Labor Costs Research Articles

Perancangan Usaha dan Kelayakan Pembukaan Usaha Kinar Mete di Kota Surabaya

Kinar Mete is a business in the food sector, especially selling cashew nuts, which has been established in 2008 with its production center in Wonogiri. The increasing public interest and consumption of nuts has made the owner of Kinar Mete see the opportunity to expand his business to the city of Surabaya. The feasibility analysis method is used to assess the feasibility of business expansion which includes market, technical, financial aspects as well as sensitivity and risk analysis. The market aspect study obtained from the results of distributing questionnaires to the people of Surabaya City shows that the potential market is 85.33%, the available market is 74%, and the target market is 0.07%. The technical aspect study shows that the business building covers an area of 260 m2 with a workforce of 4 people with working hours Monday-Friday 07.30-16.30 WIB. Financially, the project costs required are IDR 472,955,940. The results of the feasibility study show an NPV of IDR 326,737,829, an IRR of 27.89%, and a PBP of 4.13 years, which shows that the business is worth establishing. Sensitivity analysis shows that business feasibility is sensitive to an increase in raw material costs of 22.38%, labor costs of 57.68%, a decrease in demand of 11.74%, and selling prices of 8.90%. Risk analysis shows that the risk presentation that can occur is 6.35% so that the MARR with a risk of 16.35% results in an NPV value of IDR 180,581,654, an IRR of 27.89%, and a PBP of 4.37 years, which indicates the business remains viable.

The Effect of Board Chairs’ Hometown Identity on Labor Cost Stickiness

In this study, we examine the impact of board chairs’ hometown identity on labor cost stickiness in state-owned enterprises (SOEs). Using data from China, our findings demonstrate that local board chairs can effectively mitigate labor cost stickiness by leveraging their communication advantages. Mechanism analyses reveal that local board chairs primarily achieve this through salary reductions rather than layoffs. We validate two plausible channels, cultural alignment and social trust, through a series of heterogeneity tests. Our results indicate that the influence of board chairs’ hometown identity is more pronounced in SOEs located in provinces with smaller dialect diversity, less population mobility, and lower social trust levels, as well as those led by board chairs without overseas experience, serving longer terms, and far from retirement. Overall, this study contributes to existing literature by providing empirical evidence on how board chairs’ hometown identity facilitates the alleviation of labor cost stickiness in SOEs.

A Comparative Study of Precast and Cast-in Place Concrete in Construction Home Project by Life Cycle Cost Analysis

Construction industry plays a significant role in economic growth, and the industry will continue to grow with the economic expansion. However, the industry is known for its labor-intensiveness, In order to shorten construction time and address labor issues, developers have explored new systems of construction such as a precast system. This paper is to present a comparative study of precast and cast-in place concrete system in construction home project by life cycle cost analysis by dividing project period into 3 phase (Preparing phase, Construction phase and O&M phase). In modelling life cycle costs, sensitivity analysis and Monte Carlo simulation will be employed to incorporate uncertainty of material and labor costs into the proposed model. The study also found that in 6 home projects, there have risk factors that impact to life cycle cost by using sensitivity analysis are construction cost, major repair and minor repair. The result from this study is life cycle cost from precast and cast -in place systems

Modernizing agricultural workflows using artificial intelligence and deep learning: Enhancing plant species identification

Modernizing agricultural processes is imperative due to the labor-intensive nature that impedes crop production. Despite the steps of farm technologies, the manual tasks of identifying plants and weeds remain very time-consuming. The current paper asserts that Convolutional Neural Networks (CNNs), a type of Artificial Intelligence, can enhance plant species recognition efficiency. The paper focuses explicitly on how CNNs can be leveraged to improve the process of identifying different plant species, thereby promoting efficiency in plant species identification. With a dataset of images of 12 species of plants, we have come up with an entirely accurate CNN model for the classification of plant types. Implementing this AI-powered approach can reduce manual work, enhance decision-making processes, and enrich environmental sustainability in agriculture by rendering the workflow smoother. The present study has broad ramifications for agriculture, technology, and environmental science professionals seeking to embed AI solutions into conventional workflows. The proposed system is meant to show how AI can improve productivity, reduce labor costs, and allow environmentally sustainable agriculture.

