Search

Diabetes mellitus during pregnancy known as GDM is a severe disorder that develops during pregnancy and its prevalence is rapidly increasing all around the world. It arises due to two major metabolic defects; acute resistance of insulin and dysfunctioning of beta cells. Numerous organs are additionally associated with the pathological process of GDM. There are two types of factors that are associated with GDM that are modifiable and non-modifiable. GDM can cause serious long-term complications to both fetus and mother due to maternal hereditary proneness linked with the environment and fetoplacental aspects. The purpose of this manuscript is to audit GDM in detail and to provide a logical information with refreshed exploration from various angles. [ABSTRACT FROM AUTHOR]

Byline: N. Kaur, R. Miglani, R. Grover Background: Breast cancer causes significant disruption to the quality of life (QOL) of its patients, which is compounded further by lack of information [...]

Background: Women’s health disparities, often rooted in sociocultural discrimination, prompted the establishment of the ‘Women’s Wellness and Family Welfare Clinic.’ Objective: The objective of this clinic was to address the adolescents and women-related issues, screening, counselling, mental health, diet and nutrition and, family planning services under one roof. Methodology: The initiative was launched in September 2023 with collaborative efforts between the Department of Community Medicine & SPH and the Department of Gynaecology and Obstetrics at the PGIMER satellite center in Sangrur, Punjab. The clinic comprehensively, recorded socio-demographic profiles, clinical histories and various health dimensions, including physical parameters, mental well-being, social factors, NCD risks, and family planning. Positive screening results prompted referrals for further evaluation. Results: A total of 252 participants underwent screening, of whom 98 (38.9%) were aged 30-39, 32 (12.7%) were planning pregnancy, and 146 (57.6%) were using contraception, with tubal ligation being a prevalent choice 64 (43.4%). Beyond gynaecological concerns, participants reported chronic lower backache (59; 23.4%) and numbness of hands or feet (56; 22.2%) as common symptoms. More than one-third, 96 (39.09%), were found to be at risk for non-communicable diseases (NCDs), with 19 (7.5%) having hypertension, 10 (3.9%) with pre- diabetes/diabetes, and 5 (2%) presenting with breast lumps. Visual Inspection with Acetic Acid (VIA) revealed a 1.2% positivity rate among 117 participants. However, 161 (63%) participants had a BMI over 25, and 121 (48%) consumed fried snacks daily. Domestic abuse was reported by a mere 2 (0.4%) participants, shedding light on the clinic’s potential role in uncovering hidden health issues. Conclusion: The success of this initiative underscores the feasibility of the model wellness clinic in an evolving phase of satellite centre. By offering comprehensive health assessments and facilitating timely referrals, the clinic stands as a promising approach to enhance early detection and treatment, contributing to the holistic well-being of women.

The textile industry is a very resource-intensive sector, considerably contributing to environmental deterioration via substantial use of water, electricity, and chemicals. With the global transition towards sustainable practices, the notion of a circular economy has become more significant, focusing on prolonging product lifespans, reducing waste, and facilitating material recovery. An effective method for achieving circularity in the textile sector is Design for Disassembly (DfD), which facilitates the straightforward deconstruction of items for recycling or reuse. This study examines the use of Design for Disassembly (DfD) in textile production, emphasizing methods that enable the straightforward separation of fibers, trimmings, and fasteners, hence improving garment recyclability. Data was gathered via a comprehensive examination of textile product designs, production methodologies, and recycling methods throughout the sector. Findings demonstrate that the use of DfD may substantially decrease waste and enhance material recovery rates. Nonetheless, obstacles like redesign expenses, customer awareness, and compatibility with existing production techniques persist. This study presents a detailed technique for incorporating Design for Disassembly (DfD) into textile manufacturing and examines its possible effects on the circular economy. The results substantiate the assertion that the implementation of DfD may be a crucial advancement toward attaining sustainability in the textile sector.

