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Lightweight materials with better resistance to sliding wear are prominent candidates for automobile brake drums, clutch pads and cylinder block applications to facilitate fuel economy. This attempt is reserved to cater to materials with higher tribological quality needs. Less dense foamy pumice stone particles were involved in three different percentages (5, 10, and 15 wt%) to reinforce lightweight AZ31 Mg alloy. A stir-assisted squeeze casting technique was pursued to process the composite and refine the grain structure. A phase detection, elemental mapping and microstructure study were done through X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and scanning electron microscopy (SEM), respectively. An experimental dry sliding wear scrutiny was administered using a pin-on-disc apparatus by considering: (i) ambient and elevated temperature environments and (ii) three different levels of loads. The results reveal a significant drop in wear loss and a frictional coefficient for 15% pumice-loaded composite than the base alloy. Post-wear examination acknowledges the fact that the ambient temperature wear is governed by adhesive-abrasive wear and high temperature is by abrasive wear mechanisms. Worn-out scrutiny authenticates the presence of oxide layers and their role in lubrication. A comparative study with previous works upholds the novel magnesium composite is the right candidate for the mentioned automobile applications.

Breast milk, produced by the mammary glands of lactating mothers, is the most cost-effective and nutritionally balanced source of nourishment for infants during their first six months. It contains the right amounts of proteins, fats, and carbohydrates for healthy growth and development, and it’s also rich in antibodies, hormones, and immunoglobulins, which offer substantial protection against various infectious diseases. Over the past few decades, however, breast milk substitutes like infant formula have been industrially produced, but these are not only more costly than breast milk but also lack its quality and protective properties. To address the inappropriate marketing of these substitutes and to encourage breastfeeding, the World Health Assembly (WHA) adopted an International Code in 1981. Despite this, infant formulas are widely advertised to consumers through different media outlets like newspapers, online platforms, and business websites. This aggressive marketing can shape cultural perceptions, making formula use seem more common and innovative, which can have negative implications. When these products are heavily promoted and used, it can lead to a decline in the nutritional and health status of infants, especially in low-income families. A 2003 study showed that ten million children worldwide die before their fifth birthday, but breastfeeding has the potential to save 1.3 million of those lives. The use of formula has been linked to a higher risk of premature deaths and hospitalizations in infants

This study aims to determine how competence, motivation, and job satisfaction affect employee performance at PT Bank Mega, Tbk Makassar Area. The data collection technique was carried out through distributing questionnaires, with the number of samples determined as many as 132 respondents. The data analysis method used is multiple linear regression analysis. The findings of this study indicate that the competency variable has a positive and significant influence on employee performance at PT Bank Mega, Tbk Makassar Area. Work motivation variable has a positive and significant influence on employee performance. Job satisfaction variable also has a positive and significant influence on employee performance at PT. Bank Mega, Tbk Makassar Area.

Neonatal mortality remains a pressing global concern, with millions of infants succumbing to preventable causes within their first month of life. One promising solution that has gained attraction is Kangaroo Mother Care (KMC), an innovative and cost-effective approach that has demonstrated remarkable success in improving survival rates among newborns. Neonatal morbidity and mortality can be reduced by providing KMC to babies. KMC is a simple yet powerful intervention that involves continuous skin-to-skin contact between a mother and her preterm or low birth-weight infant. Pakistan bears a heavy burden of neonatal mortality, with a reported rate of 42 deaths per 1,000 live births as of the latest data. Implementing Kangaroo Mother Care in a country as diverse and populous as Pakistan presents a unique set of challenges. However, the government, non-governmental organizations (NGOs), and healthcare professionals have made commendable efforts to integrate KMC into the existing healthcare system.

This article discusses the impact and challenges of Transnational Islamic groups on strengthening Religious Moderation in Indonesia in promoting a balanced, tolerant and peaceful understanding of Islam, both at the local and global levels. This article was written using the exposition report method which relies on the strength of the author's opinion in reviewing literature facts about Transnational Islam and Religious Moderation in Indonesia. The concepts of Transnational Islam and Religious Moderation in this article are positioned in a face-to-face relationship, where Transnational Islam is positioned as a challenge for strengthening Religious Moderation in Indonesia. This article concludes that Transnational Islamic Groups, which in terms of ideology and religious practices contain a spirit of religious exclusivism, exclusivism, and violent extremism, are a challenge to religious moderation in Indonesia. These three spirits were formulated by the religious moderation program with the abbreviation REEVE. Based on these three challenge formulations, it can be understood that Strengthening Religious Moderation as a public policy is the Indonesian government's response to exclusive, extreme and radical religious practices which have a negative impact on the harmonization of social life in society and at the same time endanger the integrity of the Republic of Indonesia.

The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting aluminum metal matrix composite. Hence, this experiment examined the effects of five different tool spinning speeds, specifically 800, 900, 1000, 1100, and 1200 rpm. Weld samples are fabricated wherein the stir zone contains SiC nanoparticles. The results of the study indicate that the optimal welding rotational speed of 1000 rpm has a significant impact on the microstructure, wear rate, and microhardness. Specifically, it was observed that the stirred zone exhibited enhanced wear resistance compared to other zones. The study's findings revealed that after implementing friction stir processing (FSP), a well-defined shear zone was observed on the advancing side of the stir zone. Additionally, it is found that the uniform dispersion and strong bonding of SiC particles with an aluminium matrix further enhance wear resistance.

Background and aim: As the spleen plays a significant role in immunity, the aim was to investigate the associations of different body composition markers derived from various sources with spleen volume in a general population sample. Materials and methods: Cross-sectional data of 1095 individuals (570 women; 52%) aged between 30 and 90 years were collected in the Study of Health in Pomerania (SHIP-START-2). We measured spleen volume by magnetic resonance imaging (MRI).Body composition markers were derived from classic anthropometry, bioelectrical impedance analysis, including absolute fat mass (FM) and fat-free mass (FFM), as well as from MRI, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat content. Sex-stratified-adjusted linear regression models were used to analyze the associations of body composition markers with spleen volumes. Results: We observed positive associations of body mass index, body weight, waist circumference, hip circumference, waist-to-height ratio, absolute FM, absolute FFM, and VAT and SAT with spleen volume in men and women. An 8.12 kg higher absolute FFM was associated with a 38.4 mL (95% confidence interval [CI]: 26.7–50.1) higher spleen volume in men and a 5.21 kg higher absolute FFM with a 42.6 mL (95% CI: 26.2–59.0) higher spleen volume in women. Conclusion: Our findings indicate that obesity-related body composition markers and FFM are associated with a higher spleen volume. Particularly, higher absolute FFM showed a strong association with a larger spleen volume in both men and women. Further studies are warranted to understand the clinical significance of body composition markers on large spleen volume.

