Manual Work Research Articles

Artificial Neural Network Based Performance prediction of Exergy and Energy Analysis of Gas Turbine Generator using Cycle-Tempo Software

It is crucial for research to continuously seek innovative methods of generating additional electricity in order to meet the demands of everyone requiring it. Engineers can work to make the companies that provide electricity better. It is not possible to separate the parts of the system from how well the gas turbine generator works. This research introduces a new method to predict the performance of Gas Turbine Generators (GTGs) using Artificial Neural Networks (ANNs) combined with Cycle-Tempo software. The main emphasis of this method is on analysing the exergy and energy of the GTGs. GTGs are very important in many different industries. It is crucial to make them work efficiently to produce more energy and protect the environment. This research is new and different because it uses ANNs in a smart way to improve how research measure how well GTG works. The main advancement is in the automation part, where artificial neural networks are taught to independently collect and study data. They can adjust to changing conditions on their own, which greatly reduces the amount of manual work needed for analysis. This automation makes things easier and can change to different situations quickly. It helps industries that rely on GTG by meeting their changing needs. Moreover, the research uses complex computer algorithms to make very accurate forecasts about energy and exergy effectiveness, voltage output, and hydrogen production—an important measurement in GTG function. The results show that the accuracy is really good, with a small error of less than 1% and a high value of 0. 99 for the R-squared when compared to what was expected. The automation, detailed analysis, and accurate predictions of the novel technology help improve the efficiency and sustainability of gas turbine generators. This makes it a valuable tool for industries that rely on these generators for power.

A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

AbstractAs an effect of solar‐terrestrial activity, the aurora has always been a focus of substorm research. The expansion of the aurora is one of the important characteristics during substorms, which could be directly reflected by the change of auroral oval coverage. However, the related studies were limited because of the absence of a reliable automated method to obtain auroral ovals from a large number of images. In this paper, we propose a new strategy to achieve this. Based on the Segment Anything Model, we design a process for annotating the auroral oval region that requires little manual work. A new aurora segmentation model, HrSeg, is then developed to obtain auroral ovals more efficiently and accurately. Through 5‐fold cross‐validation, it is determined that the average intersection over union, Dice coefficient, and pixel accuracy are all greater than 0.97. Furthermore, images of 590 substorms observed by the Polar satellite Ultraviolet Imager are segmented. We present superposed epoch analyses of the auroral oval coverage calculated from the segmentation results. Generally, the coverage decreases slightly before onset, then rapidly increases for tens of minutes after onset, and finally decreases gradually. Moreover, the auroras in different magnetic local time (MLT) sectors exhibit different evolutions in coverage. It is also revealed that the evolution pattern of auroral coverage depends on interplanetary magnetic field orientations and seasonal conditions. The results quantify the variation of auroral morphology in terms of coverage, which complete the evolution pattern of aurora during substorms and provide a more comprehensive understanding of substorms.

PROPHETA: A NEW MORPHOMETRIC ANALYSIS SOFTWARE FOR INDUSTRIAL PRODUCT DESIGN

ABSTRACT: This paper introduces ProPheTA (Product Phenetics-based Typology Assessment) as a new morphometric analysis software for industrial, phenetics-based product design (PP). Phenetics is used in Biology to determine similarities between species by comparing their formal external attributes. This way, PP replicates this procedure and, by doing a morphological analysis of different models from the same product family, makes it possible to relate their morphology with the user's emotional, perceptive, hedonic and buying intention response. All this requires working with CAD models, aligning, scaling, selecting them along the X, Y and Z axes, editing images, obtaining dissimilarity indexes, and correlating them with the user's response. Various CAD and morphometric computation tools not explicitly developed for these procedures are used to fulfil these tasks. File compatibility problems and excessive manual work and working time must be circumvented. ProPheTA has been developed in Matlab to solve these problems using a single platform. Its performance in a previous PP study, which related shape to buying intention, has been evaluated to demonstrate its reliability and speed. Results show a ten-time speed improvement in the computation with identical dissimilarity results. In addition, ProPheTA offers several advantages, such as ease of use, robust batch-processing capabilities, multiple output formats and improved compatibility. These functionalities, together with its ability to analyze the outline of 3D shapes without manual selection of landmark points, make it a versatile software application which is flexible, reliable and much faster. Thus, ProPheta is arguably valuable for helping speed up the application of PP in industrial design. Keywords: phenetics, product design, morphology, computer-based assessment

Complete Denture Preclinical Training Program and Digital Tools: A Pilot Study.

