Efficient Management Research Articles

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia. The pathway from conceptualisation to deployment for the HOTspots digital surveillance platform is described.

Digilite Content Management System Digital Library Lite

Digital library is a system that organizes and provides access to digital information content. Unlike conventional publishing, which has physical content like books, journals, and journals, digital publishing offers content in electronic format that can be accessed through a computer or other device connected to the internet. It allows users to search, download, and create various types of content, such as e-books, articles, videos, and more, from various sources. In the rapidly evolving digital era, effective and efficient content management is crucial for organizations, businesses, and individuals who want to build online presence. Content Management Systems (CMS) are a useful tool for creating, editing, and distributing content on the web without technical knowledge about coding. CMS provides user-friendly tools, allowing users to easily use text, images, videos, and other elements in their sites. Web-based digital library is a platform that uses CMS to create and distribute content without requiring technical knowledge. Users can search, download, and create digital content from various sources connected to the internet. CMS also provides a user-friendly interface and allows users to manage content efficiently, making it an ideal solution for various web publishing needs, from personal blogs to large business portals. Digital library is designed for ease of use and infrastructure, avoiding various issues. It can help create a modern, literate culture and contribute to the development of a more efficient and user-friendly system.

Industrial circular water use practices through the application of a conceptual water efficiency framework in the process industry

Increased industrial water demand and resource depletion require the incorporation of sustainable and efficient water and wastewater management solutions in the industrial sector. Conventional and advanced treatment technologies, closed-water loops at different levels from an industrial process to collaborative networks among industries within the same or another sector and digital tools and services facilitate the materialization of circular water use practices. To this end, the scope of this paper is the application of the Conceptual Water Efficiency Framework (CWEF), which has been developed within the AquaSPICE project aspiring to enhance water circularity within industries in a holistic way. Four water-intensive process industries (two chemical industries, one oil refinery plant and one meat production plant) are examined, revealing its adaptability, versatility and flexibility according to the requirements of each use case. It is evident that the synergy of process, circular and digital innovations can promote sustainability, contribute to water conservation in the industry, elaborating a compact approach to be replicated from other industries.

How artificial intelligence plays a role in achieving sustainable development goals?

Artificial Intelligence (AI) is increasingly recognized as a key enabler in achieving Sustainable Development Goals (SDGs) due to its transformative potential across various sectors. This paper explores the intersection of AI and SDGs, highlighting the significant role AI plays in accelerating progress towards sustainability. AI technologies such as machine learning, natural language processing, and computer vision have been instrumental in enhancing efficiency, decision-making, and resource management in areas such as healthcare, agriculture, education, and climate change mitigation. By analyzing vast amounts of data, AI can provide valuable insights and predictive models to inform policy-making, optimize resource allocation, and enhance monitoring and evaluation processes. Furthermore, AI empowers developing countries to leapfrog traditional developmental barriers by offering innovative solutions that are cost-effective and scalable. Through initiatives like precision agriculture, telemedicine, and smart energy systems, AI enables inclusive growth while reducing inequalities and enhancing resilience to environmental challenges. While AI brings immense opportunities for sustainable development, challenges such as data privacy, bias, and ethical concerns must be addressed to ensure that AI technologies are deployed responsibly and equitably. Collaborative efforts between governments, industry, and civil society are essential to harness the full potential of AI in achieving the SDGs and creating a more inclusive and sustainable future for all.

The Impact of ESG Criteria on Firm Value: A Strategic Analysis of the Airline Industry

Environmental, social, and governance (ESG) factors are crucial in evaluating a company’s value. High ESG scores reflect ethical practices, social responsibility, and effective governance. This paper examines the impact of ESG criteria on firm value within the airline industry, focusing on their influence on operational efficiency, risk reduction, and financial performance. Using panel data analysis, the study evaluates ESG scores from 32 airline companies over the period of 2018–2023, with an explanatory power of 36.5%. The research explores how integrating environmental, social, and governance factors into strategic management can foster sustainable competitive advantage. It focuses on utilizing internal resources, meeting the needs of various interested parties, and balancing financial, social, and environmental performance. The findings indicate that while ESG practices enhance firm value through improved efficiency and risk management, they do not always lead to higher short-term firm value. Moreover, the study underscores the significance of governance in the airline industry, where robust governance structures can mitigate risks but may also increase costs. This research contributes to the literature by providing empirical evidence of the link between ESG performance and firm value in the airline industry, emphasizing the importance of integrating ESG principles into strategic management for long-term sustainability and financial success.

