Labor Requirements Research Articles

Classifying and clustering noisy images using subset learning based on convolutional neural networks

AbstractA high yield rate is a key factor related to success in the competitive global semiconductor manufacturing business market. Wafer bin maps (WBMs) can be used as one measure of the output quality of a semiconductor manufacturing process. A WBM is the image results from a number of circuit probe (CP) tests on a wafer after the completion of a manufacturing process. The specific defect patterns on WBMs provide crucial information for engineers to trace the causes of defects in the complicated manufacturing process. This study is aimed toward investigating major practical challenges in current WBM analyses. Our approach involved utilizing a small, carefully labeled subset to reduce the labor requirements and human recognition bias related to identification of very noisy images. The first proposed procedure classified the noisy defect patterns by using convolutional neural networks (CNNs) trained with a small subset of labeled WBMs in the early batches. The second proposed procedure provided the proper clusters of noisy defect patterns using the features extracted from the trained CNNs. This procedure made it possible to generate various clusters of WMBs and integrate them in label space. The third procedure separated the new pattern from the existing defect patterns with the help of a supplemental dataset from a similar wafer product. The evaluation of the three proposed procedures was done with simulation data generated from real sets of WBMs, which were added with random noise to maintain confidentiality. The evaluation results demonstrated the practical validity of the proposed procedures.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections.

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

EVENT MANAGEMENT SYSTEM

This paper describes a website-based event administration portal. This paper offers the capability of remotely adding, deleting, and retrieving information to various event attendees. This project is capable of giving the admin and everyone else involved in a specific occasion all the necessary access. It allows the administrator to view a list of participants. Administrators will also have the ability to add or remove events. The aim of this project will simplify the event management process by reducing paperwork and labor requirements. The goal of this project is to digitalize the event management processes. It is also simple to use, allowing anyone to conduct event management tasks from anywhere and at any time. Most of the fundamental features needed by any event manager to operate the event smoothly are provided by it. This is done in order to meet the requirements of both the event manager and the attendees. They can fill out their information and sign up for the program. The administrator receives this information and may use it to get in touch with the users. KEYWORDS : Event Management, Participants, Customized Service.

Using deep learning for pruning region detection and plant organ segmentation in dormant spur-pruned grapevines

Even though mechanization has dramatically decreased labor requirements, vineyard management costs are still affected by selective operations such as winter pruning. Robotic solutions are becoming more common in agriculture, however, few studies have focused on grapevines. This work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines. The Faster R-CNN network was fine-tuned using 1215 RGB images collected in different vineyards and annotated through bounding boxes. The network was tested on 232 RGB images, PRs were categorized by wood type (W), orientation (Or) and visibility (V), and performance metrics were calculated. PR detection was dramatically affected by visibility. Highest detection was associated with visible intermediate complex spurs in Merlot (0.97), while most represented coplanar simple spurs allowed a 74% detection rate. The Mask R-CNN network was trained for grapevine organs (GOs) segmentation by using 119 RGB images annotated by distinguishing 5 classes (cordon, arm, spur, cane and node). The network was tested on 60 RGB images of light pruned (LP), shoot-thinned (ST) and unthinned control (C) grapevines. Nodes were the best segmented GOs (0.88) and general recall was higher for ST (0.85) compared to C (0.80) confirming the role of canopy management in improving performances of hi-tech solutions based on artificial intelligence. The two fine-tuned and tested networks are part of a larger control framework that is under development for autonomous winter pruning of grapevines.

Improved Dynamic Geometry Control Algorithms for Efficient Aerodynamic Shape Optimization

In aerodynamic shape optimization, traditional static geometry control methods can produce suboptimal performance by introducing performance tradeoffs at various stages of optimization, enforcing arbitrary constraints on open-ended optimization, and necessitating foreknowledge of problem behavior to design an effective control scheme. These shortcomings can be mitigated through dynamic geometry control, which partly automates the geometry control design process by refining the geometry control topology throughout optimization. Such refinement can occur in a predetermined fashion (as in progressive control) or more automatically using sensitivity information to guide refinement (as in adaptive control). Both progressive control and adaptive control are implemented in the context of axial and free-form deformation geometry control, and novel contributions are made to the adaptive algorithm, including the treatment of active constraints and several novel “potential indicators” to rank candidate refinements. Application to a wide suite of aerodynamic shape optimization problems demonstrates that dynamic geometry control is effective, producing lower final drag than well-designed static schemes while reducing required iterations to convergence by 50% or more, and simultaneously reducing labor requirements on the user. These benefits are demonstrated across a wide variety of problems, representative of detailed and exploratory problems often encountered in both academia and industry.

