Efficient Management Research Articles

Improving the accuracy of terrigenous reservoir porosity modeling based on machine learning methods

ABSTRACT The purpose of this paper is to improve the prediction of porosity in Visean terrigenous sediments to increase the accuracy of digital static model of an oil field in Perm Krai. The study proposes an approach for porosity prediction based on machine learning methods, which can compensate for the shortcomings of traditional methods based on geophysical well logging data. Technical limitations of well logging and high geological heterogeneity often interfere with obtaining reliable porosity distribution data. The study uses such algorithms as Random Forest, Gradient Boosting, Support Vector method and Adaptive Boosted Decision Trees for porosity determination based on a set of geophysical methods. The developed model, trained on a specially created database using radioactive, electric and acoustic logs, model was implemented for real reservoir. Implementation of the model allowed to significantly refine the static model of the field and adjust the reserves estimation. The economic effect is achieved by reducing the cost of additional research and improving the efficiency of reservoir management. The proposed methodology has been successfully tested and can be used for other fields in the south of the Perm region, which opens up prospects for improving the efficiency of oil field development in the region.

Agile tax technology development in the U.S.: A conceptual framework for scalable and efficient enterprise solutions

The rapid evolution of tax regulations in the United States, coupled with increasing digital transformation in financial compliance, necessitates the development of agile, scalable, and efficient tax technology solutions. Traditional tax management systems, often rigid and monolithic, struggle to adapt to dynamic regulatory changes, increasing enterprise demand for flexible, technology-driven approaches. This review explores Agile Tax Technology Development as a conceptual framework that integrates agile methodologies with advanced digital solutions to enhance efficiency, scalability, and compliance in enterprise tax management. Agile methodologies, such as Scrum, Kanban, and SAFe, provide iterative and adaptive software development models that allow tax technology teams to rapidly respond to regulatory updates and evolving business needs. The review examines key components of agile tax technology, including cloud-based infrastructure, API-driven architectures, automation, and artificial intelligence, which collectively enable real-time tax processing, predictive analytics, and seamless integration with financial systems. Additionally, it discusses the role of cross-functional collaboration among tax professionals, software developers, and regulatory experts to ensure compliance and continuous improvement. Scalability is a critical factor in enterprise tax solutions, requiring cloud computing, microservices architecture, and distributed ledger technologies to efficiently process vast amounts of tax data. This study highlights best practices in Agile Tax Technology Development by analyzing case studies from leading enterprises and government initiatives. Furthermore, it addresses challenges such as integrating agile frameworks into legacy tax systems, ensuring cybersecurity in financial data processing, and overcoming organizational resistance to agile adoption. Ultimately, this review provides a forward-looking perspective on the future of tax technology in the U.S., emphasizing the need for continuous innovation, automation, and agility in response to an increasingly complex regulatory landscape. The findings serve as a guideline for enterprises seeking to modernize their tax compliance strategies while maintaining operational efficiency and regulatory adherence. Keywords: Agile Tax Technology, U.S, Efficient Enterprise Conceptual Framework.

Effectiveness of Financial Management of Start-Up Companies Before and After Using Virtual Account

Efficient payment management is a key issue for startup companies in Indonesia, as it supports cash flow and operational sustainability. The implementation of Virtual Account payment methods is expected to improve payment efficiency and accelerate financial reporting. This study aims to examine the effectiveness of using Virtual Account at PT Budi Graha Realty The Bay Apartment by comparing receivables and reporting speed during the 2023-2024 period. A qualitative approach was used, involving interviews and document analysis. The results show that the use of Virtual Account positively affects reducing delayed receivables and accelerating financial reporting compared to traditional payment methods. These findings provide empirical evidence that the implementation of Virtual Accounts can improve financial efficiency in startup companies. It is expected that this research can serve as a reference for other companies in enhancing payments and financial management. The limitation of this study is its narrow scope, as it focuses on a single company. Future research is expected to expand the scope to test the effectiveness of Virtual Account payment methods in other companies.

