Investment Costs Research Articles

Artificial Intelligence in Agriculture: Application in Developing Countries

The economic sector is significantly impacted by agriculture. Artificial intelligence is being applied in agriculture to automate numerous tasks, reduce hazards, and provide farmers with a comparatively easy-to-use farming system but a revolution is taking place in the field of agriculture, utilizing artificial intelligence (AI) to solve major issues confronting the agricultural sector in developing nations. A focus on the impact and potential benefits of AI in developing nations is explored in this paper. The widespread adoption of AI in agriculture in developing countries has been confronted by challenges such as infrastructure deficits, high investment costs, and a lack of capacity building. Governments, policymakers, researchers, and technology providers must work together to ensure sustainable and inclusive deployment of AI technologies in agriculture. A critical examination of the state of agriculture in developing countries is the primary objective of this study, as well as the identification and mitigation of farmer challenges with the use of AI technologies. But a revolution is taking place in agriculture, utilizing artificial intelligence (AI) to solve major issues confronting the agricultural sector in developing nations.

Incentive Determination for Demand Response Considering Internal Rate of Return

The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability.

Developmental studies of the sublingual and mandibular salivary glands in Japanese quails (Coturinx coturinx japonica)

Abstract Background The Japanese quail (Coturinx coturnix japonica) has a crucial role in the lives of humanity since the 12th century and continues to play main roles in our industry and scientific research. The advantages that the Japanese quail has, such as heavy egg production and high-quality meat with low cholesterol and fat contents, Moreover, the Japanese quail is easily managed, with high feeding conversion, low cost of investment, and high rate of returns. Salivary glands are a part of the lingual apparatus that secretes serios and mucous saliva. Whereas, the saliva secretions have different roles in the food variation, apprehension, and moisture of food bolus. The morphological and cytochemical analysis are done on 20 healthy Japanese quail embryos of 6th, 10th, 11th, and 13th days of incubation and 25 healthy quail chicks at hatching day old, 7th, 14th, 30th, and 60th days old. These samples are investigated histologically, histochemically, and scanned by electron microscopy serially. Our purpose of the study is to highlight the area of the oropharyngeal salivary glands and their role in food variation, as few studies spoke about that in Japanese quail. Results The primordia of the sublingual and mandibular salivary glands were noticed at the 6th and 10th days of the prehatching respectively as an epithelial bud. After hatching, both primordia were elongated and differentiated into secretory units. These glands were mucous polystomatic tubulo-alveolar paired glands, which were situated in the submucosa of the oropharyngeal floor (sublingual floor and paralingual grooves). The sublingual glands consisted of 3–5 lobes extended from the two Os ceratobranchial by their wide ends caudally, to beyond the median sulcus of the prefrenular part of the sublingual space rostrally. The taste buds were variable in size and position. The mandibular glands lay on the paralingual groove, which arose at the 10-day old embryo. The mandibular glands were located dorsomedial to the sublingual glands and extended longitudinally from the rostral border of the frenulum linguae to the caudal tips of the sublingual glands. The taste buds decreased in volume and number with advancing age. Conclusion Overall, salivary glands increase in their alcianophilic activity of the secretions with advancing age, which indicates low PH within the secretory end pieces.

Comparative performance analysis of different microfilaria testing methods for Dirofilaria immitis in canine blood