Application of the convergent education model in the development of a smart learning environment

The article presents a scientific and practical approach to the development and implementation of the concept of convergent learning in the framework of building an open educational platform to manage the process of training specialists. The presented concept of convergence education is based on the integration of educational programs, learning content, competency matrices, methods and technologies for training specialists and personalized learning trajectories in smart learning environment. To implement the model and the convergent approach, it is vital to make use of an information and educational environment with mechanisms to support convergent learning based on regional labor market analysis, actualizing of educational programs and resources, harmonization and synchronization life-cycle models thereof, adaptive customization and personalization of specialist training trajectories. The article presents the developed convergence model of educational programs and employer vacancies as a weighted graph to illustrate the relationship between competence requirement vectors of educational programs and sought-after employment. To isolate the convergence model components and evaluate the convergence degree thereof, a graph clustering algorithm based on the Louvain method has been developed. A method for synthesizing a convergence model of educational content organized as a fuzzy weighted graph has been presented, and sets of topics and sections of electronic learning resources being its vertices. Search and extraction of convergence educational content sections have been implemented by means of an algorithm for extracting fuzzy isomorphic subgraphs, and assessing the convergence degree thereof. The results of the study are used to synthesize an information and educational environment with intelligent mechanisms for supporting learning processes based on an analysis of the requirements of labor markets and actualizing educational content and training programs, as well as adaptive customization and personalization of specialist training trajectories. The architecture of the educational environment includes a set of software and tools for managing the components of the information space of the university with support for mobile, cloud and blended learning technologies.

MetaEdu: a new framework for future education

The potential of the metaverse in the field of education is an area of increasing interest, with many researchers exploring the space to increase the ease and efficacy of student education while reducing time and labor requirements to deliver effective teaching. However, there has been little work into the systematic and technological aspects of delivering education through the metaverse. To fill this gap, we propose a metaverse education system that takes good advantages of virtual reality and Web3 blockchain techologies to create a social learning environment. With this added emphasis on social aspects, learners are able to socialize and engage in collaborative efforts to improve their own knowledge. Using blockchain technology, the system can also help to ensure security and transparency while also keeping progression and grading fair for all participating students.

Development of Grooved Belt Type Fertilizer Metering Mechanism for Spot Fertilizer Applicator

Higher risks involved in excess fertilizer application require precise placement of fertilizer in adequate amount in the root zone of the crop. The drawbacks of current fertilizer application techniques (band placement, pellet application, and ring basin method) include over-fertilization, soil acidity, nutritional imbalances, soil structure damage and an increase in bulk density. Development of a spot fertilizer applicator with precise fertilizer metering was planned to address the problem. The physical and mechanical properties of di-ammonium phosphate were determined. A new grooved belt type fertilizer metering mechanism was developed for orchard application using the fertilizer properties determined. The metering mechanism was equipped with a fertilizer box of 100 kg capacity. The torque requirement of metering mechanism was determined using torque transducer to select a DC motor. The maximum torque required was 4.5 N.m at 8 m.min-1 of metering belt speed. An automatic plant detection-based spot fertilizer applicator with developed fertilizer metering mechanism has potential to save about 20% fertilizer compared to band placement along with reduced labour requirement.