Enhancing apparel supply chain resilience: a robust-stochastic approach to integrated production-distribution planning

ABSTRACT This study focuses on designing a resilient supply chain for the apparel industry. We address the challenges of uncertain replenishment orders and production capacities in the apparel industry by developing a multi-objective robust two-stage stochastic programming model. The model optimizes total costs, customer service level, and financial risk. A continuous probability distribution is employed to model uncertainty, and downside risk is used as a risk measure. Computational results based on a real-world apparel company demonstrate the model’s effectiveness in enhancing supply chain resilience. By significantly reducing financial risk while maintaining acceptable cost levels, the proposed approach provides valuable insights for industry practitioners. In order to further analyze the impact of uncertain parameters on the planning problem’s performance, we carry out a sensitivity analysis. From a practical standpoint, the results highlight the effectiveness of the robust optimization model. The optimal solutions derived from the downside risk management approach show a substantial reduction in risk, with DRΩ decreasing by an impressive 83%. This significant risk mitigation is achieved with only a minimal increase in total costs, which remains under 4%. These findings emphasize the practical advantages of robust optimization, offering critical insights for researchers and practitioners to improve efficiency, reliability and financial stability in Supply Chain management.

High cost of governance and socio-economic development in Nigeria

ABSTRACT In recent years, the high cost of governance in Nigeria has been a major concern for policymakers and citizens alike. This has had a significant impact on the country’s socio-economic development. As a result, Nigeria has struggled to achieve significant socioeconomic development, despite its vast resources and potential. In light of this, this paper conducted a thorough analysis through documentary sources of data to investigate the factors contributing to the high cost of governance and the impact it has on Nigeria’s socioeconomic development. The paper found that corruption, inefficient resource management, and excessive bureaucracy are the primary reasons for the high cost of governance in Nigeria. These factors not only increase government spending but also hinder the efficient allocation of resources. Furthermore, the study found that high cost of governance has resulted in a significant budget deficit and a heavy debt burden for the country. In conclusion, the paper recommends among others the urgent need for the Nigerian government to address the high cost of governance. By addressing corruption, improving resource management, and reducing bureaucracy, the government can lower its spending and allocate resources more efficiently, ultimately contributing to the country’s socio-economic development.

Learning-based page replacement scheme for efficient I/O processing

Recent improvements in machine learning techniques offer new opportunities for addressing challenges across various domains. A significant focus in current research is on leveraging machine learning methodologies to improve existing resource management strategies, aiming to achieve comparable performance capabilities. In particular, reinforcement learning exhibits an appealing characteristic as it performs learning by systematically exploring actions to maximize cumulative rewards. In this paper, we introduce a Learning-based Page Replacement (LPR) scheme designed for efficient I/O processing. We propose a model that learns the memory reference patterns of a given algorithm, enabling real-time determination of the optimal replacement policy. Using two replacement policies based on least/most-recently used (LRU/MRU) strategies, LPR gives rewards or penalties to each policy based on its previous decisions. Consequently, LPR evolves its own page replacement policy to minimize cumulative regrets for each decision. Notably, our scheme achieves efficient memory management without explicitly detecting application-specific memory access patterns, relying instead on self-learning. We implement and evaluate our proposed scheme, LPR, on two distinct memory subsystems: one tailored for scientific applications and the other for out-of-core graph processing. We compare the performance of LPR against existing page replacement policies using metrics such as miss ratio and execution time. Experimental results demonstrate that our scheme effectively detects changes in memory access patterns and manages page replacement online using the best-fit policy with minimal overhead.

Post-Pandemic Growth of E-Commerce Platforms: A Case Study on Taobaos Strategic Success in China

This study analyzes the reasons for the continued growth of the Taobao platform after the pandemic to reveal the key factors that enable e-commerce platforms to maintain competitiveness in a dynamic market environment. Through a case study of Taobao, it is found that its success not only relies on innovative marketing methods such as the Double Eleven shopping festival and live streaming but also on efficient supply chain management and personalized user experience. This study points out that Taobao has built a strong market advantage through the interactivity of social e-commerce and the optimization of the supply chain, successfully meeting the demand of consumers for online shopping after the pandemic. This study provides valuable practical experience for other e-commerce platforms, emphasizing that focusing on user experience, adopting innovative promotional strategies, and optimizing supply chain management are effective ways to improve market performance. These findings are of great practical significance for the continued development of the e-commerce sector.

