Manual Labor Research Articles

New-Onset Dupuytren After Surgical Treatment of Carpal Tunnel and Trigger Finger.

Dupuytren disease is a progressive fibrotic condition of the hand that often leads to contractures. Although its etiology remains multifactorial, recent studies suggest potential associations with surgical interventions for carpal tunnel syndrome (CTS) and trigger finger (TF). Understanding the incidence and risk factors for Dupuytren disease development following these procedures may improve postoperative management and early detection. A retrospective study was conducted on 426 patients who underwent surgical treatment for CTS or TF. The incidence of Dupuytren disease development postsurgery was assessed, and data on demographics, comorbidities, occupational factors, and type of surgery were collected. Statistical analysis, including odds ratio (OR) calculations, was used to identify risk factors associated with Dupuytren disease onset. Seven percent of the study population developed new-onset Dupuytren disease within an average of 15.2 weeks postsurgery, with most cases presenting as early-stage nodule formation. The fourth digit was most commonly affected (73.3%). Significant associations were observed between Dupuytren disease onset and comorbidities, such as rheumatoid arthritis (OR = 3.24) and shoulder capsulitis (OR = 9.7), as well as occupational factors like manual labor and vibration exposure (OR = 2.45). Patients treated for TF had a 2.3-fold higher risk of developing Dupuytren disease compared with those treated for CTS. The findings highlight the potential for Dupuytren disease development following CTS and TF surgeries, emphasizing the need for proactive monitoring of at-risk patients. Further research is warranted to explore underlying mechanisms and optimize preventive and management strategies for this patient population.

Evolution of Automated Testing Methods Using Machine Learning

program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses. By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques. Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage. The paper offers a thorough synthesis of current developments, contrasting ML-based testing with conventional methods in a number of areas, including efficacy and efficiency in defect identification. It also highlights important research gaps, talks about real-world implementation issues, and looks at multidisciplinary uses of machine learning technologies, such as deep learning and reinforcement learning. The paper concludes by highlighting machine learning's revolutionary influence on software testing procedures and projecting a time when testing will become more independent, flexible, and incorporated into ongoing software development processes.

Adaptive AGV Navigation Using Edge-Driven in Small-Scale Industries

ABSTRACT The Adaptive AGV Navigation System is a smart material handling solution designed to automate and optimize operations in small-scale industries. It employs a PIC16F877A microcontroller to control and coordinate various components of the Automated Guided Vehicle (AGV), including DC motors, IR sensors, and RFID-based modular instruction cards. These instruction cards, read by an EM-18 RFID reader, provide real-time task and navigation commands, allowing the AGV to adapt its path dynamically based on operational needs.To ensure accurate control and responsiveness, the system integrates edge computing modules, which locally process sensor data and navigation instructions, significantly reducing latency. The AGV structure is supported by a durable robot chassis, powered by a 5V battery supply, The hardware configuration, supported by modular and reprogrammable components like the motor driver module, allows easy system expansion and reconfiguration without extensive rewiring or infrastructure changes. This makes the solution ideal for flexible production lines, warehouse logistics, and assemblyautomation in small to medium-sized enterprises. By combining intelligent navigation, modular design, and real-time edge processing, this system offers a cost-effective and scalable AGV platform that enhances productivity, reduces manual labor, and supports the transition toward smart industrial automation.

Adaptations for Urogynecologic Surgery in Limited-Resource Settings: How to Do More with Less.

Pelvic floor disorders affect up to 50% of women in limited-resource settings (LRS) but are severely under-treated. Historically, attention has focused on urogenital fistulae, but pelvic organ prolapse (POP) is emerging as a growing issue, especially for women engaged in manual labor. Women in these regions often endure their conditions in silence owing to social stigma and mental health impacts, compounded by health care access barriers. Delivery of urogynecological services in LRS requires adaptable surgical models and skills because of limited tools and equipment. Diagnostic treatment approaches must be tailored to the unique challenges of these settings. This article presents a practical guide to managing vesicovaginal fistulae, chronic fourth-degree tears, and POP based on limited evidence and expert experience in LRS. Key diagnostic tools, surgical techniques, and case management strategies are outlined, addressing challenges such as resource scarcity and patient follow-up in LRS. The article emphasizes the importance of precise diagnosis with limited access to diagnostic testing, adaptable surgical interventions, and postoperative care, offering sustainable solutions that maximize patient outcomes despite restrictions in equipment availability. Cases are presented to illustrate practical diagnostic and surgical approaches to urinary leakage, fecal incontinence, and POP. The article underscores the need for an adaptable care model that prioritizes cost-effective, reproducible methods while considering patients' long-term health and social well-being.

