads to improved therapeutic potential, making Ni/Cu-ZnO NPs promising candidates for cancer therapy and antioxidant applications.Molecular docking recreations were performed using Auto Dock Vina software to gain more insights and validate the observed biological activities of un-doped ZnO and bi-metal doped ZnO NPs, we investigated the interaction and binding affinities of pure ZnO and bimetallic metal co-doped ZnO for their antioxidant and anticancer studies. Ni/Cu-ZnO have shown good antioxidants and exhibited remarkable anticancer activities.

Hepatitis B virus (HBV) is the causes of liver infection and associated with cirrhosis, carcinoma, and fibrosis of liver. In this research study, we conducted an observational study to evaluate the distribution of HBV infection and its genotypes in selected cities of Khyber Pakhtunkhwa, Pakistan. A total of 426 samples were collected from selected cities of Khyber Pakhtunkhwa. Mardan (n = 163), Dir (Lower) (n = 116), Malakand (n = 53), Miran Shah (n = 29), Dera Ismail Khan (n = 28), Swat (n = 22), and Hangu (n = 15). For molecular identification of hepatitis B virus infection, the ICT (Immunochromatographic techniques) kits were used for the Screening of Hepatitis B virus infection among general population. The positive samples of ICT test were further subjected to Real-time Polymerase chain reaction (PCR), of which 282 (66.2 %) were males, and 144 (33.8 %) were females. Out of 426 ICT kit positive samples, 218 (51.18 %) were found PCR positive. The maximum number of positive cases, 157 (36.86 %), was found between 15 and 34 years. Sixty samples were further processed for HBV genotyping. A mixed genotype was detected in 29 (48.3 %) individuals, followed by genotype D in 23 (38.3 %) and untypable in 8 (13.3 %). The positivity rate of HBV was recorded high among males compared to females. Most patients who reported positive for HBV were co-infected with two genotypes. This research will help to provide background information to government and health authorities to raise public awareness and improve the capacity of the healthcare delivery system for hepatitis B virus infection.

Abstract Recent advancements in single‐molecule methods have not only made it possible to obtain precise measurements for complex biological processes but also to produce simple mathematical models for intricate biochemical mechanisms, which would otherwise be speculative. These developments have strengthened our ability to respond through mathematical modeling to concepts of protein‒protein and protein‒DNA interactions on a nanometer level and address‐related questions. In this article, we examine an intriguing biological phenomenon in which a protein and an enzyme co‐jointly encounter carcinogenic adducts during transcription. We are focusing mainly on the dysregulation of the protein involved and the possible consequences that may arise. By providing a quantum mechanical model, we have demonstrated that the presence of carcinogenic adducts in a transcriptional bubble deregulates the protein that could cause lethal mutations. Next, we present a case study to explore carcinogenesis by suggesting an alternative experimental design. Our quantum mechanical model emphasizes the use of a quantized energies approach for specific mechanisms within the living cells. Radiation‐induced carcinogenicity can be prevented if radiation interacting with tissue is not given the energies that satisfy the quantization conditions.

Abstract This research scrutinizes the trends and dynamics of Intellectual Property Protection (IPP) of Intangible Cultural Heritage (ICH) in China, utilizing a dataset of 91 papers from the CNKI database spanning 2011 to 2020. The study uses CiteSpace software to visualise and analyse the literature across multiple dimensions, including article count, authorship, institutional affiliations, and keyword co-occurrence. Findings indicate a lack of robust collaboration among authors and institutions in IPP and ICH, with a scarcity of active cooperative groups. Critical research hotspots identified encompass intangible cultural heritage, intellectual property protection, inheritors, legal protection, copyright, intellectual property law, and geographical indications, with the legal safeguarding of ICH’s intellectual property, digital conservation, traditional cultural expressions, and original authentication emerging as the leading research frontiers. This investigation provides a holistic view of China’s IPP and ICH landscape, offering essential scientific insights for ongoing scholarly discourse. This study mainly benefits policymakers and stakeholders in the cultural heritage sector, underscoring the necessity of enhanced authorial and institutional collaboration and the prioritization of legal and digital protection mechanisms to safeguard China’s intangible cultural legacy for posterity. The analysis is critical, informing policy formulation and strategic planning to bolster ICH’s protection and sustainable management in China.

Background & Objective: Medical students form an integral part of future taskforce for the healthcare sector. We aimed to assess the impact of demographic charcateristics and COVID-19 pandemic affecting choice of speciality among medical graduates in Pakistan. Materials & Methods: A cross-sectional study was carried out on fresh medical graduates using two self-designed online questionnaire surveys before and after two months of house job, inquiring choice of specialty, reasons for choosing it, exposure to pandemic and its effect on choice of specialty using Chi-Square test. Results: Fifty nine percent of medical graduates had chosen field of speciality, significantly impacted by higher socioeconomic class (p=0.006), fathers with higher education (p=0.046) and advice about specialty selection (p=0.044). Internal medicine (20%), surgery (19%), obstetrics and gynaecology (O&G) (17%) and dermatology (13%) were most commonly selected. Age (p=0.027), female gender (p=0.009) and mothers with higher education (p=0.022) significantly affected choosing O&G, while advice to choose this specialty impacted choosing O&G (p=0.027) and dermatology (p=0.022). Better patient outcomes, challenging specialty, reputation/prestige and inspiration from a doctor were common reasons to choose a specialty. 9.1% changed their initial choice of specialty based on exposure to COVID-19 pandemic of whom majority switched to COVID unrelated fields, but it was not statistically significant. Conclusion: This study identifies that demographic, patient outcome and personal factors were primary determinants of specialty choice. COVID-19 pandemic did not significantly affect career choices among medical graduates.

Abstract Indium phosphide (InP) is a binary semiconductor composed of indium and phosphorus. It has a zinc blende crystal structure, which is a type of cubic lattice structure. This lattice is composed of indium and phosphorus atoms arranged in a lattice of cube-shaped cells, with each cell containing four indium atoms and four phosphorus atoms. This lattice structure is the same for all materials with a zinc blende crystal structure and is the most common type of lattice structure in semiconductors. Indium phosphide (InP) has several chemical applications. It is commonly used as a dopant in the production of semiconductors, where it helps control the electrical properties of the material. InP is also utilized in the synthesis various indium-containing compounds, which can have applications in catalysts and chemical reactions. Additionally, InP nanoparticles have been investigated for their potential use in biomedical imaging and drug delivery systems. The topological characterization of 3D molecular structures can be performed via graph theory. In graph theory, the connections between atoms are represented as edges and the atoms themselves are represented as nodes. Furthermore, graph theory can be used to calculate the topological descriptors of the molecule such as the degree-based and reverse degree-based irregularity toplogical indices. These descriptors can be used to compare the topology of different molecules. This paper deals with the modeling and topological characterization of indium phosphide $$({\text{InP}})$$ ( InP ) via degree-based and reverse irregularity indices. The 3D crystal structure of the InP is topologically modeled via partition of the edges, and derived closed form expressions for its irregularity indices. Our obtained results will be surely be helpful in investigating the QSPR/QSAR analysis as well as understanding the deep irregular behavior of the indium phosphide $$({\text{InP}})$$ ( InP ) .