This study examines the potential for enhancing carbon capture and storage (CCS) processes by machine learning to markedly improve performance across diverse capture methods, including as absorption, adsorption, membrane separation, and cryogenic distillation. Through the systematic adjustment of critical operating parameters, including temperature, pressure, flow rates, and sorbent characteristics using machine learning algorithms, we saw significant improvements in CO₂ collection efficiency. The use of optimum operating parameters, namely a temperature range of 40-60°C for absorption and a pressure range of 3-5 bar for adsorption, resulted in a 30% enhancement in capture efficiency. Moreover, machine learning models, namely Random Forest and Support Vector Machines (SVM), achieved a maximum enhancement of 20% in forecasting ideal operating parameters for membrane separation and cryogenic systems. Reduced cycle durations in adsorption processes, facilitated by predictive modeling, resulted in a 15% improvement in CO₂ removal rates. The models’ capacity to forecast sorbent regeneration conditions led to a 10% decrease in energy use. Machine learning algorithms adeptly optimized process-specific parameters, including material composition and flow dynamics, enhancing membrane performance by 18% and cryogenic systems by 12%. These results highlight the significance of using machine learning to customize CCS methods for particular materials and situations, facilitating more sustainable, efficient, and scalable carbon capture systems.

Reducing costs associated with inventory is the primary goal of conventional inventory models like the models of economic order quantity and economic production quantity. However, these models fall short of addressing defective goods or revising them. Imperfections in the manufacturing process result in flawed products alongside the final goods. To convert these flawed components into finished goods, rework is necessary. In manufacturing and reworking process produces carbon emissions which are harmful for the earth. To determine the optimal quantity for a single product manufactured in a single-stage manufacturing system that yields partially defective products that are reworked in the same cycle, is determined in real-life situations, where the inventory characteristics and objectives are not exact. Such a type of uncertainty may be characterized by fuzzy numbers. A pentagonal fuzzy number has been used to define the cost parameters. Due to fuzzy parameters, the model becomes a fuzzy quantity, and it is defuzzied by the sign distance method. this article formulates a model of manufacturing inventories with planned backorders. Furthermore, a closed form for the inventory’s system total cost is determined, and a range of actual values for defective products for which an appropriate method exists is also provided. A proper mathematical model is created to accomplish the goal, and the manufacturing lot size that reduces the overall cost is determined. The ideal amount of a production batch to reduce total cost is established in to attain this goal using an appropriate mathematical model. While formulating and solving the relevant model, the necessary and sufficient conditions for a single globally optimal solution have been determined. Examples used as visualizations are given and confirmed by data.

The Web of Things (IoT) has numerous openings to unravel issues in standard of living. accidents are demonstrating lethal and will proceed to extend. The security of prepare travelers is considered the foremost vital thing. Programmed crisis discovery has different sensors that actuate the microcontroller when an crisis is identified. When an crisis is identified, the framework employments the Bluetooth module to send an crisis notice to the prepare driver's versatile phone and naturally sends the Worldwide Situating Framework (GPS) area of the driver's mobile phone by means of Brief Message Benefit (SMS). crisis contacts. The created alarms are too overhauled within the framework recipient module utilizing the real-time cloud-based database and open alarms. The crisis direct on the passenger's crisis side is utilized to report crises to crisis responders, which may incorporate police, fire and therapeutic work force. The thought is to utilize IoT to decrease the reaction time of crisis groups.

Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.

This paper uses a thorough case study experiment to examine the real-world applications of IoT-driven innovations within the context of Industry 5.0. The factory floor has a temperature of 32.5°C, a warehouse humidity of 58%, and a safe pressure level of 102.3 kPa on the manufacturing line, according to an analysis of IoT sensor data. A 5.7% decrease in energy use was made possible by the data-driven strategy, as shown by the office's CO2 levels falling to 450 parts per million. The case study participants, who had a varied range of skills, were instrumental in the implementation of IoT, and the well-organized schedule guaranteed a smooth deployment. Key Industry 5.0 indicators, such as +2% in production efficiency, -5.7% in energy usage, -29% in quality control flaws, and +33.3% in inventory turnover, show significant gains. Key metrics evaluation, data-driven methodology, case study, Industry 5.0, IoT-driven innovations, and revolutionary potential are highlighted by these results.

The purpose of this research is to examine how data-driven dynamic urban communication affects the effectiveness and optimization of vital municipal services in a variety of contexts. Interestingly, waste management IoT sensors have an efficiency score of 9, which is remarkable and indicates the promise of data-driven approaches in this industry. An impressive 4.3 user satisfaction rating highlights how well these technologies are received. Additionally, data-driven communication techniques provide affordable options, as seen by their $2.5 service request cost, which highlights the possibility of more efficient resource allocation. This study offers strong proof that data-driven communication benefits both municipal service providers and people by increasing service consumption and lowering response times to around 5.2 minutes.