To determine whether digital training improves conceptual skills acquisition for custom tray fabrication and tooth arrangement in the preclinical curriculum for complete denture fabrication. A crossover design study compared third-year student grades after conventional (manual) and digital training. Half the students were trained with conventional techniques prior to digital, while the other half were trained with digital techniques prior to conventional. The first part of the study (DP1) focused on custom tray fabrication and involved 154 students. The second part (DP2) focused on tooth arrangement with 204 students. The effect of preliminary digital training on conceptual skills was assessed by comparing the results of the students using a Student's t-test. Results were also analyzed by ranking on the students' Perceptual Ability Test (PAT) score. Finally, students' satisfaction with the training was investigated. Students who completed the initial digital session achieved better custom tray fabrication and tooth arrangement in the subsequent manual sessions. For manual laboratory work, the most dexterous students (PAT1 group) demonstrated the best performance. However, PAT2 students had the best performance during the digital sessions during DP1, and PAT1 and PAT2 students had the best performance during the digital sessions during DP2. Additionally, 40% of DP1 and 12% of DP2 students expressed satisfaction with digital sessions. With complementary manual and digital teaching, digital tools may be useful for enhanced student learning. Digital training has contributed to improve results in manual sessions but did not affect student satisfaction and remained a limited tool to detect learning difficulties.

An Empirical Study of the Impact of NREGA Implementation on Rural Poverty Rates in India

The National Rural Employment Guarantee Act (NREGA), now known as the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), was implemented in India with the objective of enhancing livelihood security in rural areas by providing at least 100 days of wage employment per year to every household willing to do unskilled manual work. This study empirically examines the impact of NREGA on rural poverty rates in India, utilizing a Difference-in-Differences (DID) approach to analyze data from the National Sample Survey (NSS) and Census of India. The findings reveal a significant reduction in rural poverty rates in districts where NREGA was implemented compared to non-NREGA districts. The program has been particularly effective in regions with higher initial poverty levels, indicating successful targeting of the most vulnerable populations. Additionally, the study highlights regional variations in the impact of NREGA, with some areas experiencing greater benefits than others. The results suggest that while NREGA has substantially contributed to poverty reduction, its effectiveness is influenced by local governance and administrative capacities. Policy recommendations include improving administrative efficiency, enhancing the quality and sustainability of created assets, and complementing NREGA with other skill development and capacity-building initiatives. Addressing regional disparities and ensuring robust monitoring and evaluation mechanisms are also crucial for maximizing the program’s impact.

Rehabilitation guidelines following arthroscopic shoulder stabilisation surgery for traumatic instability – a Delphi consensus

BackgroundThere is no consistent approach to rehabilitation following arthroscopic shoulder stabilisation surgery (ASSS) in the UK. The aim of this study was to agree a set of post-operative guidelines for clinical practice. MethodExpert stakeholders (surgeons, physiotherapists and patients) were identified via professional networks and patient involvement and engagements groups. A three-stage online Delphi study was undertaken. Consensus was defined by the OMERACT threshold of 70% agreement. Results11 surgeons, 22 physiotherapists and 4 patients participated. It was agreed patients should be routinely immobilised in a sling for up to 3 weeks but can discard earlier if able. During the immobilisation period, patients should move only within a defined “safe zone.” Permitted functional activities include using cutlery, lifting a drink, slicing bread, using kitchen utensils, wiping a table, light dusting, pulling up clothing, washing/drying dishes. Closing car doors or draining saucepans should be avoided. Through range movements can commence after 4 weeks, resisted movements at 6 weeks. Patients can resume light work as they feel able and return to manual work after 12 weeks.Return to non-contact sports when functional markers for return to play are met was agreed. Return to contact sport is based on function & confidence after a minimum of 12 weeks. Additional factors to consider when determining rehabilitation progression: functional/physical milestones, patient’s confidence and presence of kinesiophobia. The preferred outcome measure is the Oxford Instability Shoulder Score. ConclusionThis consensus provides expert recommendations for the development of rehabilitation guidelines following ASSS. Contribution of the paper•This paper is the first to provide expert recommendations for patients undergoing rehabilitation following arthroscopic shoulder stabilisation surgery.•This paper provides clinicians and patients with expert guidance on appropriate expectations regarding functional restriction and progression following arthroscopic shoulder stabilisation surgery.•In the absence of national clinical guidelines for this patient group, these recommendations will reduce variation and improve quality of care.•The knowledge acquired from this study will also lay the foundation for clinical guidelines to be developed.•The results of this study also demonstrate the need for further, empirical work to be carried out in this area.

