Manpower Costs Research Articles

Development of A Radio Frequency Identification System for The Manufacturing Packing Process Via The DMAIC Approach

Quality has long been a critical metric for determining a company's competitiveness in the business world. The use of tools, methods, and concepts to improve and regulate product quality has been widespread. The purpose of this research is to solve the missing accessories issue that occurs during the packing process by using the Lean Six Sigma tool DMAIC (Define, Measure, Analyze, Improve, and Control). As part of the Lean Six Sigma quality effort, the DMAIC technique is frequently defined as a problem-solving methodology and a data-driven quality strategy. The actual root cause for these missing accessories is the lack of a proper validation process during the packing process. A brainstorming session was held to develop solutions to this problem, and one suggestion was to employ the radio frequency identification (RFID) validation system. This new implementation was set up during the kitting process by pasting RFID labels for all accessories. These accessories were validated back during the packing process to ensure no missing accessories after the operator sealed the carton box. After implementing the RFID system, the company managed to reduce the cost of manpower and parts that needed to be replaced in case of missing data. It also improves the efficiency of the business, as the addition of a validation RFID system avoids mistakes by detecting missing accessories before the product is delivered to the customer.

Modeling process for full forming sports underwear

Abstract Most of the sports underwear available is sewn using cutting and sewing techniques. During the exercise process, it is easy to cause discomfort to the skin due to repeated friction at the seam, and at the same time, it is also a waste of manpower and raw material costs. In order to address these issues present in existing sports underwear, during its design and development, combined with fully forming knitting technology, construction process model, so as to realize the integrated knitting of sports underwear. First of all, through the characteristics of the local style of sports underwear, the key points of its prototype were probed. Second, on this basis, its process model was established, including the forming methods and knitting rules of the body, shoulder collar, and bottom circumference. Finally, the feasibility of the technological model is proved by the braiding of a fully formed sports underwear. The change in the body size can be achieved by using the two-side braiding transfer method. The curve contour of the shoulder and neck can be obtained by using the triangular polyline segmentation method. The bottom part is divided into a cylinder part and a supporting part. The local braiding method is used to achieve the knitting. This study puts forward a process model and application method of fully formed sports underwear, which provides a certain method and reference for the design and production of fully formed sports underwear.

Evaluation of Fracturing Effect of Tight Reservoirs Based on Deep Learning.

The utilization of hydraulic fracturing technology is indispensable for unlocking the potential of tight oil and gas reservoirs. Understanding and accurately evaluating the impact of fracturing is pivotal in maximizing oil and gas production and optimizing wellbore performance. Currently, evaluation methods based on acoustic logging, such as orthogonal dipole anisotropy and radial tomography imaging, are widely used. However, when the fractures generated by hydraulic fracturing form a network-like pattern, orthogonal dipole anisotropy fails to accurately assess the fracturing effects. Radial tomography imaging can address this issue, but it is challenged by high manpower and time costs. This study aims to develop a more efficient and accurate method for evaluating fracturing effects in tight reservoirs using deep learning techniques. Specifically, the method utilizes dipole array acoustic logging curves recorded before and after fracturing. Manual labeling was conducted by integrating logging data interpretation results. An improved WGAN-GP was employed to generate adversarial samples for data augmentation, and fracturing effect evaluation was implemented using SE-ResNet, ResNet, and DenseNet. The experimental results demonstrated that ResNet with residual connections is more suitable for the dataset in this study, achieving higher accuracy in fracturing effect evaluation. The inclusion of the SE module further enhanced model accuracy by adaptively adjusting the weights of feature map channels, with the highest accuracy reaching 99.75%.

Consumer Study - Consumers Perspective Towards Drone Food Delivery Service

Drone food delivery has a large possibility of being welcomed by businesses as it provides benefits that traditional delivery methods dont have. Such as reduced reliance on manpower and sunk cost, and improved customer satisfaction. Our study aims to measure the adoption and attitudes of consumers toward drone food delivery technology in order to help us assess the feasibility and effectiveness of the service. The study found that privacy has a huge negative impact on consumer attitudes towards drone delivery. However, the Green image had a significant positive effect. People in rural areas pay more attention to the performance of drones than those in cites. At the same time, the research result can be used to analyse the future of the drone food delivery industry and commercial plans prepared for companies.

