Manual Tasks Research Articles

Comparison of Balance during Dual-Task in-between Cognitively Impaired and Nonimpaired Individuals with Parkinson’s Disease

Introduction: Parkinson’s disease manifests as bradykinesia, stiffness, tremors, and abnormalities in gait and balance. When performing dual activities, people with cognitive impairments exhibit noticeable alterations in mobility. The purpose of this study was to determine whether balance during dual tasking is related to cognitive deterioration. The aim was to compare balance during dual-task in-between cognitively impaired (CI) and nonimpaired individuals with Parkinson’s disease. The objective was to evaluate balance using timed up and go test (TUG), TUG-manual (TUG-m), and TUG-cognitive (TUG-c) and to compare its scores in both the groups. Materials and Methods: It was a cross-sectional observational study carried out at outpatient department and Parkinson’s societies. The sampling technique was purposive sampling, and the sample size was 22. Subjects were divided into two groups (by stratification method) according to Montreal Cognitive Assessment (MOCA) scores as CI and nonimpaired group. Both groups performed TUG with manual task and cognitive task. The time taken to complete all TUG tests was measured. Results: Comparison of TUG between the groups showed a highly significant difference in TUG and TUG-m tests (P < 0.001) and a significant difference in TUG-c (P = 0.028). Conclusion: The study found a significant difference in balance scores, assessed by the TUG test during dual-task conditions, between CI and nonimpaired individuals with Parkinson’s disease. This highlights the important role cognition plays in balance regulation in Parkinson’s disease.

Prevalence of Accidents in Smaller Amish Settlements: 2015–2022

Many Amish people engage in manual labor tasks at work, on the farm, and at home. As such, the daily risk for injury is high. The primary mode of transportation, horses and buggies, also creates opportunities for accidents and injuries. Much of the research on injuries among the Amish focuses on hospital reports, capturing only the most severe injuries. Research is also limited by a focus on injuries to children. Many safety programs are tailored toward people living in large Amish settlements, as more people can be reached. We use accident reports submitted to The Diary, a monthly newspaper, to determine the type and prevalence of accidents in small Amish communities. Accidents related to falls, manual labor, and buggy and/or vehicle crashes are the most prevalent forms of injury across age groups. Most accidents do not involve serious injuries and are likely to escape the purview of mainstream medicine. The prevalence of accidents at the house or on the property involving young children suggests that culturally appropriate prevention measures tailored to daily activities would be helpful to lower the incidence of injury.

Productivity improvement of control cable manufacturing machine for the automotive industry

Due to the high competitiveness that characterizes the automotive industry (AI), there is an intensive search for improvements that translates into significant impacts on the productivity, still guaranteeing the levels of quality required by the client. The requirement for flexibility is leading to a paradigm shift that arises from a constant change in the needs of consumers, who demand an ever-higher level of customization in the products. In recent years, the increasing use of automation in industry resulted in significantly improved product quality, better working environment, higher cost efficiency, and major improvement in process flexibility. The present work addresses the productivity improvement of a control cable manufacturing machine for the AI. The main objective is related to the development of a new stripping machine concept that allows the simultaneous coating removal from two cables, thus eliminating an existing bottleneck in the injection process of the cables’ first Zamak terminal. A prototype machine was built, and the new concept validated, and then implemented in the factory. An automatic cable extraction system was also designed, aiming to reduce the number of manual tasks performed by the operator, with the expectation of further improving the process productivity. The development of the two new mechanisms followed the Design Science Research (DSR) methodology. With the implementation of the new double stripping concept and automatic cable extraction system, a major increase in productivity (up to 87.8% compared to the current condition) and cost reduction were achieved.

“Career-Sync” Integrated Training and Placement Platform

Abstract: The suggested integrated training and placement platform provides a complete solution. This platform simplifies key processes during recruitment events, like selection of eligible candidates and keep students informed about their progress in the recruitment process. By digitizing student records, the system makes search operations and organization of information easier, boosts efficiency. It introduces a fresh method to handle time-consuming tasks in an organized way. The proposed platform aims to transform training and placement procedures by offering a user-friendly interface that streamlines operations, minimizes manual tasks, and guarantees a smooth experience for both students and placement officers.

