Best Management Practices Research Articles

Local Community Participation Seasonal Flood Disaster in Sidoarjo Regency, East Java

Irrigation in Sidoarjo Regency Drainage System Complex area of carrier channels to irrigation of the drainage (AFVOUR) in the display of water level regulators. High water surface in the channel causes slow flow of rain water catchment to a puddle. Conditions are exacerbated by backwater in the river channel in the area near the coast when the tide, causing floods to not be directly receding. Synergy has a combination of integration of various elements (for example, stakeholders) optimizing something better, legitimacy is considered to equalize perception hoping that actions are carried out in the desired action entity, appropriate according to value norms. Case Study Case data applies certain cases are generalized. Case study data is possible to be used to explore, examine comprehensiveness of a research object. Analysis of data using atlas, The weakness of the government's supervisor handles industrial waste into the River Peraul in Sidoarjo Regency, Urban Sprawl Social Impact Urbanization in big cities, controlling urbanization Increases development using the approach to touch the needs of the community needed Village, equity, spread of village remote development, the level of participation and understanding of the community provides feedback related to the concept of handling routine flood disasters every year. Seasonal flood disaster control in Sidoarjo Regency by involving local communities is an important approach by direct supervision by local communities, counseling on good water management practices, emergency response training, community based infrastructure development, coordinating between the government, local communities, form of concrete participation Local community in Sidoarjo Regency.

Postoperative pain management practice and associated factors among nurses working at public hospitals, in Oromia region, Ethiopia, 2021: an institution-based cross-sectional study

BackgroundManagement of postoperative pain leads to positive patient progress and shortens the duration of hospital stay. There is a lack of information on nurse’s postoperative pain management practice and its...

Data management practice of health extension workers and associated factors in Central Gondar Zone, northwest Ethiopia

IntroductionGenerating quality data for decision-making at all levels of a health system is a global imperative. The assessment of the Ethiopian National Health Information System revealed that health information system resources, data management, dissemination, and their use were rated as “not adequate” among the six major components of the health system. Health extension workers are the frontline health workforce where baseline health data are generated in the Ethiopian health system. However, the data collected, compiled, and reported by health extension workers are unreliable and of low quality. Despite huge problems in data management practices, there is a lack of sound evidence on how to overcome these health data management challenges, particularly among health extension workers. Thus, this study aimed to assess data management practices and their associated factors among health extension workers in the Central Gondar Zone.MethodAn institution-based cross-sectional study was conducted among 383 health extension workers. A simple random sampling method was used to select districts, all health extension workers were surveyed in the selected districts, and a structured self-administered questionnaire was used for data collection. The data was entered using EpiData version 4.6 and analyzed using STATA, version 16. Bivariable and multivariable binary logistic regression analyses were executed. An odds ratio with a 95% confidence interval and a p-value of <0.05 was calculated to determine the strength of the association and to evaluate statistical significance, respectively.ResultsOf the 383 health extension workers enrolled, all responded to the questionnaire with a response rate of 100%. Furthermore, 54.7% of the respondents had good data management practices. In the multivariable logistic regression analysis, being a married woman, having good data management knowledge, having a good attitude toward data management, having 1–5 years of working experience, and having a salary ranging from 5,358 to 8,013 Ethiopian Birr were the factors significantly associated with good data management practices among health extension workers. The overall data management practice was poor with only five health extension workers out of ten having good data management practices.

Long-Term Effects of Nitrogen Sources on Yields, Nitrogen Use Efficiencies, and Soil of Tilled and Irrigated Corn

Although corn is the most important and nitrogen (N)-fertilized crop, there is a lack of long-term data on the effects of organic and inorganic N fertilizers on the N balance and losses for corn systems under different tillage approaches. From 2012 to 2023, we assessed the effects of the N source on the grain yields from cultivated continuous corn receiving irrigation at a site with minimal erosion in Fort Collins, Colorado, USA, and compared these effects to no-till (NT) and strip till (ST) systems receiving inorganic N. An N balance accounting for N and carbon (C) sequestration found a system nitrogen use efficiency (NUESys) for organic N fertilizer (manure) with a tillage of 86.6%, which was higher than the NUESys of 62.6% with inorganic N fertilizer (enhanced efficiency fertilizer, EEF). Conventional tillage with manure use is a good management practice that contributed to higher grain yields (2 of 11 years), C sequestration (p < 0.05), soil organic N content (p < 0.05), and soil phosphorus (P) content than inorganic N fertilizer with tillage (p < 0.05). The tilled systems, whether receiving organic or inorganic N fertilizer, had higher yields and grain N content than the NT and ST systems receiving inorganic N fertilizer (p < 0.05). The grain production of the cultivated system receiving organic N fertilizer did not decrease with time, while the yields of the cultivated system receiving inorganic N fertilizer decreased with time (p < 0.05), suggesting that cultivated systems receiving organic N fertilizer may be more sustainable and better able to adapt to a changing climate. Additionally, a combination of manure (30% of N input) with EEF (70% of N input) contributed to a synergistic effect that increased the agronomic productivity (harvested grain yields).