Computer vision-based prototype robotic picking cum grading system for fruits

Huge harvest and post-harvest losses in conventional processes and methods demands precise applications of agricultural inputs. Computer vision based robotic picking is one of such systems that reduces time consumption, labor requirement; and enhances fruits picking and grading efficiency. In this study, an all-in-one prototype robotic picking cum grading system is developed that integrates robotic picking and fruit quality recognition based grading processes. Therein, computer vision algorithms are used for fruit detection followed by picking and placing through controlled robotic arm. For fruit detection, the steps includes color scheme conversion, masking of normal/fresh skin, masking of defects, and morpological dilation operations; and opencv library is used for implementation in Python Language on Anaconda Spyder Integrated Development Environment (IDE). The detection information is then communicated to robotic arm through microcontroller. The fabricated arm is of 4 Degrees of Freedom (4-DOF) and therein, four servo motors aid the robotic arm. The time taken by proposed computer vision algotihms scheme is as follows: 0.0035136 s, 0.0081954 s, 0.0005433 s, and 0.0002408 s for color scheme conversion, segmentation, morphological operations, and bitwise operations respectively; and total time for complete scheme is 0.0125 s. Overall, the developed system took average time of 15 s for one grading cycle in case of poor quality fruits. For this case, no action is carried out and the fruit passes the conveyor directly to storage bin. However, the time required for good quality fruits is high i.e. 21 s as the robotic arm picks and places the fruits for this case.

Infrastructure Management System

Technology cannot be disregarded because it has raised the living standards and sparked revolutions in many facets of contemporary society. In every industry, technology has decreased labor and time requirements, and enhanced production efficiency. A certain technology's innovation eventually makes it a part of society and essential to people's daily lives. The management of infrastructure manually in any organization or institution is a tedious task. In order to make the management of infrastructure in courts automated, an application is developed that maintains every detail of all infrastructures and IT stocks in the courts. Users can view the purchase date, warranty period, and location of the infrastructure in the court. A unique QR code is generated for each piece of furniture and IT stock to access its details. The Infrastructure Management System is touted as a revolution in court management. This application saves time and workload. Moreover, notifications are generated if the service period is nearing, and it would call the service person regarding the repair and service. This management system is found to be effective and efficient.

The potential impact of ChatGPT in clinical and translational medicine.

The potential impact of ChatGPT in clinical and translational medicine.

Effects of Bee Density and Hive Distribution on Pollination Efficiency for Greenhouse Strawberries: A Simulation Study

The cultivated strawberry Fragaria × ananassa Duch. is widely planted in greenhouses around the world. Its production heavily depends on pollination services. Bee pollination can significantly improve fruit quality and save considerable labor requirements. In this paper, we used a simulation model to study the effects of bee density and hive distribution on pollination efficiency for greenhouse strawberries. Simulation experiments allowed us to obtain the pollination efficiency under different conditions and track every bee, flower, pollen and fruit in detail without great planting cost. In particular, we found that fruit quality cannot be significantly improved once bee density is higher than one bee/plant due to a saturation effect. Distributing bee hives and strawberry interplanting spatially can improve fruit quality. In addition, the simulation results suggested that a continuous bee pollination process can significantly reduce the influence of stigma receptivity. This effect and the even distribution of pollen are the top two reasons explaining how bee pollination outperforms artificial pollination.

ANALISIS KEBUTUHAN TENAGA KERJA BAGIAN PELAPORAN RUMAH SAKIT MENGGUNAKAN METODE ANALISIS BEBAN KERJA KESEHATAN (ABK KES)

ABK Kes is a method of calculating the need for health human resources based on the workload carried out by each type of health human resource according to their main tasks and functions. There is a double job, the amount of data that needs to be processed results in reports that cannot be reported properly, on time and officers cannot carry out work in accordance with the description of their main tasks and functions. The purpose of this study was to determine the needs of the workforce with the health workload analysis method (ABK Kes) in the medical record unit reporting section at Muhammadiyah Lamongan Hospital.The subjects in this study were reporting officers. While the object in this study is the workload of reporting officers. Data collection methods are by interview and observation. Data processing is done by collecting, editing, tabulating and presenting data in a qualitative descriptive form. The results of this study are health facilities of the general hospital type and the type of health human resources reporting section, the available WKT is 1240 hours/year and 74400 minutes/year, the workload standard is 538,800, the FTP value is 1.91% and the STP value is 1.13%, and the results of the calculation of the labor requirements for the reporting section of the ABK Kes method were 6 people. The conclusion of this study is that the reporting section's need for health human resources is 6 health human resources currently only 3 health human resources are available. The author suggests that it is better to add 3 health human resources.

