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Abstract Polymeric insulation is a critical component of high voltage systems. However, exposure to high electric stress can cause partial discharges (PDs) to occur and may result in the deterioration of insulation and lead to dielectric failure. These PD events are accompanied by the production of acoustic pressure impulses in the polymer. Detection of these acoustic pressure impulses can reveal the presence of PDs and locate their source. However, analysing the detected acoustic emission (AE) signal is challenging. The acoustic pressure source's nature and the propagating medium's properties, such as density, viscosity, and elasticity, significantly affect the propagating AE signal. The effects of the hardness of the polyurethane (PU) on the propagating AE signal are reported by the authors based on results obtained from laboratory experiments. It was observed that the decay rate in the magnitude of the acoustic impulse was high in PU at all hardness levels following an exponential behaviour. The analysis of the frequency spectra indicates that the higher frequency components attenuate more strongly with distance. These laboratory results can be valuable for engineers and industries as they provide valuable insight into how the physical characteristics of a material affect the propagation characteristics of AE signals during the detection and location of PD source using the AE detection technique.

Abstract Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Abstract Cyanobacteria, being a prominent category of phototrophic organism, exhibit substantial potential as a valuable source of bioactive compounds and phytonutrients, including liposomes, amino derivatives, proteins, and carotenoids. In this investigation, a polyphasic approach was employed to isolate and characterize a newly discovered cyanobacterial strain from a rice field in the Garh Moor district of Jhang. Desertifilum sp. TN-15, a unique and less explored cyanobacterial strain, holds significant promise as a novel candidate for the synthesis of nanoparticles. This noticeable research gap underscores the novelty and untapped potential of Desertifilum sp. TN-15 in the field of nanomedicine. The characterization of the biogenically synthesized ZnO–NPs involved the application of diverse analytical techniques. Ultraviolet–visible spectroscopy revealed a surface plasmon resonance peak at 298 nm. Fourier transform infrared spectral analysis was utilized to confirm the involvement of biomolecules in the biogenic synthesis and stability. Scanning electron microscopy was employed to probe the surface morphology of the biogenic ZnO–NPs unveiling their size of 94.80 nm and star-shaped. Furthermore, X-ray diffraction analysis substantiated the crystalline nature of ZnO–NPs, with a crystalline size measuring 46 nm. To assess the physical stability of ZnO–NPs, zeta potential and dynamic light scattering measurements were conducted, yielding values of + 31.6 mV, and 94.80 nm, respectively, indicative of favorable stability. The antibacterial capabilities of Desertifilum sp. TN-15 are attributed to its abundance of bioactive components, including proteins, liposomes, amino derivatives, and carotenoids. Through the synthesis of zinc oxide nanoparticles (ZnO–NPs) with this strain, we have effectively used these chemicals to generate nanoparticles that exhibit noteworthy antibacterial activity against Staphylococcus aureus (MIC: 30.05 ± 0.003 µg/ml). Additionally, the ZnO–NPs displayed potent antifungal activity and antioxidant properties, as well as significant antihemolytic effects on red blood cells (IC50: 4.8 µg/ml). Cytotoxicity assessment using brine shrimps revealed an IC50 value of 3.1 µg/ml. The multifaceted actions of the biogenically synthesized ZnO–NPs underscore their potential applications in pharmacological and therapeutic fields. This study proposes a novel method for ZnO–NPs production utilizing the recently identified cyanobacterial strain Desertifilum sp. TN-15, highlighting the growing significance of biological systems in the environmentally friendly fabrication of metallic oxide nanomaterials.

Abstract Objective Here, we sought to report ABO and D antigen distribution in blood donors from Yogyakarta, Java Island, Indonesia. Phenotype data (ABO/D) from donors who donated blood between January 1, 2018, and December 31, 2023, at the Yogyakarta Blood Donor Unit were extracted from the blood donor registry, and phenotype frequency was calculated subsequently. Results In the 245,307 blood donors collected over six years, ABO phenotype frequency: O (frequency: 38.5%) > B (29.4%) > A (24.1%) > AB (8.0%). The D-positive phenotype was far more common (99.5%) than the D-negative phenotype (0.5%). The phenotypic pattern globally is similar to previous reports in Southeast Asia. The D antigen distribution is similar to world distribution as the most common blood group. For the first time in Indonesia, this distribution of ABO and D phenotype is reported in a large-scale study. This work is a pioneer in the coordinated optimization of transfusion guidelines at the national level.

Objective: This study aimed to analyse the osteogenic potential of a novel experimental material comprising of nano-hydroxyapatite grafted glass fiber scaffolds using murine osteoblast MC3T3-E1 cell lines through gene expression of biomarkers for osteogenesis. Study Design:In vitro experimental study. Place and Duration of Study: Bone Marrow Transplant Centre and Genetic Resource Centre, Rawalpindi from 10 May 2021 to 17 June 2022. Materials and Methods: Two types of nano-apatite grafted E-glass fiber scaffolds i.e., 10 wt% E-glass/90wt% Hydroxyapatite (E-10), 20% E-glass/80% Hydroxyapatite (E-20) were used, whereby pure nanohydroxyapatite (E-0) was used as control group. A comparative gene expression of Osteopontin (OP) and Collagen (Col) Type 1 was measured through reverse transcription polymerase chain reaction (RT-PCR). Results: Both experimental groups showed expression of the osteogenic proteins. The level of OP expression was higher in E-10 compared to E-20, whereas the level of Col expression was higher in E-20. Only for E-0, Col expression was almost same as OP whereas for the E-10 and E-20, OP expression was greater. Conclusions: Based on the findings, both experimental scaffolds supported the growth of cells and showed an osteogenic nature. It is anticipated that the experimental scaffolds have potential to be used for bone regeneration.

Cobalt is a key metallic element required in the manufacture of lithium-ion batteries in the electric car industry, which has led to increased demand for cobalt globally. Cobalt is found associated with laterite nickel deposits but generally at lower levels. This study aims to analyze the distribution of cobalt in the laterite zone, determine cobalt-rich zones, determine cobalt-forming minerals in the limonite, saprolite and bedrock zones. As well as determining the average cobalt content in each nickel laterite zone, and determining the correlation between Co and Ni in the laterite zone. The research methodology was carried out by conducting petrographic analysis on bedrock samples to determine cobalt-forming minerals. Furthermore, X-Ray Diffraction analysis was carried out on limonite and saprolite zone samples to determine the mineral content and cobalt-forming minerals. Furthermore, conduct X-Ray Fluorescence analysis at 12 drill points to determine the percentage of cobalt elements in each laterite zone. Based on the research results, it is known that the distribution of cobalt at 12 drill points has levels in the limonite zone ranging from 0.05-0.12% Co, saprolite zone 0.02-0.05% Co, and the bedrock zone ranges from 0.01-0.04% Co. So it can be concluded that the highest cobalt distribution is in the limonite zone and the average cobalt content in the limonite zone is 0.082%; saprolite zone is 0.029%; and bedrock zone is 0.02%. The mineral identified as the cobalt-forming mineral in the bedrock samples in the study area is the mineral antigorite. The Co-Ni correlation coefficient values range from 0.01 to 0.22.

