Efficient Resource Management Research Articles

Enhancing reconnaissance security: a 2-tier deception-driven model approach (2TDDSM)

The emergence of network security has revolutionized the way educational institutions operate, providing advanced connectivity, enhanced communication, and efficient management of resources. However, with the increasing dependence on interconnected systems, institutions and organizations became vulnerable targets for cyber threats. To address these security challenges, a two-tier deception-driven model specifically designed to for the initial phase of attacks in reconnaissance period where the adversaries is to gather information of the targets. Defending threats in this phase can provide active and proactive defense allowing the administrator to identify potential attackers and understanding their methods, motivation and potential target assets. The model's layered approach creates a resilient defense mechanism that aligns with the advanced deception techniques which aims to misguide potential threats attempting to gather intelligence within the network.

Scarce, scarcer, scarcest: Sensitivity-flexible AI-based planning of elective surgeries for efficient and effective intensive care resource management

Scarce, scarcer, scarcest: Sensitivity-flexible AI-based planning of elective surgeries for efficient and effective intensive care resource management

Engagement of forest-dependent communities in the sustainable management of Akure forest reserve

Sustainable forest management is linked to stakeholder involvement, openness, accountability of decision-makers, adherence to the rule of law, predictability, efficient and effective management of natural resources. It is essential in accomplishing the goal of good governance. This study was conducted to evaluate the involvement of the forest-dependent people in the management of the Akure forest Reserve in Ondo state, Nigeria. Two-hundred structured questionnaires were distributed to respondents in four specifically chosen communities out of the twelve communities bordering the Reserve. A total of fifty structured questionnaires were distributed to respondents in each of the four chosen localities. Data were examined using descriptive statistics and chi-square analysis. The results revealed that the majority of the community's population consists of males (72.5%) and individuals aged 43 and above (38.5%), followed by individuals aged 31-36, accounting for 28.0% of the population. The majority of the residents (40.5%) visit the reserve on a daily basis, while thirty percent (30%) visit weekly to collect firewood and for agricultural production. The level of participation of community dwellers in Forest Management and Conservation is significantly influenced by their age (χ²= 9.456, df = 6, p = 0.024), marital status (χ² = 11.873, df = 2, p = 0.003), and occupation (χ²= 44.237, df = 4, p = 0.000). The primary endeavours undertaken by the inhabitants of the reserve are agricultural in nature. Age, household size, marital status and monthly income were the primary factors influencing their decision not to participate in forest governance in the Reserve

Modeling maize growth and nitrogen dynamics using CERES-Maize (DSSAT) under diverse nitrogen management options in a conservation agriculture-based maize-wheat system

Agricultural field experiments are costly and time-consuming, and often struggling to capture spatial and temporal variability. Mechanistic crop growth models offer a solution to understand intricate crop-soil-weather system, aiding farm-level management decisions throughout the growing season. The objective of this study was to calibrate and the Crop Environment Resource Synthesis CERES-Maize (DSSAT v 4.8) model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based maize system. The model was also used to investigate the relationship between, temperature, nitrate and ammoniacal concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on maize yields. Using field data from 2019 and 2020, the DSSAT-CERES-Maize model was calibrated for plant growth stages, leaf area index-LAI, biomass, and yield. Data from 2021 were used to evaluate the model's performance. The treatments consisted of four nitrogen management options, viz., N0 (without nitrogen), N150 (150 kg N/ha through urea), GS (Green seeker-based urea application) and USG (urea super granules @150kg N/ha) in two contrasting tillage systems, i.e., CA-based zero tillage-ZT and conventional tillage-CT. The model accurately simulated maize cultivar’s anthesis and physiological maturity, with observed value falling within 5% of the model’s predictions range. LAI predictions by the model aligned well with measured values (RMSE 0.57 and nRMSE 10.33%), with a 14.6% prediction error at 60 days. The simulated grain yields generally matched with measured values (with prediction error ranging from 0 to 3%), except for plots without nitrogen application, where the model overestimated yields by 9–16%. The study also demonstrated the model's ability to accurately capture soil nitrate–N levels (RMSE 12.63 kg/ha and nRMSE 12.84%). The study concludes that the DSSAT-CERES-Maize model accurately assessed the impacts of tillage and nitrogen management practices on maize crop’s growth, yield, and soil nitrogen dynamics. By providing reliable simulations during the growing season, this modelling approach can facilitate better planning and more efficient resource management. Future research should focus on expanding the model's capabilities and improving its predictions further.