Multiprobe Amplification (MPA) with Melting Curve Analysis: A Highly Stable and Cost-Effective Platform for the Simultaneous Detection of Eight Potential Bacterial Bioterrorism Agents in Complex Samples.

We aimed to develop an efficient detection platform that can identify a larger number of suspicious samples in a single test, saving time, manpower, and material costs, and providing vital support to the public health system in coping with the current challenging and dynamic bioterrorism threat landscape, particularly in regions of turmoil and conflict. We have successfully developed a high-throughput, multitarget fluorescent array detection platform by effectively combining integrating multiprobe amplification (MPA) with melting curve analysis. Specifically, we have established reliable laboratory testing methods for eight highly pathogenic bacteria, including Bacillus anthracis, Yersinia pestis, Brucella spp., Burkholderia pseudomallei, Francisella tularensis, Vibrio cholerae, Salmonella typhi, and Staphylococcus aureus. Our method achieves sensitive and specific simultaneous detection of eight target bacteria in one well by optimizing the reaction conditions of MPA. In the assessment of 192 simulated environmental samples, both positive and negative coincidence rates were 100.00%. Among 48 simulated clinical samples, the positive coincidence rate reached 97.73%, while maintaining a perfect negative coincidence rate of 100.00%. Moreover, the detection platform holds immense potential for attaining a more comprehensive bioterrorism screening, and its high cost-effectiveness enables the provision of diverse and adaptable diagnostic methods for public health quarantine in underdeveloped countries and regions.

Development and application of artificial hydraulic lime for Chinese architectural heritage restorations

Lime is commonly employed in the restoration of historic buildings due to its strength properties, which align well with the demands of architectural heritage conservation. However, recent shortages in high-quality limestone raw materials and the commercial inclusion of finely ground limestone powder in hydrated lime powder have led to significant challenges. These include low initial strength, pronounced shrinkage and cracking, and poor resistance to frost. Although natural hydraulic lime (NHL) offers moderate strength, its high cost presents an obstacle to widespread use in architectural heritage restoration. Three types of artificial hydraulic lime (AHL) — AHL1, AHL2, and AHL3 — were prepared with the objective of low cost and easy scalability. These were formulated by incorporating white cement (WC), blast furnace slag powder (BFSP), heavy calcium carbonate powder (HCCP), and industrial-grade white sugar (WS) into industrial-grade hydrated lime powder (IHLP). To align the setting time of the AHLs with the requirements of traditional builders, WS was incorporated as a retarding agent. The addition of WC, BFSP, and HCCP to IHLP resulted in enhancements over IHLP in several key aspects: compressive strength, pore structure post-hardening, dry shrinkage rate, and frost resistance. This approach will enhance the restoration quality of architectural heritage in China, leading to fewer repeated repairs and significant savings in manpower and material costs. Ultimately, AHL2 was applied in the restoration of the Confucius Temple, proving its efficacy and alignment with traditional construction requirements. This research provides valuable theoretical and practical insights for enhancing the preservation of architectural heritage.

A new challenge for cadastral surveying in Taiwan: feasibility analysis using combination on CORS data and online PPP service

Abstract Relative GNSS positioning, a longstanding measurement standard, often incurs high manpower and equipment costs. Online Precise Point Positioning (PPP) presents a cost-effective alternative by minimizing these expenses. However, transitioning from the ITRF2014 to the TWD97[2010] coordinate system presents unique challenges. This study evaluates the efficacy of five PPP services – AUSPOS, OPUS, CSRS-PPP, magicGNSS, and RTX-PP – using 2018 Continuously Operating Reference Stations (CORS) data. Following a seven-parameter transformation, we systematically compared these services to identify the optimal solution for Taiwan’s geodetic survey needs. Our analysis reveals that RTX-PP offers superior performance, achieving 1 cm accuracy over 24 h and 4 cm accuracy over 1 h. Most stations met these accuracy standards, even during short observation intervals from September 9, 2018. Nonetheless, there is a 24.1 % likelihood of exceeding the 6 cm accuracy threshold due to variations in GNSS data quality. To address this, we employed G-Nut software to analyze station data quality across regions, recommending station selection based on data ratio and cycle slips to improve PPP solution accuracy effectively.