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Navigating the Privacy Paradox : Balancing AI Advancement and Data Protection in the Digital Age

The rapid advancement of artificial intelligence (AI) technologies has ushered in an era of unprecedented data utilization, raising critical concerns about individual privacy. This article examines the complex interplay between AI development and data protection, exploring the challenges of balancing the need for large-scale data processing with the imperative to safeguard personal information. We analyze current regulatory frameworks, including GDPR and CCPA, and their efficacy in addressing AI-specific privacy issues. The article also evaluates technical measures such as differential privacy, federated learning, privacy-preserving machine learning algorithms, and organizational best practices for responsible data management. By synthesizing computer science, law, and ethics perspectives, we propose a multidisciplinary approach to fostering AI innovation while robustly protecting individual privacy rights. Our findings underscore the need for adaptive regulations, enhanced technical safeguards, and increased collaboration between stakeholders to navigate the evolving landscape of AI and privacy in the digital age.

Identifying and assessing pond best management practice under future climate scenarios

Climate change can play important roles in the hydrological processes within watershed with ponds as the Best Management Practice (BMP). Unlike several other studies, this study integrated remote sensing technique with hydrological model to identify and simulate pond BMP. Limited studies have been carried out to evaluate pond BMP in relation to the climate change impacts on hydrology and water quality particularly in Mississippi watersheds. The objective of this study was to classify ponds on satellite imagery within the Big Sunflower River Watershed (BSRW) using Google Earth Engine (GEE) and incorporate this data with Soil and Water Assessment Tool (SWAT) model to evaluate future hydrological and water quality outputs. The SWAT model was calibrated and validated against streamflow (R2 and NSE values from 0.81 to 0.56) and sediment (R2 and NSE values from 0.91 to 0.40). Future climate data for the mid (2040–2060) and late (2079–2099) centuries were utilized to create climate change scenarios (e.g., RCP 4.5 and 8.5). Results of this study projected that the average annual flow and sediment load will increase by 26–46%, and 107-150% respectively by the late century compared to the baseline period (2002–2021). However, the projected sediment load with modified pond BMP data used in the SWAT model could decrease average annual sediment load by 44–46% under both RCP scenarios. Seasonal data analysis determined that spring, summer, and fall sediment loads were projected to decrease up to 42%, 52%, and 46% respectively under both RCP scenarios due to pond BMP. This study can be useful for the development of climate-smart management strategies in agricultural watersheds.

Community Forest Management and the Resilience of Karen Indigenous Knowledge in Northern Thailand.

This study investigates the resilience of indigenous knowledge in five Karen villages, located in Thailand's Chiang Mai, Chiang Rai, and Lampang provinces, that have been recognized for best practices in community forest management (CFM). The CFM model was initiated by local communities to promote forest conservation and community engagement. Data on CFM practices, land-use rights, social networks, and indigenous knowledge were collected through 17 in-depth interviews and 5 focus group meetings. The findings include how these Karen communities have adopted payment for environmental services (PES) to fund conservation projects and social welfare. More specifically, PES generates social capital by facilitating coordination with stakeholders from the private sector, academia, and government. These social networks promote markets for local products and advocate for indigenous land-use rights. The PES model also serves as a tool whereby Indigenous Peoples can shape their identities as innovative forest guardians and legitimize their residence within the forest. Additionally, the study reports on how Karen people have diversified their livelihoods to include hosting academic tourism, handicrafts, and livestock, reducing their dependence on forest resources.