Skeletal growth and development dictate the processes of vertebral fracture in the pediatric spine; a review emphasizing fracture biomechanics of the vertebral body during the period of skeletal immaturity

Infancy, childhood, and adolescence involve changing body proportions, muscular strength, and the complex processes of skeletal growth, contributing to a unique subset of biomechanical considerations when vertebral fractures result from falls from height, motor vehicle accidents, nonaccidental injuries, and sport and manual labour. In this review, the biomechanics of compression fractures, burst fractures, seatbelt syndrome, nonaccidental trauma, defects of the vertebral endplate, and ring apophysis fractures are all detailed regarding their manifestation in the pediatric spine. Interactions between pediatric diseases, the intervertebral disc, and the spine's facet joints are also briefly discussed, lending additional context toward the unique etiologies of pediatric vertebral fracture. The present narrative review seeks to provide a detailed overview of the key relationships responsible for the unique biomechanical considerations governing vertebral and endplate fracture, in the pediatric population.

The Influence of Artificial Intelligence on Job Searching and Professional Growth

Abstract — Artificial Intelligence (AI) is radically reshaping the workplace by reimagining the manner in which recruiting is carried out and how careers take shape. Even as some voices sound a caution that AI may overwhelm human staff and take away traditional job categories, it also brings forth a tide of stimulating new professional options in addition to optimizing the hiring process. Companies are more and more resorting to advanced AI-driven technologies to quickly screen through piles of resumes, connect candidates with matching job openings, and even conduct initial candidate screenings via conversational chatbots, minimizing manual labor and time. For those seeking employment in the job market, AI-driven platforms offer highly tailored guidance, providing personalized career advice that matches their individual strengths and goals. The tools also integrate into resume polishing to make them shine in competitive applicant pools and so identify skill-development opportunities that improve employability, making the usually intimidating process of obtaining employment more accessible. Outside of job hunting, AI-aided learning platforms play a key role in remaining competitive as professionals by offering recommended training programs and courses of study based on current industry needs and future perspectives. Instead of viewing AI as a threat to employment hanging over them, job seekers and experienced workers alike can see AI as a great ally, using its abilities to drive professional development, open up long-term success, and adjust to a constantly changing career environment.

Advancements and Applications of Robotics Technology in Modern Manufacturing and Inspection Systems

Modern robotics technology has become an indispensable part of modern manufacturing, widely applied in industries such as manufacturing and services. This article discusses the current development and research status of robotics across various fields. It concludes that in the industrial sector, welding robots enhance welding precision and quality; in logistics, industrial robots improve transportation efficiency; and in the electrical sector, inspection robots replace manual labour, reducing workload and enhancing safety. Additionally, in substations, inspection robots have replaced manual inspections, achieving cost reduction and efficiency improvement. However, challenges such as cost, precision, and safety still exist in the process of future technological iterations. Finally, this article highlights the potential of integrating artificial intelligence and 5G technology with robotics to drive future advancements, making robots more intelligent and autonomous. This integration aims to provide a comprehensive understanding of the applications of robotics in modern manufacturing and inspection systems.

Machine Vision for Recognizing Eco-Friendly and Chemical Ink Tags on Garment Labels for Recycling

With the increasing demand for sustainable practices in the fashion industry, efficient garment recycling solutions have become essential. One significant challenge in this process is accurately distinguishing garments printed with eco-friendly inks from those printed with chemical-based inks. This study proposes a novel machine vision method for the automatic identification and classification of garments, based on the printing techniques used, as indicated on the garment labels. By embedding a unique ecological ink identifier in the garment's label, this approach leverages advanced image processing techniques to precisely detect and classify the ink type used in the garment's print. This method simplifies the recycling workflow by ensuring accurate classification, reducing manual labour, and improving the overall efficiency of the recycling process. The results demonstrate the feasibility of applying machine vision for garment recycling, the effectiveness of this method in accurately detecting and classifying the ink type used in garment prints, with a high level of precision. The proposed solution proves to be scalable, offering a practical way to enhance the efficiency of garment recycling systems. The integration of machine vision for ink classification in garment recycling holds significant potential for promoting eco-friendly practices in the fashion industry. This automated approach not only simplifies the recycling workflow but also contributes to the broader goal of sustainability by facilitating the proper sorting of garments based on their ink type.