DESIGN AND EVALUATION OF A WEB-BASED MEMBERSHIP MANAGEMENT SYSTEM

In today's fast-paced and connected world, membership management systems are essential for clubs, associations, non-profits, and sports teams to streamline operations and enhance member experiences. The requirement for a membership management system arises due to the challenge of efficiently managing many members. As organisations grow and diversity, traditional manual record keeping and event management methods become impractical and error-prone. A membership management system offers a comprehensive and automated solution to handle membership-related tasks effectively. This paper aims to develop a system that provides an intuitive and user-friendly experience for administrators and members, enabling them to manage their profiles seamlessly, access personalised services, and participate in various activities. Also, it can streamline administrative processes, automate manual tasks, and centralise data to enhance association efficiency. By simplifying these aspects, associations can focus on strategic initiatives and foster more robust engagement within their community. A usability evaluation was conducted on 32 respondents. During the testing phase, participants explored the application and interacted with the website, testing its features, usability, ease of use and functionality. The evaluation findings show a positive outcome for the membership management system, with users expressing high satisfaction and providing positive feedback about the website's usability, ease of use, and functionality.

Smart Agriculture Automation System using ML

Abstract: The "Smart Agriculture Automation System using Machine Learning" project envisages a future where farming transcends manual labor and embraces data-driven automation. In response to the escalating demand for food amidst a burgeoning global population, traditional farming methods are rendered insufficient. However, Smart Agriculture Automation Systems offer a transformative solution by enhancing crop production efficiency and maintaining yield quality. Climate changeinduced challenges such as erratic weather patterns and heightened pest attacks further underscore the necessity for innovation in agriculture. At the heart of this system lies real-time sensor data, enabling precision farming practices that optimize resource utilization while fostering environmental sustainability. Leveraging Machine Learning algorithms, the system not only predicts and mitigates agricultural challenges but also detects early signs of crop diseases and nutrient deficiencies. This proactive approach ensures higher yields minimizes manual tasks and minimizes wastage. The integration of IoT technology enables remote monitoring and seamless data exchange, unlocking capabilities such as precise crop prediction, weather forecasting, and fertilizer recommendations. By analyzing historical data alongside current environmental factors, these systems accurately forecast crop yields and offer insights for informed decision-making in planting and harvesting cycles. Smart Agriculture Automation Systems embody adaptability, sustainability, and accessibility, heralding a new era of intelligent and efficient modern farming. This project integrates the Internet of Things (IoT) sensors and an innovative marketplace to revolutionize the agricultural landscape. Through this system, we aim to empower farmers with data-driven insights,

Object Detection Model for Web-Based Physical Distancing Detector Using Deep Learning

The pandemic has changed the way people interact with each other in the public setting. As a result, social distancing has been implemented in public society to reduce the virus’s spread. Automatically detecting social distancing is paramount in reducing menial manual tasks. There are several methods to detect social distance in public, and one is through a surveillance camera. However, detecting social distance through a camera is not an easy task. Problems, such as lighting, occlusion, and camera resolution, can occur during detection. The research aims to develop a physical distancing detector system that is adjusted to work with Indonesian rules and conditions, especially in Jakarta, using deep learning (i.e., YOLOv4 architecture with the Darknet framework) and the CrowdHuman dataset. The detection is done by reading the source video, detecting the distance between individuals, and determining the crowd of individuals close to each other. In order to accomplish the detection, the training is done with CSPDarknet53 and VGG16 backbone in YOLOv4 and YOLOv4 Tiny architecture using various hyperparameters in the training process. Several explorations are made in the research to find the best combination of architectures and fine-tune them. The research successfully detects crowds at the 16th training, with mAP50 of 71.59% (74.04% AP50) and 16.2 Frame per Second (FPS) displayed on the web. The input size is essential for determining the model’s accuracy and speed. The model can be implemented in a web-based application.