Abstract Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they’re needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi $$\acute{c}$$ c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid.

This study aims to investigate the Joule effect, thermal radiation, and Coriolis effects in unsteady magnetohydrodynamics (MHD) micropolar flow over a 3-D unstable sheet. A magnetic field is typically applied to the surface; additionally, it is assumed that the fluid is conducting electricity. Other micro-rotations are also considered. The physical issue is resolved with the help of the fundamental equations, and the issue's complexity is reduced with the use of similarity variables. In the present approach, the homotopy analysis method (HAM) is employed. The heat, skin's friction factor, temperature, micro movements, and velocity associated with the emerging parameters and transfer rates are considered. The boundary layer thickness is increased as the parameter of vorticity increases. When the magnitude of the magnetic field increases, the skin friction coefficient decreases. The state variables are approximated up to six decimal places numerically. The numerical values of −f″(0) and −θ′(0) are computed to higher decimal places and compared with the available literature to validate the results. The outcomes obtained are compared with the available literature; the results here are corroborated, and the performance of the HAM is demonstrated.

Objective: In the present research work, we examined the dietary Moringa oleifera effect on gut health and growth traits in chickens. Materials and Methods: There were 280 chicks (day old) that were weighted and allotted uni¬formly in seven groupings, each containing eight replicates (n = 5). Birds were supplemented with M. oleifera leaf extract (MLE) and seed extract (MSE) for 35 days. Group I was the control (fed merely basal diets), while Group II received 0.8% MLE, Group III was given 0.8% MSE, Group IV was given 1.2% MLE, Group V was given 1.2% MSE, Group VI was given 0.8% MLE + 0.8% MSE, and Group VII was given 1.2% MLE + 1.2% MSE. At the end of the fifth week, two chickens were selected from each replica, and samples (small intestine and ileal ingesta) were collected. Results: The chicken diet with MLE and MSE supplements saw significant improvement (p < 0.05) in both feed conversion ratio (FCR) and body weight gain (BWG). In the small intestine (duodenal, jejunal, and ileal), dietary MLE and MSE supplements significantly increased (p < 0.05) the surface area of the villus and the ratio of their height/crypt depth in comparison to the control group. The MLE and MSE supplements significantly increased (p < 0.05) the total goblet cell counts in the small intestine. The Lactobacillus spp. count was significantly improved (p < 0.05) and reduced (p < 0.05) in Escherichia coli counts when the bird diet was supplemented with MLE (0.8%) and MSE (0.8%). Conclusion: Results indicated that M. oleifera leaf and seed extract diet improved the growth trait and gut health in chickens. [J Adv Vet Anim Res 2024; 11(2.000): 339-348]

This work aims to investigate the Cuckoo search-active set algorithm (CS-ASA), which is based on the artificial neural network (ANN) approach to the nonlinear partial differential equations (PDEs). CS-ASA technique modifies the Cuckoo search best possible solution to the next stage. This best solution is considered from all the best-chosen sets of Cuckoo search. This technique is applied to 1D and higher-order nonlinear PDEs that work as benchmark problems (heat, forced heat, Burger's, and Allen-Cahn). The results are obtained in the form of graphs by applying the CS-ASA algorithm. The state variables are plotted for a single hidden layer to reduce the computational cost. For the overall performance of CS-ASA, the fitness function is plotted against the number of iterations. It is clear that the fitness function minimizes with higher iterations, and hence achieve an optimal solution. The error in each case is presented at the grid points. Also, the fitness function for the description of the objective function is plotted. For best possible output, CS-ASA has chosen weight functions and its range in each case study accordingly. The obtained results (L2 error) are presented in the form of a table and compared with PSO, PSO-BP, and PSO-BP-CD. It is clear that CS-ASA perform better with a minimum L2 error for each problem under consideration.

The field of 3D food printing is poised to revolutionize the gastronomic landscape by offering precise and customized food creations. This short review explores the fundamental concepts and physical qualities that influence the design, structure, and taste of 3D-printed food, examining the interplay between physics and food, including viscosity, rheology, surface tension, and heat transfer, contributing to understanding and advancing 3D food printing technology. The basics of 3D food printing, the physics-driven design and structure of printed food, the role of heat transfer and thermal effects, and the sensory aspects and flavor perception in 3D-printed food are discussed. Furthermore, it highlights the recent advances and innovations in 3D food printing, along with the challenges that lie ahead and future directions for research. This perspective underscores the importance of physics in shaping the future of 3D food printing, with potential applications ranging from personalized nutrition to sustainable food production. By embracing a physics-driven approach, we can unlock this disruptive technology's full potential and transform how we produce and experience food.

Soil flooding is a serious abiotic stress that can repress plant growth and yield. The intensity of flooding stress is continuously increasing owing to rapid climate change and changes in rain intensity and frequency. Biochar (BC) emerges as an important soil amendment to mitigate adverse impacts of abiotic stress. The role of BC against different stresses is well reported, however, its role under flooding stress is not determined yet. Thus, this experiment was conducted to determine the role of BC on the performance of brassica crops under flooding stress. The experiment was comprised of well-watered (WW) and flooding stress (FS) conditions and biochar application: control, 1% biochar, and 2.5% biochar. The results indicated that flooding stress stunted the plant growth and impaired the photosynthetic pigments, leaf water status, osmolytes yield and yield traits, and oil concentration and increased the electrolyte leakage (EL), hydrogen peroxide (H2O2) and malondialdehyde (MDA) concentration. The application of BC at the rate of 2.5% mitigated the adverse impacts of salinity and upgraded the antioxidant activities (~50-120%), reduced the oxidative stress markets (~70-300%), and increased the leaf water status, photosynthetic pigments, osmolyte accumulation, nutrient uptake, yield traits, and oil contents. In conclusion, BC application can improve the productivity and oil yield of brassica under drought stress by improving plant physiological and biochemical functioning. However, more in depth studies are direly needed to explore the mechanism of BC to induce flood tolerance.