Electrostatic separator of cannabis trichomes: an innovative approach to extraction.

Efficient separation of trichomes from plant material is critical for producing high-quality cannabis extracts. Traditional methods of separation, such as wet and dry fractionation, use the difference in mechanical properties (size, density, specific gravity) between cannabis trichomes and plant biomass. However, these methods were developed to process small quantities of raw material and are very much labor-intensive. On the other hand, the quickly growing cannabis industry requires fully automated, scalable technology for the efficient extraction of valuable trichomes from the large volume of plant biomass. Therefore, our research aimed to develop a scalable method and equipment for trichome separation. We have measured electrical properties of trichomes and plant biomass, namely electrical conductivity, dielectric permeability and particle charge, using a Keithley 6517B electrometer in a Faraday cage in controlled conditions. The fundamental forces acting on the charged particle in a strong electric field were analyzed using the theory of electrostatics. It was found that plant biomass had a positive electric charge, while trichomes had a negative electric charge. A difference in electric charge between trichomes and plant biomass suggested an electrostatic method of separation. This paper explores the application of electrostatic separation as a novel, sustainable, and efficient method for isolating cannabis trichomes. A new concept for a free-fall electrostatic separator for cannabis trichomes is proposed, and the prototype of the electrostatic separator is described. This method minimizes the need for manual labor, allowing the separation of cannabis trichomes to a desirable purity. The separator is scalable from 1 to 100kg/hour and can be fully automated.

Developing hybrid metaheuristics for the three-dimensional pallet loading considering multiple-size pallet and heterogeneous box

Intelligent operations in logistics warehousing play a crucial role in optimising efficiency and enhancing productivity. As logistics operations become increasingly complex and scaled, automation has been widely adopted in streamlined processes such as automatic picking, resulting in improved efficiency and order accuracy. However, the stacking operation in warehousing still relies heavily on manual labour and experience in loading boxes onto the pallet. By integrating metaheuristic algorithms and automated equipment such as robotic arms, the intelligence of pallet loading can be realised. This study focuses on the three-dimensional distributor pallet loading problem (3D-DPLP) with multiple-sized pallets and practical constraints. A two-phase approach is proposed to solve the problem while minimising the number of pallets required. Firstly, Fast Loading Heuristic is developed to generate an initial solution. Subsequently, a simulated annealing-based algorithm is used to refine the solution by searching for loading patterns. Furthermore, we propose improved and hybrid metaheuristic algorithms that integrate simulated annealing and differential evaluation to accelerate convergence and enhance exploration capability.

Sohn-Rethel in Naples. On plebeian creativity

This article explores German philosopher Alfred Sohn-Rethel’s little-known reflections on Naples, to begin developing the notion of a plebeian creativity. Unlike capitalist notions of innovation that reinforce the division between intellectual and manual labour, Sohn-Rethel suggested that the Neapolitan approach to technology consisted in reclaiming mostly broken technological artefacts within the context of an lived economy of use values. We suggest that the particular historical structure of Naples makes the city a privileged observatory of a similar baroque approach to technology and innovation as bazaar economies are expanding across the world. We propose that this takes part in an expanding neo-plebeian provisioning system: catering to the needs and desires of those who have been sucked in by industrial modernity to, subsequently, find themselves spat out into precarity, and in so doing ignoring the normative framework and sumptuary laws of mainstream consumer capitalism

Enhancing Motorcycle Safety and Security Using Biometric Verification and Helmet Detection Through AI and ML Integration

The automotive industry, like many others, has had to adapt to newer technologies, including biometrics, AI, and Machine Learning (ML) over the years. In this chapter, a sophisticated technique directed on improving vehicle security through biometric verification and helmet detection is described using TensorFlow and a custom-built Arduino-based system. A notable factor contributing to fatalities in these motorcycle accidents is lack of helmet use among riders. Ensuring that riders put on helmets before they get on their motorcycles is done through monitoring with CCTV cameras or by manually supervising traffic intersections by police. These methods however entail high levels of manual labor. Through these approaches, some motorcycle riders without helmets have been tracked, and their license plates captured via CCTV cameras. In this methodology, the algorithm first identifies moving objects as motorcycles or non-motorcycles. Moreover, the system assesses whether classed helmeted riders are wearing helmets. If not, the device applies an OCR technique to capture the license plate

Enhanced vegetation encroachment detection along power transmission corridors using random forest algorithm

