Mohammod Abul Kashem’s research while affiliated with Dhaka University of Engineering & Technology and other places

Industrial manipulators are essential to many manufacturing processes because they increase efficiency and productivity dramatically. However, maintaining operational safety and averting potential risks in industrial environments requires that these manipulators be stable. The development and implementation of an entirely algorithm-driven novel simulation tool intended to assess industrial manipulators’ stability in-depth are presented in this research. The suggested tool combines sophisticated mathematical models with the material properties of the manipulator, such as deflection, stiffness, and damping. To analyses the dynamic behaviour of manipulators under various operating situations, a hypothetical simulation technique to assess the stability of robot manipulators combined with material properties is taken into consideration. The simulation tool offers vital insights into the stability characteristics of manipulators, allowing engineers and designers to enhance their performance and guarantee operational safety. The simulation tool’s usefulness is showcased through case studies and comparative evaluations, emphasizing its capacity to improve the design and implementation of industrial manipulators in practical situations. In summary, this research enhances the field of industrial automation by offering a strong framework for assessing and upgrading the stability of manipulator systems. This, in turn, improves productivity and safety in industrial settings.

The demand for a comprehensive method to assess stability using manipulator material parameters is high. Various material parameters, such as the Young modulus, which represents stiffness, damping, and deflection, influence the material of the robot manipulator. The correlation between robot stability and these characteristics remains unclear, as prior studies have not yet examined the collective impact of these parameters on robot manipulators. This work considers two sophisticated manipulators, namely ABB and FANUC. The main objective of this research is to construct a stability model that considers the material properties of stiffness, damping, and deflection to assess the manipulator’s stability level, which may be categorized as low, medium, or high. Furthermore, the presented stability model examines and employs numerous modified and conventional formulas for material properties to determine the level of stability. The findings show that stiffness significantly influences the stability of robot manipulators, a relationship that applies to all the examined manipulators. We also emphasize that the choice of manipulator materials significantly impacts stability maintenance. These findings are expected to enhance the design and advancement of novel robot manipulators within the industry.

Introduction: Perioperative anemia can cause a host of complications, such as renal injury, stroke, and mortality. However, the use of transfusions preoperatively also can cause various complications, such as an overload of fluids and the risk of infection. Learning if, and how much an effect transfusion has in cardiac surgery can lead to better decision-making to reduce postoperative complications. Hypothesis: The hypothesis tested was that there is a significant difference in postoperative outcomes between cardiac transplant patients who received a blood transfusion preoperatively and those who did not. Methods: The UNOS database was used to find adult patients who received a heart transplant between 2000 and 2023 (n = 51,325). The remaining individuals were split into groups that received preoperative blood products (n=10,873) or did not (n=40,452). Endpoints analyzed include mortality, length of stay, graft status, and acute rejection. Mortality was analyzed using a Kaplan-Meier curve and log-rank tests, and Cox proportional hazards were used to find risk factors. All analysis was done in JMP. Results: After analysis, it was found that there was a significant difference in length of stay (p<0.0001), graft status (p<0.0001), and acute rejection (p=0.0430), with transfusion patients having a longer LOS, and higher percentages of graft failure and acute rejection. The Kaplan-Meier curve shows a significant difference in mortality between the two groups (p<0.0001). Life support at registration also showed significant differences, with ECMO utilization (p<0.0001), ventilator utilization (p<0.0001), and IABP utilization (p<0.0001) having higher percentages with patients with preoperative transfusions. Cox regression results showed that there is an increased risk of mortality in patients who did not receive transfusions before transplant (p<0.0001). Conclusion: While there was an increase in postoperative complications with patients with preoperative transfusions, there also appears to be a correlation between the need for life support and the use of blood products preoperatively. This, along with the reduced risk of mortality from preoperative transfusion patients, shows the possibility that differences in outcomes relating to transfusions may be due to patient conditions before the surgery rather than transfusions alone.

Objective Virtually anything can be ranked; the US News and World Report (USNWR or US News) ranks the top 50 hospitals specializing in cardiology, heart, and vascular surgery. Here the authors propose validating the effectiveness of rankings by comparing differences among the USNWR metrics across the top 50 hospitals. Methods The ranking system for the top 50 hospitals specializing in cardiology, heart, and vascular surgery was derived from 16 variant scores. Each hospital’s scores were collected from the USNWR. Hospitals were categorized into quintiles consisting of 10 institutions (1–10, 11–20, etc). An analysis of variance/χ² comprehensive statistical analysis was run alongside a Wilcoxon/Kruskal–Wallis test to compare statistical outcomes. A significant threshold was deemed to be P < 0.05. Results Significant differences were noted between quintiles for advanced technologies (P = 0.05), US News specialty score (P < 0.001), number of patient referrals (P = 0.004), and expert opinion (P < 0.001). Non-statistically significant differences were found among patient experience, public transparency, Society of Thoracic Surgery transparency, American College of Cardiology transparency, recognition as a magnet hospital, and nursing staffing. Interestingly, a large variance was noted in the average number of referrals between the first quintile (13,371) and the last (6690). Conclusions Expert opinion plays a critical role in the reputation of the USNWR’s top 10 hospitals in cardiology, heart, and vascular surgery. Although many have argued about the merits of USNWR hospital rankings, taken together, rankings fill a strong customer demand and are sticky.

Text recognition for E-Commerce boosts search engine performance, facilitates product discovery, enhances user experience, lets you create personalized recommendations, and simplifies inventory control. We've developed a novel Ensemble SVM Multinomial Naive Bayes Approach in our research project that is specifically designed to detect e-commerce text. Our dataset had four different classes: books, electronics, household, and clothing and accessories. It contained 50,425 numeric values. Our impressive training accuracy of 99.83% and validation accuracy of 98.35% were attained by applying this state-of-the-art model. The precision of e-commerce text detection has advanced significantly with this accomplishment. We truly believe that our detection technology is capable of what it does. In our opinion, it offers a sound and useful approach to the analysis of text related to online commerce. In addition, we anticipate its integration serving as a cornerstone of future e-commerce sections, promising improved functionality and precision, which excites us about its future potential to refine the e-commerce scene.

Air pollution is a big concern in developing countries due to its negative effects on both human well-being and the environment. Collecting real-time values of air quality is challenging using traditional methods. Because they have limited coverage and may not accurately reflect pollution levels in a specific location. However, Advances in Internet of Things (IoT) technology and Machine Learning (ML) algorithms, can play a vital role in collecting and analyzing large amounts of air quality data, resulting in a more complete and exact knowledge of pollution levels. Throughout this work, based on several air pollutants including sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), particulate matter (PM) 2.5, particulate matter (PM) 10 and carbon monoxide (CO) across a large urban region, we establish an IoT-based framework to collect real-time data. After collecting the real-time values, we applied two types of machine learning algorithms named regression and classification models including linear regression, decision trees (DT), random forest (RF), K-Nearest Neighbours (KNN), Naive Bayes (NB), and gradient boosting (GB), to analyze the gathered data and estimate pollution levels into good, satisfactory, moderate, poor and very poor. Among the machine learning models, RF outperforms the result. This work and the dataset will be helpful for researchers, environmental practitioners and agencies.