Abstract Diabetes mellitus (DM) is a rapidly prevailing disease throughout the world that poses boundless risk factors linked to several health problems. Vildagliptin is the standard dipeptidyl peptidase-4 (DPP-4) inhibitor type of medication that is used for the treatment of diabetes anti-hyperglycemic agent (anti-diabetic drug). The current study aimed to synthesize vildagliptin-loaded ZnO NPs for enhanced efficacy in terms of increased retention time minimizing side effects and increased hypoglycemic effects. Herein, Zinc Oxide (ZnO) nanoparticles (NPs) were constructed by precipitation method then the drug vildagliptin was loaded and drug loading efficiency was estimated by the HPLC method. X-ray diffraction analysis (XRD), UV–vis spectroscopy, FT-IR, scanning electron microscope (SEM), and EDX analysis were performed for the characterization of synthesized vildagliptin-loaded ZnO NPs. The UV–visible spectrum shows a distinct peak at 363 nm which confirms the creation of ZnO NPs and SEM showed mono-dispersed sphere-shaped NPs. EDX analysis shows the presence of desired elements along with the elemental composition. The physio-sorption studies, which used adsorption isotherms to assess adsorption capabilities, found that the Freundlich isotherm model explains the data very well and fits best. The maximum adsorption efficiency of 58.83% was obtained. Further, In vitro, anti-diabetic activity was evaluated by determining the α-amylase and DPP IV inhibition activity of the product formed. The formulation gave maximum inhibition of 82.06% and 94.73% of α-amylase and DPP IV respectively. While at 1000 µg/ml concentration with IC50 values of 24.11 μg/per ml and 42.94 μg/ml. The inhibition of α-amylase can be ascribed to the interactive effect of ZnO NPs and vildagliptin.

In the industrial revolution 4.0, everything is becoming digital. Additionally, the COVID-19 pandemic, and the difficulty of meeting face to face at that time, pushed everything to be done electronically or online. This is no exception for notaries, who have difficulty adapting to this all-electronic era. This research is doctrinal research which uses materials, namely literature and statutory regulations. The research results show that the Information and Electronic Transactions Law has accommodated the existence of electronic documents and information, but the Law on Notary Positions cannot expressly recognize the existence of electronic documents to be included in authentic deeds. Where other countries have adapted this cyber notary, Indonesia has not been able to apply it due to obstacles in these regulations. The exceptions contained in the Information and Electronic Transactions Law can be removed to provide space for cyber notaries in Indonesia, but the Notary Position Law must also be adjusted to provide clear guidelines for the implementation of cyber notaries.

The scientific and industrial communities now consider surface modification of steel-based alloys to be essential. One effective method of altering these alloys' surfaces is tungsten inert gas (TIG) cladding.The purpose of this study is to investigate how the surface characteristics of austenitic stainless steel substrates are affected by TIG-deposited composite coatings made of tungsten carbide and nickel (WC-Ni). The study also intends to investigate the influence of the WC-Ni composite coatings' travel speed, and as a result, the heat input usually has significant effects on the microstructure. The coating layers were deposited at different travel speeds (67, 107, and 122 mm/min) using pre-placed composite pastes that were 1 mm thick and contained the same weight percentage of WC (65). The clad layers showed a variety of microstructures in optical and scanning electron microscopy, primarily nickel solid solution dendrites with WC particles scattered throughout the matrix. The dendrites at various locations across the clad layers were few or dense, finer or coarser, equiaxial or columnar, densely or less densely branched, depending on the cooling rates and the density of WC in the composite. Energy dispersive spectroscopy demonstrated that the Fe element that was transferred from the substrate material to the molten pool was primarily concentrated in the matrix, not the dendrites, while the dendrite locations and orientations were amply demonstrated by the distribution of the W element.

Climate change, driven by the natural process of global warming, is a worldwide issue of significant concern because of its adverse effects on livestock output. The increasing trend of environmental temperature surging has drastically affected meat production and meat product quality, hence result in economic losses for the worldwide livestock business. Due to the increasing greenhouse gas emissions, the situation would get prolonged, and heat exposure-related stress is expected to worsen. Heat exposure causes metabolic and physiological disruptions in livestock. Ruminants and monogastric animals are very sensitive to heat stress due to their rate of metabolism, development, and higher production levels. Before slaughter, intense hot weather triggers muscle glycogen breakdown, producing pale, mushy, and exudative meat with less water-holding capacity. Animals exposed to prolonged high temperatures experience a decrease in their muscle glycogen reserves, producing dry, dark, and complex meat with elevated final pH and increased water-holding capacity. Furthermore, heat stress also causes oxidative stresses, especially secondary metabolites from lipid oxidation, severely affects the functionality of proteins, oxidation of proteins, decreasing shelf life, and food safety by promoting exfoliation and bacterial growth. Addressing the heat-related issues to retain the sustainability of the meat sector is an essential task that deserves an inclusive and comprehensive approach. Considering the intensity of the heat stress effects, this review has been designed primarily to examine the consequences of hot environment temperatures and related stresses on the quality and safety of meat and secondarily focus on cutting edge technology to reduce or alleviate the situational impact.

Background Insulin resistance (IR) is the key pathological basis of many metabolic disorders. Lack of asialoglycoprotein receptor 1 (ASGR1) decreased the serum lipid levels and reduced the risk of coronary artery disease. However, whether ASGR1 also participates in the regulatory network of insulin sensitivity and glucose metabolism remains unknown. Methods The constructed ASGR1 knockout mice and ASGR1-/- HepG2 cell lines were used to establish the animal model of metabolic syndrome and the IR cell model by high-fat diet (HFD) or drug induction, respectively. Then we evaluated the glucose metabolism and insulin signaling in vivo and in vitro. Results ASGR1 deficiency ameliorated systemic IR in mice fed with HFD, evidenced by improved insulin intolerance, serum insulin, and homeostasis model assessment of IR index, mainly contributed from increased insulin signaling in the liver, but not in muscle or adipose tissues. Meanwhile, the insulin signal transduction was significantly enhanced in ASGR1-/- HepG2 cells. By transcriptome analyses and comparison, those differentially expressed genes between ASGR1 null and wild type were enriched in the insulin signal pathway, particularly in phosphoinositide 3-kinase-AKT signaling. Notably, ASGR1 deficiency significantly reduced hepatic gluconeogenesis and glycogenolysis. Conclusion The ASGR1 deficiency was consequentially linked with improved hepatic insulin sensitivity under metabolic stress, hepatic IR was the core factor of systemic IR, and overcoming hepatic IR significantly relieved the systemic IR. It suggests that ASGR1 is a potential intervention target for improving systemic IR in metabolic disorders.