Application of Precision Farming in Horticulture: A Comprehensive Review

With the ever-increasing global population and diverse challenges caused by climate change, horticultural productivity must increase in an ecofriendly and sustainable way. Precision farming, also known as precision agriculture may play an important role to improve this situation by a data-driven farm management system that aims to identify, analyze, and manage variability within fields for optimal profitability, sustainability, and land resource protection. It involves applying the right treatments at the right time and location within a field. In horticulture, precision farming plays a crucial role in resource-efficient management as well as reducing significant labour requirement, including techniques like fertigation, greenhouse cultivation, soil and leaf nutrient-based fertilizer management, and more. This comprehensive review paper explores the application of precision farming in horticulture, emphasizing its potential impact on crop management, environmental health, and economic sustainability.

Statement of Retraction: An Efficient Resource Management Mechanism Based on Developed Political Optimizer in Fog Computing

Statement of Retraction: An Efficient Resource Management Mechanism Based on Developed Political Optimizer in Fog Computing

Unleashing the Power of Django for Construction Materials Rental

In the realm of construction equipment rental, efficient management of resources and seamless access to construction materials are paramount for project success. This paper explores the utilization of Django, a high-level Python web framework, to revolutionize the construction equipment rental industry. By harnessing the power of Django, this study proposes a comprehensive platform that streamlines the process of renting construction equipment and accessing necessary materials. The platform integrates features such as user-friendly interfaces, robust inventory management systems, real-time tracking, and secure payment gateways to enhance user experience and optimize operational efficiency. Through a combination of Django's flexibility, scalability, and rich ecosystem of plugins, this solution aims to address the challenges faced by construction companies in managing equipment rentals and material procurement. Furthermore, the paper discusses the technical implementation of Django within the context of construction equipment rental, highlighting its potential to transform traditional workflows and propel the industry towards digital innovation. This study specify the adoption of Django in construction equipment rental has the potential to unleash a new era of efficiency, transparency, and productivity, thereby driving the industry towards greater success and competitiveness in the modern era

Optimizing Cloud Computing Resources: An Energy Efficient Multi-QoS Factor-Based VM Placement Strategy

Cloud computing is experiencing unprecedented demand, offering scalable and flexible resources for a wide range of applications. However, this surge in demand has raised concerns about energy consumption and the need for environmentally sustainable solutions. Green computing has emerged as a critical consideration in this context. Virtual Machine Placement (VMP) is a key component of optimizing cloud resources, aiming to allocate virtual machines efficiently while minimizing energy consumption, cost, and load balancing. This paper addresses the VMP problem by introducing a novel approach based on multifactor optimization, specifically the Diversity Constraint Digger Snake Optimizer (DCD-SO). It offers an innovative perspective on optimizing virtual machine placement by considering energy efficiency, load balancing, and resource utilization simultaneously with the aim to reduce VM migration count, time and cost. Proposed method provides a more comprehensive and sustainable solution, aligning with the principles of green computing. Through extensive simulations and experiments, we have rigorously evaluated the performance of DCD-SO in comparison to traditional optimization techniques such as Particle Swarm Optimization (PSO) and Snake Optimization. In our analysis of actual cloud environments, we compared the results of our method with existing state-of-the-art techniques. Result outcomes determine showed that proposed approach has reduced migration count of 5 and 3 for scheduling 42VMs and 84VMs on 16 and 32 host units respectively than traditional MOGANS, GA-S, GA-N and GA-NN methods. This comprehensive evaluation reinforces the effectiveness and practicality of our approach in addressing the intricate challenges of Virtual Machine Placement (VMP) in dynamic cloud computing settings. As cloud computing continues to evolve, our study contributes to more sustainable and efficient resource management, addressing both current demands and future needs.

