Volume 13 | Issue 4 |

 Abstract

The textile industry is experiencing a significant shift in operations with the integration of Artificial Intelligence (AI) in production, quality and supply chain processes. AI-driven automation improves operation efficiency, decreases errors, and helps organizations to become more efficient, but it also can impact employee performance, job roles, and skill. This research studies the impact of AI on employees in the textile industry in relation to changes in productivity, job satisfaction, and workforce adjustment. An exhaustive literature review was carried out to assess previous research on AI in textiles. This research identifies primary challenges such as job loss, skill misalignment, and employee apprehension to AI integration, but has also found AI changes to improve productivity, work-life balance, and collaboration. The findings of this research underscore organizational needs for targeted reskilling initiatives, employee engagement programs, and human-AI integration models to maintain a sustainable workforce in textiles. This research delivers valuable contribution to textile industry employers, human resource professionals, and public policy developers to help develop AI integration models which balance operational efficiency and employee satisfaction. The research concludes that AI integration in textiles can be optimized by putting in place proactive workforce planning initiatives and employee training, alongside developing a positive approach to technological adoption in the workplace.

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 Keywords

Artificial Intelligence, Employee Performance, Textile Industry, Automation, Job Roles, Workforce Adaptation, Skill Development, AI Integration.

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 Abstract

Statement of Problem: - A descriptive study to assess the knowledge regarding anthropometric measurement of 0-6 years of age among Nursing Students of Desh Bhagat University School of Nursing Mandi Gobindgarh District Fatehgarh Sahib Punjab. Material and Method: - Quantitative research approach and non-experimental descriptive research design was used to accomplish the stated objectives data was collected with the help of self-structured knowledge questionnaire. Pilot study was done on [1/10 th sample] which constituted of students of Desh Bhagat University School of Nursing to check reliability and feasibility of the study. Subjects were chosen by the purposive sampling technique and sample size was 60. The data collected was analysed by using descriptive and inferential statistics. RESULTS The percentage distribution of Nursing students as per their knowledge regarding anthropometric measurement of 0-6 years of age. 0.0% students have Adequate knowledge,18.3% students have Moderate knowledge and a considerable portion of Nursing Students i.e 81.7% have Inadequate knowledge. The Chi-square value shows that there is significance association between the score level and demographic variables (Types of Houses). The calculated chi-square values were more than the table value at the 0.05 level of significance. There is no significance association between the level of scores and other demographic variables (Age, Gender, Religion, Education of Student, Type of Family, Total Family Income, Occupation of Father, Occupation of Mother) The calculated chi-square values were less than the table value at the 0.05 level of significance Conclusion: - The data collected was analysed by using descriptive and inferential statistics. The pie and bar diagrams were used to depict the findings. The maximum students [81.7%] were having inadequate knowledge regarding anthropometric measurement of 0-6 years of age among Nursing students of Desh Bhagat University School of Nursing, followed by [18.3%] moderate and [0.0%] were having adequate knowledge regarding anthropometric measurement of 0-6 years of age among Nursing students of Desh Bhagat University School of Nursing respectively. Pamphlets were prepared and distributed to the students to bring more knowledge on anthropometric measurements.

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anthropometric measurement, students and school.

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Pulmonary tuberculosis is a bacterial infection caused by Mycobacterium tuberculosis bacteria (MTB). It primarily affects the lungs but also affect other parts of the body. The disease spreads from one person to another person through the air. In some areas where there aren't many specialized doctors and medical resources, finding pulmonary TB in its early stages is crucial. The idea is to teach the computer to recognize TB by using various multimodal deep learning techniques like VGG16, VGG19, and Xception to help quickly and accurately identify TB in places with limited medical resources. This paper how artificial intelligence and chest X-ray images can work together to identify whether a person has TB or not.

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X-ray image, Deep learning, VGG16, VGG19, Xecption, Healthcare Accessibility, Medical Images.