ОПРЕДЕЛЕНИЕ ОПТИМАЛЬНЫХ ПАРАМЕТРОВ КАПЕЛЬНОЙ ЛЕНТЫ ДЛЯ МЕХАНИЗИРОВАННОЙ УКЛАДКИ

The introduction of drip irrigation systems in irrigated areas is impossible without the creation of special machines for laying drip tape. The advantage of in-soil laying of the tape is the absence of losses due to evaporation of irrigation water and the inadmissibility of its heating to temperatures dangerous for the plant in the hot season. To ensure high-quality laying of the drip tape in the soil, constant tension of the tape is required when the speed of the machine fluctuates or it suddenly stops. The absence of constant tension will lead to jamming of the tape in the laying pipe or its twisting, which will make further operation of the drip irrigation system impossible and will require repair work that requires additional labor costs. Constant tension of the tape ensures its precise location in the soil, which will eliminate additional pressure losses when the position of the tape changes during operation of the drip irrigation system. To maintain constant tension, a permanent brake is required. This creates additional tensile forces applied to the tape. The tape is also affected by the resistance of laying it in the soil. The research was aimed at establishing the numerical values of the forces acting on the drip tape to assess their impact. The most popular tapes on farms are those with a thickness of 0.15...0.25 mm due to their low cost. Research has established that for underground installation it is desirable to use a tape with a thickness of at least 0.2 mm. When using a tape with a thickness of 0.15 mm, tensile forces can lead to dangerous deformations of the tape, making it impossible to further use

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities.

Development and research of an autonomous mobile platform with reduced computing power requirements

Today, the market is saturated with a variety of autonomous robotic devices that offer a variety of solutions to opti-mize workflows in various industries. One of the leading areas of their application is the automation of warehouse operations, where store robots move and optimize warehouse processes, reducing labor costs and speeding up task completion. These technologies reveal some limitations and shortcomings. For example, categories of robots such as transport carts, forklifts, and automated towing vehicles are limited in their functionality, often resulting in the need to use multiple types of devices for different tasks. This leads to equipment redundancy and complicates integration into existing warehouse systems. Another significant aspect is the need to improve the routing and nav-igation algorithms for these devices. Modern autonomous robot systems often face route optimization challenges, especially in dynamic and complex warehouse environments. This can lead to inefficiencies and increased time delays [1]. The goal of this work is to develop an autonomous mobile platform that would solve possible problems with navigation, have well-developed routing algorithms combined with low cost for affordable use in small busi-nesses. The research methods involve conducting various experiments on a real mobile platform to collect data and evaluate performance, including measuring the speed, accuracy, and power consumption of transportation tasks. Development and optimization of new algorithms for managing a mobile platform, planning routes, analyz-ing signals from sensors, processing images and video from a camera. The result of the work is that the possibility of compatibility of the on-board computer (single board computer) with the flight controller for movement on the ground surface has been investigated. On this basis, a mobile platform was created, which is presented in this arti-cle. This configuration is suitable for moving the platform either autonomously or using a control panel. The role of the technical vision system in providing navigation has been studied. The results of the work highlight innovation and the use of model functionality to create effective autonomous land navigation systems, including coordinates. The mobile platform can be used for transporting goods in warehouses, for working in extreme situations, for environmental monitoring or for educational purposes.

FISCAL MANAGEMENT OF ENTERPRISE PERSONNEL IN THE CONTEXT OF DEVELOPING INTERNATIONAL ECONOMIC RELATIONS

The aim of the study is to characterize the features of fiscal management of enterprise personnel in the context of developing international economic relations. The primary task set within this article is to identify the most optimal practices for implementing fiscal management of enterprise personnel under the increased influence of changes in the international economic environment. It has been determined that the development of international economic relations requires enterprises to adapt their personnel management with consideration of fiscal aspects. Fiscal management of personnel is characterized as a means of optimizing labour costs through the use of tax incentives, subsidies, and other financial instruments, which enhance competitiveness on the international level. It has been established that effective fiscal management ensures that enterprise activities comply with international standards, allowing them to avoid fines and other legal consequences. The role of fiscal management in optimizing the processes of employee relocation between company branches in different countries, ensuring the legality and financial benefit of such relocations, has been characterized. It has been established that proper management of fiscal aspects helps companies expand into new markets by ensuring the effective integration of local and foreign workers into the corporate culture. The necessity of implementing modern technologies and enhancing the qualifications of specialists to improve the fiscal management of personnel has been characterized. It has been proven that effective fiscal management of personnel contributes to optimizing tax obligations, increasing competitiveness, and ensuring the international integration of enterprises.