Stratified flows are commonly observed in numerous industrial processes. For example, a gas-condensate pipeline typically uses a stratified flow regime. However, this flow arrangement is stable only under a specific set of operating conditions that allows the formation of stratification. In this study, the authors analyzed the flow attributes of Prandtl Eyring liquid past an inclined sheet immersed in a stratified medium. The flow also characterizes the features of the magnetic field along with a first-order chemical reaction. Convective boundary constraints associated with the thermosolutal exchange at the extremity of the domain are also prescribed. The fundamental equations of the study are formulated in dimensional PDEs and converted into dimensionless ODEs via similar variables. The numerical solution of the modelled setup is acquired by executing computations using shooting and RK-4 methods. The intelligent computing paradigm working on the mechanism of the back-propagated Levenberg-Marquardt strategy is also capitalized to forecast the behavior of related physical quantities. Graphs and tables are drawn to elaborate the impression of pertinent factors on flow distributions. It is perceived that the momentum profile diminishes with the magnetic field effect, whereas the opposite behavior is observed for the skin friction coefficient. The thermal and concentration distributions were found to dominate in the absence of stratification. Consideration of convective heating and concentration tends to elevate thermal and mass distributions.

This article aims to analyze the two-dimensional (2D) nanofluid (Ag/C2H6O2) flow past an exponentially stretched sheet. The magnetic field impact, heat source/sink, and convection in the thermal profile are taken into account. The complexity of the problem is reduced by introducing a dimensionless group of functions. The reduced model is transformed into a system of first-order ordinary differential equations (ODEs). This system is further analyzed with the artificial neural network (ANN), which is trained using the Levenberg–Marquardt algorithm. The whole dataset is sub divided into three parts: training (70%), validation (15%), and testing (15%). The impact of nonlinear heat source/sink parameter, magnetic parameter, volume fraction of nanoparticles, and Prandtl number is displayed through graphs. The heat source, volume fraction, and the Prandtl number cause an increase in the thermal profile with its larger values. The magnetic parameter causes a decline in both the thermal and momentum boundary layers with its higher values. The analysis shows that the thermal energy profile is enhanced with the larger values of the volume fraction of silver nanoparticles and heat source. For each case study, the residual error (RE), regression line, and validation of the results are presented. The performance of the proposed methodology is numerically tabulated for the nanoparticle volume fraction shown in Table 3, where the minimum absolute error (AE) is 5.3373e−11 at ϕ=0.05. Based on this, we recommend ϕ=0.05 for better performance. The AEs for the ANN and bvp4c are computed for the state variables in Tables for the magnetic parameter M=5,10, and 15. These tables show the overall performance of the ANN and further validate the present study. We have also validated the results of the ANN through the mean squared error graphically, where the accuracy of the proposed methodology is proven.

Advancement in thermodynamical characteristics of non-Newtonian fluid is achieved by induction of gyrotactic microorganisms in order to attain efficient thermal and mechanical engineering setups, such as micromixers, microfluidic instruments, biosensors and microdevices. So, the primer motive to conduct this research is to examine the effectiveness of gyrotactic microorganisms in managing heat transport in Carreau nanofluid flow over a linearly stretched surface under the effectiveness of opposing and assisting buoyancy forces. Physical aspect of magnetic field along with slip boundary constraint also entertained by employing in vertical direction. The governing equations are modelled in the form of dimensional PDE's and latterly transformed into dimensionless ODEs via similarity transformations. Solution of obtained set of differential system is solved by implementing Runge-Kutta of order 4 and shooting procedure. Visualizations about influence of flow concerning parameters on associated distributions are analyzed in comparative manner for opposing and assisting buoyancy parameter. In addition, metrics for quantities of engineering interest, such as heat and mass fluxes for nano and gyrotactic particles along with skin friction coefficient are calculated in a comprehensive manner.

This work aims to analyze the impacts of the magnetic field, activation of energy, thermal radiation, thermophoresis, and Brownian effects on the hybrid nanofluid (HNF) (Ag++silicon oil) flow past a porous spinning disk. The pressure loss due to porosity is constituted by the Darcy–Forchheimer relation. The modified Buongiorno model is considered for simulating the flow field into a mathematical form. The modeled problem is further simplified with the new group of dimensionless variables and further transformed into a first-order system of equations. The reduced system is further analyzed with the Levenberg–Marquardt algorithm using a trained artificial neural network (ANN) with a tolerance, step size of 0.001, and 1,000 epochs. The state variables under the impacts of the pertinent parameters are assessed with graphs and tables. It has been observed that when the magnetic parameter increases, the velocity gradient of mono and hybrid nanofluids (NFs) decreases. As the input of the Darcy–Forchheimer parameter increases, the velocity profiles decrease. The result shows that as the thermophoresis parameter increases, temperature and concentration increase as well. When the activation energy parameter increases, the concentration profile becomes higher. For a deep insight into the analysis of the problem, a statistical approach for data fitting in the form of regression lines and error histograms for NF and HNF is presented. The regression lines show that 100% of the data is used in curve fitting, while the error histograms depict the minimal zero error −7.1e6 for the increasing values of Nt. Furthermore, the mean square error and performance validation for each varying parameter are presented. For validation, the present results are compared with the available literature in the form of a table, where the current results show great agreement with the existing one.

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

Abstract This paper aims to demonstrate a numerical strategy via finite difference formulations for time fractional reaction–diffusion models which are ubiquitous in chemical and biological phenomena. The time-fractional derivative is considered in the Caputo sense for both linear and nonlinear problems. First, the Caputo derivative is replaced with a quadrature formula, then an implicit method is used for the remaining part. In the linear case, the proposed strategy reduces the time fractional models into linear simultaneous equations. In nonlinear cases, Quasilinearization is utilized to tackle the nonlinear parts. With this strategy, solutions of the fractional system transform into linear algebraic systems which are easy to solve. Next, the Von Neumann method is implemented to examine the stability of the scheme which discloses that the scheme is unconditionally stable. Further, the applicability of the presented scheme is tested with different linear and nonlinear models which include the one dimensional Schnakenberg and Gray–Scott models, and one and two dimensional Brusselator models. To analyze the accuracy of the present technique two norms namely, $$\mathbb {L}_{\infty }$$ L ∞ and $$\mathbb {L}_{2}$$ L 2 , and relative error are addressed. Moreover, the obtained outcomes are shown tabulated and graphically which identifies that the scheme properly works for the time fractional reaction–diffusion systems.