Abstract Background Microfilaria (MF) testing is an essential part of canine heartworm diagnostics, and it is recommended by the American Heartworm Society that a MF test be performed in tandem with antigen testing on every dog, every year, regardless of prevention status or history. There are a variety of methods that can be used to detect MF in canine whole blood; however, these methods widely vary in their sensitivities as well as practical factors, including time investment and cost. Additionally, some MF tests offer the advantage of being quantitative or allowing for morphological or molecular species identification, while other tests should only be used qualitatively. Methods The purpose of this study is to evaluate the quantitative and qualitative performance of MF tests, including the 20 μL count, wet mount, 9 μL and 40 μL hematocrit tubes, thin smear, thick smear, modified Knott test (MKT), and conventional polymerase chain reaction (PCR). Results Qualitatively, there was little difference in the performance of the 20 μL count, wet mount, MKT, and PCR. The MKT and PCR are the optimal MF tests, as these perform most reliably for detecting positives even when the MF per milliliter is relatively low, and in most cases, these two methods also allow for species-level confirmation of the identity. However, PCR tends to be a very costly test, and both PCR and MKT require a greater degree of expertise and time investment to perform than other tests. Even the lowest performance tests, including the thin smear and hematocrit tube methods, can reliably detect MF at very high burdens; although, caution should be advised when using low reliability methods, since there is a greater likelihood of failing to identify MF-positive dogs. Conclusions Microfilaria (MF) testing is an essential part of heartworm diagnosis and screening in dogs, and test selection should balance practical factors such as cost and time investment with the patient’s risk of infection based on prevention status and history, clinical signs, and antigen testing results. This approach to MF testing will help minimize cost while avoiding failure to detect MF in infected dogs, especially when MF burden is low. Graphical Abstract

Use of Sustainable Energy Sources and Technologies in Primary and Secondary School Buildings in Greece. Can They Eliminate their Carbon Footprint?

School buildings consume energy covering their energy demand. The most of them utilize fossil fuels and grid electricity while the use of renewable energies is rather limited. School buildings should eliminate their carbon emissions in order to achieve the global target for net-zero carbon emissions by 2050. The energy consumption and the carbon emissions in schools have been evaluated. School buildings in Greece consume less than 100 kWh/m2 year. Solar thermal energy, solar photovoltaic energy and high efficiency heat pumps can cover all the energy demand in school buildings in Greece replacing the use of conventional energy sources. These energy technologies are reliable, mature and cost-effective. There are 7,756 primary and secondary school buildings in Greece consuming 1.67 TWh/year. The total cost of energy renovation eliminating their carbon emissions is estimated at 1,156,000 €/school or 8.96 billion € for the 7,756 schools in the country. For achieving the target of net-zero carbon emissions by 2050, 310 Greek schools should be energy refurbished every year eliminating their carbon footprint in the next 25 years. Taking into account the availability of benign energy sources and technologies the main barrier for eliminating the carbon emissions in school buildings in Greece by 2050 is the high investment cost. Our results could be useful for the development of the required policies for decarbonization in school buildings.

A systematic methodology for selecting optimal technology for waste heat utilization in food processing industry

Utilizing waste heat that is rejected from thermal processes can greatly enhance the industry's energy balance. Identifying potential waste heat sources in the food and beverage processing industry and making estimations for their possible utilization can be an extremely challenging undertaking. To determine waste heat potentials in the food processing industry and to select an optimal technology for its utilization a novel systematic methodology based on the comprehensive energy audit and mathematical optimization has been developed. The developed mixed integer nonlinear programming model incorporates all novel and the most commonly employed technologies to utilize waste heat.The developed systematic methodology is tested on a case study, of a milk and dairy production facility. Obtained results have shown that up to 53 % of the available waste heat can be used in the scenario of limited investment costs. Otherwise, in the scenario when investment costs are not set as a limitation, 75 % of waste heat can be used, according to plant demands. Harmonising production processes is necessary to use all the waste heat. The developed systematic methodology can be applied to any food processing industry and other facilities producing waste heat because it presents a universal approach.

Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

Apple Pomace Integrated Biorefinery for Biofuels Production: A Techno-Economic and Environmental Sustainability Analysis

The combination of techno-economic process modelling and life cycle assessment is an integrated methodology that addresses quantitative operational data, and evaluates the emissions associated with any process under development. In particular, the valorisation of waste streams within the context of the circular economy could be considered a valid and promising approach, especially regarding techno-economic and environmental indicators. This manuscript aims to evaluate the integral valorisation of apple pomace from the processing industry into bioethanol, and vinasses (a byproduct of the distillation process) into biogas and digestate as biofertiliser. In addition to biogas production, lagooning and composting were considered as strategies for vinasse management. After the conceptual design of the process options was completed, the environmental profile of bioethanol production was estimated across different scenarios. When biogas production was integrated to reduce the biorefinery’s energy demand, the carbon footprint was 1.13 kg CO2eq∙kg−1. This footprint increased to values around four when lagooning and composting were used as vinasse management strategies. Although the economic dimension posed a significant limitation due to high investment costs, the eco-efficiency analysis showed that the scenario of the co-production of bioethanol and biogas is the best alternative. Despite the promising results, further research is needed to explore the recovery of additional co-products to develop a high-potential strategy for apple pomace.

AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

This article explores the multifaceted applications of Artificial Intelligence (AI) technologies in precision agriculture, focusing on their potential to significantly enhance crop yields while optimizing resource utilization. The article examines five key areas where AI is making substantial impacts: predictive analytics for crop management, intelligent irrigation systems, automated pest and disease detection, precision fertilizer application, and robotic harvesting. By integrating data from various sources and employing advanced machine learning algorithms, these AI-driven systems demonstrate remarkable improvements in efficiency, accuracy, and sustainability. The article highlights significant advancements, such as a 15% improvement in yield prediction accuracy, up to 30% reduction in water usage, and a 20% decrease in fertilizer use without compromising crop yields. While acknowledging challenges such as data privacy concerns and initial investment costs, the article underscores the long-term benefits of AI adoption in agriculture, including increased profitability, environmental sustainability, and improved food security. This comprehensive analysis provides insights into how AI-driven precision agriculture is reshaping modern farming practices and its potential to address global food production challenges while reducing agriculture's environmental footprint.

Energy Flexibility in Aluminium Smelting: A Long-Term Feasibility Study Based on the Prospects of Electricity Load and Photovoltaic Production

This paper investigates the economic feasibility of utilising energy flexibility in aluminium production as a viable solution to leverage electricity surpluses arising from the increasing number of photovoltaic (PV) system installations. Future trends suggest that the generation capacity of PV systems will soon surpass consumption, leading to significant electricity surpluses, particularly during the summer. This surplus electricity, which is anticipated to be available at low prices, offers a unique opportunity to evaluate different investment and utilisation scenarios for aluminium production while simultaneously decreasing its environmental impact. The results demonstrate that, despite their high initial investment cost, large-scale PV power plants can potentially deliver maximum economic gains over a ten-year period. Conversely, the direct utilisation of surpluses without substantial investment can yield savings of up to EUR 17 million within the same time frame for Slovenia’s case with an aluminium smelter, which has a maximum power usage of 60 MW. The findings of this study have significant implications in terms of shaping future energy strategies and policies, emphasizing the value of integrating renewable energy sources and industrial processes for enhanced economic and environmental outcomes.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Exploring Internet of Things adoption challenges in manufacturing firms: A Delphi Fuzzy Analytical Hierarchy Process approach.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh's manufacturing sector to survive and thrive in today's cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry. We delve deeply into the complex terrain of IoT adoption challenges by synthesizing expert insights and a meticulously selected body of contemporary literature. We employ a robust methodology combining the Delphi method with the fuzzy Analytical Hierarchy Process to systematically analyze and prioritize these challenges. Using this methodology, we leveraged the combined expertise of domain specialists and subsequently employed fuzzy logic techniques to address the inherent ambiguities and uncertainties within the data. Our findings highlight this clear path. They reveal that among the myriad barriers, "Lack of top management commitment to implementing new technology" (B10), "High initial implementation investment costs" (B9), and "Risks associated with switching to a new business model" (B7) loom most extensive, demanding immediate attention. These insights are not confined to academia but serve as a pragmatic guide for industrial managers. Armed with the knowledge gleaned from this study, managers can craft tailored strategies, set well-informed priorities, and embark on a transformational journey toward harnessing the vast potential of IoT in the Bangladeshi industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and industry leaders with the tools necessary to navigate these challenges effectively. This strategic navigation, in turn, contributes significantly to enhancing the competitiveness and sustainability of Bangladeshi manufacturing in the IoT era.