HNCGAT: a method for predicting plant metabolite-protein interaction using heterogeneous neighbor contrastive graph attention network.

The prediction of metabolite-protein interactions (MPIs) plays an important role in plant basic life functions. Compared with the traditional experimental methods and the high-throughput genomics methods using statistical correlation, applying heterogeneous graph neural networks to the prediction of MPIs in plants can reduce the cost of manpower, resources, and time. However, to the best of our knowledge, applying heterogeneous graph neural networks to the prediction of MPIs in plants still remains under-explored. In this work, we propose a novel model named heterogeneous neighbor contrastive graph attention network (HNCGAT), for the prediction of MPIs in Arabidopsis. The HNCGAT employs the type-specific attention-based neighborhood aggregation mechanism to learn node embeddings of proteins, metabolites, and functional-annotations, and designs a novel heterogeneous neighbor contrastive learning framework to preserve heterogeneous network topological structures. Extensive experimental results and ablation study demonstrate the effectiveness of the HNCGAT model for MPI prediction. In addition, a case study on our MPI prediction results supports that the HNCGAT model can effectively predict the potential MPIs in plant.

Logistics of the First Anglo-Burma War, 1824–1826

ABSTRACT One of the principal props of British imperialism in South Asia was the British Imperial Army in India. Logistics was important for supplying and transporting the army at the distant theatres to annex new territories and cow down the rebels. The First Anglo–Burma War occurred between the British-Indian Empire and the ruler of Ava in Burma. This conflict, which lasted from 5 March 1824 to 24 February 1826, was an amphibious war. The primary aim of this article is to study the logistics of the British Imperial Army in India, and the secondary objective is to throw some light on the Burmese logistics. An attempt is made in this essay to show the interconnections between ecology, logistics and military organisations of the British-Indian state and Ava. The First Anglo–Burma War can be located at the interface of preindustrial and industrial wars. The animal and human resources of Bengal and Madras presidencies, utilisation of certain indigenous ‘technologies’ (like country boats, rafts, etc.) and technological superiority in gunpowder weapons (both on land and at sea, like gunboats, corned powder, cast iron guns, etc.) enabled the East India Company (EIC) to muddle through and emerge victorious, albeit at a high financial and manpower cost.

Evaluation of the time, labor, and money required for manual and automated nucleic acid (RNA) isolation for the detection of SARS-COV-2 by QRT-PCR using the qiamp viral RNA mini kit and kingfisher flex

We undertook this cross-sectional investigation to assess the time, manpower, and average run cost per sample using manual Qiamp Viral RNA micro kit (Qiagen) and automated kingfisher flex instrument extraction methods for SARS-Cov-2 identification.The study used 120 Viral Transport Media-collected nasopharyngeal/ oropharyngeal swabs.Magnetic bead-based RNA extraction was performed using the Thermo Fisher Scientific kingfisher flex instrument and manual Extraction was Silica membrane-based Qiagen spin column kits. The TaqPath™ COVID-19 Combo Kit from Thermo Fisher Scientific was used for detecting SARS-CoV-2 target genes.Human technique took 40 minutes longer than automation. It cost more to automate than to manually labor. These disparities in time, effort, and cost affect laboratory operations, offering pros and cons for each method. This suggests that positive or negative was consistent regardless of viral load or RNA concentration.The study found that automated RNA extraction yielded better results compared to manual extraction. The automated sample processing system saved time, people, and money. In resource-limited or low-throughput labs, manual extraction may be preferable. Manual methods are laborious, require more hands-on time, and risk cross-contamination and technical blunders.