In this study, biocrude was successfully produced by the hydrothermal liquefaction of municipal solid waste collected from the landfill site of Lahore, the capital of Punjab, Pakistan, boasting a population of 12 million and an annual waste collection of 10 million tons. The hydrothermal liquefaction process was performed at reaction parameters of 350 °C and 165 bars with 15 min of residence time. The solid waste was found to have 78 % dry matter, 22 % moisture contents, 22.2 % ash, 22.69 MJ/kg higher heating value, 52.062 % C, 8.007 % H, 0.764 % N, and 39.164 % O. Non-catalytic process only produced 10.57 % oil, however when using the catalytic process, the biocrude yield improved to 17.61 %, with 22.61 % energy recovery for biocrude and 12.14 % for solids, when using 2 g dose of K2CO3. The resultant biocrude has a 28.61 MJ/kg higher heating value, having 60.28 % C and 9.28 % H. In contrast, the aqueous phase generated had 4.43 pH, 71.5 g/L TOC, and 1.35 g/L Total Nitrogen. TGA indicated that biocrude contains approximately 80 % of volatile fractions of different fuels. The organic compounds having the six highest peak areas in GC-MS were Ethyl ether 25.74 %, 2-pentanone, 4-hydroxy-4-methyl 9.08 %, 2-propanone, 1,1-dimethoxy 5.62 %, Silane, dimethyl (docosyloxy) butoxy 5.08 %, 1-Hexanol, 2-ethyl 4.53 %, and. Phenol 4.07 %. This work makes the first-ever successful use of indigenous solid waste from a landfill dumping site in Lahore to successfully produce useful biocrude with aims of waste reduction and management, circular economy, and energy recovery.

This paper focuses on the derivation of a new two-parameter discrete probability distribution. The new model is derived by mixing Poisson and Loai distributions and is named “Poisson Loai Distribution”. The paper explores various mathematical properties of the new model, introducing a count-regression model based on this distribution. The parameters of the model are estimated using the maximum likelihood estimation method. A comprehensive simulation study is utilized to assess the behavior of derived estimators. The importance of the proposed distribution is confirmed through the analysis of three real datasets. It is found that the suggested distribution has the greatest match when compared to all rival distributions, and it may be a viable alternative for assessing dispersed count data.

This article discusses the determinism of Islamic theology in shaping the laws and movement patterns of Indonesian history. The aim is to explore how religions in Indonesia play a central role in shaping individual and group actions, as well as determining the movement patterns of modern Indonesian history. This discussion uses the speculative historical philosophy concept of historical determinism. In conclusion, the Indonesia historical driving force or it historical pattern are influenced by the theological ideas of religions in Indonesia, including Islam. This is reflected in the formulation of periodization of Indonesian history which is based on the periodization of Indonesian history, which is built on the basis of power entities characterized by religion and the identity of its rulers which is related to a particular religion. Apart from that, its influence can also be read in the style of historiography that developed in Indonesia which is related to religious affairs.

The human JC polyomavirus (JCV) is a widespread, neurotropic, opportunistic pathogen responsible for progressive multifocal leukoencephalopathy (PML) as well as other diseases in immunosuppressed individuals, including granule cell neuronopathy, JCV-associated nephropathy, encephalitis, and meningitis in rare cases. JCV classification is still unclear, where the ICTV (International Committee on Taxonomy of Viruses) has grouped all the strains into human polyomavirus 2, with no classification on clade and subclade levels. Therefore, JCV strains were previously classified using different genomic regions, e.g., full-length, VP1, and the V-T intergenic region etc., and the strains were grouped into several types related to various geographic locations and human ethnicities. However, neither of these classifications and nomenclature contemplates all the groups described so far. Herein, we evaluated all the available full-length coding genomes, VP1, and large T antigen nucleotide sequences of JCV reported during 1993–2023 and classified them into four major phylogenetic clades, i.e., GI-GIV, where GI is further grouped into two types GI.1 and GI.2 with five sub-clades each (GI.1/GI.2 a-e), GII into three (GII a-c), GIII as a separate clade, and GIV into seven sub-clades (GIV a-g). Similarly, the phylogeographic network analysis indicated four major clusters corresponding to GI-GIV clades, each with multiple subclusters and mutational sub-branches corresponding to the subclades. GI and GIV clusters are connected via GI.1-e reported from Europe and America, GII, GIII and GIV clusters are connected by GII-b and GII-c strains reported from Africa, while GIV cluster strains are connected to the Russia-Italy JCV haplotype. Furthermore, we identified JCV-variant-GS/B-Germany-1997 (GenBank ID: AF004350.1) as an inter-genotype recombinant having major and minor parents in the GI.1-e and GII-a clades, respectively. Additionally, the amino acid variability analysis revealed high entropy across all proteins. The large T antigen exhibited the highest variability, while the small t antigen showed the lowest variability. Our phylogenetic and phylogeographic analyses provide a new approach to genotyping and sub-genotyping and present a comprehensive classification system of JCV strains based on their genetic characteristics and geographic distribution, while the genetic recombination and amino acid variability can help identify pathogenicity and develop effective preventive and control measures against JCV infections.

In a variety of industries, including transportation, agriculture, and manufacturing, diesel engines are often employed. Due of rising prices and environmental concerns, researchers examined whether biodiesels might replace diesel. The current study looks into American Saffron Oil's feasibility as a feedstock for biodiesel production. The transesterification technique is used to extract American saffron oil methyl este(ASOME), which is then examined for its physical and chemical properties in accordance with ASTM standards. Diesel fuel and American Saffron Oil methyl ester are mixed on a volume basis to create a variety of fuel blends, including B20, B40, and B60. The test results showed that the 20 % ASOME mix had better performance and reduced emissions. Also, utilizing DIESEL-RK simulation software, diesel engine tests are conducted for the B20, B40, and B60 under identical operating circumstances. Moreover, diesel engine testing for the B20, B40, and B60 are carried out using DIESEL-RK simulation software under comparable operating circumstances. Results of simulation software tests show improved engine performance and reduced pollutants. When experimental data is compared to DIESEL-RK modeling software, it is found that brake thermal efficiency increased by 5.7 % and emissions of hydrocarbon and carbon monoxide decreased by 2.5 % and 14.3 %, respectively.