Optimal energy management and scheduling of a microgrid considering hydrogen storage and PEMFC with uncertainties

Renewable energy sources have been widely installed and operated in power systems, particularly in microgrids in the form of distributed generation units. This issue requires efficient energy management tools which take into account the inherent uncertainties of such energy resources. Thus, this paper presents a stochastic framework aimed at scheduling the renewable energy-based and thermal units in a coordinated way. The generation units comprise fuel cell units with proton exchange membrane known as PEMFC-CHP producing heat and power, concurrently. Moreover, the uncertainties arising from wind and solar power as well as market prices are characterized by deploying scenario-based optimization. The mentioned framework considers storing hydrogen and the model is presented within a stochastic mixed-integer nonlinear programming (MINLP) framework. The resulting problem is simulated on a modified 33-bus distribution network and tackled using the modified marine predators algorithm (MMPA)algorithm. The obtained results indicate that the revenue increases by more than 5% compared to other optimization algorithms. Furthermore, taking into account CHP will increase the total profit of the system by more than 15%.

Cancer stem cells in meningiomas: novel insights and therapeutic implications.

Meningiomas (MGs), which arise from meningothelial cells of the dura mater, represent a significant proportion of primary tumours of the central nervous system (CNS). Despite advances in treatment, the management of malignant meningioma (MMG) remains challenging due to diagnostic, surgical, and resection limitations. Cancer stem cells (CSCs), a subpopulation within tumours capable of self-renewal and differentiation, are highlighted as key markers of tumour growth, metastasis, and treatment resistance. Identifying additional CSC-related markers enhances the precision of malignancy evaluations, enabling advancements in personalised medicine. The review discusses key CSC biomarkers that are associated with high levels of expression, aggressive tumour behaviour, and poor outcomes. Recent molecular research has identified CSC-related biomarkers, including Oct-4, Sox2, NANOG, and CD133, which help maintain cellular renewal, proliferation, and drug resistance in MGs. This study highlights new therapeutic strategies that could improve patient prognosis with more durable tumour regression. The use of combination therapies, such as hydroxyurea alongside diltiazem, suggests more efficient and effective MG management compared to monotherapy. Signalling pathways such as NOTCH and hedgehog also offer additional avenues for therapeutic development. CRISPR/Cas9 technology has also been employed to create meningioma models, uncovering pathways related to cell growth and proliferation. Since the efficacy of traditional therapies is limited in most cases due to resistance mechanisms in CSCs, further studies on the biology of CSCs are warranted to develop therapeutic interventions that are likely to be effective in MG. Consequently, improved diagnostic approaches may lead to personalised treatment plans tailored to the specific needs of each patient.

Monitoring surgical nociception using multisensor physiological models

Monitoring nociception, the flow of information associated with harmful stimuli through the nervous system even during unconsciousness, is critical for proper anesthesia care during surgery. Currently, this is done by tracking heart rate and blood pressure by eye. Monitoring objectively a patient's nociceptive state remains a challenge, causing drugs to often be over- or underdosed intraoperatively. Inefficient management of surgical nociception may lead to more complex postoperative pain management and side effects such as postoperative cognitive dysfunction, particularly in elderly patients. We collected a comprehensive and multisensor prospective observational dataset focused on surgical nociception (101 surgeries, 18,582 min, and 49,878 nociceptive stimuli), including annotations of all nociceptive stimuli occurring during surgery and medications administered. Using this dataset, we developed indices of autonomic nervous system activity based on physiologically and statistically rigorous point process representations of cardiac action potentials and sweat gland activity. Next, we constructed highly interpretable supervised and unsupervised models with appropriate inductive biases that quantify surgical nociception throughout surgery. Our models track nociceptive stimuli more accurately than existing nociception monitors. We also demonstrate that the characterizing signature of nociception learned by our models resembles the known physiology of the response to pain. Our work represents an important step toward objective multisensor physiology-based markers of surgical nociception. These markers are derived from an in-depth characterization of nociception as measured during surgery itself rather than using other experimental models as surrogates for surgical nociception.