EXPLORING THE ASSOCIATION BETWEEN HOUSEHOLD SANITATION AND WOMEN’S MENSTRUAL HYGIENE MANAGEMENT IN RURAL ODISHA, INDIA

IntroductionAccess to water, sanitation, and hygiene (WASH) facilities has been globally recognized as a critical need, especially for women and girls to ensure their menstrual hygiene. This study aimed to investigate the association between household sanitation and women’s menstrual management experience in Odisha, India. MethodsA cross-sectional study was conducted in Balasore district of Odisha state in India among 694 rural women and girls aged 15-45 years selected using a multi-stage sampling method. The Performance Monitoring and Accountability (PMA) 2020 questionnaire was used for data collection. Multivariate logistic regression analysis was carried out using SPSS version 25. ResultsMore than two-third (68.4%) of rural families used improved sanitary facilities. Around 30% of households have been using an unimproved sanitary facility, which means the practice of open defecation is still prevalent in rural areas. About 40.6% of women and girls reported using clothes as their primary menstrual hygiene material. Further, women and girls with improved sanitation facilities (aOR = 1.56, 95% CI: 1.09 - 2.59) were more likely to change menstrual materials at the main household sanitation facility than those with an unimproved sanitation facility. In contrast, households using improved sanitation facility were less likely to use bedroom/sleeping areas to change the menstrual materials (aOR=0.65, 95% CI: 0.42-0.99) than those with an unimproved facility. ConclusionAvailability of improved household sanitation is critical to good menstruation management practices. State-specific policy for menstrual health and hygiene could provide the much-needed impetus to strengthen WASH infrastructure in rural and tribal regions of Odisha.

An integrated tool for cost-effectively applying nutrient management practices for corn, soybeans, and wheat.

Harmful algal blooms (HABs) problem in Lake Erie has become critical recently-primarily triggered by phosphorus losses from cropland in the Maumee River watershed (major crops of corn/soybeans/wheat). Implementing agricultural best management practices (BMPs) is crucial to reduce excess nutrient loadings. Nutrient management is the management of nutrient applications for crop production that maximizes nutrient use efficiencies and minimizes nutrient losses. However, an integrated watershed-scale tool is needed for cost-effectively applying nutrient management practices for corn/soybeans/wheat considering the 4Rs (Right nutrient source, Right rate, Right time, and Right place of nutrient applications). In this study, by combining an improved Soil and Water Assessment Tool (SWAT) for nutrient management (SWAT-NM) and an improved BMP Cost Evaluation Tool (BMP-COST) for economic evaluations of nutrient management (BMP-COST-NM) considering the 4Rs for corn/soybeans/wheat, an integrated tool SWAT-COST-NM was created. SWAT-COST-NM was demonstrated in the AXL watershed (a typical agricultural area in the Maumee River watershed). The impacts of single nutrient management practices (single-NM, which separately changed the rate, place, time, or nutrient source of fertilizer applications) and combined-NM practices (multiple single-NM practices combined as one nutrient management practice) for corn/soybeans/wheat were evaluated. Tradeoffs in yearly net costs, crop yields, and March-July/yearly nutrient losses (Total Phosphorus-TP, Dissolved Reactive Phosphorus-DRP, and Total Nitrogen-TN) existed. Nutrient management did not necessarily lead to sufficient increases in crop yields to generate extra revenues that match or exceed the additional costs of the activities (compared to existing practices). One of the combined-NM practices could simultaneously reduce March-July TP, DRP, and TN losses by 5.89%, 8.19%, and 8.23%, respectively, while increasing crop yields with additional income (0.50 $/ha/yr of cropped area). SWAT-COST-NM, which can quantify various factors and tradeoffs when evaluating the impacts of nutrient management practices for corn/soybeans/wheat, can assist decision-makers in cost-effectively applying nutrient management practices considering the 4Rs.

Combined impacts of climate and land use scenarios on sediment loads in small agricultural watershed in Tunisia

This study investigates the impact of conservation practices, specifically mulching and terracing, on soil erosion in the Kamech watershed (2.6 km²) situated in the Cap Bon region of northeastern Tunisia, under both current and future climate scenarios. Utilizing the Soil and Water Assessment Tool (SWAT) model, the research evaluates the effectiveness of these Best Management Practices (BMPs) in reducing soil loss, revealing significant reductions of 1.5 t ha⁻¹ yr⁻¹ for mulching and 2 t ha⁻¹ yr⁻¹ for terracing. The findings indicate that these practices play a crucial role in enhancing soil conservation amidst climate change challenges, including altered precipitation patterns and increased temperatures. Additionally, the study highlights a notable decline in average monthly rainfall projected for the period 2051 to 2100 under the RCP8.5 scenario, further emphasizing the importance of implementing effective soil management strategies. This paper indicates that both methods, mulching and terracing, significantly contribute to reducing soil erosion under future scenarios (2051-2100). Their implementation proves essential for effective soil conservation, helping to mitigate the impacts of climate change and maintain soil health in the Kamech watershed. The results contribute to a better understanding of BMP performance in addressing soil erosion, water quality, and hydrological responses in the face of evolving environmental conditions. By enhancing our understanding of these interactions, this research aims to provide valuable insights for sustainable watershed management, particularly in regions prone to water scarcity and land degradation.