Biomechanical analysis of different back-supporting exoskeletons regarding musculoskeletal loading during lifting and holding

Industrial back support exoskeletons (BSEs) are a promising approach to addressing low back pain (LBP) which still affect a significant proportion of the workforce. They aim to reduce lumbar loading, the main biomechanical risk factor for LBP, by providing external support to the lumbar spine. The aim of this study was to determine the supporting effect of one active (A1) and two passive (P1 and P2) BSEs during different manual material handling tasks. Kinematic data and back muscle activity were collected from 12 subjects during dynamic lifting and static holding of 10 kg. Mean and peak L5/S1 extension moments, L5/S1 compression forces and muscle activation were included in the analysis. During dynamic lifting all BSEs reduced peak (12–26 %) and mean (4–17 %) extension moments and peak (10–22 %) and mean (4–15 %) compression forces in the lumbar spine. The peak (13–28 %) and mean (4–32 %) activity of the back extensor muscles was reduced accordingly. In the static holding task, analogous mean reductions for P1 and P2 of L5/S1 extension moments (12–20 %), compression forces (13–23 %) and muscular activity (16–23 %) were found. A1 showed a greater reduction during static holding for extension moments (46 %), compression forces (41 %) and muscular activity (54 %). This pronounced difference in the performance of the BSEs between tasks was attributed to the actuators used by the different BSEs.

Labor market adjustment to extreme heat shocks: Evidence from Brazil

Despite ample evidence on the labor productivity impact of heat shocks, little is known about how heat shocks affect labor adjustment over time. This paper uses administrative employer–employee linked data from Brazil to examine margins of labor market adjustment to extreme heat. Tracking workers over time and across job spells, I find that quarterly heat shocks increase the probability of formal manufacturing layoffs. In the medium run, there is limited intersectoral and interregional reallocation, as well as a significant failure rate to reallocate back to formal sectors, indicating job transitional costs. Finally, the employment impact of heat shocks is larger for workers in more routine manual task intensive, less abstract occupations.

Postural control during gait termination and prehension

BackgroundChallenges to postural stability emerge in the transition from locomotion to a standing posture as during gait termination, often accompanied by another task (e.g., opening a door), which may complicate control. However, less is known about postural control during terminating gait while engaged in a secondary manual task. Research questionWhat are the changes in postural control when terminating gait with and without a prehension task? MethodsIn a cross-sectional design, 15 healthy young adults (M=8, F=7; 27±2 years; 69±13 kg; 171±8 cm) underwent both a single task gait termination (GTO) and dual task (gait termination plus reaching; GTR). Postural Time-to-Contact (TtC) was measured using Center of Pressure (CoP) and the sternum position in anterior-posterior (AP) and medial-lateral (ML) directions over two different phases: preparatory phase and stabilization phase. Five successful trials were recorded to obtain a mean TtC. For statistical analysis of TtC, a two-tailed paired t-test was used (p =.05) as normality was satisfied. ResultsFor the preparatory phase, there were no differences for the CoP, but TtC of the sternum position in AP was shorter in GTR than GTO (p =.001). Meanwhile, for the stabilization phase, TtCs of both the CoP and sternum position were longer in GTR in both AP and ML directions (p’s <.001). SignificanceWe suggest that for the preparatory phase, the shorter TtC of the sternum position with intact TtC of the CoP in GTR indicates that healthy young individuals are flexible, in that they smoothly integrate CoP control with the upper body demands required to also perform the prehension task. Meanwhile, for the stabilization phase, the longer TtC in dual termination and prehension task indicates that the perturbation imposed by the prehension movement did not result in reduced stabilization when returning to an upright posture.

SOCIAL IMPACT OF AUTOMATED ACCOUNTING SYSTEMS: A REVIEW: ANALYZING THE SOCIETAL AND EMPLOYMENT IMPLICATIONS OF THE RAPID DIGITIZATION IN THE ACCOUNTING INDUSTRY

This study examines the social impact of automated accounting systems, focusing on the implications for employment, ethical considerations, and broader societal effects. Employing a systematic literature review combined with content analysis, the research draws on recent academic articles, industry reports, and regulatory documents published between 2015 and 2024. The methodology aims to capture a comprehensive view of how automation and artificial intelligence (AI) are reshaping the accounting profession. Key findings reveal that automation acts as a dual-edged sword: it enhances efficiency and accuracy, enabling accountants to focus on higher-value strategic roles, but also raises concerns about job displacement for roles centered on manual tasks. Ethical and privacy considerations emerge as critical issues, necessitating robust frameworks to ensure data protection and maintain trust. The future landscape of accounting is poised to be significantly influenced by ongoing technological advancements, with a transformation in employment patterns and a heightened focus on ethical standards. To navigate this transition, the study recommends strategic initiatives for stakeholders, including updating educational curricula to include relevant digital age skills, developing regulations to address ethical and privacy concerns, and fostering industry collaboration to establish best practices. Lastly, the study underscores the transformative potential of automated accounting systems, highlighting the need for a balanced approach that leverages technological advancements while addressing associated challenges. Future research is encouraged to explore the long-term impacts of automation on the profession, focusing on employment patterns, ethical standards evolution, and societal implications. Keywords: Automated Accounting Systems, Employment, Accounting Industry, Digital Transformation.