Trade‐offs between labour migration and agricultural productivity: Evidence from smallholder wheat systems in Nepal

AbstractRural labour out‐migration has become a major contributor to off‐farm income through remittances and plays a crucial role in supporting the livelihoods of rural households in developing economies. However, research on the simultaneous on‐farm and off‐farm impacts of labour migration is still lacking. This study assesses the impacts of household labour migration on wheat productivity, labour and total costs, profitability and off‐farm income among smallholder wheat growers in Nepal. We use endogenous switching regression and two‐stage least squares regression models to control for potential endogeneity. The findings reveal that labour migration boosts off‐farm income due to remittances but negatively affects wheat productivity and profitability due to labour shortages. In addition, heterogeneous effects are observed, with large farms, cooperative membership, use of farm mechanization and non‐marginalized castes recording positive impacts. The study suggests that social institutions, such as cooperatives, and farm mechanization can create synergies between labour migration and agricultural productivity in Nepal.

Weed management options in drum seeded wetland rice

High labour requirement and cost for raising nursery and transplanting of the seedlings has necessitated the shift from transplanted rice to direct-seeded rice. To reduce the requirement of large quantity of seeds, incidence of pests and diseases in broadcasting and to ensure uniform seed distribution for consistent crop growth and easier maintenance, mechanization is needed in direct-seeded rice. The major constraint experienced in wet DSR is the occurrence of high weed infestation as emerging seedlings in direct-sown fields are less competitive with concurrently emerging weeds. An experiment was conducted at M. S. Swaminathan Rice Research Station, Moncompu, Kerala Agricultural University during kharif and rabi 2021-22, with eight treatments and three replications in randomised complete block design to identify suitable cost-effective agronomic management practices to enhance the productivity of drum seeded rice and to maximise its resource-use-efficiency. The results revealed wet DSR using drumseeder as a viable crop establishment method for reducing the seed requirement, cultivation time, energy, cost of cultivation, pest and disease infestation, minimise plant stress due to transplanting and sustaining yield. There was significant variation in the yield and yield attributes in drum seeding depending on the weed management strategies adopted viz., two conoweedings, post emergent herbicide application, pre followed by post emergent herbicide application, post emergent herbicide application followed by conoweeding. Post-emergence application of Chlorimuron ethyl + Metsulfuron methyl @ 0.004 kg/ha and Penoxsulam + Cyhalofop butyl @ 0.135 kg/ha as tank mix at 18 DAS followed by conoweeding at 30 DAS was found economical in drum seeding which was on par yield with broadcasted crop yield under post emergent herbicide application.

Combine harvester outsourcing services and seasonal rural non‐farm employment in Myanmar

AbstractPrior research on mechanization outsource services has not explored seasonal variations in drivers and outcomes of adoption by smallholders. These omissions are important because seasonality of crop cultivation may influence intensity of demand for machines, while seasonality of demand for non‐farm labor may influence the availability and cost of agricultural workers and the opportunity costs of on‐farm self‐employment. We analyze seasonal correlates of combine harvester outsource service use in Myanmar. Surprisingly, adoption is not associated with lower labor costs or higher net margins from paddy cultivation. Rather, using combines releases family labor into more remunerative dry season non‐farm employment.

Establishment of clary sage (Salvia sclarea L.) regeneration system and development of glyphosate-tolerant clary sage plants through Agrobacterium-mediated genetic transformation