Background and Aim: Foot-and-mouth disease (FMD) is an infectious disease of cloven-hoofed animals, including buffalo, cattle, sheep, goats, and pigs, causing major economic losses to the local farmers and, overall, to the national economy of the country. This study aimed to detect FMDV serotypes in year-round FMD outbreaks, hematological and biochemical changes, and oxidative stress in FMDV-infected cattle and buffaloes in the district of Quetta, Balochistan, Pakistan, and the socioeconomic impact of FMD outbreaks on farmers. Materials and Methods: We conducted a cross-sectional study in the district of Quetta, Balochistan, Pakistan, where FMD virus (FMDV) serotypes were detected by enzyme-linked immunosorbent assay (ELISA). Hematological, biochemical, and oxidative analyses were performed by analyzing the blood of FMDV-infected and non-infected animals. Information on the associated risk factors was obtained through a structured questionnaire by interviewing farmers in each FMD-affected farm. Results: Thirty-four out of 38 farms (89%, 95% confidence interval [CI]: 75%–97%) were positive for FMD by ELISA. Higher FMD infection was detected in farms with a herd size of 100 animals (32%, 11/34) and 51–100 animals (18%, 6/34). Fifty-seven percent (114/200, 95% CI: 50%-64%) of animals were positive for FMD. Of these, 61% (69/114) were cattle and 39% (45/114) were buffalo. FMD positivity was higher in females (86%, 98/114) than in males (14%, 16/114) and higher in animals older than 2 years of age (52%, 59/114). On average, farmers lose U.S. dollars 3000 annually due to FMD outbreaks. Animals infected with FMDV had significantly (p ≤ 0.05) white blood cell counts and significantly (p ≤ 0.05) lower hemoglobin and total protein concentrations in buffalo and cattle, whereas infected cattle showed significantly (p ≤ 0.05) lower albumin levels. Globulin levels were lower in buffaloes infected. Alanine aminotransferase levels were lower in infected cattle (p ≤ 0.05). Creatinine levels were higher in infected buffalo (p ≤ 0.05). Urea and phosphorus concentrations were higher in FMDV-infected cattle and buffalo (p ≤ 0.05). Calcium levels were lower in infected cattle and buffalo (p ≤ 0.05). Catalase enzyme activity in infected cattle and buffaloes was significantly lower (p < 0.05). Lipid peroxidation was significantly higher in FMDV-infected cattle and buffalo (p ≤ 0.05). Conclusion: This study confirmed serotype O circulation among cattle and buffalo in year-long FMD outbreaks in the Quetta District of Balochistan. Blood analysis identified a parameter deviated from the normal level due to FMDV infection. In addition, the outbreak of FMD has a significant negative economic impact on livestock farmers.

Machine learning and deep neural network developments have evolved image classification. This study offers a new neural network design with segmentation and localization, different crack types, categorization accuracy, and efficiency. In the proposed work, an advanced and innovative Spliced Multimodal Residual18 Neural Network (SMR18-NN) Model is presented that departs from previous models. For data authentication, augmentation is done by proposing the new and effective model known as an Augmented Minority Over-sampling Technique (AMOST). The SMR18 NN model combines the well-established ResNet18 framework with the Faster RCNN architecture, incorporating parts from the modified Fast RCNN and the Region Proposal Network. This work aims to revolutionize crack-type recognition and categorization in images. A Support Vector Machine strategically improves the network’s data classification. A modified ResNet18 model is ultimately implemented and compared with the proposed innovative SMR18-NN model. Both network’s parameters, such as epochs, number of iterations, etc., were kept the same for fair evaluation. Innovative frameworks and properly selected benchmark datasets supported this. The empirical results of this comparison study are convincing. ResNet18 training and testing accuracy was 90.60% and 85.20%, respectively. The SMR18-NN outperformed these results with 96.20 training and 92.00% testing accuracy. The experiment concluded with SMR18-NN accurately detecting image features, proving its superiority in image classification.

The integration of unmanned aerial vehicles (UAVs) in the fifth-generation (5G) network has emerged as a promising solution to deliver seamless services and provide connectivity because of their mobile nature. In vehicular networks, connectivity between vehicles and the network can be affected by ground-level obstacles and false messages created by some deceitful vehicles. UAVs are integrated into vehicular networks to provide line-of-sight communication. This integration also poses specific challenges, such as maintaining reliable communication links and handling operational complexities related to coordination and routing, which minimizes the network throughput and packet delivery ratio (PDR) and increases energy consumption, routing overhead, and communication delay. This paper proposes improved trust analysis and artificial gorilla troops optimization (ITA-AGTO) to address the challenges associated with the security and deployment of UAVs in vehicular networks. ITA improves network security and reduces congestion by discarding incoming messages from a deceitful vehicle. It filters false messages by calculating the trust values corresponding to every message and determining whether to accept or discard the incoming message. The artificial gorilla troops optimization (AGTO) approach positions the UAVs and finds the optimal solution to transmit the data sent by the RSU or vehicle to a destination directly or through the ground base station. AGTO enhances communication intelligence and reduces the complexity of the network, thereby contributing to an overall performance improvement. The proposed approach is evaluated in terms of packet delivery ratio (PDR), communication delay, network throughput, energy efficiency, energy consumption, and routing overhead. Simulation results validate the effectiveness of the proposed approach over state-of-the-art approaches by achieving a maximum PDR (93.28%), throughput (658.17 Kbps), energy efficiency (88.19%), minimum routing overhead (865 packets), energy consumption (1.13 KJ) and communication delay (125.46 ms). The findings indicate the superior performance of the ITA-AGTO approach.

The DC-DC converters are essential in power electronics as they maintain a stable output voltage even when there are changes in input voltage and load current. This study introduces an advanced Proportional-Derivative (PD) compensator for buck converters. The compensator enhances stability and transient responsiveness by employing a unique modulation technique that has not been previously applied in this context. The proposed method entails applying an input of 28 volts, which yields an output amplification of 15 volts, even in the presence of interference. The small signal transfer function of the buck converter is meticulously derived, considering the converter’s dynamic behavior to achieve exceptional results. The transfer function comprehensively explains the intricate relationship between the input and output voltages, providing the theoretical basis for our distinctive control approach. The MATLAB code accurately generates the Bode diagram of the buck converter by employing the small signal transfer function. The graph illustrates the frequency response of the converter, a crucial factor in enhancing the stability and quality of the output voltage. The proposed research is substantiated by mathematical data shown through several simulated figures, distinguishing it from conventional methodologies. The implemented research achieves the maximum efficiency exceeding 95% with a minimum ripple factor.