Background: Gynecological issues and sexually transmitted infections (STIs) pose significant challenges to women's health, particularly in developing nations. These challenges are exacerbated by limited access to modern reproductive healthcare facilities, economic constraints, and entrenched cultural norms. Consequently, most of the Pakistani population relies on traditional ethno-medicinal healthcare systems. This preference stems from the ease of access, affordability, widespread availability, and inherent trust placed in these alternative healthcare methods. Aim/objective: The inquiry aimed to report details on the application of conventional uses of plants in the health field in rural areas that could contribute to advancing the natural discovery of drugs. The objective of this analysis is to provide researchers with information on conventional and empirical knowledge of plant species concerning women's diseases. Methodology: Information on the common use of medicinal plants in treating women's diseases was gathered from electronic databases. As a keyword for the quest, ethnobotany, and ethnopharmacology were used together with gynecological complications. Result: The work of the current analysis has revealed that 217 plant species belonging to 89 families have been used in Pakistan's rural communities. The majority of plant species belong to the Apiaceae family, followed by the Asteraceae, Fabaceae, Solanaceae, Rosaceae, Lamiaceae, and other families. The biological interpretation of plants used in rural communities of Pakistan revealed that herbs and trees are the dominant forms with 58 % and 23 % respectively while shrubs and sub-shrubs with a low percentage of 17 % and 2 %. In natural preparation, leaves 29 %, flowers 22 %, seeds 14 %, fruits 14 %, roots 13 %, bark 7 %, and stems 5 % were the most used parts respectively and aerial parts, dried pericarp, bulb, bud, berry, latex, wood, rhizome, husk, fruit coat, oil, resins, twigs, and shoot were also used in minimum percentage. A multitude of plant species have found extensive application in the management of diverse women's health issues. These encompass concerns such as fatigue, mood fluctuations attributed to menstrual problems, gonorrhea, complications related to pregnancy, cravings for specific foods, throbbing breast pain, abdominal and pelvic cramps, excessive vaginal discharge, mastitis, irritability, abortion-related matters, headaches, uterine hemorrhage, Menorrhagia, Amenorrhea, Menopause, Vomiting Abortion, infertility and lactation challenges, as well as the regulation of lochia flow. Conclusion: This review provides remarkable information about the use of medicinal plants against women's diseases in the rural communities of Pakistan. It opens the gateway for the discovery of natural drug development.

Non-Fourier relation elucidates conduction of heat subjected to finite thermal wave proliferation speed whereas non-Fick relation reports anomalous diffusion differing from orthodox Fickian conduct. Their applications encompass the modeling of heat-mass transference in biological tissues, environmental engineering and micro/nanoscale systems. These models improve prediction precision in heterogeneous, complex or non-equilibrium environments. Here an attempt is made to model Darcy-Forchheimer dual convected viscoplastic material flow confined by stratified slippery surface. Modeling is based on magnetohydrodynamics, thermal stratification, temperature-dependent conductivity, temperature-dependent diffusivity, chemical reaction and solutal stratification. Deploying similarity variables, the nonlinear coupled complex partial differential expressions are transfigured into a system of coupled ordinary differential expressions. This process streamlines the problem, making it more convenient and assisting the utilization of numerical schemes to solve the simplified differential system. This system is then computed numerically deploying bvp4c algorithm. The outcomes related to drag force are derived and compared with previously reported findings in the existing literature. The comparison demonstrates a high level of agreement between the results obtained in this study and those from prior research. This study provides a comprehensive inspection of specific parameters and their characteristics on concentration, velocity and temperature. Results show that the Hartman number, inertia coefficient parameter, Casson parameter, porosity parameter and velocity slip parameter reduce Casson fluid velocity while reverse scenario is witnessed for mixed convection parameter. Besides the concentration distribution is an escalating function of Hartman number, solutal ratio, mass diffusivity and chemical reaction parameters whereas a reducing trend is found for solutal relaxation time, power index, solutal stratified parameters and Schmidt number.

This study aims to investigate the blends of supplementary cementitious ground granulated blast furnace slag (GGBFS) with limestone powder (LSP) as mineral admixtures to partially replace cement (up to 30 %) in self-consolidating paste (SCP) systems made at respective water demands. Besides the control formulation (100 % cement), the first set of SCP systems includes GGBFS alone as a partial substitute of cement (5 %, 10 %, 15 %, and 20 %), whereas the second set contains blends of 10 % GGBFS with 10 % and 20 % LSP. For all the seven formulations (1 control, 4 binary and 2 ternary), tests were performed to determine their water demand, setting times and flow behavior, hydration kinetics, mechanical strengths and microstructural properties. Results showed that the fresh and hardened state properties of SCP systems incorporating GGBFS and LSP led to an increase in strength, hydration kinetics were slightly improved and hence enhancement of strength and microstructure of SCP systems. Reduction in initial and final setting times recorded with increased LSP from 10 % to 20 % in ternary SCP formulations. Early compressive strength due to LSP increased in ternary SCP systems as compared to binary formulations, which reflected synergy between GGBFS and LSP in SCP systems. Moreover, environmental assessment results indicate a significant reduction in CO2 emissions by substituting cement with GGBFS and LSP. These findings indicate that both GGBFS and LSP can be successfully used in developing high performance and environment friendly self-compacting concrete (SCC).

Ordinary Portland cement (OPC) is proving to be hazardous to the environment. To replace the OPC, geopolymers (GPs) are introduced. However, to fully replace the OPC by GPs extensive laboratory tests are required to assess the long-term and short-term properties of GPs in different scenarios. Given the shortage of time for performing such extensive testing, artificial intelligence (AI) is used to analyze the properties of GPs. In this study, different AI techniques such as artificial neuro network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) are used to obtain the predictive models for estimating the compressive strength of fly ash and ground granulated blast furnace slag-based GP concrete. Different statistical parameters are used to evaluate the performance of predictive models. Similarly, sensitivity and parametric analysis are also conducted on the input parameters. Additionally, multiple linear regression was also performed on the whole database. After comparing all the results, it was concluded that GEP is the best AI technique to predict the compressive strength of GP-based concrete.

Sustainable development in the building industry can be achieved through the use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites can create materials with enhanced performance and numerous applications. The utilization of carbon nanotubes (CNTs) in the construction industry has great promise for developing efficient solutions to establish a sustainable ecosystem with diverse characteristics. However, forecasting the characteristics of these composites is a significant challenge due to their intricate composite structure and nonlinear behavior. Designing and conducting laboratory experiments on diverse samples and across multiple age groups is challenging, time-consuming, and costly. Moreover, there is presently a lack of a forecasting model that can predict concrete's compressive strength (fc') with nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), and artificial neural network (ANN), were used to predict the fc' of nanocomposites containing CNTs in this research. A thorough database consisting of 282 data entities for the CNTs-based concrete and the model's reliability was assessed using the R2 test and statistical error analysis. The ANN model had the most outstanding R2 value of 0.885, while the KNN and LR models had R2 values of 0.838 and 0.744, respectively. Moreover, RReliefF analysis is utilized to evaluate the primary components in predicting concrete outcomes. This research shows that the properties of CNT-based concrete composites are greatly affected by the water-to-binder ratio, followed by the proportions of cement and coarse aggregates. The ML algorithms exhibited superior generalization capabilities, suggesting their enhanced potential for accurate predictions of CNTs-based concrete properties.

Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs.

The optimized use of nanomaterials in concrete for developing resilient and eco-friendly infrastructure is vital for our built environment. Therefore, the current study aims to optimize the dosages and particle sizes of colloidal nano-silica in developing high-performance, sustainable cement mortar. For this purpose, compressive strength, water absorption, porosity, and microstructure tests were performed on mortars containing different dosages and particle sizes (NS80, NS250, NS250Al, NS500) of colloidal nano-silica. For comparison, tests were also performed on control mortars and mortars containing metakaolin and silica fume. The microstructure of cement pastes was examined through SEM-based BSE imaging with elemental mapping through EDS and FTIR analyses. Additionally, global warming potential (GWP) as well as embodied energy (EE) were also analyzed for all mortar mixtures. The results demonstrated that smaller nano-sized particles (NS500) at a lower dosage (3%) showed improved compressive strength and densified microstructure compared to all other mixes. However, larger nano-sized particles (NS80) at medium (5%) and high dosages (10%) had the highest impact on compressive strengths due to the formation of low and medium Ca-based C-S-H and C-A-S-H phases. Furthermore, the results of greater compressive strength of NS80 mortars under medium and high dosages were supported by their lowest water absorption and apparent porosity. When compared with the control and all other mixes, the mortar mixes containing medium and high doses of NS80 had lower embodied energy/MPa and CO2-eq intensity/MPa, indicating their significant contribution to producing green concrete with a lower environmental impact.

Abstract Background Radiation exposure is a significant hazard associated with invasive Cardiology, with most studies based on primary operator exposure. This prospective, observational study aimed to find out over lead radiation exposure as effective dose acquired by non-physician staff comprising scrub technicians and rotating staff in the cath laboratory. Effective dose (ED) measured per procedure via Raysafe i2®dosimeter badges worn by both rotating staff and scrub technicians over lead aprons along with dose area product (DAP), fluoroscopy time (FT) and procedure time (PT) in minutes was collected prospectively over forty-six invasive Cardiology procedures. Results This study shows that rotating staff acquire higher ED in comparison with their scrub technician colleagues in diagnostic, interventional and electrophysiology cases. However, a statistically significant difference in radiation exposure of both staff groups was demonstrated in diagnostic and interventional Cardiology procedures, with p values of 0.04 and 0.01, respectively. Conclusions These findings highlight occupational role and mobility around fluoroscopic sources as major factors in radiation exposure, which should be addressed within current radiation protection practices.

Environmental sustainability concerns have led to exploring alternative fuels like biodiesel in transportation. However, biodiesel engines emit pollutants like NOx, CO, and PM, posing health and environmental risks. This review explores the use of Aluminium Oxide (Al2O3), Ruthenium Oxide (RuO2), Titanium Oxide (TiO2), Cerium Oxide (CeO2), Graphene Oxide, Multi-walled Carbon Nanotubes (CNT) and other nanoparticles, in biodiesel engine. It focuses on their unique properties, characterization, emission control, environmental impact, and engine performance. The study emphasizes the significance of different biodiesel blends, compositions, and nanoparticle additions in determining engine performance and emissions. Results vary based on nanoparticle type, size, concentration, and blend composition. The review examines the impact of nanoparticles on various aspects of biodiesel blends, including density, viscosity, cetane number, calorific value, and flash points. It found that nanoparticle additives significantly influence Brake Thermal Efficiency and combustion efficiency. The study also found that nanoparticle-enhanced biodiesel blends have improved ignition properties, faster evaporation, higher oxygen content, and elevated cetane numbers, leading to cleaner combustion and more environmentally friendly engine operation. The research supports the beneficial effects of nanoparticles on biodiesel characteristics and emissions reduction. The review suggests that nanoparticles in biodiesel engines can improve fuel characteristics, engine performance, and emissions reduction but cautions against potential environmental and health risks. The findings suggest further research and optimization for sustainable and efficient engine performance in pursuing greener transportation fuels, highlighting the potential of nanoparticles in biodiesel blends.

The reduction in compressive strength (CS) of cementitious composites incorporating waste plastic is the main concern limiting its applicability in the building sector. Using industrial wastes as cement substitutes to enhance the CS of plastic mortar is a sustainable approach. This study used fine powdered waste materials such as silica fume (SF), marble powder (MP), and glass powder (GP) in plastic-based mortar for their effectiveness in enhancing CS. Plastic mortar specimens were cast using plastic waste in 5–25 % contents as sand replacement by mass, and their 28-day CS was recorded as a reference. SF, GP, and MP were utilized in plastic mortar mixtures separately in proportions of 5–25 %, with a 5 % increment, substituting cement by mass. These waste powders were also used in combinations of two (SF+GP, SF+MP, and GP+MP) and three (SF+GP+MP) in plastic mortar mixtures. Moreover, prediction models were built using the experimental database for the CS of plastic mortar. Gradient boosting and bagging ensemble machine learning (ML) techniques were chosen for model development. The decrease in CS was limited by substituting SF, GP, and MP for cement in plastic mortar. It was determined that the most effective substitution levels for SF, GP, and MP in plastic mortar, according to the strength enhancement, were 15, 10, and 15 wt.% of cement, respectively. The ML models closely matched experimental results, and in terms of R2 and error evaluations, bagging model outputs were more accurate than gradient boosting. The gradient boosting and bagging models had R2 of 0.89 and 0.94, respectively, with average absolute errors of 0.87 and 0.65 MPa.