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Industrial design has a critical role in developing technology by combining beauty with functionality and optimizing user experience. The following paper investigates its influence through legal, economic, and strategic aspects. Industrially, protection for industrial design stimulates creativity by establishing the right of creators and facilitating competitive positioning on the basis of intellectual property legislation. Prominent cases, including Apple vs. Samsung, demonstrate how design rights have an impact on market results. Economically, design leads to product differentiation, consumer preference, and value creation, particularly in design-intensive sectors such as electronics, automotive, and fashion. It is an important contributor to brand identity, customer loyalty, and business success. Strategically, firms that incorporate design thinking in their innovation processes tend to gain a competitive advantage. Industrial design is a key driver of R&D, improves usability, and reinforces branding, allowing firms to effectively meet market demands. In conclusion, the article brings to the forefront industrial design as the major driver of technological advancement and an important aspect in defining a company's long-term success and competitive edge in a fast-changing global market.

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Keywords: Industrial design, technological advancement, intellectual property, design protection, Apple vs. Samsung, product differentiation, user experience, brand identity, competitive advantage, design thinking, legal aspects, economic impact, strategic value, R&D, innovation.

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Premenstrual Syndrome (PMS) is a cyclic disorder characterized by a range of emotional, behavioral, and physical symptoms that occur during the luteal phase of the menstrual cycle. It significantly affects the daily functioning and quality of life of many women. While conventional treatments focus primarily on symptom suppression through hormonal therapy, analgesics, or antidepressants, these methods may come with side effects and do not always offer long-term relief. This study aimed to evaluate the effectiveness of individualized homeopathic treatment in managing PMS. Method: 30 cases diagnosed with PMS were selected based on specific inclusion criteria. All participants were treated exclusively with individualized homeopathic remedies tailored to their unique symptom profiles and overall constitution. The study was conducted over a defined treatment period, with regular follow-ups to assess changes in the intensity and frequency of PMS symptoms. Result: Out of the 30 cases, 27 showed marked improvement in emotional and physical symptoms, while 3 cases remained unimproved. The results demonstrated a high success rate, indicating that homeopathy may offer a safe, effective, and holistic approach to managing PMS. The individualized nature of the treatment appeared to play a key role in addressing the root causes of the condition rather than merely suppressing symptoms. These findings support the potential of homeopathy as a reliable alternative in the treatment of PMS. Conclusion: Further research with larger sample sizes and more extended follow-up periods is recommended to validate these results and explore the scope of homeopathy in women's health care.

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Premenstrual Syndrome (PMS), Homeopathy, Individualized Treatment, Case Series, Holistic Medicine.

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The classification of satellite images is vital in remote sensing for tasks such as mapping land cover, monitoring disasters, and conducting environmental assessments. Nonetheless, deep learning models frequently encounter substantial computational requirements, which hinder their use in real-time settings. To mitigate this issue, we introduce an Embedded Teacher-Student CNN framework that utilizes knowledge distillation for more efficient classification of satellite images. ResNet50 and VGG16 act as teacher models, capturing spatial features and producing soft labels to train a lightweight EfficientNet-B0 student model. Experimental results demonstrate high classification accuracy with reduced inference time, making the model suitable for real-time and resource-constrained applications.

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Satellite Image Classification, Knowledge Distillation, Teacher-Student Learning, Convolutional Neural Networks (CNN), ResNet50, VGG16, EfficientNet-B0, Hybrid Loss Function, Remote Sensing, Computational Efficiency