Comparative modeling of household electricity consumption in France: Insights from path analysis and classical models

Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.

Research on Project Management Based on PPP Model

With the continuous development of the global economy, the public-private partnership (PPP) model is increasingly used in the field of project management. Through in-depth cooperation between the government and social capital, the PPP model achieves optimal allocation of resources and reasonable sharing of risks, brings new solutions to infrastructure construction and the improvement of social public services, and achieves a win-win situation. This article analyzes the main characteristics of the PPP model through the literature review method, discusses the main advantages of PPP project management, focuses on the existing problems of PPP project management, and proposes specific solutions to the problems to help project managers carry out projects under the PPP model. Scientific and efficient project management provides theoretical support and practical guidance to ensure that enterprises maximize economic benefits, improve project quality, and promote the healthy and sustainable development of the PPP model in more fields.

Research on Financial Risk Management of Electric Power Enterprises under the "Dual Carbon" Background

Under the "dual carbon" background, the power industry, as one of the pillar industries of China's national economy, is undergoing a critical transformation. It is shifting from a traditional power generation model based on fossil fuels to one that utilizes non-fossil fuels, such as wind, solar, and hydropower. The operational development of China's power industry is significantly constrained by a series of policies, including power system reform and electricity price regulation, which has placed a burden on power companies and may lead to corresponding financial risks. This paper analyzes the issues in financial risk management of power enterprises under the "dual carbon" background from three aspects: enterprise financing channels, debt-to-asset ratio, and operating costs. Targeted measures to improve the management of financial risks are proposed, such as constructing diversified financing channels, optimizing asset management efficiency, and enhancing cost management levels, in order to provide valuable insights for financial risk management in power enterprises under the "dual carbon" framework.

Chromium "(VI)" phytoremediation using Azolla pinnata: effects on Vicia faba growth, physiology, cytogenetics, and gene expression profiling

BackgroundOne of the primary challenges that the expanding population faces is water scarcity. Thus, a global imperative has been established to safeguard extant water resources and optimize their utility through sustainable practices and efficient management. In the present investigation, Azolla pinnata, a pteridophyte (fern), was employed to phytoremediate Cr (VI) from chromium-polluted water. The potential of this treated water for agricultural purposes was verified through the use of Vicia faba plants.ResultsIn vitro, A. pinnata effectively remediates Cr (VI) from an array of liquid concentrations (0.05 to 90 ppm) in a ratio of 25:1 {volume (mL): fresh weight of Azolla (g)} after 2 days incubation period at room temperature. At low concentrations (0.1 ppm), the phytoremediation capacity peaked at 70%, falling to 19.53% at a high concentration (90 ppm). Upon continuous irrigation with Cr-polluted water (0.05 to 50 ppm), the in vivo pot experiment on Vicia faba plants revealed high Cr accumulation in the roots reached 52.5 mg Kg-1 dry weight (Dwt) at the 50 ppm Cr treatment. Nevertheless, a reduced Cr content of 19.5 mg Kg-1 Dwt was observed when the plants were irrigated with 50 ppm Cr-polluted water that had been treated with Azolla. At 50 ppm of Cr, Azolla's treatment significantly increased shoot length, fresh weight, and Chl a content to 25.25 cm, 3.4 g, and 6.5 mg g-1 Dwt, respectively, up from 10.25, 1.8, and 4.7 in untreated plants. The chromosomal aberrations were significantly induced in the dividing cells of all Cr treatments, with the highest value of 4.8% at 50 ppm. This value was reduced to 2.88% at the same concentration when treated with Azolla. At a concentration of 10 ppm Cr, the mitotic index was significantly improved to 6.99% when combined with Azolla, as opposed to 3.63% when the same concentration was used without Azolla. The DNA degradation assay showed partial DNA degradation at 50 ppm Cr, which the Azolla treatment eliminated. Furthermore, the gene expression levels of both the PM H+-ATPase and the calcium-dependent protein kinase CDPK5 were upregulated in response to Cr, despite the fact that the expression level was altered in a dose- and concentration-dependent manner by Azolla treatment.ConclusionAzolla exhibits substantial potential for reducing the detrimental effects of chromium stress including oxidative stress on plants. It modulates stress-related gene expression, protects DNA integrity, enhances cell mitosis, and reduces chromosomal damage. These results indicate that Azolla has the potential to be a valuable asset in phytoremediation strategies for chromium-contaminated environments, and that it may enhance plant survival and growth under Cr stress conditions.Key messageAzolla pinnata can be effectively utilized as an environmentally-friendly method to remediate chromium-contaminated water for agricultural usage.

