Explore the vast range of titles available.

Fused deposition modeling (FDM) is an additive manufacturing technique used to build complete three-dimensional models from a range of materials for various applications. Brass alloys as an additive were found to be noteworthy composites used in additive manufacturing. Till date, limited research data are available on the tensile properties of PLA–brass composites manufactured using the FDM process. As such, the present research investigates the tensile properties of brass–PLA composite at various infill patterns and compositions (15% and 70%). The significant parameter affecting the mechanical parameters was determined using response surface methodology (RSM). Mechanical qualities were mathematically modeled using the response surface methodology, anticipating the needed output value for various compositions, and infill patterns. In conclusion, for the tensile test, the concentric pattern achieves the highest value for elastic modulus, ultimate tensile strength, and yield strength (0.2% offset) for both compositions and the octa-spiral pattern has the weakest properties. The highest value of elastic modulus, ultimate tensile strength, and yield strength (0.2% offset) obtained was 0.333 GPa, 7.758 MPa and 4.539 MPa, respectively. The higher infill composition was found to decreases the tensile behavior of the composite. Adapting RSM, a mathematical model to estimate tensile properties has also been developed to ease the future FDM printed PLA – brass tensile properties.

Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors. Using Exploratory Data Analysis (EDA), the study identifies temperature as the most critical factor influencing crop yields, with notable interactions observed between rainfall patterns and macronutrient levels. Advanced regression models, including Decision Tree Regressor, Random Forest Regressor, and LightGBM Regressor, achieved exceptional predictive performance, with R² scores reaching 0.92, mean squared errors as low as 0.02, and mean absolute errors of 0.015. Additionally, XAI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enhanced the interpretability of the predictions, offering actionable insights into the relative importance of key features. These insights inform strategies for agricultural decision-making and climate adaptation. By integrating AI-driven predictions with XAI-based interpretability, this research presents a robust and transparent framework for mitigating the adverse effects of climate change on agriculture, emphasizing its potential for scalable application in precision farming and policy development.

Diarrhoeal diseases are major causes of morbidity and mortality in developing countries. This longitudinal study aimed to identify controllable environmental drivers of intestinal infections amidst a highly contaminated drinking water supply in urban slums and villages of Vellore, Tamil Nadu in southern India. Three hundred households with children (<5 years) residing in two semi-urban slums and three villages were visited weekly for 12–18 months to monitor gastrointestinal morbidity. Households were surveyed at baseline to obtain information on environmental and behavioural factors relevant to diarrhoea. There were 258 diarrhoeal episodes during the follow-up period, resulting in an overall incidence rate of 0·12 episodes/person-year. Incidence and longitudinal prevalence rates of diarrhoea were twofold higher in the slums compared to rural communities (P < 0·0002). Regardless of study site, diarrhoeal incidence was highest in infants (<1 year) at 1·07 episodes/person-year, and decreased gradually with increasing age. Increasing diarrhoeal rates were associated with presence of children (<5 years), domesticated animals and low socioeconomic status. In rural communities, open-field defecation was associated with diarrhoea in young children. This study demonstrates the contribution of site-specific environmental and behavioural factors in influencing endemic rates of urban and rural diarrhoea in a region with highly contaminated drinking water.

In the field of pharmacology, medicinal plants are of great importance to researchers as most pharmaceutical industries depend on medicinal plant for their raw materials. Hybanthus enneaspermus and Hybanthus travancoricus belong to the family Violaceae and are well known for its medicinal properties. The present study was carried out to evaluate the possible bioactive components present in the above plant species. Petroleum ether, benzene, ethyl acetate, methanol, ethanol, and aqueous extracts were subjected to qualitative test for the identification of phytoconstituents as per standard procedure. The functional groups of plant powders were identified using FT-IR analysis. The ethanol extracts were analyzed via GC-MS techniques. The phytochemical screening of different solvent extract of H. enneaspermus and H. travancoricus revealed the presence of alkaloids, flavonoids, phenols, saponins, tannins, steroids, terpenoids, glycosides, carbohydrates and xanthoproteins. The FT-IR analysis confirmed the presence of hydroxyl, phenolics, alcohols, ester, amine, aromatic, alkanes and others. GC-MS analysis showed the presence of 15 for H. enneaspermus and 10 compounds for H. travancoricus, which include n-hexadecanoic acid, hexadecanoic acid, methyl ester, undecane, neophytadiene, etc. The phytochemical profile and GC-MS analysis of H. enneaspermus and H. travancoricus revealed the presence of important bioactive compounds with medicinal properties. Hence, the presence of these phytoconstituents is responsible for the therapeutic effects.