Field effects and work‐based learning: The case of school–work alternance in Italy

AbstractThis essay analyses the implementation of a work‐based learning policy in upper secondary schools in Italy. The policy aims to improve student orientation and enhance their soft skills. Based on two ethnographic research studies in Italy, this essay utilises Pierre Bourdieu's theoretical framework, particularly the ‘field’ concept, to examine how this policy has been implemented in different tracks of the Italian school system in a peripheral region. The policy has challenged the autonomy of the scholastic field, primarily advocated by teachers and students in the dominant pole, represented by the classical and scientific lyceums. The policy has been translated into a series of projects in museums, libraries and third‐sector associations in this field segment. These initiatives reinforce students' citizenship values while highlighting their detachment from manual work. In the dominated pole of the field, represented by vocational schools primarily located in the suburbs of large cities and rural areas, the policy has been well received as it aligns with the expectations of teachers and students. However, due to the prevalence of small family companies, planning truly formative work‐based learning experiences becomes challenging. Consequently, this leads to an early, albeit tense, socialisation of students within the hierarchical order of small companies.

Research on Sentiment Analysis of Micro-blog based on Attention-BiLSTM

The rapid development of the Internet gives birth to massive data. These data have the characteristics of multiple types, low density, and fast transmission speed. As one of the products of the era of big data, micro-blog provides users with a highly interactive platform. In order to explore the value and sentiment contained in micro-blog comment texts, and solve the problem of semantic loss and over-reliance on manual work in traditional text sentiment analysis, this paper proposes a deep learning model based on Attention-BiLSTM. In the embedding layer, the CBOW method of Word2vec is used to transform the text data into word vectors, and the attention mechanism is integrated to dynamically weight the input word vectors. Secondly, BiLSTM is used to extract text features, then fuse them in concat layer. After that the classification is realized by Softmax whose results of the output text are positive or negative. Finally, by comparing with TextCNN, LSTM and BiLSTM, the result shows that the Attention-BiLSTM model does have better classification effect, strong generalization and practicability.

The racist devaluation of manual work and professional education in Brazil

No presente artigo, partindo da hipótese de que a educação profissional no país é desvalorizada por conta do preconceito racista que se tem em relação às pessoas que realizam trabalhos manuais, pretendemos, por meio da metodologia do ensaio como conversa, explicitar argumentos que apontam e comprovam tal afirmação. O pressuposto classista de que o trabalho braçal é indigno é mobilizador de um tipo particular de racismo. Este é muito mais presente no ideário modernizador da educação oficial do que costumamos admitir. Faremos esse experimento do ensaio como conversa ao longo do texto, no intuito de apontar como o preconceito em relação ao trabalho manual e às/aos suas/seus portadoras/res nos atrapalha na criação da educação integrada.

The “Villas de Sesimbra” Aparthotel and The Importance of Training Low-Skilled Workers