Clary sage (Salvia sclarea L.) is well known as a highly aromatic herbaceous plant and is commonly used in aromatic products and medicine. The biggest problem in the cultivation of clary sage is weeds, which not only entails a great cost of labor for weeding, but also leads to a great fluctuation in the yield and quality of its products. Therefore, there is a need to breed herbicide-resistant clary sage using genetic engineering. However, the lack of suitable regeneration and genetic transformation systems for clary sage has limited the process of molecular breeding. In this study, we firstly reported the regeneration system of clary sage, where explants from hypocotyls of sage induced adventitious shoots at a rate of 22.22% when cultured on MS medium supplemented with 2.5mg·L-1 6-BA, 0.15mg·L-1 IAA, and 0.25mg·L-1 VC. Rooting of adventitious shoots was achieved at a rate of 94.44% on 1/2 MS medium containing 0.125mg·L-1 NAA, 0.5mg·L-1 IAA, and 0.1mg·L-1 IBA. Secondly, based on the regeneration system, an efficient and stable Agrobacterium-mediated genetic transformation system was established in this paper, and the rate of genetic transformation was determined to be in the range of 3%-5% by transforming the hypocotyls of clary sage with pCAMBIA1301 vector and pCAMBIA1301-GR79-GAT vector. Finally, glyphosate tolerance was obtained from GR79 and GAT co-expressed clary sage plants in this study. This study not only lays the foundation of genetic transformation for the functional gene research and in-depth investigation of various biological problems of clary sage, but also provides a feasible genetic engineering solution for its industry to solve the weed problem, as well as a promotional role for precise molecular breeding of clary sage.

Intelligent Dance Motion Evaluation: An Evaluation Method Based on Keyframe Acquisition According to Musical Beat Features

Motion perception is crucial in competitive sports like dance, basketball, and diving. However, evaluations in these sports heavily rely on professionals, posing two main challenges: subjective assessments are uncertain and can be influenced by experience, making it hard to guarantee timeliness and accuracy, and increasing labor costs with multi-expert voting. While video analysis methods have alleviated some pressure, challenges remain in extracting key points/frames from videos and constructing a suitable, quantifiable evaluation method that aligns with the static–dynamic nature of movements for accurate assessment. Therefore, this study proposes an innovative intelligent evaluation method aimed at enhancing the accuracy and processing speed of complex video analysis tasks. Firstly, by constructing a keyframe extraction method based on musical beat detection, coupled with prior knowledge, the beat detection is optimized through a perceptually weighted window to accurately extract keyframes that are highly correlated with dance movement changes. Secondly, OpenPose is employed to detect human joint points in the keyframes, quantifying human movements into a series of numerically expressed nodes and their relationships (i.e., pose descriptions). Combined with the positions of keyframes in the time sequence, a standard pose description sequence is formed, serving as the foundational data for subsequent quantitative evaluations. Lastly, an Action Sequence Evaluation method (ASCS) is established based on all action features within a single action frame to precisely assess the overall performance of individual actions. Furthermore, drawing inspiration from the Rouge-L evaluation method in natural language processing, a Similarity Measure Approach based on Contextual Relationships (SMACR) is constructed, focusing on evaluating the coherence of actions. By integrating ASCS and SMACR, a comprehensive evaluation of dancers is conducted from both the static and dynamic dimensions. During the method validation phase, the research team judiciously selected 12 representative samples from the popular dance game Just Dance, meticulously classifying them according to the complexity of dance moves and physical exertion levels. The experimental results demonstrate the outstanding performance of the constructed automated evaluation method. Specifically, this method not only achieves the precise assessments of dance movements at the individual keyframe level but also significantly enhances the evaluation of action coherence and completeness through the innovative SMACR. Across all 12 test samples, the method accurately selects 2 to 5 keyframes per second from the videos, reducing the computational load to 4.1–10.3% compared to traditional full-frame matching methods, while the overall evaluation accuracy only slightly decreases by 3%, fully demonstrating the method’s combination of efficiency and precision. Through precise musical beat alignment, efficient keyframe extraction, and the introduction of intelligent dance motion analysis technology, this study significantly improves upon the subjectivity and inefficiency of traditional manual evaluations, enhancing the scientificity and accuracy of assessments. It provides robust tool support for fields such as dance education and competition evaluations, showcasing broad application prospects.