Occupational Mobility of Routine Workers

AbstractThis paper demonstrates that the decline in routine occupations and concurrent rise in abstract occupations are largely due to workers changing jobs. The reduction in routine manual tasks is further explained by workers transitioning to unemployment or retirement. In contrast, the increase in non‐routine manual occupations is primarily driven by the entry of young or unemployed individuals into the workforce. Plant closure information is used to identify involuntary job separations. These findings indicate that routine cognitive workers can adjust to smaller employment disruptions compared to routine manual workers among women. However, a contrasting pattern is observed for men.

Stimulating Preconception Care Uptake by Women With a Vulnerable Health Status Through a Mobile Health App (Pregnant Faster): Pilot Feasibility Study.

A low socioeconomic status is associated with a vulnerable health status (VHS) through the accumulation of health-related risk factors, such as poor lifestyle behaviors (eg, inadequate nutrition, chronic stress, and impaired health literacy). For pregnant women, a VHS translates into a high incidence of adverse pregnancy outcomes and therefore pregnancy-related inequity. We hypothesize that stimulating adequate pregnancy preparation, targeting lifestyle behaviors and preconception care (PCC) uptake, can reduce these inequities and improve the pregnancy outcomes of women with a VHS. A nudge is a behavioral intervention aimed at making healthy choices easier and more attractive and may therefore be a feasible way to stimulate engagement in pregnancy preparation and PCC uptake, especially in women with a VHS. To support adequate pregnancy preparation, we designed a mobile health (mHealth) app, Pregnant Faster, that fits the preferences of women with a VHS and uses nudging to encourage PCC consultation visits and engagement in education on healthy lifestyle behaviors. This study aimed to test the feasibility of Pregnant Faster by determining usability and user satisfaction, the number of visited PCC consultations, and the course of practical study conduction. Women aged 18-45 years, with low-to-intermediate educational attainment, who were trying to become pregnant within 12 months were included in this open cohort. Recruitment took place through social media, health care professionals, and distribution of flyers and posters from September 2021 until June 2022. Participants used Pregnant Faster daily for 4 weeks, earning coins by reading blogs on pregnancy preparation, filling out a daily questionnaire on healthy lifestyle choices, and registering for a PCC consultation with a midwife. Earned coins could be spent on rewards, such as fruit, mascara, and baby products. Evaluation took place through the mHealth App Usability Questionnaire (MAUQ), an additional interview or questionnaire, and assessment of overall study conduction. Due to limited inclusions, the inclusion criterion "living in a deprived neighborhood" was dropped. This resulted in the inclusion of 47 women, of whom 39 (83%) completed the intervention. In total, 16 (41%) of 39 participants visited a PCC consultation, with their main motivation being obtaining personalized information. The majority of participants agreed with 16 (88.9%) of 18 statements of the MAUQ, indicating high user satisfaction. The mean rating was 7.7 (SD 1.0) out of 10. Points of improvement included recruitment of the target group, simplification of the log-in system, and automation of manual tasks. Nudging women through Pregnant Faster to stimulate pregnancy preparation and PCC uptake has proven feasible, but the inclusion criteria must be revised. A substantial number of PCC consultations were conducted, and this study will therefore be continued with an open cohort of 400 women, aiming to establish the (cost-)effectiveness of an updated version, named Pregnant Faster 2. RR2-10.2196/45293.

Short-term effects of running exercise on pinch strength, grip strength, and manual dexterity of the dominant and non-dominant hands

Occupations including first responders and military require manual tasks; therefore changes in hand strength and dexterity could affect performance. We hypothesised that pinch strength, grip strength, and dexterity will change after unloaded and loaded exercise. Twenty-four male (25 ± 4.0 yrs; 86.3 ± 9.3 kg) and 10 female (25 ± 6.0 yrs; 62.1 ± 5.9 kg) participants completed 3 conditions for 5 minutes: (1) no exercise (2) run with no load at 3.0 m/s and (3) run wearing a 9.1 kg belt. Heart rate was different among conditions (p ≤ 0.05). Pinch strength was significantly different for the non-dominant hand after exercise (p = 0.005) for male participants, but not for the dominant hand. Grip strength was significantly different for the non-dominant hand between loaded and unloaded run (p = 0.035) for male participants. Pinch and grip strength did not change after exercise for female participants. Dexterity times were not different after exercise, but female participants were significantly faster (p ≤ 0.039) than male participants.