Estimation of the valorization potential of municipal solid waste under a bioeconomy approach: contribution to the achievement of SDG 11 and 12 in the city of Chachapoyas, Amazonas, Peru

Objective: The objective of this study is to analyze the characterization of municipal solid waste (MSW) from the city of Chachapoyas, Peru, exploring its transformation potential and identifying the economic and social challenges to improve its valorization. The research focuses on proposing a bioeconomic model that promotes sustainable waste management aligned with Sustainable Development Goals (SDGs) 11 and 12. Methods: A detailed characterization of MSW was carried out using the "Guide for the Characterization of Municipal Solid Waste" of the Peruvian Ministry of the Environment. Data collection included physical sampling, socioeconomic segmentation and environmental impact assessment. In addition, operational and financial analyses were conducted to evaluate the results of organic and inorganic waste valorization. Originality/Relevance: This research introduces a bioeconomic model adapted to the specific context of the city of Chachapoyas, addressing local challenges such as infrastructure limitations and inefficiencies in waste management. By integrating community participation and innovative technologies, the study provides scalable and sustainable solutions for developing cities. Results: The MSW characterization identified that 41.75% of the waste is organic, with a high potential for composting, while 15.3% corresponds to recyclable inorganic waste. The application of the bioeconomic model allowed for a 50% reduction in waste sent to the landfill and avoided the emission of 4,602 tons of CO2eq in 2023. The model generated combined revenues of US$407,372.30 from organic compost and recycled materials, in addition to supporting the creation of 60 jobs.

Proposal of a hybrid dosimetry method for improving work efficiency in CT dose management.

In recent years in JAPAN, with the reform of work styles and the expansion of radiologists' duties, there has been a demand for more work improvement. Among them, many regulations surrounding computed tomography (CT) tasks require a large amount of time and manpower. In this study, we propose a hybrid method between leakage X-ray dosimetry and CT dose index (CTDI) measurement using a CTDI phantom used for CTDI measurement and elucidate the usefulness of the hybrid method. The results of the CTDI phantom generated more scattered radiation in the CT room; therefore, the previously used thoracoabdominal phantom could be replaced with the CTDI phantom. The number of exposures can be reduced by a factor of 15, work hours by 20min, and the number of workers by two when evaluated as an average of four facilities using the hybrid method. The hybrid method enables the determination of the reproducibility of the output X-ray dose during leakage X-ray dosimetry, thereby ensuring the reliability of periodic leakage X-ray dosimetry. The use of the proposed hybrid method is reasonable because it is effective for work improvement, such as reducing work time and the number of workers.