Factors that affecting firm performance mediated by digital transformation in Indonesia’s state-owned enterprises plantation and forestry industries

Digital transformation refers to the integration of digital technology into all areas of a business, fundamentally changing the way it operates and adding value to a firm's performance. This can involve adopting new technologies, reorganizing business processes, and creating a digital culture within the organization. One of the State-Owned Enterprises (SOE) in Indonesia, PT Perkebunan Nusantara III (Persero) with a focus on agriculture and main commodities, namely Palm Oil and Rubber, is carrying out a digital transformation initiative with the aim of improving firm performance. Along the way, the company faced many obstacles related to implementing digital transformation and the large investment costs incurred. This research aims to test the influence of Digital Transformation Leadership, IT Capability, Digital Mindset, Work Culture Change, Organizational Agility, Human Capital and IT Alignment with Business and test the influence of these variables on Firm Performance with Digital Transformation as a mediating variable. This research uses quantitative research methods by distributing online questionnaires via Google Form to 234 company employees who use Digital Farming, GIS and SCADA applications. Data was analyzed using the SEM-PLS analysis method with Smart-PLS Software version 3.3.9. The results of the research show that IT Capability, Work Culture Change, IT Alignment With Business have a positive effect on Digital Transformation. Apart from that, this research also shows the positive influence of Digital Transformation Leadership on Digital Transformation which is mediated by IT Capability and Digital Mindset on Digital Transformation which is mediated by Work Culture Change. Then, research also shows that Digital Transformation has a significant effect on Firm Performance. the research outcomes can serve as valuable insights for what factors influence firm performance mediated by digital transformation in the SOE plantation and forestry industry business cluster in Indonesia.

A lightweight MMC topology with recombined half‐bridge submodules for DC fault ride‐through

AbstractThe lightweight of modular multilevel converter (MMC) and the DC faults ride‐through ability are main challenges for MMC‐high voltage direct current (HVDC) transmission systems. By introducing the concept of time‐division multiplexing, an arm multiplexing MMC (AM‐MMC) topology with high utilization of submodules is presented to reduce the weight and volume of MMC. In order to block the DC side fault current, this paper proposes a novel submodule in AM‐MMC, instead of using full‐bridge submodules. The proposed recombined half‐bridge submodules of AM‐MMC (RHAM‐MMC) contains four half‐bridge submodules and an IGBT with reverse parallel diodes. The topology and operating principle of RHAM‐MMC are introduced in detail. The time‐division multiplexing of middle arms between upper and lower arms is achieved by introducing arm selection switches. Thus, a new type of arm switch and switching method is designed based on the switch state. The DC faults ride‐through strategy is carried out based on its DC fault characteristic analysis. In addition, the economy analysis is conducted on the switching loss and operating loss of RHAM‐MMC. Compared with the fault ride‐through capability of other sub‐modules (SMs), RHAM‐MMC performs better in terms of investment cost and device losses. The simulation results based on MATLAB/Simulink reveal that RHAM‐MMC can achieve the DC side fault ride‐through and show effectiveness of the DC fault ride‐through control strategy.

Experimental analysis on the prototype of decentralised air handling unit with ball-packed regenerator

Decentralised, wall mounted ventilation is one of alternative to typical – cross flow or “heat wheel” recovery air handling units (AHU) system when it is not possible to install it. It solves problems such as the need for more space, power consumption of fans, and investment costs. This experimental study aims to analyse the performance of the unique constructed decentralised air handling unit (DAHU) prototype that has a special balls-packed regenerative heat exchanger. The development of the DAHU also aimed to simplify the operation and production of the unit to make it more affordable and easier to manufacture. As the DAHU is constructed from individually sized components, the pressure losses of the diffuser, the regenerator, the screens to hold the balls, the filter and the fan were measured and the actual DAHU airflow was determined. The general operation of the DAHU was tested in a climatic chamber to determine the appropriate cycle times for this type of device, as well as the heat recovery efficiency at different outdoor air temperatures. Five different DAHU operating cycles were analysed. It was found that the duration of supply air must be longer than the duration of extract air to achieve higher efficiency. Therefore, DAHU's subsequent heat exchanger studies were performed for four airflows. The highest DAHU heat recovery efficiency was achieved (above 70%) with a supply and extract duration of 150 s. It has been found that the efficiency of this DAHU increases with decreasing outdoor air temperature because of the storage properties of the balls. The analysis showed that this type of DAHU could be suitable for use in cooler and colder climates.