ECONOMIC AND FINANCIAL SITUATION OF THE FOOD INDUSTRY IN POLAND IN THE YEARS 2019-2023

The aim of the article is to assess the economic and financial situation of the food industry and its main industries in Poland in 2023 compared to 2019. The analysis used the method of synthetic assessment of the financial result developed by Bednarski. A comparative assessment of the dynamics of quantitative and qualitative indicators determining changes in the value of individual production factors and the efficiency of their use was carried out, both for the entire food industry and its main industries. The research was further enhanced by a cause-and-effect analysis of the financial result, which determined the level of influence of individual production factors on the growth of revenues and profit. The study used published and unpublished data from the Central Statistical Office. The synthetic assessment of the financial result of the food industry shows that despite the fact that entities in the audited period faced many challenges, m.in. related to price pressure, their economic and financial situation in 2023 was better than in 2019. In-depth research has shown that the development of the sector took place mainly through the increase in the technical equipment of labor and the improvement of the economic efficiency of the human factor involved in economic activity. Such a situation occurred in most industries (in 13 out of 18 respondents), and only in a few did not improve the net financial result. In the coming years, companies will have to face challenges related to, m.in others, high costs of raw materials and labor, but also the energy transition, which is why achieving sufficiently high processing margins is a priority for enterprises.

Typification of Projects for the Transition to Cloud Services

Introduction. Digital solutions make the operation of the company clearer, reduce staff costs, and provide data security. Various aspects of automation, digitalization and cloud technologies are described in the literature. The question is raised about the quality of the methodological basis for such transformations. Global and narrow technical approaches are presented. As a rule, materials are presented from the perspective of experts in the implementation of digital technologies. In this paper, for the first time, the author's scheme is proposed that can allow not only providers, but also their customers to navigate the upcoming transition to the cloud. The latter will receive systematic information on how to select a contractor and the most economically feasible option of cooperation.Materials and Methods. The information model was based on the semantic network as a system of nodes, their characteristics and connections. Management of the cloud migration project and the migration itself were visualized. The contraction practice between providers and their customers was summarized. The specifics of the tasks of such projects were taken into account. The part of the subject area related to the implementation of a cloud service is algorithmic — a step-by-step transition to the cloud, a generalized scheme of the process taking into account the hierarchy of elements are presented.Results. For the first time, a method of self-preparation of a company for the implementation of cloud solutions is proposed. The algorithm systematizes the cloud migration processes. The activities related to goal setting, audit, selection of cloud environment and services, calculation of the economic efficiency of the project, planning and implementation of migration, technical support and scaling of processes are described. The possibilities of determining the economic feasibility of measures for the transition to the cloud are shown. The costs of equipment, data storage and processing, software licenses, salaries, information security, etc., are taken into account. The amount received is compared to the providers' offers. For the final decision, the costs of infrastructure support are taken into account — by the customer or the outsourcer. The best option is selected. As a result, the customer gets the opportunity to work with better profitability and scale the project. Feedback is provided, and processes are adjusted, starting with IT reaudit.Discussion and Conclusion. The proposed solution will give the customer's management a system view of the execution sequence when migrating to the cloud, the issues and tasks to discuss with a potential outsourcer. Providers can use the algorithm to typify and unify projects, which can eventually simplify the coordination of the list of services and the migration procedure with customers. In this way, the parties can free up significant resources in terms of time, labor and other costs. In addition, customers and providers can partially use the described semantic network to develop not only the organizational, but also the technical aspect of the project.

Exploration of Deep-Learning-Based Approaches for False Fact Identification in Social Judicial Systems

With the many applications of artificial intelligence (AI) in social judicial systems, false fact identification becomes a challenging issue when the system is expected to be more autonomous and intelligent in assisting a judicial review. In particular, private lending disputes often involve false facts that are intentionally concealed and manipulated due to unique and dynamic relationships and their nonconfrontational nature in the judicial system. In this article, we investigate deep learning techniques to identify false facts in loan cases for the purpose of reducing the judicial workload. Specifically, we adapt deep-learning-based natural language processing techniques to a dataset over 100 real-world judicial rules spanning four courts of different levels in China. The BERT (bidirectional encoder representations from transformers)-based classifier and T5 text generation models were trained to classify false litigation claims semantically. The experimental results demonstrate that T5 has a robust learning capability with a small number of legal text samples, outperforms BERT in identifying falsified facts, and provides explainable decisions to judges. This research shows that deep-learning-based false fact identification approaches provide promising solutions for addressing concealed information and manipulation in private lending lawsuits. This highlights the feasibility of deep learning to strengthen fact-finding and reduce labor costs in the judicial field.