Application of Mobile Technology in Enterprise Management: From Mobile Office to Intelligent Decision Support

With the rapid advancement of information technology, the application of mobile technology in enterprise management has become increasingly widespread. From mobile office solutions to intelligent decision support systems, the proliferation of mobile technology has not only enhanced operational efficiency but also provided more flexible and convenient management methods. Existing research indicates that the adoption of mobile office solutions enables enterprises to overcome limitations of time and space, improving work efficiency and employee satisfaction. Additionally, the implementation of intelligent decision support systems allows enterprises to make faster and more accurate business decisions. However, current studies often focus on individual mobile technologies or application scenarios, lacking a systematic exploration of mobile office data sharing and intelligent decision-making collaboration. Moreover, most research overlooks privacy protection issues in the enterprise management decision-making process. To address these gaps, this paper proposes a scheme for mobile office data sharing and intelligent decision-making collaboration and examines the integration of privacy protection in the decision-making process. The findings not only enrich the theoretical framework of management studies but also provide enterprises with a systematic and practical solution for mobile technology applications, enhancing management efficiency and intelligence while effectively safeguarding data privacy and security.

Strategies for the Development of Tilapia Fish Floss Processing Businesses: A Case Study in Cijambe Village, Cijambe Sub-district, Subang District

Aims: This study aims to analyze the strategic development of UMKM Ibu AAS's tilapia fish floss processing business, evaluating its internal and external factors to determine appropriate growth strategies. Study Design: The research follows a case study design focusing on a single business unit, UMKM Ibu AAS, to understand its operational, financial, and strategic aspects. Place and Duration of Study: The study was conducted at UMKM Ibu AAS, Cijambe Village, Subang District, over a period of six months from January to June 2021. Methodology: This study employed qualitative and quantitative methods, including SWOT analysis, financial analysis, and observational techniques. The financial analysis covered short-term and long-term aspects, while the SWOT analysis helped identify the strengths, weaknesses, opportunities, and threats of the business. Primary data were collected from business records, interviews with key personnel, and direct observations. Results: The study revealed that UMKM Ibu AAS, despite having solid operational strengths and a strategic location, faces challenges in capital limitation and high production costs. The business shows potential for growth, indicated by a strong market demand and advantageous relationships with consumers. Financial analysis suggest profitability with an R/C ratio of 2.027, indicating efficient revenue generation against costs. The SWOT analysis positions the business in Quadrant I, suitable for aggressive growth strategies focusing on exploiting strengths and opportunities. Conclusion: UMKM Ibu AAS has a promising future in the tilapia fish floss industry, contingent on strategic capital investment and efficient management. The study recommends adopting aggressive growth strategies, enhancing production technology, expanding market reach, and establishing stronger partnerships to capitalize on the available opportunities.

Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.

Analisis Penggunaan Helicopter Stand dalam Menunjang Keamanan dan Keselamatan Penerbangan di Bandar Udara Tjilik Riwut Palangka Raya

Tjilik Riwut Palangka Raya Airport is one of the main gateways in Central Kalimantan province. In 2023, the airport will serve around 40,000 passengers, indicating an increase in aviation activity that requires high efficiency in facility management. Helicopters at Tjilik Riwut Airport have an important role in various operations, including medical evacuation, goods delivery and government missions. Currently, helicopters only land in regular commercial aircraft parking areas, which are not specifically designed for helicopter needs. This can cause a buildup of activity, disrupting flight schedules. This research aims to analyze the needs and benefits of building a Helicopter Stand at Tjilik Riwut Airport, as well as assessing its impact on the smooth running of flights. This research uses qualitative methods and the types of data used are primary data and secondary data. The data collection techniques used in this research are observation, interviews and documentation. By using analytical techniques in the form of data reduction, data presentation and conclusions. Testing the validity of the data used in this research is technical triangulation and source triangulation. The results of this research show that the helicopter stand facilities at Tjilik Riwut Airport are currently not fully adequate, it is necessary to reorganize the area to ensure clearer separation between helicopters and commercial aircraft in order to reduce the potential for accidents and avoid the potential for overlapping with other aircraft in the parking stand area.