Vegetation encroachment along power transmission corridors poses significant risks to infrastructure safety and reliability, necessitating effective monitoring and management strategies. This study introduces an innovative methodology for detecting vegetation encroachment using a combination of manual and automatic processes integrated with the random forest algorithm. The issue of vegetation encroachment is critical as it can lead to power interruptions and safety hazards if not addressed promptly. The objective of this research is to develop a scalable and cost-effective solution for vegetation management in power infrastructure maintenance. The methodology involves manual patch extraction and labeling to ensure the accuracy of the training dataset, combined with automatic feature extraction techniques to capture relevant information from satellite imagery. Leveraging the random forest algorithm, the model constructs an ensemble of decision trees based on the extracted features, achieving robust classification accuracy. Findings from this study demonstrate that the proposed approach enables consistent and timely identification of vegetation encroachment in new satellite imagery. Stored model parameters facilitate efficient testing, enhancing the system's ability to provide proactive interventions. This scalable solution significantly reduces reliance on manual labor and offers a cost-effective method for continuous monitoring, ultimately contributing to the resilience and safety of power transmission infrastructure.

Attention U-Net-based semantic segmentation for welding line detection

In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. Traditional manual visual inspection is a time-consuming and costly endeavor. If the opportunity arises, replacing manual visual inspection with AI could lead to significant efficiency gains. However, simply judging the correctness or incorrectness of a process is often insufficient; quantitative attributes must also be associated with visual inspection. This paper proposes a solution for replacing manual visual inspection with AI specifically for welded joints. The aim is not only to detect the presence of weld joints but also to assess their geometric dimensions. Leveraging a proposed Attention U-Net architecture in combination with rule-based metrics, the proposed method offers a novel solution for identifying welding lines in images. By integrating semantic segmentation techniques, the method effectively distinguishes weld joint elements, while rule-based metrics facilitate the identification of critical cases requiring human intervention. Experimental results demonstrate the method’s capability to automate a significant portion of inspection tasks, thereby reducing the reliance on manual labor and enhancing overall process efficiency and reliability.

An Integration of Sensor-Driven Automation in Water Surface Cleaning Robots for Environmental Remediation

The water cleaning robot is an innovative device designed to clean the surface of lakes, rivers, ponds, and oceans by collecting floating debris, plastics, and pollutants. It operates autonomously or semi-autonomously, using sensors to detect and gather waste into a built-in storage container. Powered by eco-friendly energy sources like solar panels, the robot minimizes environmental impact while reducing manual labour and minimizing human exposure to harmful substances. Its deployment not only improves water quality and protects aquatic life but also raises awareness about environmental conservation. These robots can be deployed in both urban and remote areas, making them a practical solution for large-scale water management and pollution control.

OPTIMIZACIÓN DIMENSIONAL DEL BRAZO DE ROBOT AGRÍCOLA 7-DOF

Agricultural automation has emerged as a potential solution to meet the growing need in the quickly changing farming business. For example, the tasks in palm oil plantations require precision, efficiency, and adaptability to navigate the complex environment. Robotics solve these challenges by enhancing farming and executing tasks such as cutting fresh fruit bunches that surpass manual labor. By examining the optimal dimensions synthesis for the robotic arm and integrating the Denavit-Hartenberg (DH) parameters for kinematic modeling, this study aims to enhance the degree of freedom, enabling precise and flexible movements, which is crucial for navigating around palm oil trees. This research evaluates two optimization algorithms, artificial bee colony (ABC) and particle swarm optimization (PSO), specifically tailored for robotic arms in the agricultural sector and intended to improve performance. Kinematic modeling simulations are conducted using MATLAB software. This research emphasizes optimization methods to ensure the accurate and efficient execution of tasks. The results indicate that the PSO algorithm outperforms the ABC algorithm in terms of error minimization. Specifically, the mean square error for PSO is 5.0433 x 10-6, compared to 9.3904 x 10-6 for ABC. These results demonstrate that the PSO algorithm provides more accurate and efficient task execution for the robotic arm in agricultural applications. Key Words: Optimization; Topological; Dimensional; Agricultural; Forward Kinematics; Inverse Kinematics

AI-Based River Cleaning Robot for Plastic Waste: Design, Development, and Implementation