ABSTRACTIntroduction Diabetes increases preventative sickness and costs healthcare and productivity. Type 2 diabetes and macrovascular disease consequences cause most diabetes-related costs. Type 2 diabetes greatly costs healthcare institutions, reducing economic productivity and efficiency. This cost of illness (COI) analysis examines the direct and indirect costs of treating and managing type 1 and type 2 diabetes mellitus.Methodology According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, Cochrane, PubMed, Embase, CINAHL, Scopus, Medline Plus, and CENTRAL were searched for relevant articles on type 1 and type 2 diabetes illness costs. The inquiry returned 873 2011–2023 academic articles. The study included 42 papers after an abstract evaluation of 547 papers.Results Most articles originated in Asia and Europe, primarily on type 2 diabetes. The annual cost per patient ranged from USD87 to USD9,581. Prevalence-based cost estimates ranged from less than USD470 to more than USD3475, whereas annual pharmaceutical prices ranged from USD40 to more than USD450, with insulin exhibiting the greatest disparity. Care for complications was generally costly, although costs varied significantly by country and problem type.Discussion This study revealed substantial heterogeneity in diabetes treatment costs; some could be reduced by improving data collection, analysis, and reporting procedures. Diabetes is an expensive disease to treat in low- and middle-income countries, and attaining Universal Health Coverage should be a priority for the global health community.

AbstractInvestors may find it challenging to invest due to economic fluctuations during COVID-19. This study aims to examine the relationship between economic fluctuations and the Indonesian sectoral stock market in the consumer goods sector (CGI), basic industrial and chemical sector (BIC), and miscellaneous industry (MSI), both before and during the COVID-19 pandemic in Indonesia. The monthly time-series data used in the empirical approach cover the period from January 2008 to December 2020. The analysis used forecast error variance decomposition, vector autoregression, impulse response function analysis, and causality investigation. The econometric results showed that previous period shocks in each industrial sector stock market had a disadvantageous effect on future stock market earnings. Additionally, while the CGI stock market positively affects the Rupiah exchange rate, the MSI industrial sector is negatively impacted by inflationary pressures. Also connected to the MSI stock market are fluctuations in inflation. Conversely, the exchange rate affects MSI and CGI. Furthermore, for the CGI and BIC stock markets, a one-way causation relationship is observed. Another notable result was that all three industrial sectors responded positively when inflation and exchange rates were disrupted. It implies that, for convenience, investors will seek out other areas of the stock market. Therefore, a quick government response is needed to handle the economy during economic fluctuations accompanied by the COVID-19 pandemic so that it does not have an impact on the future.

The study proposed the removal of Brilliant Green, a cationic dye, by adsorption process from wastewater solution utilizing a low-cost adsorbent such as Nutraceutical Industrial Pepper Seed Spent (NIPSS). The study comprises an investigation of the parametric influence on the adsorption process. The parameters identified are pH, dye concentration, process temperature, quantity of the adsorbent, and particle size. The study of statistics found from experiments was carried out by incorporating Freundlich, Brouers-Sotolongo, Langmuir, Toth, Sips, Jovanovic, and Redlich-Peterson isotherm models. The adsorption kinetics were determined by implementing pseudo-first-order and second-order models, diffusion film models, and Dumwald-Wagner and Weber-Morris models. The experimental adsorption capacity qe was found to be about 130 mg/g. This value was closest to the maximum adsorption of 144.6mg/g predicted by the Brouers-Sotolongo isotherm which had a correlation coefficient (R2) of 0.998. The adsorption kinetics data was confirmed to be a pseudo-second-order model. The change in free energy, enthalpy change, and entropy change were vital thermodynamic factors in concluding that adsorption is almost spontaneous and endothermic process. Change in enthalpy (ΔH°) reduced value indicates the physical nature of the process. The adsorption of BG dye on the adsorbent surface was authenticated by FTIR spectroscopy and SEM imaging. A Central Composite Design (CCD) Quadratic model under Response Surface Methodology (RSM) was implemented for statistical optimization of adsorption capacity for the five parameters studied, namely, time, temperature, concentration of the dye, weight of the adsorbent, and pH. Software Design Expert 7.0 was used to evaluate 3D contour plots. The process of optimization yielded a value of 350 mg/g. Thus, incrementing the adsorption process by 84.2 %. The study provides insights on various dye and adsorbent interaction possibilities and derives that NIPSS is an efficient adsorbent to extract BG dye from wastewater solutions.

OBJECTIVES This study aimed to evaluate the clinical outcomes of Anterior Cervical Discectomy and Fusion (ACDF) treatments for cervical disc degenerative disease (CDDD) using a Zero Profile cage. METHODOLOGY A retrospective study of 26 patients with cervical disc disease treated with a zero-profile cage was designed and followed up for an average of 12 months in descriptive research. For arm and neck pain, the Neck Disability Index (NDI) and Visual Analogue Scale (VAS) scores were used to assess function. Nurick’s myelopathy classification scheme based on gait impairments was also documented. RESULTS The average age of the 26 patients was 48.96 13.13 years (mean SD), with 44 percent falling into the 40-60-year age bracket. The male/female gender distribution was 21 (84%) and 04 (16%) male/female. Radiculomyelopathic symptoms were detected in 11 (28%) of the patients, with radicular pain occurring bilaterally in seven (28%) of the patients and on the left side in four (16%). A radiological examination of fusion was performed at six months and one year. The fusion had a success rate of 95 percent at six months (19 patients) and 100 percent at one year. CONCLUSION A zero-profile device provides biomechanical stability and fusion rates with excellent outcomes for one- and two-level ACDFs. Advantages include low rates of dysphagia, decreased operative time, restoration of cervical lordosis and disc height, and lack of cage subsidence or screw back out.

This research proposes a fuel system conversion from a carburetor to an electronic fuel injection (EFI), to save fossil fuels using gasoline-ethanol combined with water injection. Beneficial as an offer for Indonesian motorcycle environmental friendliness in terms of exhaust emissions. The goal of this study is to improve engine performance and accommodate technology for the deployment of ethanol gasoline implementations. The experiment was conducted using fuel with a ration octane number (RON) of 92, gasoline-ethanol was executed up to E25 then applied water injection (Wi). The results show that converting a conventional carburetor to EFI and additional water injection increases engine performance, mileage, and reduces exhaust emissions.

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs. This research presents an AI-driven adaptive learning platform implemented across four different courses and 300 students at a university in Faisalabad-Pakistan. A controlled experiment compares student outcomes between those using the AI platform and those undergoing traditional instruction. Quantitative results demonstrate a 25 % improvement in grades, test scores, and engagement for the AI group, with a statistical significance of a p-value of 0.00045. Qualitative feedback highlights enhanced experiences attributed to personalized pathways. The DL analysis of student performance data highlights key parameters, including enhanced learning outcomes and engagement metrices over time. Surveys reveal increased satisfaction compared to one-size-fits-all content. Unlike prior AI research lacking rigorous validation, our methodology and significant results deliver a concrete framework for institutions to implement personalized, AI-driven education at scale. This data-driven approach builds on previous attempts by tying adaptations to actual student needs, yielding measurable improvements in key outcomes. Overall, this work empirically validates that AI platforms leveraging robust analytics to provide customized and adaptive learning can significantly enhance student academic performance, engagement, and satisfaction compared to traditional approaches. These findings have insightful consequences for the future of higher education. The research contributes to the growing demand for AI in education research and provides a practical framework for institutions seeking to implement more adaptive and student-centric teaching methodologies.