Abstract The protein Bcl-2, well-known for its anti-apoptotic properties, has been implicated in cancer pathogenesis. Identifying the primary gene responsible for promoting improved cell survival and development has provided compelling evidence for preventing cellular death in the progression of malignancies. Numerous research studies have provided evidence that the abundance of Bcl-2 is higher in malignant cells, suggesting that suppressing Bcl-2 expression could be a viable therapeutic approach for cancer treatment. In this study, we acquired a compound collection using a database that includes constituents from Traditional Chinese Medicine (TCM). Initially, we established a pharmacophore model and utilized it to search the TCM database for potential compounds. Compounds with a fitness score exceeding 0.75 were selected for further analysis. The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis identified six compounds with favorable therapeutic characteristics. The compounds that successfully passed the initial screening process based on the pharmacodynamic model were subjected to further evaluation. Extra-precision (XP) docking was employed to identify the compounds with the most favorable XP docking scores. Further analysis using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) method to calculate the overall free binding energy. The binding energy between the prospective ligand molecule and the target protein Bcl-2 was assessed by a 100 ns molecular dynamics simulation for curcumin and Epigallocatechin gallate (EGCG). The findings of this investigation demonstrate the identification of a molecular structure that effectively inhibits the functionality of the Bcl-2 when bound to the ligand EGCG. Consequently, this finding presents a novel avenue for the development of pharmaceuticals capable of effectively addressing both inflammatory and tumorous conditions.

Abstract Reservoir simulation is crucial for understanding the flow response in underground reservoirs, and it significantly helps reduce uncertainties in geological characterization and optimize methodologies for field development strategies. However, providing efficient and accurate solutions for the strong heterogeneity remains challenging, as most of the discretization methods cannot handle this complexity. In this work, we perform a comprehensive assessment of various numerical linearization techniques employed in reservoir simulation, particularly focusing on the performance of the nonlinear solver for problem dealing with fluid flow in porous media. The primary linearization methods examined are finite difference central (FDC), finite forward difference (FDF), and operator-based linearization (OBL). These methods are rigorously analyzed and compared in terms of their accuracy, computational efficiency, and adaptability to changing reservoir conditions. The results demonstrate that each method has distinct strengths and limitations. The FDC method is more accurate particularly in complex simulations where strong heterogeneity are introduced but is generally slower in convergence. The OBL on the other hand, is more efficient and converges quickly, which makes it suitable for scenarios with limited computational resources and simple physics, while the FDF method provides a balanced combination of precision and computational speed, contingent upon careful step size management of the derivative estimations. This paper aims to guide the selection of appropriate linearization techniques for enhancing nonlinear solvers’ accuracy and efficiency in reservoir simulation .

Abstract Cervical cancer is still the leading cause of cancer mortality worldwide even after introduction of vaccine against Human papillomavirus (HPV), due to low vaccine coverage, especially in the developing world. Cervical cancer is primarily treated by Chemo/Radiotherapy, depending on the disease stage, with Carboplatin/Cisplatin-based drug regime. These drugs being non-specific, target rapidly dividing cells, including normal cells, so safer options are needed for lower off-target toxicity. Natural products offer an attractive option compared to synthetic drugs due to their well-established safety profile and capacity to target multiple oncogenic hallmarks of cancer like inflammation, angiogenesis, etc. In the current study, we investigated the effect of Bergenin (C-glycoside of 4-O-methylgallic acid), a natural polyphenol compound that is isolated from medicinal plants such as Bergenia crassifolia, Caesalpinia digyna, and Flueggea leucopyrus. Bergenin has been shown to have anti-inflammatory, anti-ulcerogenic, and wound healing properties but its anticancer potential has been realized only recently. We performed a proteomic analysis of cervical carcinoma cells treated with bergenin and found it to influence multiple hallmarks of cancers, including apoptosis, angiogenesis, and tumor suppressor proteins. It was also involved in many different cellular processes unrelated to cancer, as shown by our proteomic analysis. Further analysis showed bergenin to be a potent-angiogenic agent by reducing key angiogenic proteins like Galectin 3 and MMP-9 (Matrix Metalloprotease 9) in cervical carcinoma cells. Further understanding of this interaction was carried out using molecular docking analysis, which indicated MMP-9 has more affinity for bergenin as compared to Galectin-3. Cumulatively, our data provide novel insight into the anti-angiogenic mechanism of bergenin in cervical carcinoma cells by modulation of multiple angiogenic proteins like Galectin-3 and MMP-9 which warrant its further development as an anticancer agent in cervical cancer.

Aim: Inappropriate emergency department (ED) use represents an important problem for health systems across the world. This study was designed to determine the reasons for inappropriate ED visits at different ED care levels during different seasons. Materials and Methods: The study was conducted in three hospitals with different ED care levels in two periods (March and July). Physicians determined the appropriateness of ED visits. Data were collected through face-to-face interviews using a researcher-made questionnaire. Results: The inappropriate ED visit rate was 75.6%. The most frequent reasons for inappropriate ED visits according to different ED care levels were as follows: Level 1, easy access (61.2%); Level 2, no other units providing health services outside of working hours (43.0%); and Level 3, perceived need for serum/injection treatment (58.2%). Inappropriate ED visits due to these reasons occurred more frequently in July than in March, with significant differences observed across the various levels of ED (p

In vitro embryo production (IVEP) encompasses a series of three basic steps, namely in vitro maturation (IVM), in vitro fertilisation (IVF), and in vitro embryo culture. IVEP approach has shown to be a valuable tool in advancing our comprehension of early mammalian development and expediting genetic enhancement in livestock. The aim of this research was to compare the impact of small and large follicles on the total number of oocytes recovered through the IVEP process. Therefore, 305 Ewe genitalia were collected at the Al-Fallujah abattoir in the Al-Anbar province. The follicles were identified, and their diameter was measured using an automated vernier calliper. Subsequently, the oocytes were classified into three distinct groups, namely excellent (A), acceptable (B), and poor (C). The results of the maturation rate of large follicles were 43.9% (51/116) and small follicles 30.7% (20/65). There was a statistical difference in maturation rate (p