Background and Objectives: The present study was designed to evaluate the effect of intravenous dexmedetomidine on spinal anesthesia with 0.5% of hyperbaric bupivacaine. Materials and Methods: One hundred American Society of Anesthesiologists (ASA) physical status I/II patients undergoing elective surgeries under spinal anesthesia were randomized into two groups of 50 each. Immediately after subarachnoid block with 3 ml of 0.5% hyperbaric bupivacaine, patients in group D received a loading dose of 1 μg/kg of dexmedetomidine intravenously by infusion pump over 10 min followed by a maintenance dose of 0.5 μg/kg/h till the end of surgery, whereas patients in group C received an equivalent quantity of normal saline. Results: The time taken for regression of motor blockade to modified Bromage scale 0 was significantly prolonged in group D (220.7 ± 16.5 min) compared to group C (131 ± 10.5 min) (P < 0.001). The level of sensory block was higher in group D (T 6.88 ± 1.1) than group C (T 7.66 ± 0.8) (P < 0.001). The duration for two-dermatomal regression of sensory blockade (137.4 ± 10.9 min vs. 102.8 ± 14.8 min) and the duration of sensory block (269.8 ± 20.7 min vs. 169.2 ± 12.1 min) were significantly prolonged in group D compared to group C (P < 0.001). Intraoperative Ramsay sedation scores were higher in group D (4.4 ± 0.7) compared to group C (2 ± 0.1) (P < 0.001). Higher proportion of patients in group D had bradycardia (33% vs. 4%) (P < 0.001), as compared to group C. The 24-h mean analgesic requirement was less and the time to first request for postoperative analgesic was prolonged in group D than in group C (P < 0.001). Conclusion: Intravenous dexmedetomidine significantly prolongs the duration of sensory and motor block of bupivacaine spinal anesthesia. The incidence of bradycardia is significantly higher when intravenous dexmedetomidine is used as an adjuvant to bupivacaine spinal anesthesia. Dexmedetomidine provides excellent intraoperative sedation and postoperative analgesia.

Improving understanding of the pathogen-specific seasonality of enteric infections is critical to informing policy on the timing of preventive measures and to forecast trends in the burden of diarrhoeal disease. Data obtained from active surveillance of cohorts can capture the underlying infection status as transmission occurs in the community. The purpose of this study was to characterise rotavirus seasonality in eight different locations while adjusting for age, calendar time and within-subject clustering of episodes by applying an adapted Serfling model approach to data from a multi-site cohort study. In the Bangladesh and Peru sites, within-subject clustering was high, with more than half of infants who experienced one rotavirus infection going on to experience a second and more than 20% experiencing a third. In the five sites that are in countries that had not introduced the rotavirus vaccine, the model predicted a primary peak in prevalence during the dry season and, in three of these, a secondary peak during the rainy season. The patterns predicted by this approach are broadly congruent with several emerging hypotheses about rotavirus transmission and are consistent for both symptomatic and asymptomatic rotavirus episodes. These findings have practical implications for programme design, but caution should be exercised in deriving inferences about the underlying pathways driving these trends, particularly when extending the approach to other pathogens.

A physically accurate and computationally effective pure finite-element-based methodology for Resin Transfer Molding (RTM) process simulations is presented. The formulations are developed starting with the time-dependent mass conservation equation for the resin flow. Darcy's flow approximations are invoked for the velocity field, thereby forming a transient governing equation involving the pressure field and the resin saturation fill factor which tracks the location of the resin front surface. Finite element approximations are then introduced for both the fill factor and the pressure field, and the resulting transient discrete equations are solved in an iterative manner for both the pressures and the fill factors for tracking the progression of the resin front in an Eulerian mold cavity. The formulation involves only a pure finite-element Eulerian mesh discretization of the mold cavity and does not require specification of control volume regions and has no time increment restrictions that exist as in the traditional explicit finite-element-control volume based formulations. The present formulations accurately account for and capture the physical transient nature of the mold-filling process while maintaining improved numerical and computational attributes. Mold-filling simulations involving various geometrically complex mold configurations are presented, demonstrating the applicability of the developments for practical manufacturing process simulations.

Modern criminal investigations include various cutting-edge scientific methods, including hybrid models, game theory, and machine learning. Criminal investigations consist of the examination of data that is utilized as evidence. In this sense, many significant and innovative studies on the crime uncertainty research of criminals have been carried out in the past, and one method for resolving this ambiguity is called Neutrosophic logic. The criminal in the crime application platform's initial inquiry may be a new involved or a previous offender. In this paper, the framework describes identifying offenders in a criminal investigation. Crime data for a wide range of offences were collected. This approach uses a game theory model to compare the present crime to previous crimes committed by the same individual. Making the right decision about the crime and exercising sound judgment depend on identifying the actual offender. When assessing the empirical data, taking in the incident's location and early criminal prediction is essential.