Building a theoretical framework for financial risk management in emerging markets: Applying global best practices to the oil and gas industry in developing economies

Financial risk management is crucial for the sustainability and growth of the oil and gas industry in developing economies, where volatility and regulatory uncertainties present significant challenges. This abstract explores the construction of a theoretical framework for financial risk management, adapting global best practices to the specific contexts of emerging markets within the oil and gas sector. In developing economies, the oil and gas industry faces unique risks such as geopolitical instability, currency fluctuations, and regulatory changes. Effective financial risk management mitigates these risks through proactive strategies that balance risk exposure with strategic objectives. Adopting global best practices involves the integration of comprehensive risk assessment methodologies, scenario planning, and hedging techniques tailored to local market conditions. Key components of the theoretical framework include risk identification through comprehensive analysis of market, operational, and financial factors. This approach enables oil and gas companies to anticipate and mitigate risks before they escalate, ensuring financial stability and resilience in volatile environments. Furthermore, the application of global best practices in financial risk management fosters transparency and accountability, enhancing investor confidence and facilitating access to capital. By aligning risk management strategies with international standards and benchmarks, developing economy oil and gas firms can navigate uncertainty more effectively, thereby optimizing operational performance and sustaining long-term growth. However, challenges such as limited access to sophisticated financial instruments and expertise may hinder effective implementation. Overcoming these barriers requires capacity building, collaboration with experienced partners, and continuous adaptation of strategies to evolving market dynamics. In conclusion, building a theoretical framework for financial risk management in the oil and gas industry in developing economies is essential for mitigating volatility and maximizing opportunities. By applying global best practices tailored to local contexts, companies can strengthen their resilience, attract investment, and drive sustainable development in emerging markets.

Strategic leadership and management within non-profit organizations: An in-depth review of practices contributing to sustainable impact

This study provides an in-depth review of strategic leadership and management practices within non-profit organizations (NPOs) and their contribution to sustainable impact. Employing a systematic literature review and content analysis, the research aims to explore the evolving landscape of strategic leadership, define the scope of leadership and management in achieving sustainable impact, and identify innovative management practices that enhance organizational sustainability. The methodology involved a comprehensive search across peer-reviewed journals, conference proceedings, and grey literature from 2013 to 2023, focusing on empirical evidence, theoretical frameworks, and case studies relevant to NPOs' strategic leadership and management. Key findings reveal that adaptive leadership, innovative management practices, stakeholder engagement, and technology integration are crucial for NPOs to navigate operational complexities, engage stakeholders effectively, and achieve mission-driven objectives. The study proposes strategic recommendations for NPOs to enhance their sustainable impact, including adopting adaptive leadership styles, fostering innovation, integrating technology, and building resilient organizational frameworks. The study underscores the importance of policy development to guide non-profit leaders towards best practices in strategic leadership and management. It also highlights future research directions, emphasizing the need to explore the impact of emerging technologies, globalization, and cross-cultural dynamics on NPOs. Conclusively, the research affirms that effective strategic leadership and management are indispensable for enhancing the sustainable impact of NPOs, advocating for continuous innovation and adaptability in the non-profit sector.