Abstract Revolutionizing construction, the concrete blend seamlessly integrates human hair (HH) fibers and millet husk ash (MHA) as a sustainable alternative. By repurposing human hair for enhanced tensile strength and utilizing millet husk ash to replace sand, these materials not only reduce waste but also create a durable, eco-friendly solution. This groundbreaking methodology not only adheres to established structural criteria but also advances the concepts of the circular economy, representing a significant advancement towards environmentally sustainable and resilient building practices. The main purpose of the research is to investigate the fresh and mechanical characteristics of concrete blended with 10–40% MHA as a sand substitute and 0.5–2% HH fibers by applying response surface methodology modeling and optimization. A comprehensive study involved preparing 225 concrete specimens using a mix ratio of 1:1.5:3 with a water-to-cement ratio of 0.52, followed by a 28 day curing period. It was found that a blend of 30% MHA and 1% HH fibers gave the best compressive and splitting tensile strengths at 28 days, which were 33.88 MPa and 3.47 MPa, respectively. Additionally, the incorporation of increased proportions of MHA and HH fibers led to reductions in both the dry density and workability of the concrete. In addition, utilizing analysis of variance (ANOVA), response prediction models were created and verified with a significance level of 95%. The models' R2 values ranged from 72 to 99%. The study validated multi-objective optimization, showing 1% HH fiber and 30% MHA in concrete enhances strength, reduces waste, and promotes environmental sustainability, making it recommended for construction.

Sub-Saharan African (SSA) nations are falling behind in human development when compared to other regions globally, despite their abundant natural resources. Several Sub-Saharan African states have an underdeveloped financial structure, leading to underutilization of natural resource profits for human development. This research examines how the level of financial development impacts the relationship between natural resource rents and sustainable human development in 40 SSA countries from 2005 to 2019. This research used the dynamic panel threshold (DPT) approach, an advanced econometric methodology designed to address concerns often seen in panel data such as endogeneity, cross-sectional dependence, and heterogeneity. The cointegration findings showed a long-term association among the variables. The dynamic panel threshold analysis showed that natural resource rent had a detrimental influence on human capital development when financial development was below a certain threshold. Conversely, the outcomes were different when financial development exceeded the threshold. Financial system progress in Sub-Saharan African states considerably uses resource windfalls to improve sustainable human development. This research conducted robustness tests by using alternative estimates excluding outlier sample countries and extra proxies. The conclusions remained unchanged. Policy suggestions were introduced to enhance the financial sector and improve institutional quality in order to ensure sustainable growth in the SSA area.

Myeloid leukemia factor 1 (Mlf1) was identified as a proto-oncoprotein that affects hematopoietic differentiation in humans. However, its cellular function remains elusive, spanning roles from cell cycle regulation to modulation of protein aggregate formation and participation in ciliogenesis. Given that structurally conserved homologs of Mlf1 can be found across the eukaryotic tree of life, we decided to characterize its cellular role underlying this phenotypic pleiotropy. Using a model of the unicellular eukaryote Giardia intestinalis, we demonstrate that its Mlf1 homolog (GiMlf) mainly localizes to two types of cytosolic foci: microtubular structures, where it interacts with Hsp40, and ubiquitin-rich, membraneless compartments, found adjacent to mitochondrion-related organelles known as mitosomes, containing the 26S proteasome regulatory subunit 4. Upon cellular stress, GiMlf either relocates to the affected compartment or disperses across the cytoplasm, subsequently accumulating into enlarged foci during the recovery phase. In vitro assays suggest that GiMlf can be recruited to membranes through its affinity for signaling phospholipids. Importantly, cytosolic foci diminish in the gimlf knockout strain, which exhibits extensive proteomic changes indicative of compromised proteostasis. Consistent with data from other cellular systems, we propose that Mlf acts in the response to proteotoxic stress by mediating the formation of function-specific foci for protein folding and degradation.

The basic purpose of this work was to develop environmentally friendly, biodegradable, and biocompatible polymeric nanoparticles of sorafenib that can effectively release the desired drug in a customized and controlled manner for targeting hepatocellular carcinoma. The solvent evaporation technique was employed for the synthesis of sorafenib-loaded PLGA–chitosan nanoparticles, followed by various experimental specifications and compatibility studies using poloxamer 407 as the stabilizer. The best nanoparticles thus synthesized were selected to be used for cytotoxicity investigations through in vitro and in vivo assessments. For the in vitro drug release tests, the dialysis bag diffusion technique was used. For both chitosan nanoparticles and PLGA loaded with sorafenib, a biphasic release pattern was found, exhibiting a protracted release lasting 10 days after a 24-h burst release. As experimental animals, rabbits were utilized to evaluate different in vivo pharmacokinetic properties of the selected formulations. Plasma samples were extracted with acetonitrile and analyzed through the developed HPLC method. Pharmacokinetic parameters such as AUC0-t, Cmax MRT, Vd, and half-life (t1/2) were enhanced significantly (p ≤ 0.001), while clearance was considerably decreased (p ≤ 0.001) for the chosen synthesized nanoparticles in contrast to the commercially accessible sorafenib formulation (Nexavar®). The cytotoxicity of the reference drug and sorafenib-loaded PLGA and chitosan nanoparticles was calculated by performing an MTT assay against HepG2 cell lines. The developed polymeric sorafenib nanoformulations possess the appropriate physicochemical properties, better targeting, surface morphology, and prolonged release kinetics. The pharmacokinetic parameters were improved significantly when the results were compared with commercially available sorafenib formulations.

Load inconsistency has disrupted the power system, causing rotor angle fluctuation that leads to angle instability in the system. This research suggests an innovative proportional integral derivative with filter (PIDF)-based thyristor-controlled series compensator (TCSC) controller that utilise an evolutionary programming sine cosine algorithm (EPSCA) for hybrid optimisation to increase the angle stability of the power system. The challenge of the PIDF-TCSC design is transformed into an optimal control problem with respect to performance indices, such as the maximum imaginary part of system eigenvalues, damping ratio and damping factor, where another multi-objective function is utilised to determine the best stabiliser settings. Eigenvalue analysis is used to conduct the stability study in a linearised paradigm of the single-machine infinite-bus (SMIB) network. The resilience of the PIDF controller was tested using a SMIB power network under various operating circumstances. Simulation results are used to evaluate the system's effectiveness with the proposed optimised PIDF-TCSC controller to that of the system using the proportional integral derivative, proportional integral, and base case PIDF-TCSC approaches. The research findings demonstrate the efficacy of EPSCA in implementing PIDF-TCSC motif and its excellent resilient performance for improving power system stability as related to other strategies in various situations.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, show great potential in advancing the United Nations (UN) Sustainable Development Goals (SDGs), a collection of 17 worldwide targets outlined by the United Nations (UN) to tackle diverse social, economic, and environmental issues and foster sustainable development by 2030. As per the Federal Aviation Administration (FAA), UAVs can significantly improve areas of sustainability like wildlife and wildfire monitoring, precision agriculture, as well as healthcare. Furthermore, drones with communication capabilities are seen as key elements of non-terrestrial networks, and as enabling technologies for future 6G communication networks. This systematic literature review focuses on the role of UAV-enabled communications to achieve the SDGs. Our study is based on a thorough selection and analysis of scientific articles. Initially, we performed a broad search across three major databases: IEEE Xplore, Web of Science, and Scopus, yielding a set of 10,499 papers. Following a strict selection methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis - PRISMA), we narrowed this set down to 28 key journal articles. Our analysis highlights six global targets that greatly benefit from UAV-enabled aerial communication. In order of impact, these are: “Sustainable Cities and Communities,” where UAVs improve security and disaster management; “Life on Land,” where drones help with wildlife monitoring and reforestation; “Industry, Innovation, and Infrastructure,” where UAVs (Unmanned Aerial Vehicles) aid in infrastructure inspection; “No Hunger,” where drones improve agricultural practices; “‘Good Health and Wellbeing,” where UAVs assist with disease prediction and monitoring; and “Clean water and sanitation,”where drones play a crucial role in efficiently monitoring water quality and distribution. In summary, this paper offers a detailed look at the applications of UAVs in achieving the SDGs through aerial communications. By identifying areas of major impact and spotting gaps and weaknesses in current research, this review article aims to guide future research and policy-making in sustainable drone applications.