Highly Thermally Conductiveand Flame-Retardant Waterborne Polyurethane Composites with 3D BNNS Bridging Structures via MagneticField Assistance

The microstructure design for thermal conduction pathways in polymeric electrical encapsulation materials is essential to meet the stringent requirements for efficient thermal management and thermal runaway safety in modern electronic devices. Hence, a composite with three-dimensional network (Ho/U-BNNS/WPU) is developed by simultaneously incorporating magnetically modified boron nitride nanosheets (M@BNNS) and non-magnetic organo-grafted BNNS (U-BNNS) into waterborne polyurethane (WPU) to synchronous molding under a horizontal magnetic field. The results indicate that the continuous in-plane pathways formed by M@BNNS aligned along the magnetic field direction, combined with the bridging structure established by U-BNNS, enable Ho/U-BNNS/WPU to exhibit exceptional in-plane (λ//) and through-plane thermal conductivities (λ⊥). In particular, with the addition of 30 wt% M@BNNS and 5 wt% U-BNNS, the λ// and λ⊥ of composites reach 11.47 and 2.88 W m-1K-1, respectively, which representing a 194.2% improvement in λ⊥ compared to the composites with a single orientation of M@BNNS. Meanwhile, Ho/U-BNNS/WPU exhibits distinguished thermal management capabilities as thermal interface materials for LED and chips. The composites also demonstrate excellent flame retardancy, with a peak heat release and total heat release reduced by 58.9% and 36.9%, respectively, compared to WPU. Thus, this work offers new insights into the thermally conductive structural design and efficient flame-retardant systems of polymer composites, presenting broad application potential in electronic packaging fields.

Knowledge, attitude and practice of health care workers on solid medical waste management in two urban hospitals of Bangladesh: a mixed-method study

BackgroundProper management of solid medical waste is a crucial component of an efficient healthcare system. The objective of the present study was to assess the knowledge, attitude, and practices of medical waste management among the healthcare workers of two selected hospitals of Dhaka city of Bangladesh.MethodsThis cross-sectional, mixed-method study was conducted in two tertiary care hospitals of Dhaka, Bangladesh–icddr, b Dhaka hospital (Private) and Dhaka Shishu hospital (Public) during November, 2011. A number of 138 healthcare professionals (29 doctors, 80 nurses and 29 cleaners) participated in the study. The study had three parts of data collection: (i) survey with health care workers to determine their knowledge, attitudes, and practices about medical waste management; (ii) in-depth interviews with health officials of infection control committee; and (iii) observation using a facility checklist to determine the current practice of medical waste management.ResultsOverall, participants from IDH had better knowledge, and practice regarding solid medical waste management compared to DSH. Doctors of IDH demonstrated better knowledge regarding the responsible person for waste management and a better attitude regarding the statement that it was a teamwork (p-value < 0.05). Similarly, nurses and cleaners of IDH showed superior level of knowledge of waste handling as well as safer practices, including consistent use of personal protective equipment. Workers of DSH reported higher incidence of errors and sharp injuries also. From the observation, it was found that IDH authority was more likely to follow the waste management guideline. Poor knowledge and attitude were the major barriers for proper waste management in IDH while lack of funds, human resource and logistics were the major barriers in DSH.ConclusionsBoth hospitals, particularly DSH, demonstrated significant deficiencies in knowledge, attitude, and practice of proper solid medical waste management. Despite limitations, IDH, exhibited more efficient waste management procedures.

Working capital management efficiency in large firms during crisis: implications for community-based enterprises

Purpose This study aims to examine the impact of the COVID-19 pandemic on the working capital management (WCM) efficiency of resilient and nonresilient firms listed on India’s BSE 500 index. It focuses on key WCM components such as cash conversion cycles (CCC), accounts receivable periods, inventory conversion periods and accounts payable periods. Design/methodology/approach Panel data from 2012 to 2023 is analyzed using the System Generalized Method of Moments model. This study differentiates between resilient and nonresilient firms based on liquidity stress tests and cash flow performance before and during the pandemic. Findings Resilient firms demonstrated superior WCM efficiency, maintaining shorter CCCs, effective receivables and inventory management and stable payables. Nonresilient firms faced significant inefficiencies, including extended CCCs and slower receivables and inventory turnover, exposing gaps in their WCM practices. Research limitations/implications This study is limited to the pandemic period. Future research could explore broader timeframes to understand the long-term effects on WCM. Practical implications Managers should enhance WCM strategies, focusing on cash flow optimization to strengthen firm resilience during crises. Social implications Efficient WCM supports job retention, preserves supplier relationships and stabilizes local economies, contributing to broader community resilience during crises. Originality/value This study extends the resource-based view by emphasizing WCM as a critical internal resource that supports firm resilience during economic crises. It contributes new insights into how Indian firms adapted their WCM strategies in response to COVID-19.