Development of an automated process for monitoring of amiodarone treatment

Abstract Background Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to risk of side effects during long-term administration. Robotic process automation (RPA) represents the use of software robots to automate repetitive and routine tasks typically performed by humans. Purpose We aimed to develop an RPA using a diagnostic classification algorithm and clinical support system for follow-up of amiodarone treatment. Methods Based on international guidelines and clinical specialist advisory we constructed an RPA which was studied in a test environment in parallel with routine amiodarone treatment. Expert advice was integrated through a series of collaborative workshops, ensuring alignment of the algorithm with current best practices in thyroid and liver disease management. The RPA provided recommendations on the time interval to next laboratory testing and suggestions for patient management to the physician, who was serving as a human-in-the-loop responsible for clinical decisions. Results After iterative improvements the RPA was evaluated against physician orders (n=390 paired orders) without technical failures. The recommended time interval to next follow-up was (mean ± SD) 4.5 ± 2.4 vs. 3.1 ± 1.4 months for RPA vs. physician (p<0.0001). While normal laboratory findings resulted in recommended follow-up in 6 months by the RPA (72.2%) this was only ordered in 9.9% by the physician (Figure). Physicians’ orders clustered between 3-4 months (58.5%). All patients diagnosed with new side effects (n=12) were detected and communicated correctly by the RPA, whereas only 8 were correctly detected and responded to by the physician (Table). Conclusions An automated process constitutes a reliable alternative to the current fully manual management of amiodarone follow-up. It may reduce manual labour, frequency of laboratory testing and detect side effects with increased precision, thereby reducing costs and enhancing patient value. While the RPA shows promise, further research is needed to explore its adaptability to other treatment scenarios.

The adoption of Best Management Practices: empirical evidence from oil palm farming in rural Ghana

ABSTRACT Purpose We examine how farmer characteristics, farm, institutional, perception and shock factors influence the adoption of the Best Management Practices (BMPs). A thorough understanding of how relevant drivers affect the adoption decision of diverse BMPs can contribute to the development of evidence-based policies. Approach We use a household survey of 1031 smallholder oil palm farmers in the forest zones of Ghana to assess the determinants of farmers’ adoption decisions. We employ a seemingly unrelated regression (SUR) model after a principal component analysis is used to extract five latent components. Findings Our findings indicate that different farmer characteristics, farm, institutional, perception and shock factors have differential associations with the adoption of diverse BMPs. Key among these are the positive perception of BMPs, access to mills, perception of soil fertility and pests and diseases. Practical implications Specific BMPs must be tailored to meet the prevailing individual socio-economic characteristics and challenges of farmers to facilitate their adoption and scale-up. Educational programmes and dissemination of information by extension agents and research institutions are needed in the promotion of BMPs to enhance effective uptake. Theoretical implication The paper contributes to the growing literature especially on the uptake of diverse management practices, utilizing both behavioural and socio-economic theories underpinning adoption, thus providing empirical evidence that adoption is influenced by both behavioural and socio-economic factors. Originality/value In the bid to increase agricultural productivity, while preserving ecosystem services, the adoption of sustainable management practices remains key for all stakeholders and policy makers. This paper presents credible information for policy makers in Ghana and Africa in designing polices to influence their adoptions.

Effect of Different Weed Management Practices on growth and yield in Potato at Khumaltar, Lalitpur, Nepal

Potato (Solanum tuberosum L.) is the fifth most important crops in the world in terms of production with an estimate of 375 million metric tons. In Nepal, potato ranks second in terms of production and sixth in terms of area coverage. Potato production is affected by weed infestation. Weeds compete with crops for nutrients, water and other resources, which eventually reduce the crop yield. The critical period of crop-weed competition for potato is 25-40 days from planting depending on the climatic-zone and planting seasons. The most common method of weed control in Nepal is manual method, while herbicides are uses in less areas. Weeds are considered as the major problem in conventional and organic farming, due to lower yields by weeds and higher costs of labor for weed control. Mulching is one of the best and safest practice for effective weed management in potato due to which micro-climate manipulation is developed. Metribuzin is widely used herbicides in potato that controls a range of broadleaf weed species and suppresses many grassy weeds. To study the performance of different weed management practices in potato ten treatments were set up viz., three herbicides (pendimethalin@ 1.0 kg a.i ha-1, metribuzin@ 0.5 kg a.i ha-1 and atrazine @ 0.25 kg a.i ha-1) constituting seven treatments, one mulching with rice straw, one treatment with two manual hoeing, and another treatment with no any weed management practices as control.The treatments were laid out in RCB design with 4 replications. The potato variety Janak Dev was planted on 12th Feb 2021 and 23rd Jan 2022 with tuber rate of 4.1 t.ha-1 with row spacing of 60 cm and plant to plant 20 cm in a plot size of 4 x 3 meter in flat bed. Growth parameters such as plant height, plant fresh and dry biomass were recorded. Data on weed density, weed dry biomass, and tuber yield were taken.The economics of the different treatments were calculated. Among the treatments metribuzin@ 0.25 kg a.i ha-1 as pre-emergence proved effective in controlling weeds showing highest weed control efficiency (200.7 %) and tuber yield (32.79 t ha-1) . However, the treatment rice straw mulch @ 6 t ha-1 produced the highest tuber yield (38.31 t ha-1), 73.4 % increased yield over control, weed control efficiency(147.1%), economic benefit (NRS 13,40,850), benefit cost ratio (4.93).The treatment rice straw mulch @ 6 t ha-1 showed superiority among the different weeds management practices.