This study presents an innovative framework to examine the interplay between foreign direct investment and economic development from 1996 to 2021. Using the Panel NARDL approach, our study provides an empirical framework for exploring these complex relationships. We enhance the reliability of our results by integrating additional methodologies, such as augmented mean group and common correlated effects mean group. A decline in carbon emissions is commonly hypothesized to be correlated with economic expansion, foreign direct investment inflows, advancements in renewable energy, and implementation of environmental technologies. On the contrary, detrimental disturbances have the potential to result in protracted ecological deterioration, especially in situations where there are setbacks in economic expansion and foreign direct investment. It is worth noting that although short-run and long-run coefficients exhibit comparable directional trends, there are discrepancies in their magnitudes and levels of statistical significance. This sophisticated comprehension empowers countries such as China, among others, to allocate resources strategically towards sustainable energy initiatives, promoting a systematic approach that prioritizes the adoption of renewable energy, advancements in technology, attraction of foreign investment, and transformation of structures.

OBJECTIVES This study aims to evaluate the midterm clinical and radiological outcomes of TLIF in patients with single-level lumbar disc degeneration. The focus is on assessing the procedure's effectiveness in terms of pain relief, functional improvement, and any potential complications. METHODOLOGY A retrospective cohort study was conducted at Hayatabad Medical Complex, Peshawar involving 120 patients who underwent single-level TLIF for lumbar disc degeneration between 2018-2023. The patients with a diagnosis of single-level lumbar disc degeneration confirmed by MRI, failure of conservative treatment for at least 6 months, and who underwent TLIF during the study period were included in the study. Data were collected on preoperative and postoperative pain scores, Oswestry Disability Index (ODI), and radiological parameters. SPSS version 24 was used. RESULTS The mean follow-up period was 36 months (range 24-48 months). There was a statistically significant improvement in both pain scores (p < 0.001) and ODI scores (p < 0.001). Fusion rates were high, with 92% of patients achieving solid fusion by the final follow-up. Complications were observed in 15% of the cases, with adjacent segment disease being the most common. CONCLUSION TLIF provides significant pain relief and functional improvement in patients with single-level lumbar disc degeneration at midterm follow-up. The procedure demonstrates a high fusion rate with an acceptable complication profile, making it a reliable option for this patient population

Huntington’s disease (HD) is an inherited neurodegenerative disease characterized by neuropsychiatric symptoms including chorea, dementia, and depression. It is inherited in an autosomal dominant fashion and exhibits anticipation leading to earlier and more severe symptoms in the affected offspring of a patient. With a much lower prevalence in Asia than in Europe and other parts of the world, its diagnosis can be missed easily in the early stages due to mildness of the symptoms and significant overlap between its symptoms and those of other diseases. We present the case of a 38-year-old male of Pashtun ethnicity presenting with mild cognitive impairment and clumsiness who was eventually diagnosed with HD, but his mild clinical features, no documented history of HD in his parents, and his relatively young age coupled with the relatively low prevalence of HD in his geographical location presented a significant challenge in our diagnosis of his condition. This case underscores the importance of keeping a high clinical suspicion for HD in patients with chorea despite a negative parental history, especially in resource-limited areas where the parents may have gone undiagnosed and highlights the need for further research on HD’s prevalence in different parts of the world as well as the barriers to its diagnosis.

Traditional infant monitoring systems are evolving into intelligent solutions, offering real-time notifications and automation to cater to the demands of multitasking parents. However, most IoT solutions in parental care focus solely on monitoring, lacking automatic smartphone control. In response to this gap, we developed an IoT-based system for automatic cradle monitoring. This system not only enables smartphone-based monitoring but also allows parents to control the cradle's swing speed and receive notifications through a mobile application. Our solution integrates PIR and sound sensors, an ESP8266 microcontroller, and a Wi-Fi module to create a robust monitoring system that tracks a baby's movements and sounds, sending instant notifications to the smartphone. The user can activate the system and adjust the cradle's motion using a slider based on sensor data. This IoT-enabled smart baby cradle enhances convenience and reduces parental stress, ensuring infant well-being and safety. A survey conducted among Universiti Teknologi MARA instructors revealed that 70% preferred automatic swing cradles over traditional ones, and 80% expressed the need for IoT integration in infant cradles, highlighting the growing demand for such advanced solutions.

The underwater environment is harsh and challenging for human life, prompting companies and researchers to develop advanced technologies for exploration. Building on previous work that applied a CNN-based method for underwater object classification, this paper focuses on the design and implementation of an interactive embedded system for a compact Remotely Operated Vehicle (ROV) with specific dimensions and weight. The primary goal is to capture real-time underwater video using remote control communication via Ethernet. The ROV is powered by five brushless motors controlled by a smart PID controller. Precise pulse-width modulation signals enhance stability during movements along three axes, enabling high-resolution video capture. The system utilizes the Raspberry Pi 3's computing power for motion control, positioning, temperature monitoring, and video acquisition. Experimental results demonstrate the system's capability to process 42 frames per second. A user-friendly graphical interface allows for remote ROV control across various operating systems. With a depth rating of 100 meters and speed of 0.148 m/s. This ROV surpasses human divers' limitations and holds significant potential for applications in surveillance, operations, maintenance, and measurement tasks underwater. The proposed ROV contributes to the advancement of underwater exploration technology by combining high performance with cost-effectiveness.