Assessment of the triple-bottom line for development and management of human settlements: Case study of ward 12, uMlalazi local municipality, South Africa

The global adoption of sustainable development practices is gaining momentum, with an increasing emphasis on balancing the social, economic, and environmental pillars of sustainability. This study aims to assess the current state of these pillars within the uMlalazi Local Municipality, South Africa, and evaluate the initiatives in place to address related challenges. The purpose is to gain a deeper understanding of how effectively these three pillars are being addressed in the context of local governance. Using qualitative research methods, the study gathered data from a sample of five key informants, including three local government officials, one councillor, and one chief information officer from the local police. Data was collected through open-ended interview questions, with responses recorded, transcribed, and analysed for thematic content. The findings reveal significant gaps in the municipality’s approach to sustainability, including the absence of formalized trading areas, limited community input in planning and decision-making, high crime rates, and persistent unemployment. These issues were found to be interlinked with other challenges, such as inefficiencies in solid waste management. Additionally, the study confirms that the three pillars of sustainability are not treated equally, with economic and social aspects often receiving less attention compared to environmental concerns. This highlights the need for the municipality to focus on formalizing trading areas, encouraging local economic growth, and enhancing public participation in governance. By implementing incentives for greater community involvement and addressing the imbalances between the sustainability pillars, uMlalazi can make significant progress toward achieving more sustainable development.

Omilayers: a Python package for efficient data management to support multi-omic analysis

Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapid storage and retrieval of data with varying numbers of features and mixed data-types, ensurance of reliable and secure database transactions, extension of stored data row and column-wise and facilitation of data distribution. SQLite and DuckDB are embedded databases that fulfil these requirements. However, they utilize the structured query language (SQL) that hinders their implementation by the uninitiated user, and complicates their use in repetitive tasks due to the necessity of writing SQL queries. This study offers Omilayers, a Python package that encapsulates these two databases and exposes a subset of their functionality that is geared towards frequent and repetitive analytical procedures. Synthetic data were used to demonstrate the use of Omilayers and compare the performance of SQLite and DuckDB.

Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture.

Train Human Resources with Buddhist Teachings for Sustainable Economic Development

Sri Lanka is currently facing a profound economic crisis rooted in internal issues such as management inefficiencies, attitudes, and political interventions. This study aims to explore the integration of Buddhist teachings on economic management to address these challenges and foster sustainable development. Utilizing a qualitative approach, the research analyzes the Buddhist Pali canon alongside contemporary economic theories. The findings emphasize the relevance of Buddhist principles in tackling poverty, economic mismanagement, and social issues. Key teachings from Buddhist scriptures such as the Kūṭadanta sutta and Sigalovada Sutta offer frameworks for eradicating poverty, achieving full employment, and ensuring the basic needs of the population. The study also highlights the importance of ethical governance as discussed in the Mahaparinibbana Sutta and Cakkavattisihanada Sutta, suggesting that transparency, accountability, and compassion are crucial for economic stability. Additionally, Buddhist concepts of contentment and mindful consumption, as articulated in the Dhammapada, provide insights into sustainable resource use and environmental conservation. The integration of these teachings into education and professional training can cultivate values of mindfulness, ethical conduct, and social responsibility. The practical application of Buddhist teachings in economic policies, community initiatives, and fair trade practices can enhance social harmony and reduce economic disparities. Despite potential challenges such as cultural shifts and policy implementation, the study concludes that Buddhist teachings offer a holistic approach to addressing Sri Lanka's economic crisis and achieving sustainable development. By applying these principles, Sri Lanka can create a more equitable and prosperous society that values both material and spiritual well-being.