Developing a two-decadal time-record of rice field maps using Landsat-derived multi-index image collections with a random forest classifier: A Google Earth Engine based approach

Historic maps showing the temporal distribution of rice fields are important for precision agriculture, irrigation optimisation, forecasting crop yields, land use management and formulating policies. However, mapping rice fields using traditional ground surveys is impractical when high cost, time and labour requirements are considered, and the availability of such detailed records is limited. Although satellite remote sensing appears to be a viable solution, conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation classes. To this end, we explored a novel, Google Earth Engine (GEE) based multi-index random forest (RF) classification approach to map rice fields over two decades. Landsat images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from GEE and a multi-index RF classification algorithm was applied to distinguish the rice fields. The results showed above 80% accuracy for both training and validation, when compared against high spatial resolution Google Earth imagery. In essence, multi-index sampling and RF together synergised the compelling classification accuracy by effectively capturing vegetation, water (ponding) and soil characteristics unique to the rice fields using a single-click approach. The maps developed in this study were further compared against the MODIS land cover type product (MCD12Q1) and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed approach. Future work seeking effective index combinations is recommended, and this approach can potentially be extended to other crop analyses elsewhere.

Synthetic Datasets for Rebar Instance Segmentation Using Mask R-CNN

The construction and inspection of reinforcement rebar currently rely entirely on manual work, which leads to problems such as high labor requirements and labor costs. Rebar image detection using deep learning algorithms can be employed in construction quality inspection and intelligent construction; it can check the number, spacing, and diameter of rebar on a construction site, and guide robots to complete rebar tying. However, the application of deep learning algorithms relies on a large number of datasets to train models, while manual data collection and annotation are time-consuming and laborious. In contrast, using synthetic datasets can achieve a high degree of automation of annotation. In this study, using rebar as an example, we proposed a mask annotation methodology based on BIM software and rendering software, which can establish a large and diverse training set for instance segmentation, without manual labeling. The Mask R-CNN trained using both real and synthetic datasets demonstrated a better performance than the models trained using only real datasets or synthetic datasets. This synthetic dataset generation method could be widely used for various image segmentation tasks and provides a reference for other computer vision engineering tasks and deep learning tasks in related fields.

Weed Smothering in Mustard (Brassica spp) by its High-Density Uniform Broadcast Sowing

Manual weed control in mustard is very expensive because of its high labour requirement i.e., 150-250 labourers/ha. Chemical weed control again imposes environmental hazards. Mechanical weed control in mustard is not still that popular till date. Due to severe weed competition, the yield reduction in Indian mustard may go as high as 70 per cent. Thus weed control in zero till mustard using its high-density sowing (HDS) was studied in different farmers’ field, at ICAR-CRIJAF, in North 24 PGS and Purulia, from 2018-2022. Fast growing and high- density uniform mustard canopy (100-200 m2) developed at early stages hinders sunlight penetration (up to 99.98 per cent) below its canopy (at 35- 40 days), leading to scanty and under developed growth of different composite weed species at harvest, those germinate or grow below mustard canopy. Weed population below mustard canopy was reduced by 92 to 97 per cent at mustard harvest. It was only 10-80/m2 at harvest over 350-1000/m2 in weedy situation at initial germination. The reduction of dicot weed biomass below matured mustard at harvest was 81-99 percent (5-150g/ m2) percent over weedy plots (500-800/ m2). Reduction of effective flowers/pods of different weeds under matured mustard canopy were up to 99.5 percent. Proper agronomic management practices of HDS mustard e.g., seed rate, date of sowing, uniformity of sowing, irrigation and fertiliser application will eliminate the herbicide application or manual weeding in mustard. Nearly 80 percent mustard plants remain active at harvest. It saves 150-250 labours /ha depending on weed species and its density of germination. Mustard grain yield ranged from 15-30 q/ha depending on management and varieties used. This weed smothering merit of mustard can be used for eco friendly weed control in other wide spaced field/horticultural crops using its dwarf variety (Toria).