Effects of restricting wastage to only staff who reach the peak of their career in manpower planning models

Wastage is the exit of employees from an organization which may be voluntary or involuntary. The concept of wastage in manpower planning models in literature is being expanded in this study by restricting the concept of wastage to only employees who got to the highest rank in their careers before their exit from service. A new approach which involves backward recursive procedure in dynamic programming (DP) is formulated and applied to evaluate the wages of retirees who exit from service. The athematical analysis of the proposed model indicates that the wages of employees who got to the highest rank in their profession earn higher wages compare to other employees recruited to replace retirees in all the periods of manpower cost being considered.

Inversion of soil carbon, nitrogen, and phosphorus in the Yellow River Wetland of Shaanxi Province using field in situ hyperspectroscopy

Soil nitrogen and phosphorus are directly related to soil quality and vegetation growth and are, therefore, a common research topic in studies on global climate change, material cycling, and information exchange in terrestrial ecosystems. However, collecting soil hyperspectral data under in situ conditions and predicting soil properties, which can effectively save time, manpower, material resources, and financial costs, have been generally undervalued. Recent optimization techniques have, however, addressed several of the limitations previously restricting this technique. In this study, hyperspectral data were taken from surface soils under different vegetation types in the wetlands of the Shaanxi Yellow River Wetland Provincial Nature Reserve. Through in situ original and first-order differential transformation spectral data, three prediction models for soil carbon, nitrogen, and phosphorus contents were established: partial least squares (PLSR), random forest (RF), and Gaussian process regression (GPR). The R2 and RMSR of the constructed models were then compared to select the optimal model for evaluating soil content. The soil organic carbon, total nitrogen, and total phosphorus content models established based on the first-order differential had a higher accuracy when modeling and during model validation than those of other models. Moreover, the PLSR model based on the original spectrum and the Gaussian process regression model had a superior inversion performance. These results provide solid theoretical and technical support for developing the optimal model for the quantitative inversion of wetland surface soil carbon, nitrogen, and phosphorus based on in situ hyperspectral technology.

Potential of <i>Mikania micrantha</i> for biogas production

Invasive weeds are one of the major ecosystem-degrading elements and also a threat to crop production. The conventional approaches of weed control appear ineffective for Mikania micrantha which is one of the notorious invasive weeds having vigorous propagation characteristics. The manual method of control is otherwise effective provided the manpower cost involved in the collection is recovered. This study attempts an innovative approach to energy recovery from Mikania micrantha biomass. The suitability of Mikania biomass as a source of biogas production is investigated using a commercially available biogas reactor. The study primarily aims to understand the techno-economic feasibility of achieving the benefits of weed control and the generation of clean cooking fuel taking into consideration of collection, and handling of Mikania biomass and its subsequent conversion into biogas for a typical rural situation. In general biogas production potential of hydrolyzed Mikania is comparable with several weed biomasses and even more than cow dung. The time spent to remove Mikania micrantha to protect crops and the ecosystem is justified by its fuel value as biogas. Outcomes of the current study concerning the benign control of invasive weed, will be useful to materialize the multifaceted benefits of biogas technology such as clean fuel (an alternative renewable source of energy) and nutrient recycling from the large-scale conversion of Mikania biomass into biogas and organic fertilizer.