Objective: To determine the frequency of diabetic nephropathy in type 2 diabetic patients using spot urine ACR ratio and identify its associated risk factors. Study Design: Descriptive, cross-sectional study. Place and Duration of Study: This research was carried out at the Department of Medicine, Muhammad Teaching Hospital (MTH) Peshawar, Pakistan from February 01, 2023, to July 31, 2023. Methods: A total of 150 patients having type 2 diabetes mellitus were investigated. The sample size was calculated with a WHO sample size calculator using a reference study with a 10.8% prevalence of diabetic nephropathy in individuals with type 2 diabetes mellitus; confidence interval = 95% and the margin of error was 5%. Results: With an SD ± 5.146, the mean age was 49 years old. There were 87 (58%) females and 63 (42%) males present. Out of 150 individuals, the spot urine ACR was analyzed. Of these, 47 (31%) had microalbuminuria (less than 300 mg/m) while, 28 patients (19%) had macroalbuminuria (more than 300 mg/m), whereas end-stage renal disease was found in 6 (4%) with GFR < 30 ml/m -1.73 m2. A study was conducted on 150 individuals to determine the status of diabetic nephropathy. Of these, 81 (54%) had diabetic nephropathy and 69 (46%) did not. Conclusion: Using the spot urine ACR ratio, our study suggests that 46% of type-2 diabetic patients had diabetic nephropathy. The incidence was 24% in people with diabetes for 5–10 years and 26% in patients with diabetes for 11–15 years. Patients with diabetes for 16 to 20 years had a 50% incidence. In conclusion, those with diabetes for a longer period are at a much greater risk of diabetic nephropathy. How to cite this: Ullah A, Kausar SAZ, Ayaz M, Khan MS, Ullah S, Saeed RF. Frequency of Diabetic Nephropathy in Type 2 Diabetic Patients Using Spot Urine ACR and Its Associated Risk Factors. Life and Science. 2024; 5(4): 459-464. doi: http://doi.org/10.37185/LnS.1.1.805

In electromechanical systems, backlash in gear trains can lead to a degradation in control performance. We propose a drive–anti-drive mechanism to address this issue. It consists of two DC motors that operate in opposite directions. One motor acts as the drive, while the other serves as the anti-drive to compensate for the backlash. This work focuses on switching between the drive and anti-drive motors, controlled by a switched-mode PID controller. Simulation results on an inverted pendulum demonstrate that the proposed scheme effectively compensates for backlash, improving position accuracy and control. This switched controller approach enhances the performance of electromechanical systems, particularly where gear backlash poses challenges to closed-loop performance.

Introduction: Pancreaticoduodenectomy (PD) is the only potentially curative treatment for pancreatic head adenocarcinoma. This study aimed to determine short-term outcomes of PD performed over one year at a newly established Hepato-Pancreatico-Biliary (HPB) unit in Khyber Pakhtun Khwa (KPK) Province of Pakistan. Material and methods: A retrospective analysis of a prospectively maintained Hospital Information System (HIS) was undertaken of all patients referred to the unit between May 2021 to August 2022. Data were collected from the medical records of patients in the hospital information system. Data were analyzed for primary location, age, complications, and operative parameters. Results: The primary site of disease were ampulla (n=18, 52.9%), pancreas (n=11, 32.4%), and duodenum (n=5, 14.7%). The median duration of surgery was 7 hours. 16 (47.1%) patients required blood transfusion either intraoperatively or in the peri-operative period. Patients with preoperative biliary drainage (PBD) were more likely to have multidrug-resistant positive bile cultures with a p-value of 0.2 [n=12(35.3%) vs n=5(14.7%)]. Overall morbidity was 38.2%. The most common complications were wound infection (n=12, 35.3%), delayed gastric emptying (n=6, 17.6%), and type B pancreatic fistula (n=3, 8.8%). The complication rate was higher in patients with biliary stenting [n=11(32.4%) vs n=2 (5.9%); p-value 0.06]. The median length of hospital stay for patients without complications was less (6 vs 12 days; P

Objective: To determine the delay in Clozapine initiation in patients with treatment-resistance schizophrenia who presented to the Tertiary psychiatric facilities. Study Design: Cross-sectional study. Place and Duration of Study: Department of Psychiatry Combined Military Hospital, Peshawar and Department of Psychiatry Combined Military Hospital, Nowshera Pakistan, from Jan to Dec 2023. Methodology: We recruited 103 treatment-resistant schizophrenia and Clozapine naïve patients after obtaining their consent. Patients were included by reviewing their medical history and records. The diagnosis was made by clinical interview and use of Positive and Negative syndrome scale. The data were recorded using a semi-structured questionnaire. Results: Out of 103 patients, 78(75.7%) were male and 25(24.3%) were female. Regarding the initiation of Clozapine treatment, the mean duration from diagnosis to treatment initiation was 6.23±4.82 years. Patients were categorised based on the time to Clozapine initiation into early initiators (≤4 years), intermediate initiators (>4 years to 15 years). The distribution across these categories was 51(49.5%) for early initiators, 47(45.6%) for intermediate initiators, and 5(4.9%) for delayed initiators. Conclusion: There is a significant delay in Clozapine initiation despite adequate evidence to initiate it early. Clinicians should start Clozapine whenever there is a need.

Chitin and chitosan are the most significant polysaccharides, having a wide range of uses in biopolymers, the biological sciences, and other domains. Its derivatives, hydrogels, are frequently employed in biomedical settings. Adsorbents for metals and dyes from industrial waste are among the other uses. Chitosan-based hydrogels are commonly used in pharmaceuticals for tablets and proteins. Most of these hydrogels consist of polysaccharides, such as chitosan and its derivatives. Their chemical structure is depicted as a chemical blueprint, and the arrangement of components and vertex data is analyzed. In this work, polynomials of chitosan-derived polysaccharides are determined for various topology coindices using a non-adjacent pairing vertices approach. Extensions of M-polynomials for neighboring vertices are called CoM-polynomials. Real numbers known as topology coindices are used to describe a chemical compound's molecular structure and can be used to investigate physicochemical properties relevant to QSPR/QSAR (Quantitative structure- activity relationship) research. CoM-polynomials are calculated for chitosan derivatives, including gamma, beta, and alpha chitin.

This study introduces an innovative approach involving the synthesis of lead-free halide double perovskite (LFHDP) solar cell Cs2AgBiBr6 and that with the doping of trans-polyacetylene (t-PA) via the sol–gel method. Through X-ray diffraction (XRD) analysis, we confirm the cubic structure and, in addition, an observable growth in grain size attributed to t-PA doping. A comprehensive exploration of optical parameters, including band gap, refractive index, electronegativity, extinction coefficient, and dielectric constant was conducted through UV–Vis spectrophotometry. The pristine Cs2AgBiBr6 exhibited a band gap energy (Eg) of 1.9 eV, while with t-PA doping the Eg decreased to 1.84 eV, indicating a notable enhancement in light absorption. The calculated optical electronegativity is 0.49 which suggests a shift towards a more covalent nature. The J-V curve shows parameters for pristine thin film as Jsc of 5.13 mA/cm2, Voc of 0.92 V, FF of 0.69, and the efficiency of 3.29 %. On the other hand, the t-PA doped thin film exhibits improvement of the parameters which are Jsc of 5.7 mA/cm2, Voc of 0.929 V, and FF of 0.75, leading to a significantly elevated efficiency of 3.98 %. This enhanced efficiency is 20.97 % higher than that of the pure Cs2AgBiBr6, clearly showing an uplifted performance. In addition to the experiment, computational method based on finite difference time domain technique was used to check the stability of the material. The study shows the mechanical and dynamical stability of the Cs2AgBiBr6.