Cost-Effectiveness Analysis of Nearly Zero-Carbon Office Buildings in Taiwan

The formulation of an affordable and economically-feasible policy for nearly zero-carbon building design is a challenge that countries encounter in promoting net-zero building. In 2022, Taiwan promulgated Building Energy Rating System (BERS), the world's first building energy efficiency assessment system tailored for subtropical climates, as a basis for evaluating its goal of achieving nearly zero-carbon buildings by 2050. However, whether this nearly zero-carbon building policy is financially feasible has remained unknown to the government and the industry. To address the issue, the objective of the paper is to conduct a cost-benefit and payback period analysis of newly constructed nearly zero-carbon office buildings in accordance with the Building Energy-efficiency Rating System for New Buildings (BERSn) assessment framework, and thereby examines the financial affordability and feasibility of Taiwan's net-zero building policies.Firstly, this study introduces existing building energy performance evaluation methods in Europe, America, and Japan, and explains the methodology of Taiwan's Building Energy Rating System (BERS). The paper then reviews international Cost-Benefit Analyses of Nearly Zero Energy Office Buildings and parallelly conducts a Cost-Benefit Analysis of Nearly Zero-Carbon Office Buildings in Taiwan. Following the BERS methodology, this paper identifies the baseline efficiency level and the Nearly Zero-Carbon Building (NZCB) efficiency level, along with their associated solutions and investment costs, for air conditioning and lighting systems. In this study, a questionnaire survey of 28 experts identifies the air conditioning unit costs of Basic Mechanical Equipment Efficiency (BME) and energy-saving control technologies (R) for energy-efficient design of Fan Coil Unit (FCU), Air Handling Unit (AHU), and Variable Refrigerant Flow (VRF) system. Accordingly, 33 air conditioning design solutions and their corresponding construction costs complying with the BERSn requirements of EAC≦0.5 for nearly-zero carbon office buildings are identified. Regarding the nearly zero-carbon lighting design, the simulations reveal that the use of LED panel luminaires or T8 LED luminaires (including those with energy-saving labels) can achieve an average operational lighting illuminance of 500 LUX and the BERSn requirements of EL ≤ 0.5 for lighting. Finally, the study further analyzes the payback period and premium cost ratio of NZCB by employing the budget data of seven building projects. The analysis shows that the premium cost ratio and the payback period of NZCB range from 1.33%-1.50% and 6.4-7.7 years, respectively. The results demonstrate that while a higher budget is commonly associated with NZCB by the public, it is in fact economically and technologically feasible to realize NZCB with prevailing energy-efficient measures.

Optimizing public investments: A Sustainable Economic, Environmental, and Social Investment Multi-criteria Decision Model (SEESIM)

One of the most important aspects for the economic and social growth of a country is high efficiency of public investments. This imperative has never been more critical, especially in the context of Europe and Italy, where the integration of environmental, economic, and social goals is essential to address the multifaceted challenges of the 21st century. This research introduces the Sustainable Economic, Environmental, and Social Investment Model (SEESIM), a novel multi-criteria decision-making framework designed to ensure the sustainability of projects as their validity over time, especially from a National Recovery and Resilience Plan perspective. Utilizing the Analytic Hierarchy Process (AHP), SEESIM integrates environmental, economic, and social criteria as useful tool to evaluate a project in the post-funding phase, ensuring a balanced approach to efficiency and sustainability. The model is adaptable to various regional contexts, providing a transparent and replicable methodology for assessing the impacts and costs of public investments. An experimental scenario demonstrates SEESIM’s utility in guiding investment decisions, highlighting its potential to significantly enhance the effectiveness of public resources in achieving long-term sustainability goals. SEESIM represents a pivotal advancement in sustainable development strategies, offering a comprehensive tool for integrating multi-dimensional sustainability criteria into public investment decisions.