Sustainability Reporting Under GRI Standards: An Empirical Study of Indian Companies

The accumulation of industrial waste at the global level is distressing, as the World Bank estimated in 2016 that the growth of solid waste will hit 3.4 billion tonnes by 2050. India faces similar issues related to inefficient waste management and enfeebled waste reporting practices as the Energy and Resources Institute (TERI) estimated that more than 62 million tons of industrial waste are generated annually, of which around 12 million tons are treated before disposal, 31 million tons are diverted towards landfills, and the majority is unprocessed and unaccounted for. In this response, this study analyses the waste reporting practices adopted by the top corporate social responsibilities ranked Indian companies with a motivation to align the Indian reporting pattern with the Global Reporting Initiative (GRI). The study finds inconsistent, unstandardized, and unstructured waste reporting practices when analysed under the GRI across the selected Indian companies concerning massive waste generation per turnover and earnings per share. The findings suggest the need for integrated compliance towards reporting standards to control any negative impact on the environment, as shown through waste intensity ratios. The findings have largely contributed towards understanding the anomalies between the Indian corporate reporting practices and the global reporting under GRI.

Optimizing the food supply chain through the integration of financial models and big data in procurement: A strategy for reducing food prices

Optimizing the food supply chain through the integration of financial models and big data analytics presents a transformative strategy for reducing food prices. In an increasingly complex global market, managing procurement effectively is crucial for cost control and price stabilization. Financial models, such as cost-benefit analysis, financial forecasting, and risk assessment, provide valuable insights into procurement decisions, helping organizations anticipate price fluctuations and manage expenses. When combined with big data analytics, these models enable more precise predictions and strategic planning. Big data analytics harnesses vast amounts of information from various sources, including market trends, supplier performance, and consumer behavior. By analyzing this data, organizations can identify patterns, optimize inventory levels, and forecast demand with greater accuracy. This data-driven approach enhances decision-making in procurement, allowing for better negotiation with suppliers, efficient inventory management, and the reduction of wastage. The integration of financial models with big data provides a holistic view of the supply chain, enabling companies to implement dynamic pricing strategies and adjust procurement practices based on real-time insights. This integration helps in minimizing operational costs, improving supply chain efficiency, and ultimately reducing food prices for consumers. For example, predictive analytics can identify potential supply disruptions and adjust procurement strategies proactively, while financial models can evaluate the cost-effectiveness of various sourcing options. Challenges such as data integration, quality, and privacy must be addressed to fully leverage these technologies. Moreover, investment in advanced analytics tools and skilled personnel is essential for successful implementation. In conclusion, the synergy between financial models and big data analytics offers a strategic approach to optimizing the food supply chain. By enhancing procurement practices and leveraging real-time insights, this strategy contributes to reducing food prices and improving overall supply chain efficiency, benefiting both businesses and consumers. Keywords: Food Supply Chain, Financial Models, Big Data Analytics, Procurement Optimization, Cost Control, Price Reduction, Inventory Management, Predictive Analytics, Supply Chain Efficiency.

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.

ClusFC‐IoT: A clustering‐based approach for data reduction in fog‐cloud‐enabled IoT

SummaryThe Internet of Things (IoT) is an ever‐expanding network technology that connects diverse objects and devices, generating vast amounts of heterogeneous data at the network edge. These vast volumes of data present significant challenges in data management, transmission, and storage. In fog‐cloud‐enabled IoT, where data are processed at the edge (fog) and in the cloud, efficient data reduction strategies become imperative. One such method is clustering, which groups similar data points together to reduce redundancy and facilitate more efficient data management. In this paper, we introduce ClusFC‐IoT, a novel two‐phase clustering‐based approach designed to optimize the management of IoT‐generated data. In the first phase, which is performed in the fog layer, we used the K‐means clustering algorithm to group the received data from the IoT layer based on similarity. This initial clustering creates distinct clusters, with a central data point representing each cluster. Incoming data from the IoT side is assigned to these existing clusters if they have similar characteristics, which reduces data redundancy and transfers to the cloud layer. In a second phase performed in the cloud layer, we performed additional K‐means clustering on the data obtained from the fog layer. In this secondary clustering phase, we stabilized the similarities between the clusters created in the fog layer further optimized the data display, and reduced the redundancy. To verify the effectiveness of ClusFC‐IoT, we implemented it using four different IoT data sets in Python 3. The implementation results show a reduction in data transmission compared to other methods, which makes ClusFC‐IoT very suitable for resource‐constrained IoT environments.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