Rolling bearings are essential components in a wide range of equipment, such as aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete system failure, and it accounts for approximately 45 %–50 % of failures in rotating machinery. Hence, it is imperative to establish a thorough and accurate predictive maintenance program that can efficiently foresee and prevent mishaps or malfunctions. The literature has employed a variety of techniques and approaches, from conventional methods to contemporary machine learning (ML) and ML-integrated IoT-based solutions, to categorise bearing faults. This article provides an overview of the most recent research and models used in the classification of bearing faults. The literature summary highlights various significant challenges in current models, such as issues with the classification function, complexities in the neural network structure, unrealistic datasets, dynamic working conditions of rotating machines, noise in the dataset, limited data availability, and imbalanced datasets. In order to tackle the problems, researchers have endeavored to improve and apply different methods, such as convolutional neural networks, deep belief neural networks, and LiNet, among others. Researchers have primarily developed these approaches using datasets from publicly accessible sources. This study also identified research gaps and deficiencies, including limited data availability, data imbalance, and difficulties in data integration. The nascent technologies in the field of problem diagnosis and predictive maintenance are acknowledged as Internet of Things-based ML and vision-based deep learning techniques, which are currently in their initial phases of advancement. Ultimately, the study puts forth several prospective suggestions and recommendations.

Papaver somniferum L. produces therapeutically-useful alkaloids like morphine, codeine, papaverine, and thebaine that accumulate in the latex of its capsule. Apart from its alkaloids, the edible seeds of the plant have high nutritional value and culinary use, with the seed oil being rich in the health-promoting unsaturated fatty acids. Sujata is low alkaloid-producing latex-less culinary variety of P. somniferum developed from an alkaloid-rich gum harvest parent (Sampada) for curtailing the narcotic menace of morphine and opium (dried latex). Earlier, it has been shown that the expression of stearoyl-acyl-carrier protein desaturase (PsSAD) in the young capsule and the proportion of unsaturated fatty acids in the seeds are higher in Sujata as compared to Sampada. Here, we studied bacterial endophytes isolated from Sujata tissues for their role in defining the fatty acid (saturated stearic versus unsaturated oleic) profile in its leaves and seeds. Besides, a potyvirus infecting P. somniferum was characterized through its coat protein gene sequencing and transmission electron microscopy and the two genotypes (Sujata and Sampada) contrasting in their phenotypes (oil/fatty acid, alkaloid and latex profiles) were compared for their susceptibility towards it through indicator plant- and qRT-PCR-based virus infectivity assays. In both the assays, Sujata was found to have higher susceptibility to the potyvirus, as compared to Sampada. The most plausible reason for this could be the higher PsSAD expression in the leaves of Sujata, which resulted in higher relative levels of oleic acid and lower relative levels of stearic acid in its leaves as compared to that of Sampada.

Abstract Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and $$\pi $$ π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution Statement This study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model’s proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.

The partial discharge (PD) event in high-voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of the acoustic pulse and the acoustic reflections pose fundamental challenges to the acoustic emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse’s shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate the reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system.

Green supplier selection has been an important technique for environmental sustainability and reducing the harm of ecosystems. In the current climate, green supply chain management (GSCM) is imperative for maintaining environmental compliance and commercial growth. To handle the change related to environmental concern and how the company manages and operates, they are integrated the GSCM into traditional supplier selection process. The main aims of this study were to outline both traditional and environmental criteria for selecting suppliers, providing a comprehensive framework to assist decision-maker in prioritizing green supplier effectively. In order to address issue to simulate decision-making problems and manage inaccurate data. A useful technique of fuzzy set was proposed to handle uncertainty in various real-life problems, but all types of data could not be handled such as incomplete and indeterminate. However, several extensions of fuzzy set were considered, such as intuitionistic fuzzy set, Pythagorean fuzzy set, q-rung orthopair fuzzy set, and q-rung orthopair fuzzy soft set considering membership and nonmember ship grade to handle the uncertainty problem. However, there was a lack of information about the neutral degree and parameterization axioms lifted by existing approaches, so to fill this gap and overcome the difficulties Ali et al. proposed a generalized structure by combining the structure of picture fuzzy set and q-rung orthopair fuzzy soft set, known as q-rung orthopair picture fuzzy soft sets, characterized by positive, neutral and negative membership degree with parameterization tools and aggregation operator to solve the multi criteria group decision-making problem. Additionally, the TOPSIS method is a widely utilized to assist individuals and organizations in selecting the most appropriate option from a range of choices, taking into account various criteria. Finally, we demonstrate an illustrative example related to GSCM to enhance competitiveness, based on criteria both in general and with a focus on environmental consideration, accompanied by an algorithm and flow chart.

Introduction: Fine needle aspiration cytology of head and neck region is well-accepted as a diagnostic procedure. It is a safe, simple, rapid, cost-effective, and minimally invasive way of diagnosing inflammatory, noninflammatory and neoplastic lesions. Aim : To study the role of FNAC in diagnosing head & neck lesions in the paediatric age group. Material and Method: This is the hospital-based study of 120 lesions of the head and neck region belonging to the age group of 0-18 years. Cytological results are interpreted and analyzed according to anatomical site and then categorized based on interpretation. Result: Out of 120 cases 20 % cases belong to the age group 0-5yrs, 24.16% belong to 6-10yrs & 55.83% belong to the 11-18yrs age group. According to the anatomical distribution of lesions maximum cases accounting for 81.67% are lymph node lesions followed by thyroid lesions, miscellaneous, and then least salivary gland lesions which account for 2.5%. In the lymph node, thyroid, salivary gland, and miscellaneous lesions predominant lesions are respectively reactive lymphoid hyperplasia (71.42%), thyroiditis (42.85%), sialadenitis (33.34%) &epidermal cyst (40%) Conclusion: FNAC in resource-limited settings, healthcare providers should realize the importance of FNAC as an initial screening tool for superficial lesions in paediatric population. The presumptive diagnosis after FNAC of palpable lesions in paediatric age group avoids the unnecessary definitive operative procedure. Thus, FNAC is an easy, simple, rapid, and cost-effective diagnostic procedure for paediatric age group.

Recently, max-min fuzzy relation inequalities (FRIs) have been used to model a (peer-to-peer) P2P network system. Any feasible scheme in the P2P network system is reflected by a solution of the max-min FRIs. One of the objectives of system managers is to decrease network congestion. To satisfy this objective, we attempt to minimize a weighted minimax function motivated by existing research. As a consequence, we establish a weighted minimax programming model in which the constraint is the max-min FRIs. Our goal in this work is to develop an effective algorithm to obtain the optimal solution of the optimization model. The so-called SCP-based algorithm is proposed to find the optimal solution. A numerical example shows the efficiency of our proposed SCP-based algorithm.