Objective(s): The current study aimed to investigate the control and treatment of biofilm-producing isolates of Pseudomonas aeruginosa using silicon nanoparticles (SiNPs). Materials and Methods: Biofilm-producing isolates of P. aeruginosa were recovered from various food samples and identified through fluorescent green colony formation on selective and differential media, as well as the amplification of oprI and oprL genes. Tube methods, Congo-red agar method, and scanning electron microscopy (SEM) were used to study biofilm phenotypes. The effect of SiNPs was evaluated by broth dilution assay.Results: The biofilm assay revealed that these isolates formed biofilms on glass surfaces within 72 hr of incubation. Scanning electron micrographs showed that the biofilm communities were composed of multicellular clusters of P. aeruginosa encased in matrix material. However, these isolates were unable to form biofilms on SiNPs-coated surfaces. The results showed that the planktonic isolates of P. aeruginosa were comparatively sensitive to the antibacterial properties of SiNPs, with minimum inhibitory concentration (MIC) ranging from 100 to 200 µg/ml. Contrarily, the biofilms were found to be 500 times more tolerant to the highest concentration of SiNPs (MIC of 500 µg/ml) and were more resistant. Under static conditions, the sedimentation of SiNPs resulted in their ineffectiveness. However, under shaking conditions, the biofilms were effectively dispersed and the cells were lysed. The results showed that SiNPs were effective against both the planktonic and the metabolically inactive forms of P. aeruginosa.Conclusion: This study suggests that SiNPs could be a useful tool for preventing the formation of biofilms and removing pre-existing biofilms.

Background. Cervical cancer is a serious medical condition that affects hundreds of thousands of individuals worldwide annually. The selection and analysis of suitable gene targets in the early stages of drug design are crucial for combating this disease. However, overlooking the presence of various protein isoforms may result in unwanted therapeutic or harmful side effects. Objective. This study aimed to provide a computational analysis of the interactions between cervical cancer drugs and their targets, influenced by alternative splicing. Methods. Using open-access databases, we targeted 45 FDA-approved cervical cancer drugs that target various genes having more than two distinct protein-coding isoforms. To check the conservation of binding pocket in isoforms of the genes, multiple sequence analysis was performed. To better understand the associations between proteins and FDA-approved drugs at the isoform level, we conducted molecular docking analysis. Results. The study reveals that many drugs lack potential targets at the isoform level. Further examination of various isoforms of the same gene revealed distinct ligand-binding pocket configurations, including differences in size, shape, electrostatic characteristics, and structure. Conclusions. This study highlights the potential risks of focusing solely on the canonical isoform, and ignoring the impact of cervical cancer drugs on- and off-target effects at the isoform level. These findings emphasize the importance of considering interactions between drugs and their targets at the isoform level to promote effective treatment outcomes.

In this study, energy production by two solar energy technologies, namely concentrated solar power (CSP) and photovoltaic (PV) power, is compared from a technical, economic and environmental perspective. Initially, a 50 MW CSP plant is modeled and simulated at four selected sites in Pakistan. Then, the most feasible location of the CSP plant is compared with the solar PV plant of the same capacity. The effect of the solar thermal storage size and cooling system of the CSP system is investigated, while the photovoltaic tracking system is investigated to evaluate the technical and economic performance of the power plants. Technical performance is evaluated based on energy generation and capacity factors metrics, while economic performance is evaluated with respect to levelized cost, payback period and net present value. In addition, environmental criteria such as reducing greenhouse gas emissions (GHG), saving fossil fuels, and life-cycle water consumption are evaluated. From the results, it was concluded that the CSP plant located in Quetta is technically and economically viable. The capacity factor of the CSP plant is 36.6% compared to 19.8% for the PV plant, while the solar-to-electrical efficiency of the CSP plant is 14.2% compared to 20.8% for the PV plant. The required land area is 2.77 acres/GWh for the CSP plant and 2.33 acres/GWh for the PV plant, while the net capital cost of the CSP plant is five times higher than that of the PV plant. Various design parameters are optimized to obtain the minimum levelized cost of energy (LCOE) for both CSP and PV plants. The results of CSP and PV plants indicate that the LCOE can be reduced to 11.57 cents/kWh and 4.69 cents/kWh, respectively. Thus, the CSP plant performs better from the technical point of view while the PV plant performs better from the economic perspective.

The cultivation of halophytes is an alternative approach to sustain agricultural productivity under changing climate. They are densely equipped with a diverse group of metabolites that serve multiple functions, such as providing tolerance to plants against extreme conditions, being used as a food source by humans and ruminants and containing bioactive compounds of medicinal importance. However, some metabolites, when synthesized in greater concentration above their threshold level, are considered antinutrients. Widely reported antinutrients include terpenes, saponins, phytate, alkaloids, cyanides, tannins, lectins, protease inhibitors, calcium oxalate, etc. They reduce the body's ability to absorb essential nutrients from the diet and also cause serious health problems. This review focuses on antinutrients found both in wild and edible halophytes and their beneficial as well as adverse effects on human health. Efforts were made to highlight such antinutrients with scientific evidence and describe some processing methods that might help in reducing antinutrients while using halophytes as a food crop in future biosaline agriculture.

This research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as “energy management”, “demand-side innovation”, “decentralized energy trading”, and “strategic optimization”, covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets.