INVESTMENT ANALYSIS IN SOYBEAN UBS FOR OWN USE: A CASE STUDY IN ALTO GARÇA – MT

Soybean (Glycine max) is the most widely produced legume in the world, playing an important economic, social and environmental role, in addition to influencing the commodities market in Brazil and worldwide. This has attracted many national producers to its cultivation, making it a highly competitive market with investment in research, innovation and development in all links of the chain. Therefore, the objective of this study was to evaluate the feasibility of investing in the installation of a Seed Processing Unit (UBS) for soybeans, on private property, with the purpose of producing the crop's own seed. To this end, a case study was carried out on a property in the municipality of Alto Garça - MT, using the descriptive method of exploratory nature, in which the economic impact of soybean seed production on the property was evaluated to the detriment of the reduction in production costs obtained in years following its implementation. Thus, the 2016/2017 to 2020/2021 harvests were analyzed in their cost composition and investment result from the 2017/2018 harvest. It was possible to verify the reduction in the seed cost per hectare by 38.33% in the first cycle, and by 66.87% compared to the last cycle analyzed, concluding that the investment was viable.

The Impact of Leadership Training on Burnout and Fulfillment Among Direct Reports

SUMMARY Goal Burnout, decreased professional fulfillment, and resultant attrition across the medical professions are increasingly recognized as threats to sustainable and cost-effective healthcare delivery. While the skill level of leaders as perceived by their direct reports has been correlated with rates of burnout and fulfillment, no studies, to our knowledge, have directly evaluated whether intervention via leadership training impacts burnout and fulfillment among direct reports. The goal of this study was to evaluate the effectiveness of a leadership training intervention on direct reports’ perceptions of the leadership skills of supervising residents and subsequently on the well-being of the direct reports. Methods We implemented a leadership training program with supervising (i.e., chief) resident volunteers in two surgical residency programs. The leadership training included two sessions of approximately 2 hours each that consisted of interactive didactic and small group activities. The training focused on the following themes: defining leadership (i.e.,characteristics and behaviors), team building, fostering trust, managing conflict, navigating difficult conversations, and feedback. We administered pretraining and posttraining surveys to the direct reports (i.e., junior residents) to assess the perceived leadership skills of supervising residents, as well as burnout and professional fulfillment. Principal Findings Leadership scores significantly improved following the leadership training intervention. Additionally, improvement in leadership scores following training was positively correlated with professional fulfillment among the junior residents (direct reports). Practical Applications The results of this study suggest that incorporating leadership training into residency programs may serve as an appropriate initial intervention to improve the leadership skills of supervising residents, and in turn, improve professional fulfillment and retention among medical professionals. This intervention involved minimal cost and time investment, with potentially significant returns in combating the well-being and attrition crisis. These findings may be applicable across the healthcare field to tackle the impending healthcare worker crisis.

Optimal Intermediate Pressure Investigation in a CO₂ Transcritical Distributed Compression Refrigeration Cycle{fr}Recherche sur la Pression Intermédiaire Optimale dans un Cycle de Réfrigération à Compression Distribuée Transcritique au CO₂

The CO2 transcritical distributed compression cycle proposal provides a novel approach for refrigerant cooling in traditional transcritical cycles. This paper employed an exhaustive search method to derive the correlation formula for the optimal intermediate pressure. From the perspectives of thermodynamic performance and economics, a comparative analysis was conducted between the cycle established under the optimal intermediate pressure obtained in this study, the cycle based on equal compression ratios in traditional two-stage compression cycles, and the cycle established using the optimal secondary compression ratio method based on low-pressure stage discharge pressure mentioned in previous literature study. The research results indicate that the system's COP calculated using the obtained optimal intermediate pressure correlation method can be improved by up to 7.26% and 5.32%, respectively, compared to the traditional and literature-based methods. The exergy loss with the optimal intermediate pressure method is less than with the other two methods. The entransy dissipation rate of the system obtained using the optimal intermediate pressure correlation method is 24.61% and 50.14% lower than that of the traditional and literature-based methods, respectively. The investment cost of the main components using the optimal intermediate pressure correlation method is about 5% higher than that of the traditional method and about 1% higher than that of the optimal pressure ratio method. However, the total annual cost rate of the system is the lowest. The research enriches and improves the theory of the CO₂ transcritical distributed compression cycle, thereby facilitating the advancement of practical applications based on this cycle theory.