Child care involves parents, namely father and mother, as parties who have responsibility for their children. As time goes by, sometimes disputes occur between husband and wife which result in divorce. The purpose of this writing is to find out about child maintenance after divorce according to statutory regulations. Child care after divorce is regulated in Law Number 35 of 2014 concerning Child Protection and Law Number 1 of 1974 concerning Marriage. Child maintenance after marriage in the case study of Decision Number 395/Pdt.G/2021/PN Jkt.Utr. It started with the Plaintiff (Wife) and the Defendant (Husband) who divorced, they had two sons, namely Brother A and Brother B. In this case there has been a divorce, then the parents, either father or mother, are obliged to provide maintenance for their children. until they are adults or able to stand on their own. These children's rights are supported by Article 14 of the Child Protection Law where children have the right to meet directly or have personal contact with their parents, children have the right to receive care, maintenance, education and protection for the growth and development process from their parents according to their abilities, talents and interests, the right to receive living expenses from both parents, and the right to obtain other children's rights. So that the two children of the Plaintiff and Defendant are entitled to receive maintenance in the form of a place to live with the mother because she is the closest person to the child and receives support and education from father to adult.

Abstract In this paper, we propose a fractional-order mathematical model to explain the role of glucagon in maintaining the glucose level in the human body by using a generalised form of a fractal fractional operator. The existence, boundedness, and positivity of the results are constructed by fixed point theory and the Lipschitz condition for the biological feasibility of the system. Also, global stability analysis with Lyapunov’s first derivative functions is treated. Numerical simulations for fractional-order systems are derived with the help of Lagrange interpolation under the Mittage-Leffler kernel. Results are derived for normal and type 1 diabetes at different initial conditions, which support the theoretical observations. These results play an important role in the glucose-insulin-glucagon system in the sense of a closed-loop design, which is helpful for the development of artificial pancreas to control diabetes in society.

Background: Gaming is a new phenomenon and there are increasing concerns over its negative impact. After the official recognition by diagnostic and statistical manual-5 and International Classification of Disorder-11, much of the research is directed toward understanding the different constructs of the gambling disorder. Peer influence is an established factor in substance use but less researched in gaming. Aims of the Study: The aim of this study is to explore and understand the influence of peer pressure on Internet gaming. Materials and Methods: Individuals between 15 and 35 years were asked to participate after explaining the study objectives and assessed on the Peer Pressure Scale and Internet Gaming Disorder (IGD) Scale. Responses were analyzed using the R language with R studio as the integrated development environment. Generalized additive model was used to identify the best-fit model. The Akaike information criterion was used to select the best model. Results: Battle Ground Mobile India is the most played game. Peer pressure alone accounts for 46.2% of the variability in Internet gaming and along with factors such as student; real friends; strategy games; online mode of playing, night time playing; and hours spent playing, increasing it to 56%. Peer pressure is positively correlated with IGD (P < 0.001, R = 0.65). Conclusions: Peer pressure is directly associated and plays a role in developing Internet gaming along with factors such as being a student; playing with real friends; strategy game genre; online mode of playing games; and nightly playing. Substance use such as tobacco may influence peer pressure and Internet gaming, acting as a co-factor for both.

We presented a methodology for detecting acute lymphoblastic leukemia (ALL) based on image data. The approach involves two stages: Feature extraction and classification. Three state-of-the-art transfer learning models, InceptionResnetV2, Densenet121, and VGG16, were utilized to extract features from the images. The extracted features were then processed through a Global Average Pooling layer and concatenated into a flattened tensor. A linear support vector machine (SVM) classifier was trained and tested on the resulting feature set. Performance evaluation was conducted using metrics such as precision, accuracy, recall, and F-measure. The experimental results demonstrated the efficacy of the proposed approach, with the highest accuracy achieved at 91.63% when merging features from VGG16, InceptionResNetV2, and DenseNet121. We contributed to the field by offering a robust methodology for accurate classification and highlighted the potential of transfer learning models in medical image analysis. The findings provided valuable insights for developing automated systems for the early detection and diagnosis of leukemia. Future research can explore the application of this approach to larger datasets and extend it to other types of cancer classification tasks.

Passing is one of the fundamental techniques in football. Good passing skills enable players to participate in the game effectively and maintain possession of the ball. Paired short passing drills also help improve individual skills. This research aims to find out whether short passing training in pairs can increase the accuracy of soccer passing in adolescent soccer players. The quantitative method used in the research is a pre-experimental design through one group pretest and posttest. The research instrument used a ball passing accuracy test for 30 seconds and used the short passing practice method in pairs. The population in this study consisted of 20 male Rekminers Club soccer players aged 15-17 years. The sample was selected using a total sampling technique, and the entire population sampled was 20 players. The data analysis technique uses the t-test to test the hypothesis to increase soccer passing accuracy in the pretest and posttest treatment using SPSS version 26. Based on the results of hypothesis testing using the t-test in paired short passing exercises with football passing accuracy ability, there is a significant influence based on pretest and posttest data analysis of 0.000 < 0.05. This exercise was successfully carried out to improve the accuracy of football passing. The research results show that short passing training in pairs is an effective method for improving the passing accuracy of football players.

Objectives: Keeping in view the economic and veterinary public health importance of brucellosis, this research was conducted to determine its seroprevalence and associated risk determinants in small ruminants in district Khanewal, Southern Punjab, Pakistan. Materials and Methods: Two-stage cluster sampling technique was used for sampling, and the sample size was calculated using C-survey 2.0. Accordingly, sera samples (n = 392) were collected from small ruminants in the study area from October 2022 to July 2023. All the samples were tested for the presence of anti-Brucella antibodies by Rose Bengal Plate Test (RBPT), followed by confirmation of all the samples using an enzyme linked immunosorbent assay (ELISA) kit (ID.vet®, France; sensitivity and specificity=100%, each). Results: The seropositivity rate of brucellosis was 7.14% [n = 28/392; 95% confidence interval (CI) = 4.87%–10.12%] by RBPT, whereas the results of ELISA showed an overall seroprevalence rate of 7.40% (n = 29/392; 95% CI = 5.11%–10.37%) in the study population. Univariate analysis of risk factors revealed that abortion history (AH), retained fetal membranes (RFMs), repeat breeding, flock size (FS), educational status of farmers (ESFs), awareness about brucellosis (AB), and farm hygiene had a significant association with the seroprevalence of brucellosis (p < 0.05). The multivariate analysis using a binary logistic regression model revealed that variables including tehsil, FS, AH, RFM, ESF, AB, and farming system were significant factors (p < 0.05) associated with brucellosis in the target population. Conclusion: Brucellosis is prevalent in small ruminants in Khanewal, Pakistan. The disease burden can be reduced by improving the reproductive health of animals, farm hygiene, and farmers' awareness about the diseases. Further studies are needed on a larger scale to devise stringent disease control strategies to avoid losses associated with brucellosis at regional, national, and global levels. [J Adv Vet Anim Res 2024; 11(1.000): 9-18]

Groundwater is very important part of life and source of survival on earth; and the clean water is one of the most important issues of the developed or developing world. In addition to other secondary problems such as solid waste and resource waste management, wastewater management is a major environmental obstacle to the development of the textile industry. Textile industry is the largest industrial sector of the world and hold very important part for the development of economy in all countries. Although the industry generates a lot of chemicals, including dyes in the form of wastewater. The textile industry is most fresh water consuming industry; simultaneously it discharges the huge load of contamination in our environment. The textile industry is the largest producer of wastewater during its different production processes. The wastewater creates several problems such as health problems, aquatic life including water pollution. The textile industry wastewater is treated by different methods such as coagulation, adsorption, membrane, and biological treatment. This paper reviews the different treatment technologies for textile wastewater along with its advantages and disadvantages. The objective of this study is to contribute a summary on the best and latest treatment techniques for textile industry effluent.