This case study describes the complex process of qualifying accommodation employees at the “Villas de Sesimbra” aparthotel, in the context of an organizational change and the development of a prismatic structural configuration that favours leadership and management by teams, an organization who learns. This is, effectively, a case study of management and employee satisfaction based on the consolidation of the strategic “pillar”, par excellence, of People Management, constituted by “continuous and qualifying professional training, in practice” (Lopes, 2012), in relation to all 12-hotel staff (permanent and seasonal). By proceeding in this way, the hierarchical pyramid is considerably reduced, and may even nullify the tradition of maintaining indefinitely the organization of work based on low-skilled workers. As a result, it could evolve into project management. The qualification of these workers was assumed to be strategic by the administration, which also accepted a re-elaboration of the structure (from hierarchical centralization to evolving into a “prismatic” structure that would allow for success based on “project management”). Training would follow the “ANEFA” model, of on-the-job learning, according to the four skills considered as core (project - Saber+): (i) literacy (knowing more); (ii) citizenship (participation in business life); (iii) numeracy (mathematics for life); (iv) technology (judicious use of ICTs). The workers wrote their manual personalized work on the topic of “accommodation management”. Organizational reconversion increased management autonomy, with a dynamic focus on the events and company meetings market, while the effectiveness and productivity of accommodation workers increased, with them being hired as permanent workers and transferred to situation of multi-purpose employees, capable of serving tables and supporting breakfast services. Organizational change, developed as a function of resolving a single initial problem (Kotter, 1995; Weick and Quinn, 1999), in fact involved the entire structure and led it to a stage of organizational learning (Senge, 1990).

Glaucoma Detection Using Explainable AI and Deep Learning

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed.
 OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.
 RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS).
 CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

HRConnect

Human Resource Management System (HRMS) seems to matter for Human Resource Management (HRM) in the current digital economy. Many firms transform their HRM functions from the manual work processes to the computerized work processes. Information technology is expected to drive Human Resource (HR)'s transition from a focus on Human Resource Management (HRM) to Strategic Human Resource Management (SHRM). This strategic role not only adds a valuable dimension to the HR function, but also changes the competencies that define HR professional and practitioner success. Interestingly, an HRMS is utilized for administrative purposes rather than analytical purposes. The main objective is to know the payroll software's used in the organization and the roll of HR in payroll. The secondary objectives of this study are, To understand the HR roll in Payroll, To review the effectiveness of the Payroll process of HRConnect, To find about the software's used in Payroll process and satisfactory level of employees using this software's To analysis whether these software's are user friendly, To suggest the Latest Software's in achieving organizational objective.

Efeitos das Diferenças na Política Contábil de Leasings entre o FASB e o IASB

Objective: This research investigates the effects of the 2019 US lease standards for US lessee companies, comparing with the potential effects of IFRS 16 to explore the economic reasons that would have led the FASB and the IASB to diverge from their joint lease standard project. Method: We hand-collected detailed data on leases for 500 US lessees firms’ 2019 financial statements to first analyze Topic 842’s effects on financial ratios and to simulate these effects had the FASB adopted the same earnings recognition criteria in IFRS 16. Next, we investigate the value relevance of these observed and simulated effects on the stock market. Results: Topic 842 generated significant variations in companies' financial indicators, but it was not possible to identify the value relevance of these variations due to model limitations. The IFRS 16 simulation shows a significant increase in EBIT and operating cash flows, but a drop in net income, which seems to have been incorporated into stock prices. This suggests the FASB’s decision to diverge from the IASB could include the desire to avoid lower earnings and their negative impacts on the stock market. Contributions: This research adds to the literature first by focusing on the US when most of the recent studies investigated the IFRS world and, second, by exploring economic reasons rather than the hierarchy of power between the FASB and the IASB. Finally, we add a novelty derived from the detailed manual work which allowed us to estimate the effects had the US firms been under IASB’s jurisdiction.

Research on AGV path planning in new energy battery workshop

In recent years, more and more factories have begun to use automatic guided vehicles (AGVs) instead of manual work in production, transportation, and other links, which has broad application prospects and market prospects. This paper takes the new energy battery workshop as the research object, analyzes the AGV operation plan in the workshop according to the overall workflow of the workshop material distribution, and uses AnyLogic to simulate the map to complete the algorithm test. A* global algorithm is optimized and improved, and the optimal shortest route with more smoothness from the beginning of the AGV to the end of the AGV is planned, which greatly improves the operation efficiency of the whole scheduling and has high practicability.