Plastic waste contamination of rivers and other water bodies has become a pressing global environmental challenge. Conventional waste management techniques, which often rely on manual labour, are inefficient and struggle to handle the vast scale of pollution. This study introduces an AI-powered river-cleaning robot designed to autonomously identify, collect, and manage floating plastic waste to address this issue. The system enhances detection and waste retrieval efficiency by incorporating Artificial Intelligence (AI), the Internet of Things (IoT), and computer vision. The robot utilizes advanced technologies such as Convolutional Neural Networks (CNN) and the YOLO (You Only Look Once) algorithm for real-time waste identification, while a conveyor belt mechanism facilitates efficient collection. Additionally, IoT-enabled monitoring ensures real-time data transmission and performance tracking. The robot uses a dual-energy system, combining a rechargeable battery with solar power to enhance sustainability. This innovative approach aims to minimize reliance on manual labour, improve the effectiveness of waste removal, and promote environmentally responsible waste management. Experimental evaluations confirm the system’s high accuracy in detecting and collecting plastic debris, demonstrating its potential as a scalable and efficient solution for river pollution control

Revolutionizing Indian Agriculture through Innovations in Mechanization and Digitalization

As the global population continues to grow and the demand for food rises, the agricultural sector faces the challenge of increasing productivity while minimizing resource consumption. In India, a country with a significant agrarian economy, the need to enhance farming practices is of paramount importance. The innovative technologies such as farm mechanization, drones, and robotics has potential to revolutionize farming practices in India. Like other economic sectors, agriculture is increasingly affected by the digital revolution. The use of advanced technologies results in precise matching of agricultural inputs with needs and thus significantly increases profitability. By using technology as a sustainable and scalable resource, agriculture of the country can be taken to new heights, keeping farm to fork in our future. In July, 2021, Honourable Prime Minister addressed chiefs of top 100 technological institutes emphasized that inventions and innovations in agriculture are very important. Agriculture was listed among key sectors like Defence, Education, Health, climate change and cyber security. He also urged scientists to provide solutions to various issues in agriculture through modern biotechnology, artificial intelligence, block chain technology and drone technology to counter issues like hunger, poverty and malnutrition. Future agriculture will be led by knowledge, technology innovation and skill. In fact, mechanization and automation in agriculture has been one of the top 20 invention of 20thcentury in the world. Traditional farming practices often rely on manual labor and outdated methods, leading to inefficiencies, resource wastage, and yield variability. The integration of advanced technologies like mechanization, drones, and robotics holds the promise of addressing these challenges and propelling Indian agriculture towards greater productivity and sustainability.

Behind the produce curtain: exploring occupational health of Connecticut migrant farmworkers in a warming climate

Abstract Climate change is increasing global temperatures and heat exposure, especially for outdoor manual laborers like farmworkers in the United States. Farmworkers have many occupational health concerns, but heat is an understudied issue among this population, particularly in northeastern states like Connecticut. This qualitative study had three aims: 1) to explore the health impacts of heat exposure among Connecticut migrant farmworkers; 2) to understand heat and health awareness among their healthcare providers; and 3) to understand barriers to improving migrant farmworker conditions and protections against heat. We conducted focus groups with migrant farmworkers (N=29) and individual in-depth interviews (N=10) with their healthcare providers, as well as workplace safety officers, in Connecticut. Thematic analysis was conducted using NVivo software, yielding the following core themes: adverse health outcomes, work environment, barriers to improving work conditions, barriers to receiving healthcare, and solutions and interventions. The adverse health outcomes theme included both heat and non-heat related illnesses. The work environment theme revealed varying treatment of farmworkers by employers, including a wide range of policies on breaks from work, and that personal protective equipment was provided through the federally funded Connecticut River Valley Farmworkers’ Health Program. Barriers to improving work conditions included lack of enforcement by outside agencies, exploitative practices by growers, lack of knowledge of the minimal existing work protections, and the fear to exercise them. Barriers to receiving healthcare included reluctance to take time off, inability to pay, and overwhelmed federally qualified health centers. Finally, suggested solutions and interventions consisted of farmworker education on heat-related illnesses, expansion of healthcare access, and proactive protective measures. Farmworkers represent a socially vulnerable population, and future work should further consider their environmental exposures, beyond heat, particularly in geographic regions where their occupational health has not been well-studied.

Impact of Agricultural Engineering and Technology on India’s Growth Story

Agricultural engineering and technology have significantly contributed to India’s growth story by boosting crop yields, improving food security, increasing farmer income, promoting resource efficiency, and enabling economic development, particularly through the Green Revolution, by introducing advanced irrigation systems, high-yielding crop varieties, and mechanization, which reduced manual labor and increased productivity across the agricultural sector; this has been crucial in transforming India from a food-deficient to a food-surplus nation.