Background: Forward head posture is associated with the upper trapezius myofascial trigger points and cause pain, restricted range of motion, and referred pain in the arm. The positional release technique is likely to focus on evaluating the efficacy of this manual therapy approach. Objective: To determine the effects of positional release technique for myofascial trigger points of the upper trapezius with forward head posture Method: A randomized control trial study was conducted in the Physiotherapy Department of THQ Hospital Wazirabad, Pakistan, from February 2021 to June 2021 with a sample size of n=32 male participants. Participants were recruited with pain intensity of at least 3 points on the Visual Analogue Scale at baseline, at least one active TrP in the upper trapezius that was diagnosed using the method described by Travel and Simon, and computer users who spend at least 6 hours per day in a sitting posture and are between the ages of 18 and 45. All subjects were randomly and equally allocated into the Positional Release Therapy (PRT) group and a conventional Physical Therapy group. A total 12 treatment sessions were given with 3 days a week for consecutive 4 weeks. The data was collected at the baseline and after the 12th session. The outcome measures for trigger point sensitivity were pain pressure threshold (PPT), visual analogue scale (VAS), active contra lateral flexion (ACLF), cranio-vertebral angle (CVA), and neck disability index (NDI). Data analysis was done through SPSS version-27. Results: The mean age of 34.3±6.57 years. The positional release therapy post-intervention results showed more significant improvement (p≤0.05) in all outcomes PPT, VAS, ACLF, CVA, and NDI between the groups with large effect size. Conclusion: Positional release therapy (PRT) is superior to conventional therapy in decreasing pain intensity and threshold with disability in patients of upper trapezius trigger points Keywords: cranio-vertebral angle; forward head posture; neck disability index; pain pressure threshold; positional release therapy; trigger points; upper trapezius; visual analogue scale.

Dye-polluted wastewater poses a serious threat to humans’, animals’ and plants’ health, and to avoid these health risks in the future, the treatment of wastewater containing dyes is necessary before its release to environment. Herein, a biological approach is used; the textile azo dye brown 703 is degraded utilizing Pseudomonas aeruginosa. The bacterial strain was isolated from textile wastewater dumping sites in Mingora, Swat. The optimization for bacterial degradation was carried out on the nutrient broth medium, which was then subjected to a variety of environmental physicochemical conditions and nutritional source supplementation before being tested. Under micro-aerophilic circumstances, the maximum decolorization and degradation of dye occurred at a 20 ppm dye concentration within 3 days of incubation at a neutral pH and 38 °C. The decrease in the intensity of the absorbance peak in the UV–Vis spectrum was used to measure the extent of decolorization. Initially, 15 bacterial strains were isolated from the textile effluent. Out of these strains, Pseudomonas aeruginosa was found to be the most potent degrading bacteria, with a degradation extent of around 71.36% at optimum conditions. The appearance and disappearance of some new peaks in the FT-IR analysis after the degradation of brown 703 showed that the dye was degraded by Pseudomonas aeruginosa. The GC–MS analysis performed helped in identifying the degraded compounds of azo dye that were utilized in illustrating the under-study process of brown 703 degradation. The biodegradation brought about by Pseudomonas aeruginosa can be employed successfully in the future as an eco-friendly approach with far reaching results.

Abstract A numeric quantity that characterizes the whole structure of a network is called a topological index. In the studies of QSAR and QSPR, the topological indices are utilized to predict the physical features related to the bioactivities and chemical reactivity in certain networks. Materials for 2D nanotubes have extraordinary chemical, mechanical, and physical capabilities. They are extremely thin nanomaterials with excellent chemical functionality and anisotropy. Since, 2D materials have the largest surface area and are the thinnest of all known materials, they are ideal for all applications that call for intense surface interactions on a small scale. In this paper, we derived closed formulae for some important neighborhood based irregular topological indices of the 2D nanotubes. Based on the obtained numerical values, a comparative analysis of these computed indices is also performed.

Plants engage in a variety of interactions, including sharing nutrients through common mycorrhizal networks (CMNs), which are facilitated by arbuscular mycorrhizal fungi (AMF). These networks can promote the establishment, growth, and distribution of limited nutrients that are important for plant growth, which in turn benefits the entire network of plants. Interactions between plants and microbes in the rhizosphere are complex and can either be socialist or capitalist in nature, and the knowledge of these interactions is equally important for the progress of sustainable agricultural practice. In the socialist network, resources are distributed more evenly, providing benefits for all connected plants, such as symbiosis. For example, direct or indirect transfer of nutrients to plants, direct stimulation of growth through phytohormones, antagonism toward pathogenic microorganisms, and mitigation of stresses. For the capitalist network, AMF would be privately controlled for the profit of certain groups of plants, hence increasing competition between connected plants. Such plant interactions invading by microbes act as saprophytic and cause necrotrophy in the colonizing plants. In the first case, an excess of the nutritional resources may be donated to the receiver plants by direct transfer. In the second case, an unequal distribution of resources occurs, which certainly favor individual groups and increases competition between interactions. This largely depends on which of these responses is predominant (“socialist” or “capitalist”) at the moment plants are connected. Therefore, some plant species might benefit from CMNs more than others, depending on the fungal species and plant species involved in the association. Nevertheless, benefits and disadvantages from the interactions between the connected plants are hard to distinguish in nature once most of the plants are colonized simultaneously by multiple fungal species, each with its own cost-benefits. Classifying plant–microbe interactions based on their habitat specificity, such as their presence on leaf surfaces (phyllospheric), within plant tissues (endophytic), on root surfaces (rhizospheric), or as surface-dwelling organisms (epiphytic), helps to highlight the dense and intricate connections between plants and microbes that occur both above and below ground. In these complex relationships, microbes often engage in mutualistic interactions where both parties derive mutual benefits, exemplifying the socialistic or capitalistic nature of these interactions. This review discusses the ubiquity, functioning, and management interventions of different types of plant–plant and plant–microbe interactions in CMNs, and how they promote plant growth and address environmental challenges for sustainable agriculture.

This work aims to analyze the transfer of heat through new fractional-order convective straight fins by using the Riemann-Liouville type fractional derivatives. The convection through the fins is considered in such a way that the thermal conductivity depends on the temperature. The transformed fractional-order problems are constituted through an optimization problem in such a way that the L2 norm remains minimal. The objective functions are further analyzed with the hybrid Cuckoo search (HCS) algorithm that use the artificial neural network (ANN) mechanism. The impacts of the fractional parameter β, the thermo-geometric parameter of fin ψ, and dimensionless thermal conductivity α are explained through figures and tables. The fin efficiency during the whole process is explained with larger values of ψ. It is found that the larger values of ψ decline the fin efficacy. The fractional parameter declines the thermal profile as we approach the integer order. The convergence of HCS algorithm is performed in each case study. The residual error touches E−14 for the integer order of α. The present results are validated through Table 6 by comparing with HPM, VIM and LHPM, while the error for HCS-ANN touches E−13. This proves that the proposed HCS is efficient.