Numerical simulation and experimental study on seafloor sampling of an anchor-type mud collector based on SPH-FEM coupling method

Marine sediments are important for research in scientific fields such as marine geology, environmental testing of waters, marine biology and seabed resource exploration. Among them, mud miner is an important way to obtain sediments. However, due to the complexity of the marine environment, the seabed sampling operation is a relatively difficult and complicated project. The structural design of the sampler, the operation mode, and the interaction between the sampler and the sediment affect the sampling effect, which leads to the low efficiency of the sampler operation. In order to investigate the main factors affecting the drag force of the sampler during seafloor sampling, this paper takes a simple and portable anchor-type mud collector surface sediment sampler as a study. This paper introduces the mechanical structure and working principle of an anchor-type mud collector, establishes a mechanical model of the mud collector seafloor sampling process and derives the main factors affecting the dragging force: internal friction angle; the horizontal angle of the bi-directional shaft rod; undercut angle [Formula: see text] of bottom cover and dragging speed. A FEM-SPH method based on the coupling of the finite element method (FEM) and the smooth particle hydrodynamics method (SPH) was proposed to simulate the dynamic process of mud collector subsea sampling, and the mechanical data of fine sand and clay were obtained through land-based experiments. Based on the comparison between experimental data and numerical simulation data, the simulation validity of the FEM-SPH method was verified. The results show that the drag force of the clay was greater than that of the fine sand in the experiment of cutting the lower cover of the mud collector into the fine sand and clay, the internal friction angle of the clay is greater than that of the fine sand; with the increase in horizontal inclination angle and the decrease in undercut angle, the dragging force gradually increased; The dragging speed ranged from 0.2 to 0.6 m/s, with an increase in the mass of clay and fine sand collected with increasing speed, resulting in a gradual increase in dragging force. This paper provides a new method to study the force of the mud collector, provides a theoretical method to reduce the intensity and difficulty of manual work in the sampling process and increase the efficiency of sampling.

Annular Ligament Instability

Purpose: Lateral elbow pain is frequently attributed to epicondylalgia; yet, less common pathologies with limited literature representation exist. This study aims to delineate the diagnostic significance of alterations in the biomechanics and morphology of the annular ligament as a distinctive factor in lateral elbow pain. Methods: A retrospective observational analysis was conducted on patients surgically treated for lateral elbow pain with arthroscopic assistance from January 2015 to December 2018. Initial preoperative diagnoses primarily indicated epicondylalgia. Results: The study encompassed 82 patients (48 men, 34 women; mean age: 44.6 ± 5.3 years), with 91.46% involved in demanding manual work. Notably, 17 patients underwent surgery due to recurrent symptoms. Arthroscopic assessments revealed diverse morphological and biomechanical variations in annular ligaments, designating 36 patients with an identified incompetent annular ligament. Conclusions: Our arthroscopic study on lateral epicondylalgia cases involved a comprehensive analysis of annular ligament anatomy and radiocapitellar joint biomechanics. The formulated anatomic classification, incorporating crucial variables such as radial head coverage and ligament tension, unveiled implications for joint instability. Recognition of simultaneous pathologies, notably annular ligament lesions, signifies a potential, yet unexplored, avenue for differential diagnosis in cases of lateral elbow pain.

Analysis of Energy Consumption of Robotic Welding Stations

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency.

GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.

Research report on automatic unpacking operation of measuring instrument storage buffer area

In order to realize the automatic, digital and intelligent research and development of the key process of unpacking, this paper designs an intelligent unpacking equipment based on visualization of the makeup and unloading system, and the efficiency is seriously not matched. intelligent unpacking equipment based on visual positioning algorithm, including the main frame of the unpacking machine, the film winding device, the automatic film cutting and the film clamping. automatic film cutting and the film clamping mechanism. Based on the above design, the test simulation is carried out. In the actual work, the intelligent unpacking workstation can significantly improve the current manual unpacking work efficiency (increase efficiency > 50%), and realize the automation, digitalization and intelligence of the current manual unpacking work efficiency (increase efficiency > 50%), and realize the automation, digitalization and intelligence of the operation process in the front area of the library. In the actual work, the intelligent unpacking workstation can significantly improve the current manual unpacking work efficiency (increase efficiency > 50%, and realize the automation, digitalization and intelligence of the operation process in the front area of the library.

Bootstrapping Cognitive Agents with a Large Language Model

Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. In contrast cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantiate. In this work, we combine the best of both worlds: bootstrapping a cognitive-based model with the noisy knowledge encoded in large language models. Through an embodied agent doing kitchen tasks, we show that our proposed framework yields better efficiency compared to an agent entirely based on large language models. Our experiments also indicate that the cognitive agent bootstrapped using this framework can generalize to novel environments and be scaled to complex tasks.