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1. Study Design A three armed clinical trial is a clinical research experiment in which a new proposed treatment is compared against existing standard form of treatment. It consists of 'Control group' which continues with the homoeopathy treatment. 'Experimental group' which receives allopathic treatment with homeopathic medicines. An 'Observational group' which is given the allopathic treatment. In this study we shall be comparing the effects of homoeopathic medicine. Eupatorium perfoliatum in dengue fever of adult population with a Experimental group of individual taking antiviral medications and other allopathic drugs according to symptomatology -TAMIFLU, AZTREONAM AND CEFACTIVE for viral fever 2.Operational Definitions: Dengue fever is mosquito borne infections, transmitted by Aedes aegypti,clinically manifested as High/low grade fever( pattern classically biphasic or saddleback & breaking), Rash( maculopapular) ,Thromboctopaenia, Myalgia , Arthralgia and Gastrointestinal symptoms like nausea & vomiting and lasting for 3-14 days( Incubation Period). The study will be carried out on adults suffering from Dengue fever based on Dengue Severity Scale. Out of 30 cases, 10 cases are under allopathic treatment and 10 cases will be given homoeopathic medicine with allopathic medicine to assess its effects and remaining 10 cases will receive pure homeopathic medicine Eupatorium perfoliatum. Result: Out of 30 cases, 10 are homoeopathic cases, in which 8 cases got improved and 2 cases did not improve. Next 10 cases are of allopathic, in which 6 cases get improved with allopathic treatment and 4 cases did not improve. The last 10 cases are allopathic cases intervention with homoeopathic cases, in which 5 cases get improved with both Pathy treatment and 5 cases did not get improved. Maximum patients belonged to the age group 30 -80 years of age; 7 patients belonged to the age group of 15 -30 years of age, 23 patients belonged to 30 - 80 years of age. Around 60 percent of patients were male and 40 percents were female. Conclusion: Cases in which homeopathic medicine is prescribed, the result are quick and faster in relief of complaints.

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Dengue Fever, Adult Population, Eupatorium Perfoliatum, Clinical Trail, Etc.

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The study examines the impact of induction programs in shaping employees' experiences as they first enter the private sector in the banking environment, as viewed through an Organizational Behavior (OB) lens. A key study was on the ability of a structured induction process to develop new employees' levels of integration, engagement, and ultimately retention. Taking into account various qualitative and quantitative data from fiction and non-fiction research and its accompanying interviews, this research presented how organization culture - exemplified by socialization and other tactics - and organization leadership can, or were, able to create new hires who had or turned into "organization insiders" or members of the organization who were committed to its objectives. The research presented opportunities for organizations to improve their employee induction programs based on OB principles to better facilitate, and transitions from outsiders to insiders.

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Induction Programs, Organizational Behavior, Organizational Culture, New Joiners, Employee Engagement

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The Traffic signs are important in communicating information to drivers. Thus, comprehension of traffic signs is essential for road safety and ignorance may result in road accidents. Traffic sign detection has been a research spotlight over the past few decades. Real- time and accurate detections are the preliminaries of robust traffic sign detection system which is yet to be achieved. This study presents a voice-assisted real-time traffic sign recognition system which can assist drivers. This system functions under two subsystems. Initially, the detection and recognition of the traffic signs are carried out using a trained Convolutional Neural Network (CNN). After recognizing the specific traffic sign, it is narrated to the driver as a voice message using a text-to-speech engine. An efficient CNN model for a benchmark dataset is developed for real-time detection and recognition using Deep Learning techniques. The advantage of this system is that even if the driver misses a traffic sign, or does not look at the traffic sign, or is unable to comprehend the sign, the system detects it and narrates it to the driver. A system of this type is also important in the development of autonomous vehicles. Initially, the detection and recognition of the traffic signs are carried out using a trained Convolutional Neural Network (CNN). After recognizing the specific traffic sign, it is narrated to the driver as a voice message using a text-to-speech engine. An efficient CNN model for a benchmark dataset is developed for real-time detection and recognition using Deep Learning techniques. The advantage of this system is that even if the driver misses a traffic sign, or does not look at the traffic sign, or is unable to comprehend the sign, the system detects it and narrates it to the driver. A system of this type is also important in the development of autonomous vehicles.

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Convolutional Neural Network (CNN), Dataset, Image Processing

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Along with the rise in technology, there is an increase in attacks involving digital platforms. It is very important to stay secure in this digital world. To respond to these attacks, we propose a method that detects phishing websites by categorizing the Internet URL and domain names of websites with the Random Forest classifier algorithm according to seventeen predetermined features. To illustrate the highest accuracy rate, the use of Random Forest algorithm is preferred. In this method, a dataset with 10,000 URLs in which 5000 URLs are non-phishing websites and 5000 URLs are phishing websites to be labelled according to seventeen predetermined features.

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Cybersecurity, Phishing detection, Phishing domains, Legitimate domains, Random Forest.

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Creative Commons Attribution 4.0 and The Open Definition