The use of Supplementary Cementitious Materials (SCMs) or industrial wastes as a partial replacement for cement in the production of concrete is an urgent need in the construction industry due to cement's growing environmental challenges and rising cost. In respect of this, we conducted research work on proportioning binary concrete mixes. Fly ash (FA) replaced 10 %, 20 %, and 30 % of the cement, while silica fume (SF) replaced 5 %, 10 %, and 15 % of the cement. A control concrete mix was also developed with 100 % cement and no SCM. The results showed no increase in compressive strength for FA concrete compared to control at the early age of 3–28 days, but a maximum increase in compressive strength of 4 % was discovered at a later age of 56 days for concrete with 20 % FA. For 5 % SF concrete, a considerable strength increase of 15 % was seen at the early age of 3 days. Like with FA concrete, 2 % improvement in strength was recorded at the later age of 56 days for 10 % SF concrete. This study further focused on the concrete's temporal evolution of compressive strength by developing a strength evolution model (SEM) using nonlinear regression analysis at a 95 % confidence level. Pearson correlation coefficient was used to determine the correlation between the model values and the experimental results. For comparison, the fib Model Code 2010 was applied to the experimental data, and a good agreement was observed among the proposed model, the fib Model values, and the experimental results. The proposed model can be expanded to address further regression-related problems. Finally, environmental life cycle assessment revealed that utilizing 10 %, 20 %, and 30 % of FA lowered Global Warming Potential (GWP) by 9 %, 19 %, and 29 %, respectively. Likewise, using 5 %, 10 %, and 15 % of SF reduced the GWP by 5 %, 9 %, and 14 %.

Helicobacter pylori (H. pylori) is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that H.pylori colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the H. pylori infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen.

Research is continuously being pursued to treat cancer patients and prevent the disease by developing new medicines. However, experimental drug design and development is a costly, time-consuming, and challenging process. Alternatively, computational and mathematical techniques play an important role in optimally achieving this goal. Among these mathematical techniques, topological indices (TIs) have many applications in the drugs used for the treatment of breast cancer. TIs can be utilized to forecast the effectiveness of drugs by providing molecular structure information and related properties of the drugs. In addition, these can assist in the design and discovery of new drugs by providing insights into the structure-property/structure-activity relationships. In this article, a Quantitative Structure Property Relationship (QSPR) analysis is carried out using some novel degree-based molecular descriptors and regression models to predict various properties (such as boiling point, melting point, enthalpy, flashpoint, molar refraction, molar volume, and polarizability) of 14 drugs used for the breast cancer treatment. The molecular structures of these drugs are topologically modeled through vertex and edge partitioning techniques of graph theory, and then linear regression models are developed to correlate the computed values with the experimental properties of the drugs to investigate the performance of TIs in predicting these properties. The results confirmed the potential of the considered topological indices as a tool for drug discovery and design in the field of breast cancer treatment.

A topological index is a structural descriptor of any molecule/nanostructure that characterizes its topology. In the QSAR and QSPR research, topological indices are employed to predict the physical characteristics associated with bioactivities and chemical reactivity within specific networks. 2D nanostructured materials have many exhibit numerous chemical, mechanical, and physical features. These nanomaterials are exceptionally thin, displaying high chemical functionality and anisotropy. For applications necessitating robust surface interactions on a small scale, 2D materials stand out as the optimal choice due to their expansive surface area and status as the thinnest among all discovered materials. This paper characterized the neighborhood irregular topological invariants of nanostructures TUC4C8[p, q] and GTUC[p, q] and derived closed form expressions for them. A comparative analysis is then performed on the basis of these computed indices.

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Background: Understanding students' perception of the educational environment and their cognitive load in this context is crucial for optimizing the effectiveness of e-learning platforms. Objective: To compare educational environment perception and cognitive load in under-graduates Doctor of Physical Therapy (DPT) and post-graduate Master in Science of Physical Therapy (MSPT) students having online learning experiences. Methodology: In this comparative cross-sectional study, data was collected through non-probability convenient sampling from n=274 under-graduates DPT (n=225) and post-graduates MSPT(n=49) students of either gender having one-semester experience of online learning, Dundee Ready Education Environment Measure (DREEM) for measuring educational environmental perception and Rating Scale of Mental Effort (RSME) for measurement of cognitive load. Online self-structured was developed questionnaire and shared through communication media platform and data analysis was made through SPSS version 28. Results: There were n=225 under-graduates (DPT) and n=49 post-graduates (MSPT) students in which, there were n=208 females and n=66 males. The overall DREEM score showed that MSPT students are more significantly positive (p

Advanced-stage lung cancer (LC) causes significant morbidity and impacts patients’ quality of life (QoL). Exercise has been proven to be safe, feasible, and beneficial for symptom reduction and QoL improvement in many types of cancers, but research is limited in advanced-stage LC patients. This systematic review evaluates the effect of exercise interventions on the symptoms and QoL in patients with advanced-stage LC. Twelve prospective studies (744 participants) were included, evaluating different combinations of exercises and training such as aerobics, tai chi, strength, inspiratory muscle training, and relaxation. Studies found outcomes including but not limited to improved QoL, symptom burden, psychosocial health, functional status, and physical function. The results of this review support that exercise is safe and feasible with evidence supporting improved QoL and symptom mitigation. Integration of exercise should be considered in the individualized management of advanced-stage LC patients under the guidance of their healthcare providers.

Abstract Mutations, the genetic variations in genomic sequences, play an important role in molecular biology and biotechnology. During DNA replication or meiosis, one of the mutations is transposons or jumping genes. An indigenous transposon nDart1-0 was successfully introduced into local indica cultivar Basmati-370 from transposon-tagged line viz., GR-7895 (japonica genotype) through conventional breeding technique, successive backcrossing. Plants from segregating populationsshowed variegated phenotypes were tagged as BM-37 mutants. Blast analysis of the sequence data revealed that the GTP-binding protein, located on the BAC clone OJ1781_H11 of chromosome 5, contained an insertion of DNA transposon nDart1-0. The nDart1-0 has “A” at position 254 bp, whereas nDart1 homologs have “G”, which efficiently distinguishes nDart1-0 from its homologs. The histological analysis revealed that the chloroplast of mesophyll cells in BM-37 was disrupted with reduction in size of starch granules and higher number of osmophillic plastoglobuli, which resulted in decreased chlorophyll contents and carotenoids, gas exchange parameters (Pn, g, E, Ci), and reduced expression level of genes associated with chlorophyll biosynthesis, photosynthesis and chloroplast development. Along with the rise of GTP protein, the salicylic acid (SA) and gibberellic acid (GA) and antioxidant contents(SOD) and MDA levels significantly enhanced, while, the cytokinins (CK), ascorbate peroxidase (APX), catalase (CAT), total flavanoid contents (TFC) and total phenolic contents (TPC) significantly reduced in BM-37 mutant plants as compared with WT plants. These results support the notion that GTP-binding proteins influence the process underlying chloroplast formation. Therefore, it is anticipated that to combat biotic or abiotic stress conditions, the nDart1-0 tagged mutant (BM-37) of Basmati-370 would be beneficial.