Background: The recent global crisis afflicted by Coronavirus has led to psychological consequences like anxiety, fear and insecurity among health care workers. Fear of contracting infection and transmitting to close contacts has emerged as a serious concern. Through this study, we aimed to evaluate different psychological sequel experienced by medical and dental students and frontline health workers during COVID-19 pandemic. Methods: A cross sectional online survey was opted using a link platform targeting frontline health care workers and medical students. Data was collected from September until November 2020. Descriptive statistics was used to address demographic characteristics of participants. Associations among selected qualitative variables were determined using Chi square test. All analysis was done by using SPSS and a p value of

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83–0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

Objective: To compare the frequency of post-spinal anaesthesia hypotension occurring with co-loading versus pre-loading in patients undergoing caesarean section. Study Design: Cross-sectional analytical study. Place and Duration of Study: Department of Anaesthesiology, Pak-Emirates Military Hospital, Rawalpindi Pakistan, Sep 2020 to Mar 2021. Methodology: A total of 70 patients undergoing spinal anaesthesia for caesarean section and meeting the sample selection criteria were included. Patients with emergency procedures or a history of blood pressure disorders were excluded. Patients in Group-A underwent co-loading with ringer lactate, while patients in Group-B underwent pre-loading. All participants had their systolic and diastolic blood pressure and mean arterial pressure measured at the time of anaesthesia induction and then at 5, 10, 15, and 30 minutes post anaesthesia induction. Results: There was difference in mean systolic pressure at 10 and 15 minutes, p=0.001 and p=0.027, respectively. The difference in diastolic blood pressure was only significant at 10 minutes, p=0.001. While, mean arterial pressures were significantly higher at 10 and 15 minutes with co-loading, p

This study aimed to identify efficient Trichoderma isolate(s) for the management of Fusarium wilt in peas. Four different pea germplasms (Sarsabz, Pea-09, Meteor and Supreme) were evaluated for resistance against Fusarium oxysporum in pot assay. Resistant germplasm exhibits a varying range of disease severity (23%) and percent disease index (21%), whereas susceptible and highly susceptible germplasm exhibit maximum disease severity (44–79%) and percent disease index (47–82%). The susceptible germplasm Meteor was selected for in vivo experiment. Five different Trichoderma spp. (Trichoderma koningii, T. hamatum, T. longibrachiatum, T. viride, and T. harzianum) were screened for the production of hydrolytic extracellular enzymes under in vitro. In-vitro biocontrol potential of Trichoderma spp. was assayed by percentage inhibition of dry mass of Fusarium oxysporum pisi (FOP) with Trichoderma spp. metabolite filtrate concentrations. Maximum growth inhibition was observed by T. harzianum (50–89%). T. harzianum metabolites in filtrate conc. (40%, 50%, and 60%) exhibited maximum reduction in biomass and were thus used for in vivo management of the disease. The pot experiment for in-vivo management also confirmed the maximum inhibition of FOP by T. harzianum metabolites filtrate at 60% by reducing disease parameters and enhancing growth, yield, and physiochemical and stress markers. Trichoderma strains led to an increase in chlorophyll and carotenoids (34-26%), Total phenolic 55%, Total protein content 60%, Total Flavonoid content 36%, and the increasing order of enzyme activities were as follows: CAT > POX > PPO > PAL in all treatments. These strains demonstrate excellent bio-control of Fusarium wilt in pea via induction of defense-related enzymes. The present work will help use Trichoderma species in disease management programme as an effective biocontrol agent against plant pathogens.

The purpose of this study is to analyze ethnic conflict management in Aceh (Indonesia) and Moro (Philippines) conflicts. The Aceh conflict was considered resolved after the signing of the Helsinki agreement, which was mediated by the Crisis Management Initiative (CMI), an international NGO based in Finland, on 15 August 2005 between Indonesian government and the Free Aceh Movement (Gerakan Aceh Merdeka, GAM). Meanwhile, the Moro (Philippines) conflict is still ongoing despite the TRIPOLI agreement signed and mediated by the OIC (Organization of Islamic Conference) between the Philippine government and the Moro National Liberation Front (MNLF) on 23 December 1976 and also with the MILF in 2001. This research method uses a qualitative approach. Primary data was obtained through interviews with figures who were considered competent in the Aceh conflict and the Moro conflict. The results of this study show that CMI in Aceh has succeeded in changing the situation of ethnic conflict into a situation that can reduce and reduce escalation and direct conflict actors towards conflict resolution. Peacemaking, peacekeeping and peace building have been played well by CMI involving the international community such as ASEAN and the European Union to manage the conflict. Meanwhile, in Moro the OIC failed to bring the conflict actors to solve their problems. The Philippine government lacks a robust vision, framework, and the necessary political will to effectively address the Moro conflict, which is of concern to the Organization of Islamic Cooperation (OIC).

High-performance concrete is an excellent choice for modern engineering structures due to its superior performance, low maintenance costs, and long service life. Therefore, this study was aimed to produce high-performance concrete by incorporating a high volume of basaltic volcanic ash along with metakaolin (MK), micro-silica (MS), and nano-silica (NS). An investigation was performed in regard to the mechanical and durability properties, autogenous and drying shrinkage, and microstructural and pore-structure properties. A control mix, binary mixes, and ternary mixes were examined. The binary mixes contained fine (VA) and ultra-fine (VAF) volcanic ashes, and the ternary mixes contained combinations of VA with MK, MS, and NS. Test results show that ternary mixes with 30% and 40% cement substitution demonstrated better strength development and higher resistance to chloride penetration and water absorption. All binary and ternary mixes displayed lower autogenous shrinkage, as well as lower or comparable drying shrinkage than the control mix. Moreover, the combination of VA with MK, MS, and NS up to 40% resulted in micro and pore structure refinement by forming high-density gels. The findings indicate that VA in combination with MK, MS, and NS, can effectively substitute cement at up to 40% for the production of sustainable high-performance concrete.