Investment Analysis of New Fire Truck: A Case Study of Purchase or Rent Option

PKT, as a petrochemical company with high potential hazards, prioritizes emergency preparedness and response by complying with regulations and standards. In addition to serving its own needs, PKT’s fire and rescue department also provides emergency services to four neighboring companies through an annual fee cooperation scheme. To enhance the emergency response services, PKT conducted an evaluation and identified the need for an additional fire truck unit with up-to-date technology. To determine the most suitable option, PKT is considering purchasing or renting the new fire truck units. A financial modeling analysis was conducted using income from health and safety services. The financial feasibility analysis, considering parameters such as NPV, IRR, and discounted payback period, resulted in a feasible outcome. The Purchase option exhibited better economic value, with a predicted NPV of Rp. 569,276,495, an IRR of 11.56% (higher than the discount rate of 10.5%), and a discounted payback period of 9.35 years. Sensitivity analysis, using tornado and spider charts, revealed that the annual fee from clients or joint venture companies had the most significant impact on the project’s financial performance, followed by manpower costs. A 20% increase in annual fees led to a significant increase in NPV Rp. 6,639,013,466, while a 20% decrease resulted in a significant decrease in NPV (−Rp. 5,500,460,477). Similarly, a 20% increase in manpower costs resulted in a decreased NPV of Rp. −3,500,609,667. Based on the sensitivity analysis, the two significant factors influencing the purchase option were identified as the annual fee and manpower cost. Recommendations that are also put forward for this study are identification to anticipate increases in labor costs and to keep manpower costs under control. The increase in manpower costs must be accompanied by an increase in income. Exploring opportunities to attract additional clients, particularly companies in the industrial complex that do not yet have an agreement with PKT, is also advised. By implementing these recommendations and ensuring cost control, PKT can optimize its emergency response services and maintain a financially viable operation.

Automatic fruit picking technology: a comprehensive review of research advances

In recent years, the fruit industry has become an important part of agricultural development, and fruit harvesting is a key stage in the production process. However, picking fruits during the harvest season is always a major challenge. In order to solve the challenges of time-consuming, costly, and inefficient fruit picking, researchers have conducted a lot of studies on automatic fruit picking equipment. Existing picking technologies still require further research and development to improve efficiency and reduce fruit damage. Aiming at the efficient and non-destructive picking of fruits, this paper reviews machine vision and mechanical fruit picking technology and the current research status, including the current application status, equipment structure, working principle, picking process, and experimental results. As a promising tool, machine vision technology has been widely researched and applied due to its low hardware cost and rich visual information. With the development of science and technology, automated fruit picking technology integrates information technology, integrates automatic perception, transmission, control, and operation, etc., saves manpower costs, and continuously promotes the development of modern agriculture in the direction of refinement of equipment technology, automation, and intelligence. Finally, the challenges faced by automated fruit picking are discussed, and future development is looked forward to with a view to contributing to its sustainable development.

Optical Properties Prediction for Red and Near-Infrared Emitting Carbon Dots Using Machine Learning.

Functional nanostructures build up a basis for the future materials and devices, providing a wide variety of functionalities, a possibility of designing bio-compatible nanoprobes, etc. However, development of new nanostructured materials via trial-and-error approach is obviously limited by laborious efforts on their syntheses, and the cost of materials and manpower. This is one of the reasons for an increasing interest in design and development of novel materials with required properties assisted by machine learning approaches. Here, the dataset on synthetic parameters and optical properties of one important class of light-emitting nanomaterials - carbon dots are collected, processed, and analyzed with optical transitions in the red and near-infrared spectral ranges. A model for prediction of spectral characteristics of these carbon dots based on multiple linear regression is established and verified by comparison of the predicted and experimentally observed optical properties of carbon dots synthesized in three different laboratories. Based on the analysis, the open-source code is provided to be used by researchers for the prediction of optical properties of carbon dots and their synthetic procedures.

Performance of tracked excavators equipped with grapple-saw stacking treetops in poplar plantations

ABSTRACT In recent years, the cost of manpower increased in Southern Europe. This has encouraged companies to improve the mechanization level of all activities. Purpose-built machines are very expensive, so in this transition phase, there is a tendency to purchase multipurpose machines to reduce depreciation costs. In this scenario, excavators play a fundamental role because they can be used in many activities by changing the working tools. In poplar plantations, excavators with grapple saws and tracks are usually used to move the residual biomass. This work aimed to analyze the performance of excavators with a grapple-saw during the stacking of residual biomass in poplar plantations. Six excavators that varied in power and mass were analyzed. The data analyzed showed that productivity is directly proportional to the machine’s power and mass (0.15 t h−1 kW−1 and 0.75 t h−1 t−1). Furthermore, productivity is linked to the opening size of the grapple (6.43 t h−1 m−1 of opening). In conclusion, on the basis of the results obtained, it is possible to confirm that the excavators can be valid machines also in forestry activities independently of their nominal power and gross mass.