Enhanced pulse electrocatalytic reductive-oxidative degradation of chlorinated organic pollutants based on a synergistic effect from Pd single atoms and FeCo2O4 nanowire

Targeting complete dechlorination and decomposition of chlorinated organic pollutants in an environmentally friendly and resource efficient manner. In this investigation, a three-dimensional multi-structured electrode (Pd-NC@Fe/CoCF) comprising Pd monatomic and nitrogen-doped carbon-coated FeCo2O4 nanowire cluster arrays were effectively synthesized. This electrode demonstrated efficient generation of H* and the production of •OH through a (2+1) e- oxygen reduction mechanism under the synergistic action of Pd single atoms and FeCo2O4 nanowire. When integrated with a square-wave pulsed electrocatalytic system, the chlorinated organic pollutants could be fully degraded within 45 min under the most favorable process parameters, which include a pulse voltage of −0.7 V, a duty cycle of 50 %, and a pulse frequency of 0.1 Hz. This results in a removal rate of 92.3 % for TOC and an energy consumption of 1.27 kWh·kg−1. This study presents a sustainable and environmentally friendly approach to the electrochemical degradation of chlorinated organic pollutants.

Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase—from 0.1083 W m-2μm−1sr−1 in 2001 to 0.1468 W m-2μm−1sr−1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

CONTABILIDADE APLICADA AO FUTEBOL: ANÁLISE FINANCEIRA DOS CLUBES E FATORES DETERMINANTES PARA O DESEMPENHO ESPORTIVO DURANTE A PANDEMIA DE COVID-19

This research delves into the impacts of accounting applied to soccer, unraveling the determining factors for sports performance and their correlation with the financial situation of clubs in the pandemic extract. It is based on the justification that sport is of enormous importance to society and its direct influence on the national GDP, so investigating it provides subsidies for the entities and agents involved in the accounting statements. The production was materialized by reviewing the literature on the subject in databases, based on a qualitative approach. The article aims to analyze the economic indicator of financial equilibrium, determine the competitiveness of the championships and explore the historical context of the legislation adopted by the associations. This dive into the data platforms made it possible to see the relationship between sporting performance and the financial situation of the clubs, along with efficient management of resources, maximization of revenue and transparent and responsible administration of the aforementioned, making it clear that changes to for-profit companies need to go hand in hand with modern managers, for professionalization and regularization of sports in a holistic context.

DEVELOPMENT OF INNOVATIVE TECHNOLOGIES IN CONSTRUCTION AND CIVIL ENGINEERING IN THE CONTEXT OF DIGITALIZATION OF THE UKRAINIAN ECONOMY

Purpose. Construction and civil engineering, traditionally perceived as conservative industries, also feel the need to adapt to the demands of the time and introduce innovative technologies. Ukraine, entering the era of digital transformation, is actively developing and implementing new technologies in the construction industry. Therefore, systematization and coverage of key aspects of the impact of digital Transformation into the construction industry and civil engineering in Ukraine is the main task today. Methodology. The introduction of Internet of Things technologies in the construction industry expands the possibilities of building control and management. Findings. Sensors located in different parts of the structures allow you to monitor the condition of the building in real time, identify possible problems and ensure the safety of objects. Artificial intelligence is becoming an integral part of the digital transformation of construction. Machine learning algorithms help predict and optimize construction processes, which increases Efficiency of planning and resource management. The 3D printing system in construction is one of the promising and revolutionary innovations that is already influencing traditional approaches to construction processes. This digital technique will bring significant changes to the field of construction, accelerating and optimizing a number of stages from design to execution Originality. Analysis of current problems, especially now, in the difficult times of war, industrial construction in Ukraine, as well as around the world, is a promising direction for the development of other industries in general. Industrial construction is an important branch of the construction industry that specializes in the creation and operation of buildings and infrastructure facilities for industrial needs. Practical value. The introduction of innovative technologies in construction and civil engineering in the context of digitalization of the Ukrainian economy has a significant practical impact on various aspects of the industry: Improving Construction Productivity and Quality: Use of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), allows you to increase the efficiency of construction processes, reduce the time of work and avoid errors; Cost optimization: Using machine learning algorithms to predict resource needs, such as materials and labor, allows you to rationalize costs and reduce construction costs.

Increasing the performance of a hospital department with budget allocation model and machine learning assisted by simulation

ABSTRACT The COVID-19 pandemic highlighted the critical need for efficient resource management in healthcare. In this study, the internal medicine outpatient clinic in a hospital is modelled by simulation method. Appropriate statistical distributions of the parameters are derived from past data. The results of a limited number of simulation runs are used as training data for machine learning techniques and an estimation model is selected among them. The estimation results are considered as input to a mathematical model which determines the optimal budget allocation for improving the system performance. Analysis considers patient waiting times and system throughput under varied parameters. A significant amount of time is saved by using machine learning to predict the simulation model outcomes, which had previously taken a total of around 7 hours reduced to 30–40 minutes. Time savings through machine learning are projected to be notably greater for more complex simulations comparing to current case.