Abstract Background Early pregnancy nutritional status can be associated with adverse birth outcomes such as small-for-gestational age (SGA) and low birth weight (LBW). BMI (Body Mass Index) and MUAC (Mid-upper arm circumference) are easy to use assessments and are indicative of the pre-pregnancy nutritional status if obtained in the first trimester. This study primarily assesses the association of maternal nutritional status using BMI and MUAC with SGA in a community-based cohort of Pakistani women. It also aims to determine the predictive ability of MUAC and BMI in predicting SGA. Secondarily, we assessed the association between maternal nutrition and large for gestational age (LGA) and LBW. Methods This study is a secondary analysis of an ongoing pregnancy cohort “Pregnancy Risk Infant Surveillance and Measurement Alliance (PRISMA)“in Ibrahim Hyderi and Rehri Goth, Karachi. PRISMA participants who were enrolled between January 2021 to August 2022 were included given they had a gestational age

Accurate burst pressure prediction is critical for ensuring oil and gas pipeline safety, guiding maintenance decisions, and lowering costs and risks. Traditional methods have limitations, including high experimental costs, conservative empirical models, and computationally expensive numerical algorithms. Machine learning (ML) models have supplanted traditional methods in recent years. However, small and imbalanced datasets are the big challenge to build a ML model that can generate more accurate results. Moreover, the lack of generalization in ML models trained on a dataset of pipelines with specific material grids prevents them from producing superior results on other pipeline types. First, FEA was used to make a dataset. Then, a new way to improve machine learning (ML) model generalization for burst pressure prediction is suggested: combine publicly available datasets of different pipeline specifications. In this combined dataset, some pipelines have a higher number of data samples, and some have fewer, which causes a class imbalance issue. The Synthetic Minority Oversampling Technique (SMOTE) technique was applied to address the issue of class imbalance. The performance of various ML models, Extra Trees (ET), Extreme Gradient Boosting (XGBR), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Decision Tree (DT), was evaluated to validate the model's prediction and generalization on pipelines of various material grids. Results show that all the selected ML models produced high R-squared, i.e., >0.95, on balanced data compared to the imbalance dataset. These results show that SMOTE-based augmentation is a beneficial way to fix dataset imbalance and make ML models better at predicting burst pressure in oil and gas pipelines.

The world's population is proliferating, and the growing population needs more food resources. Meat is one of the essential dietary components for humans, but the available meat resources are insufficient to fulfill consumers' demands. In this scenario, the meat alternative could be a sustainable resource for fulfilling the meat demand. Cultured meat is one of the best meat alternatives and also has the potential to fulfill the meat demand of the growing population. Cultured meat is produced by cultivating animal stem cells and allowing them to undergo a growth and division process that closely mimics the natural cellular development inside living organisms. The primary reasons behind cultured meat production are to minimize the environmental effect by reducing methane emissions, to minimize the meat cost so it will be economical for the consumer, and to provide antibiotic-free meat. This review explains cultured meat production and new technologies being introduced to create opportunities to improve the overall production of cultured meat. It also explores the cultured meat sector's market opportunities and challenges to the cultured meat industry.

Objective: To determine the risk factors for early post-operative arrhythmias in children undergoing congenital heart surgeries at a tertiary care hospital. Study Design: Prospective longitudinal study. Place and Duration of Study: National Institute of Cardiovascular Disease, Karachi Pakistan, from Jan to Jul 2020. Methodology: We included 143 patients of both genders undergoing open heart surgery for congenital heart diseases. We noted all patients' pre-operative, intra-operative, and post-operative clinical characteristics. Patients were monitored in the pediatric cardiac intensive care unit. We analyzed the development of postoperative arrhythmias and the factors associated with them. Results: In 143 patients, the mean age at the time of surgery was 7.52±6.0 years. There were 83(58.0%) male patients. Tetralogy of Fallot was the most common type of congenital heart disease noted in 57(39.0%) patients. The mean cardiopulmonary bypass time was 83.9±25.5 minutes, while the mean aortic cross-clamp time was 58.1±21.9 minutes. Post-operatively, arrhythmia was observed in 39(27.3%) children. Intra-operative arrhythmias (p

Objective: To compare the surgical outcome of Tetralogy of Fallot patients with and without right ventricular diastolic dysfunction after repair. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Cardiology, National Institute of Cardiovascular Disease, Karachi Pakistan, from May to Oct 2021. Methodology: Patients above 5 months of age, of either gender, undergoing surgery for Tetralogy of Fallot were enrolled. Patients were divided into two groups according to the Gatzoulis’ criterion for right ventricular restriction i.e. Group-A had right ventricle with restriction, and Group-B had right ventricle without restriction. Velocities of E and A waves was recorded, and their ratio (E/A) was calculated. The outcome such as aortic cross clamp, cardiopulmonary bypass time, ventilation time, intensive care unit stay, drain time, and transannular patch was noted. Results: Of 54 patients, restrictive physiology patients had significantly higher mean for aortic cross-clamp (p-value 0.004, 95%CI 3.54 - 17.35), ventilation time (p-value

Millions of people in developing countries lack access to modern energies and for long, these countries have relied on energy subsidies to scale up their access rate. However, there is no evidence that those energy subsidies contribute to energy poverty reduction. Similarly, while good governance and strong institutions are touted as critical for implementing energy policy and improving access rate, empirical evidence of their impact remains very scanty. This study assesses the impact of energy subsidies and institutional quality on access to electricity in selected developing countries. Result from the Praise-Winston regression revealed that energy subsidies as a policy tool for achieving universal electrification do not hold in the selected developing countries. We also found that energy subsidies accompanied by a strong governance mechanism can help developing countries scale up their electricity access rate. We recommend practical ways to help developing countries achieve universal access to modern energy solution.