Improving the Digital Transformation Capacity of Vietnam's Retail Businesses

The Fourth Industrial Revolution is bringing about major transformations: a new order is emerging, new ecosystems, new business models are emerging and creating challenges as well as opportunities for every individual, and every business. Business competition in the digital age is increasingly intense, requiring constant innovation and improve to adapt to changing customers and the economy. Digital transformation is a requirement for emerging economies like Vietnam. Retail business is detailed, specific, and complex. Retailers need to find ways to reduce shipping costs and keep improving their inventory position. Retailers also need to be aware of enhancing value-added services to customers, cutting the cost of manpower, and running while expanding sales channels. So the application of technology to business and retail management is essential nowadays. In this article, the author discusses the reality of the Vietnam retail industry in its approach to technology, opportunities, and challenges, suggesting several solutions to enhance the digital transformation capacity of retail enterprises in the context of digital transition.

Brain tumor segmentation using U-Net in conjunction with EfficientNet.

According to the Ten Leading Causes of Death Statistics Report by the Ministry of Health and Welfare in 2021, cancer ranks as the leading cause of mortality. Among them, pleomorphic glioblastoma is a common type of brain cancer. Brain cancer often occurs in the brain with unclear boundaries from normal brain tissue, necessitating assistance from experienced doctors to distinguish brain tumors before surgical resection to avoid damaging critical neural structures. In recent years, with the advancement of deep learning (DL) technology, artificial intelligence (AI) plays a vital role in disease diagnosis, especially in the field of image segmentation. This technology can aid doctors in locating and measuring brain tumors, while significantly reducing manpower and time costs. Currently, U-Net is one of the primary image segmentation techniques. It utilizes skip connections to combine high-level and low-level feature information, leading to significant improvements in segmentation accuracy. To further enhance the model's performance, this study explores the feasibility of using EfficientNetV2 as an encoder in combination with U-net. Experimental results indicate that employing EfficientNetV2 as an encoder together with U-net can improve the segmentation model's Dice score (loss = 0.0866, accuracy = 0.9977, and Dice similarity coefficient (DSC) = 0.9133).

Building a Flexible and Resource-Light Monitoring Platform for a WLCG-Tier2

Software development projects at Edinburgh identified a desire to build and manage our own monitoring platform. This better allows us to support the developing and varied physics and computing interests of our Experimental Particle Physics group. This production platform enables oversight of international experimental data management, local software development projects and active monitoring of lab facilities within our research group. Larger sites such as CERN have access to many resources to support generalpurpose centralised monitoring solutions such as MONIT. At a WLCG Tier2 we only have access to a fraction of these resources and manpower. Recycling nodes from grid storage and borrowed capacity from our Tier2 Hypervisors has enabled us to build a reliable monitoring infrastructure. This also contributes back to our Tier2 management improving our operational and security monitoring. Shared experiences from larger sites gave us a head-start in building our own service monitoring (FluentD) and multi-protocol (AMQP/STOMP/UDP datagram) messaging frameworks atop both our Elasticsearch and OpenSearch clusters. This has been built with minimal hardware and software complexity, maximising maintainability, and reducing manpower costs. A secondary design goal has also been the ability to migrate and upgrade individual components with minimal service interruption. To achieve this, we made heavy use of different layers of containerisation (Podman/Docker), virtualization and NGINX web proxies. This presentation details our experiences in developing this platform from scratch with a focus on minimal resource use. This includes lessons learnt in deploying and comparing both an Elasticsearch and OpenSearch clusters, as well as designing various levels of automation and resiliency for our monitoring framework. This has culminated in us effectively indexing, parsing and storing >200GB of logging and monitoring data per day.