Factoring labour when comparing in situ rainwater harvesting technologies for semi-arid areas of central Tanzania

ABSTRACT Erratic rainfall, high evapotranspiration rates and droughts are major factors limiting crop production in semi-arid areas. Tied ridges that have crossed ties within the furrow are among the physical soil and water conservation measures. During the 2018/19 and 2019/20 seasons, we examined the efficacy of repaired tied ridges for maize crop (Zea mays) and sorghum (Sorghum bicolor) in Kongwa district of Tanzania as an alternate labour saving strategy for managing climate risks associated with variable rainfall. Treatments consisted of three tillage methods: conventional tillage (CT) which involved the preparation of a flat seedbed using handhoe, annually constructed tied ridges (ATR) and tied ridges that had been constructed during the previous season and had been repaired (residual tied ridges–RTR). Data were collected on labour requirements and crop performance. RTR increased economic returns by 29% and 80% over ATR and CT, respectively. Maize grain yield shows a trend of RTR >ATR>CT with values ranging from 2465 kg ha−1 to 4185 kg ha−1 (P < 0.01). While tillage and/or variety did not influence sorghum grain yield significantly (P > 0.05). The use of RTR is recommended because of low labour requirements and greater economic benefits than CT and ATR under maize cropping systems.

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects.

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.

Analysis of Manufacturing Methods and Die Design for Rice Transplanter Claws and Combine Harvester Blades in Bangladesh

High labor wages brought on by a shrinking agricultural labor force, as well as promises of increased crop yields and decreased labor requirements, have spurred the adoption of agricultural mechanization in Bangladesh. Due to these factors, the market for more complex agricultural machinery, such as rice transplanters and combine harvesters, is growing substantially in Bangladesh. As a result, a market has developed for agricultural workshops to manufacture spare parts within the local communities. However, the current manufacturing practices of local agricultural workshops limit their ability to produce high-quality parts that can compete with spare parts imported from China. This paper presents the analysis of the manufacturing processes used in the manufacturing of two key spare parts for the mechanization of planting and harvesting in Bangladesh: rice transplanter claws and combine harvester blades. The manufacturing processes proposed in this paper use machine tools and manufacturing processes that are ubiquitous in Bangladesh, such as grinders and drill presses. Additional manufacturing processes, such as blanking and forming with punch and die sets, are proposed as the methods for increasing product quality and reducing the manufacturing time required to produce complex geometries, such as those found on rice transplanter claws and combine harvester blades. The results from the experimental testing and the finite element simulations of the deformation processes indicate that local manufacturing of high-quality rice transplanter claws and combine harvester blades is viable with the proposed manufacturing processes.

Potential of soil and water conservation technologies for dryland agriculture and forestry

ABSTRACT Objective: Drylands cover 41% of the earth’s terrestrial landmass while in Kenya; they cover 80% of the land surface with production in these areas often impeded by soil moisture limitation thereby necessitating soil moisture conservation. The study aimed at identifying feasible soil moisture conservation technologies for agriculture and forestry development. Methodology and Results: The current metadata analysis evaluated 80 reference materials comprising of journal papers, book chapters and conference proceedings; 38.8% being research conducted in Kenya, 31.2% was research undertaken in other parts of Africa while 30.0% was research done in other parts of the globe. Available data shows that mulching enhanced infiltration by 81% reduced runoff by 54% and enhanced crop yield by 39%; terraces enhanced infiltration by 8% and reduced runoff by 30%. Micro-catchments enhanced moisture content by 45%, reduced runoff by 63% and enhanced crop yield by 17%. Further, the use of polymers enhanced crop yield by up to 45%. In Kenya, soil and water conservation technologies have been tested in Machakos, Embu, Kitui, Busia, Makueni and Tharaka Nthi counties. The main impediments to adoption of these technologies in Kenya and other parts of the globe include increased risk of pest and diseases incidences; high labour requirements; require large land sizes and increased cost of production resulting from labour requirements. The research gaps identified by different researchers concerning soil water conservation technologies are: lack of effective dissemination platforms to highlight efficiency of the different technologies; cost-benefit analysis of the different soil water conservation technologies and limited data on potential of various soil water conservation technologies for tree growing in ASALs. Conclusion: This review has revealed that adequate information on soil and moisture conservation technologies is needed and tailored to specific farmers’ needs to enhance level of adoption. In order to realize the full potential of soil and moisture conservation technologies, effective dissemination platforms such as use of demonstration plots and farmers’ field schools are required. Key words: soil moisture, technologies, conservation, adoption, ASALs