Cardiovascular diseases, particularly coronary artery disease, pose big challenges to human life. Deployment of the stent is a preferable treatment for the above-mentioned disease. However, stents are usually made up of shape memory alloy called Nitinol. The poorer surface finish on the machined nitinol stents accelerates the migration of Nickel ions from the implanted nitinol stent, which is considered toxic and can lead to stenosis. The current study deals with controlling surface quality by minimising surface roughness and improving corrosion resistance. Femtosecond laser (fs-laser 10−15 s) micromachining was employed to machine the Nitinol surface to achieve sub-micron surface roughness. The Grey relational analysis (GRA)-coupled design of the experimental technique was implemented to determine optimal levels of four micromachining parameters (laser power, pulse frequency, scanning speed, and scanning pattern) varied at three levels to achieve minimum surface roughness and to maximise the volume ablation. The results show that to yield minimum surface roughness and maximum volume ablation, laser power and scanning speed are in a higher range. In contrast, the pulse frequency is lower, and the scanning pattern is in a zig-zag manner. ANOVA results manifest that scanning speed is the predominant factor in minimising surface roughness, followed by pulse frequency. Furthermore, the corrosion behaviour of the machined nitinol specimens was evaluated, and the results show that specimens with lower surface roughness had lower corrosion rates.

The present study used the techniques of gene expression programming (GEP) and multi-expression programming (MEP) to assess the compressive strength (CS) and flexural strength (FS) and develop predictive models of sustainable mortar modified with waste eggshell powder (WEP) and waste glass powder (WGP) as a replacement of cement. In order to get more insights into the impact and relation of raw components on the CS and FS of a developed sustainable mortar, a comprehensive study using the SHapley Additive exPlanations (SHAP) methodology was performed. When comparing the efficiency of both employed models, it was seen that the MEP model exhibited superior performance with an R2 value of 0.871 and 0.894 for CS and FS, as compared to the GEP model, which had an R2 value of 0.842 and 0.845 for CS and FS respectively.

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.

Evaluating educational climate (EC) is imperative for ensuring postgraduate trainees' competencies and quality in residency training programs. This study assessed the EC experiences of pathology postgraduate residents (PGRs) during their postgraduate training in pathology residency programs-a cross-sectional study design assigned EC scores in the pathology residency program at a prestigious institution in Islamabad, which were measured using the Dutch Residency Educational Climate Test (D-RECT) questionnaire. Scores from the D-RECT were employed to conduct descriptive statistics and comparison of means across groups to evaluate EC scores by years of training and compared to assess where the differences were located. Among FCPS-II pathology residents, most of whom were females (94.4%), the mean age was 28.11±2.91 years. A mean positive score was observed among all pathology residents (M≥3.6) for all D-RECT subscales except for the feedback subscale: the average score for feedback was below the average mean score of 3.6 (M = 3.19). A significant difference p = 0.016 was observed in EC scores across different groups through the Analysis of Variances (ANOVA) test. The most significant difference was between less than two and greater than two groups p = 0.027, followed by the difference between equal to two groups and greater than two groups p = 0.052. Overall, positive scores for EC in the pathology residency program were observed. Thus, targeted interventions are needed to increase feedback scores and address observed differences in EC scores by years of training.

The secret keys produced by current image cryptosystems, which rely on chaotic sequences, exhibit a direct correlation with the size of the image. As the image dimensions expand, the generation of extensive chaotic sequences in the encryption and decryption procedures becomes more computationally intensive. Secondly, a common problem in existing image encryption schemes is the compromise between privacy and efficiency. Some existing lightweight schemes reveal patterns in encrypted images, while others impose heavy computational burdens during encryption/decryption due to the need for large chaotic sequences. In this study, we introduce a lightweight image encryption scheme that involves partitioning the image into uniformly sized tiles and generating a chaotic sequence accordingly. This approach diminishes the necessity to create extensive chaotic sequences equal to the tile size, which is significantly smaller than the original image. As a result, it alleviates the processing burden associated with generating sequences equivalent to the original image size. The results confirm that our proposed scheme is lightweight and secure compared to the latest state-of-the-art image encryption schemes. Additionally, sensitivity analysis demonstrates that the proposed image encryption technique, with a UACI value of 33.48 and NPRC value of 99.96, affirms its resistance to differential attacks.

Despite its collapsible nature and weakness, desert sand can be used for construction purposes all over the world if properly stabilized. Therefore, the aim of this study is to evaluate the effectiveness of cement and fiber in stabilizing locally available dune sand. A test plan was used to investigate the effects of varying quantities of alkali resistance glass (ARG) fiber (F: 0%, 0.2%, 0.4%, and 0.6%) and portland cement (C: 0.0%, 1.0%, 3.0%, and 5.0%) on the mechanical and microstructural properties of dune sand. Mechanical properties such as unconfined strength (UCS), strain at failure (εf), California bearing ratio (CBR), and modulus of elasticity (Es) were evaluated, and microstructure properties were investigated using Raman spectrum and laser-scanning microscopy (LSM) tests on stabilized sand samples. The results of the experimental study showed that the percentage of cement in the treated sand has a more significant impact on the investigated properties of the treated sand than the percentage of fibers. In addition, increasing fiber content results in an increase in the ductility of the sand mix. Raman analysis revealed significant interactions between sand mix components. Moreover, LSM results showed that fiber–cement interaction increased with increasing cement percentage, as calcium silicate hydrates (CSH) formed in the mix and filamentous and intrastrand binding occurred. The findings of this study indicate that ARG fiber and cement can be effective in the stabilization of dune sand for construction purposes even with the use of low percentages of ARG fiber (0.2%–0.4%) and cement (3%).

The material on the basic laws of chemistry taught at the high school level so far is still not optimal according to the demands of the curriculum. This can be seen based on the low national exam results. The national exam scores for basic chemical laws in 2018 and 2019 were 16.6 and 35.2, respectively. This study aims to: (1) Describe the feasibility of TTW based LKPD developed on basic chemical laws material, (2) Describe students responses to TTW based LKPD developed on basic chemical laws material, (3) Describe educators responses to TTW based LKPD developed on basic chemical laws material. This research method uses R&D with the 4-D model, namely define, design, develop, and disseminate. The subjects of this study were students of class X IPA 1 SMA Negeri 12 Banda Aceh totaling 25 people. Data collection was carried out using instruments. The research instrument is the LKPD validity assessment sheet by experts, a questionnaire sheet for students and educators responses. The results of the feasibility assessment of LKPD obtained very good, namely 93.3%. The responses of students and educators to the LKPD each obtained 86% and 94.8% in the very good category. Based on the results of the study, it can be concluded that the LKPD based on think talk write on the material of the basic laws of chemistry is feasible to use as teaching material