Effects of Action Observation Plus Motor Imagery Administered by Immersive Virtual Reality on Hand Dexterity in Healthy Subjects.

Action observation and motor imagery (AOMI) are commonly delivered through a laptop screen. Immersive virtual reality (VR) may enhance the observer's embodiment, a factor that may boost AOMI effects. The study aimed to investigate the effects on manual dexterity of AOMI delivered through immersive VR compared to AOMI administered through a laptop. To evaluate whether VR can enhance the effects of AOMI, forty-five young volunteers were enrolled and randomly assigned to the VR-AOMI group, who underwent AOMI through immersive VR, the AOMI group, who underwent AOMI through a laptop screen, or the control group, who observed landscape video clips. All participants underwent a 5-day treatment, consisting of 12 min per day. We investigated between and within-group differences after treatments relative to functional manual dexterity tasks using the Purdue Pegboard Test (PPT). This test included right hand (R), left hand (L), both hands (B), R + L + B, and assembly tasks. Additionally, we analyzed kinematics parameters including total and sub-phase duration, peak and mean velocity, and normalized jerk, during the Nine-Hole Peg Test to examine whether changes in functional scores may also occur through specific kinematic patterns. Participants were assessed at baseline (T0), after the first training session (T1), and at the end of training (T2). A significant time by group interaction and time effects were found for PPT, where both VR-AOMI and AOMI groups improved at the end of training. Larger PPT-L task improvements were found in the VR-AOMI group (d: 0.84, CI95: 0.09-1.58) compared to the AOMI group from T0 to T1. Immersive VR used for the delivery of AOMI speeded up hand dexterity improvements.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Chatbot Design and Implementation: Towards an Operational Model for Chatbots

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and a model is developed and validated to support the planning and implementation of chatbot projects. The presented model can serve as an exemplary guide for researchers and practitioners working in this field. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience in chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Operator selection for human-automation teaming: The role of manual task skill in predicting automation failure intervention

Humans working in modern work systems are increasingly required to supervise task automation. We examined whether manual aircraft conflict detection skill predicted participants’ ability to respond to conflict detection automation failures in simulated air traffic control. In a conflict discrimination task (to assess manual skill), participants determined whether pairs of aircraft were in conflict or not by judging their relative-arrival time at common intersection points. Then in a simulated air traffic control task, participants supervised automation which either partially or fully detected and resolved conflicts on their behalf. Automation supervision required participants to detect when automation may have failed and effectively intervene. When automation failed, participants who had better manual conflict detection skill were faster and more accurate to intervene. However, a substantial proportion of variance in failure intervention was not explained by manual conflict detection skill, potentially reflecting that future research should consider other cognitive skills underlying automation supervision.

Artificial intelligence in liver cancer - new tools for research and patient management.

Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Crucially, research demonstrates that AI can analyse histopathology, radiology and natural language in liver cancer, and can replace manual tasks and access hidden information in routinely available clinical data. However, for liver cancer, few of these applications have translated into large-scale clinical trials or clinically approved products. Here, we advocate for the incorporation of AI in all stages of liver cancer management. We present a taxonomy of AI approaches in liver cancer, highlighting areas with academic and commercial potential, and outline a policy for AI-based liver cancer management, including interdisciplinary training of researchers, clinicians and patients. The potential of AI in liver cancer is immense, but effort is required to ensure that AI can fulfil expectations.

Benefits of Running Databases on Amazon Relational Database Service over Traditional On-premises Servers

Amazon RDS is a web service provided by Amazon to provide easy way to operate and scale relational database in traditional database to cloud. In Traditional or on-premises server, CPU, memory, storage comes together as a bundle. RDS stipulate managed database service for handling most management tasks. After elimination of manual task which is most tedious part ,RDS focus on application and users. This paper delves the migration of database to the cloud. The study highlights introduction to amazon RDS and its comparative study with traditional database. It also throws a light on comparative analysis like cost,performance,security ,maintenance etc by taking use cases.