Estimation of the impact of investment in fixed assets and employment on GDP using the Cobb-Douglas function

The main objective of the study is to determine the degree of influence of labor and investment in fixed capital on the total volume of output in Kazakhstan. The paper uses the multiple regression analysis method based on the Cobb-Douglas production function. The results of the empirical analysis showed that with a change in investment in fixed capital by 1%, the volume of production will increase by an average of 0.747%. Accordingly, with a change in the number of employed people by 1%, the volume of production will increase by an average of 2.589%. The study revealed a low contribution of technological progress to the development of the manufacturing industry. Based on the analysis, a conclusion was made about the presence of increasing returns to the scale of production, since the sum of the elasticity coefficients of production for capital and labor is greater than one. The high rate of contribution to labor costs (2.589%) indicates that the growth of labor intensity in the manufacturing industry of Kazakhstan has a stimulating effect on the economic growth of production, and also brings a positive economic effect to production.

The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

Rapid investment casting: a techno-economic analysis for evaluating VAT photopolymerisation processes

Adopting additive manufacturing in investment casting, known as rapid investment casting, can significantly reduce lead time and cost. Despite many publications on these technologies, no quantitative techno-economic analyses refer to rapid investment casting. The paper provides helpful insights and a decision-making approach for replacing conventional production technologies (i.e. wax injection) with additive manufacturing technologies. The study is based on a techno-economic analysis framework. It allows assessing key performance indicators (e.g. crossover points and payback periods) for stakeholders to decide the suitable VAT photopolymerisation processes (i.e. stereolithography, digital light processing, and material jetting) according to specific production scenarios (i.e. load factors). Analytical cost models were developed in the frame of this work to assess the time and cost of the overall manufacturing process of the resin model for investment casting, considering the post-processing stages for each of the technologies investigated. The cost models were used to assess the benefits introduced by additive manufacturing in rapid investment casting. A sensitivity analysis evaluated the impact of energy, material, and labour costs for both key performance indicators (crossover points and payback periods). The techno-economic analysis yielded the following results: (i) crossover points in terms of production costs vary, ranging from thousands of units for small components in the jewellery or fashion industries to a few dozen units for larger parts in the mechanical industry, and (ii) crossover points in terms of time are lower than those related to cost. Additionally, digital light processing is regarded as the most promising technology, offering the best crossover points and payback periods across various scenarios, mainly when 3D printers are used at a low utilisation rate.

Efficiency of the BioBoi® homeopathic product combined with proper milking management in mastitis treatment in lactating cows

The dairy production chain is one of the main economic activities in Brazil, with primary production present in almost all Brazilian municipalities, involving more than one million rural producers, and generating jobs in all segments of the chain. The growing consumption of dairy products, and the pursuit of quality, lead to increasing concern with food and nutritional safety. Among the factors that affect milk composition are management and the incidence of diseases, with mastitis being one of the most frequent infections affecting dairy cattle. This results in economic losses due to reduced production and milk quality, increased costs for labor, medication, and veterinary services, and the early disposal of animals. Treatment with commercial homeopathic products presents an alternative to avoid problems found in the continuous use of allopathic medicines, such as residues in animal-derived products, selection of multi-resistant strains, and reactions in hypersensitive individuals. The general objective of this study was to evaluate the efficiency and effectiveness of using a commercial homeopathic product in the treatment of bovine mastitis when combined with proper milking management. Our specific objectives were to evaluate the milker’s handling before, during, and after milking, to determine the incidence and prevalence of clinical and subclinical cases of mastitis in the herd before and after treatment, and to perform comparative analyses of the data obtained at the beginning and end of the experiment. The results showed that positive behaviors by the milker had greater influence on the cows' behavior, which remained calm and stress-free throughout the milking process, as well as a correlation with obtaining low somatic cell counts (SCC). Regarding the prevalence of mastitis in the herd, although it decreased, there was no statistically significant difference. However, there was evidence of the positive interference of the homeopathic medicine on its incidence. It is concluded that the homeopathic medicine is effective in inhibiting the emergence of new cases of mastitis, as well as in treating acute cases of mastitis and reducing the herd's SCC, being efficient in the control and prevention of mastitis; it is also concluded that the milker plays an important role in this process, making it essential for them to be aware and valued for this.