An Integrated Spatial Fuzzy‐Based Site Suitability Assessment Framework for Agricultural BMP Placement

ABSTRACTAssigning crisp class boundaries to landscape features can result in the loss of vital information for land evaluation objectives, especially when these boundaries lack clear definitions. This challenge becomes particularly pronounced when land suitability is assessed for implementing agricultural best management practices (BMPs)—conservation measures aimed at reducing the environmental risks of farming activities to aquatic ecosystems while simultaneously achieving water quality and economic objectives. To address the limitations associated with Boolean suitability assessment frameworks, we have introduced an integrated spatial, fuzzy‐based land evaluation framework that considers a range of hydrological and economic determinants for BMP placement. By employing data‐driven fuzzy membership functions and overlay operators, this framework generates a joint suitability index for BMP placement across agricultural watersheds. The application of the proposed framework to the Thames River Watershed in southwestern Ontario, Canada, produced the first joint suitability index of the watershed. Further analysis of the average farm‐level joint suitability scores identified statistically significant clusters of highly suitable and unsuitable lands for BMP placement, with 85% of highly suitable lands being situated in the upper basin areas. The proposed framework is adaptable to various agricultural production geographies, especially in data‐limited environments, allowing for strategic BMP placement to mitigate the global impacts of anthropogenic nutrient loadings on aquatic ecosystems. For optimal results, context‐specific applications should prioritize research on locally relevant fuzzy membership functions and BMP implementation drivers.

Optimizing pharmaceutical inventory management: A global framework for efficiency and cost reduction

Pharmaceutical inventory management is critical in ensuring continuous drug availability and cost efficiency in healthcare institutions globally. However, maintaining optimal inventory levels is challenging due to factors such as overstocking, stockouts, and global supply chain disruptions. This paper reviews best practices for efficient pharmaceutical inventory management, emphasizing the integration of data analytics and predictive models to forecast demand, reduce waste, and streamline procurement. It explores strategies for implementing unified inventory systems across diverse healthcare settings, balancing cost efficiency with uninterrupted drug availability. Additionally, the paper examines the impact of global supply chain disruptions on pharmaceutical supply and recommends solutions for building resilience. Through real-world examples and recommendations for future improvements, this review highlights the potential for enhanced global inventory management practices to reduce costs while ensuring the continuous availability of essential medications. Keywords: Pharmaceutical Inventory Management, Data Analytics, Cost Efficiency, Predictive Models, Supply Chain Resilience, Drug Availability

Streamlining budgeting and forecasting across multi-cloud environments with dynamic financial models

This review paper explores the complexities of financial management in multi-cloud environments, emphasizing the need for streamlined budgeting and forecasting processes. It begins by addressing the challenges inherent in managing finances across multiple cloud platforms, such as integration difficulties, data silos, and cost management issues. The paper then provides an overview of dynamic financial models, detailing their components and benefits in overcoming these challenges. It highlights the role of automation, real-time data utilization, and predictive analytics in enhancing financial accuracy and efficiency. Furthermore, the paper discusses the importance of collaborative platforms in fostering stakeholder communication and coordination. Finally, strategic planning, best practices for multi-cloud financial management, and emerging trends and prospects are outlined. The review underscores the significance of aligning financial models with organizational goals and leveraging advanced technologies to achieve optimal financial outcomes in multi-cloud settings. Keywords: Multi-Cloud Financial Management, Budgeting and Forecasting, Dynamic Financial Models, Automation in Finance, Predictive Analytics.

Commons land management in Southern, post-unification Italy: New evidence from a micro analysis on Salerno province

Abstract The aim of this study is to observe the ability of Southern Italian municipalities to respond to and redefine the management of common lands following the national privatisation and partition policies promoted by the State during the second half of the nineteenth century. The hypothesis tested is whether, through the analysis of new archival sources such as management rules, it is possible to identify good practices of management of common lands, along with the presence of resilience on the part of local communities during a period defined as one of uncontrollable ‘decline’ for these resources. Three Apennine areas (Campania Region) were identified as the observation sample. The analysis revealed that for these specific cases, it is imprecise to speak of decline and a lack of propensity to develop good management practices, but rather of institutions that are attentive to the protection and preservation of their commons through specific rules.