The incorporation of nanomaterials (NMs) in concrete will produce the utmost properties. Nevertheless, investigational work takes a great deal of time with efforts to measure the strength of concrete. Thus, the process of estimating the properties of cement paste can be accelerated by employing soft machine learning (SML) techniques. Individual machine learning approaches (MLAs) like random forest (RF), decision tree (DT), and support vector machine (SVM) were utilized. These individual MLAs models then ensemble with two approaches namely bagging and boosting. The outcome of the model depicts a robust performance by showing significant correlations (R2) as compared to individual MLAs. Data points with 205 having contribution factors (e.g., curing time (days), water to binder (w/b), micro-silica (%), and nano-silica (%) were used as input parameters, and compressive strength as output for MLAs modeling. To ensure that each model is as accurate as possible, cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE) were utilized. Thus, interpretable approaches with AdaBoost give a strong correlation of R2 ranging from 0.85 to 0.90 respectively with fewer errors. Out of which DT with ensemble Adaboost and RF give a robust performance. Moreover, the results of the cross-validation show that the response was accurate, with fewer errors. Therefore, the ensemble model represents a robust correlation. Therefore, its usage with individual ML would enhance the model's performance. In addition, the graphical user interface (GUI) is created by training a model that can predict the values of the desired outputs when the necessary input parameters are provided. It simplifies the procedure and provides a handy tool for employing the model's capabilities in the civil engineering domain.

This research is a case study research using qualitative descriptive research methods. The purpose of this study is to describe the phenomenon of the Bat population in Watansoppeng City and describe the benefits of research results from the Bat population phenomenon in Watansoppeng City to be used as a source for learning Ecosystem Biology. This research was conducted at the Environmental Service of Soppeng Regency and in Botto Village, Lalabata District, Soppeng Regency. The informants of this research were 7 people, namely the Head of the Soppeng Regency Environmental Service, the Secretary of the Soppeng Regency Environmental Service and 5 people from the community. Determination of informants in this study using a purposive sampling technique. Data collection techniques used are observation, interviews and documentation by using two types of data, namely primary data and secondary data. Primary data comes from interviews and direct observations at the research site, while secondary data comes from literature review. The data analysis used is data reduction, data presentation and conclusion drawing/verification. Test the validity of the data used is triangulation. The results obtained are the phenomenon of the Bat population in Soppeng Regency which has existed for a long time due to several factors such as habitat suitability, adequate food sources, protected and preserved so that bats can live and be well preserved. Thus, the phenomenon of the Bat population as a characteristic of the city of Watansoppeng can be used as a source of learning ecosystem material biology.

This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs.

Alkali-activated materials (AAMs) have gained significant attention in recent years due to their potential for sustainable construction and outstanding strength and durability characteristics. However, their wide application is constrained by their weak behavior under flexure and tensile loading, leading to brittleness. This study examines the formulation of an alkali-activated fiber-reinforced mortar, using fly ash and hydrated lime as main precursors, and investigates the capacity when reinforced with polyvinyl alcohol (PVA) and polypropylene (PP) fibers. The performance was measured through experimental investigation in terms of flowability, setting time, compressive strength (CS), water absorption, flexure strength (FS), and load-displacement curve under flexure loading. The durability was assessed through acid and sulfate attack tests after 120 days. The findings showed that adding both types of fibers improved the mechanical and durability performance of the one-part AAM. The highest CS of 44.95 MPa and 42.04 MPa and FS of 7.8 MPa and 6.9 MPa were achieved for PVA and PP fibers at a 1.5% fiber content. The FS was increased by 18.94% and 28.2% for PP1.5 and PVA1.5 fibers respectively. The control sample, PP 1.5 and PVA 1.5 recorded 31.1%, 12.5%, and 11% loss in CS after the acid attack test, respectively, after 120 days. A reduction of 15.3% and 13.3% was recorded in flow diameter with the addition of PP and PVA fibers at 2%. Overall, mortars reinforced with PVA fibers have shown better flowability, mechanical strength, and chemical resistance than PP fibers.

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of a robust cementitious matrix with utmost properties. The strength of HPC when subjected to compression test is determined by the combination and quantity of the materials used in its production. Thus, making its mixed design process challenging and ambiguous. The objective of this research is to forecast the strength of HPC containing RHA, by using a diverse range of machine learning techniques, including both individual and ensemble learners such as bagging and boosting. This study will cause significant implications for sustainable construction practices by facilitating the development of an efficient and effective method for forecasting the strength of HPC. Individual machine learning (ML) algorithms are incorporated with ensemble methods such as bagging, adaptive boosting, and random forest algorithms. These ensemble techniques is use to create twenty different sub-models. Afterward, these sub-models is train and optimized for achieving the best possible value for R2. The sub-models were further fine-tuned to obtain the best value for R2. In order to assess or evaluate the quality, reliability, and generalizability of the test data, the K-Fold cross-validation method is utilized. Furthermore, the index for measuring the statistical performance of models is use to validate and compare the assessment of ensemble models with individual models. The findings indicate that using bagging and boosting techniques enhances the prediction accuracy of individual or weak models, with minimum errors and an R2 value > 0.92 is achieved using bagging with decision tree and random forest. In general, the performance of the model is optimized by using ensemble learner methods in machine learning (ML).

Background: Parenting stress is an important factor that affects the marital satisfaction of parents of children with Neurodevelopmental disorders. When parents are faced with a challenging situation, such as having a child with a neuro-developmental disability, resilience facilitates a more positive coping style and improves marital satisfaction. Objective: to investigate the relationship between parenting children with neurodevelopment disorders, stress and marital satisfaction among parents while moderating the role of resilience. Methodology: The cross-sectional analytical study was conducted on n=300 parents of children with ADHD, an intellectual disability, and autism. Purposive sampling was employed to get a sample from RHS Rehabilitation Centre Islamabad. The mothers (n=150) and fathers (n=150) were selected with the age range of 25 to 50 years. Parenting stress was measured using the Parental Stress Scale after translating it into Urdu. To determine the Resilience, the Urdu version of the Connor-Davidson Resilience scale was used. Whereas marital satisfaction was determined using an Urdu-translated version of the Dyadic Adjustment Scale. Results: A negative correlation was revealed in the findings between parenting stress and marital satisfaction (p < 0.05). Moderation analysis demonstrated a significant effect of resilience on the relationship between parenting stress and marital satisfaction (R2 = 0.70, B = 0.002, β = -0.71, p < 0.05). Conclusion: In conclusion, it was found that high stress experienced by parents of children with neurodevelopmental disorders has a negative impact on their marital satisfaction. Both their marriage and parental lives were affected equally. For both fathers and mothers, resilience can be a useful tool in resolving marital problems. Keywords: marital satisfaction, neurodevelopmental disorders, parenting stress, resilience