AbstractMore than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time‐consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

Environmental impact of modern agricultural practices: Strategies for reducing carbon footprint and promoting conservation

Modern agricultural practices have significantly transformed food production, leading to increased efficiency and productivity. However, these advancements have also had notable environmental impacts, particularly concerning carbon emissions and resource depletion. This study explores the environmental impact of contemporary agricultural practices, emphasizing the need for strategies to mitigate carbon footprints and enhance conservation efforts. Agriculture contributes substantially to greenhouse gas emissions through sources such as livestock, synthetic fertilizers, and fossil fuel consumption. These emissions contribute to climate change and global warming. Additionally, modern farming practices often lead to deforestation, habitat loss, and soil degradation, exacerbating biodiversity loss and reducing soil health. Water usage in agriculture also poses significant challenges, with high water consumption and pollution from runoff affecting water resources and aquatic ecosystems. To address these issues, several strategies can be employed to reduce the carbon footprint of agriculture. Adoption of sustainable farming practices such as conservation tillage and no-till farming can enhance soil carbon sequestration, improving soil health and reducing emissions. Agroforestry and cover cropping further contribute by capturing atmospheric carbon and improving soil structure. The integration of renewable energy sources like solar and wind power in farming operations, along with energy-efficient technologies, can also reduce reliance on fossil fuels and lower overall greenhouse gas emissions. Promoting conservation involves implementing practices that protect and restore natural resources. Soil conservation methods, including erosion control techniques like terracing and contour plowing, help maintain soil health and prevent degradation. Efficient water management strategies, such as drip irrigation and rainwater harvesting, reduce water consumption and mitigate pollution from runoff. Biodiversity conservation can be supported through the preservation of natural habitats and the promotion of biodiverse farming systems, which enhance ecosystem resilience and stability. Effective policy frameworks and support mechanisms are crucial for driving the adoption of these strategies. Government policies, including environmental regulations and incentives, play a pivotal role in encouraging sustainable practices. Research and innovation are essential for developing new technologies and practices, while education and extension services ensure the dissemination of knowledge and best practices to farmers. Modern agricultural practices have significant environmental impacts, but through the adoption of sustainable practices and innovative strategies, it is possible to reduce carbon footprints and promote conservation. Continued research, supportive policies, and effective implementation of conservation strategies are essential for achieving a more sustainable agricultural future. Keywords: Environmental Impact, Agricultural Practices, Carbon Footprint.

A study on water quality and pollution source investigation of Jinkengling Reservoir

Context Eutrophication pollution has emerged as a significant ecological concern on a global scale, with the quality of drinking water closely intertwined with the sustainability and advancement of rural communities. Aims The objective of this study was to conduct a comprehensive examination of pollution sources within the Jinkengling Reservoir, assess the extent of eutrophication pollution, and offer insights for the efficient management and regulation of the reservoir. Methods The investigation of exogenous pollution sources entailed an assessment of the pollution load, whereas the investigation of endogenous pollution sources centred on sediment testing and the analysis of sediment accumulation within the reservoir. Key results The examination of exogenous pollution sources in the reservoir indicated that nitrogen and phosphorus predominantly emanated from pollution resulting from municipal solid waste, fertiliser, and livestock and poultry farming. The analysis of endogenous pollution sources illustrated that sediment functions as a significant contributor of nutrient substances in the water. Conclusions The examination of exogenous pollution sources indicates the necessity for thorough control measures, whereas the study of endogenous pollution sources underscores the significance of sediment dredging in the reservoir. Implications This study offers insights to improve the ecological environment of local water bodies, and also has universal and reference significance for solving the global eutrophication pollution problem.