The role of information technology in managing the human resource development of educators in inclusion-based primary schools in Greece

The rapid growth in technology offers both benefits and challenges to traditional teaching and learning methods in the field of education. Not only is information technology (IT) a study topic itself, but it also has a significant impact on all other academic fields. The ways in which teaching, learning, and human resource management are conducted in educational institutions are changing as a result of the integration of IT, particularly in inclusive settings. The purpose of this project is to investigate the effects of using a variety of IT tools and integrating IT into the curriculum in inclusive elementary schools in Greece. It looks at the ways in which these variables affect the development and management of human resources, with an emphasis on teachers working in inclusive environments. A questionnaire survey was sent to 396 education sector experts, which made it possible to get quantifiable information about these experts' opinions on the influence and integration of IT in learning environments. The findings show that the deployment of different IT tools in inclusive schools and the integration of these technologies into the inclusive curriculum have a favorable impact on the efficient management and development of human resources in these learning settings. The study confirms that integrating IT into the classroom is essential to prepare pupils for life in a knowledge-based society. To keep up with the rapid improvements in technology and economic globalization, educational institutions must proactively innovate and change their methods to human resource management.

Gas Level and Leakage Detection with Automatic Booking

Gas Level and Leakage Detection with Automatic Booking

The Perception of Teachers Regarding Entropy in University Institutional Management

The Perception of Teachers Regarding Entropy in University Institutional Management

Business Management as a Link Between Competitiveness and Sustainable Development in the Textile Sector

Efficient resource management plays a crucial role in the sustainable development of companies, aiming to maximize economic performance, minimize environmental impacts, and contribute to social welfare. This quantitative and explanatory study analyzed resource management in textile microenterprises in Metropolitan Lima, with a sample of 370 participants. The methodology used was non-experimental and cross-sectional, allowing for a detailed analysis of the relationship between business management and sustainable development in this specific context. Two questionnaires of 20 questions each were administered. Both instruments were validated by five experts in the business area. The Aiken's V for the first instrument was 0.83 and for the second instrument was 0.79. Additionally, a pilot test was conducted for instrument reliability, obtaining 0.89 for the first instrument and 0.81 for the second. The results highlight the significant relationship between business management and sustainable development, especially in terms of competitiveness and general management aspects. However, there is a relatively lesser association of eco-efficiency, eco-effectiveness, and continuous improvement in the variability of sustainable development. It is concluded that, while business management is considerably related to sustainable development, aspects such as eco-efficiency and continuous improvement show a less pronounced relationship in sustainable development variability. These findings underscore the importance of sustainability-focused business management to enhance competitiveness and foster equitable and long-term sustainable development. Received: 15 February 2024 / Accepted: 26 April 2024 / Published: 5 May 2024

Effect of artisanal gold mining on woody plant diversity in Western Burkina Faso

Although artisanal gold mining strongly contribute to enhance people incomes in West Africa, this activity can lead to habitat fragmentation and loss of woody species. Therefore, studying the impact of artisanal gold mining on woody plant diversity is needed for efficient management of forest resources. In this study, we investigated the impact of artisanal gold mining on woody plant diversity and dynamics in the Kari classified forest in Western Burkina Faso. Forest inventories were carried out at two sites including a gold panning site and a control site, following a systematic sampling design. We identified all woody species and measured all woody species with a diamtere at breast height ≥5 cm. A total 60 plots of 900 m² were surveyed. Diversity indices and dendrometric parameters were calculated and compared between the two sites. The results showed a higher species richness (59 woody species distributing in 39 genera and 18 families) in the control site compared to the gold panning site (38 species distributing in 25 genera and 12 families). The mean values of the number of species per plot and the Shannon diversity index were higher in the control site (15.50 ± 8.80; 2.16 ± 0.43, respectively) than in the artisal mining site (5.47 ± 2.71; 1.40 ± 0.53, respectively), showing that the control site is more diverse than the site that is under gold panning. The control site also had the highest density (683.33 ± 262.99 ft/ha) and stable woody stands compared to the less gold panning site (137.41 ± 102.31 ft/ha) where unstable woody stands were found. While the gold-panning site was characterised by biophysical impacts such as destruction of the plant cover and soil excavation, no visible impact was observed at the control site.Theses findings highlight the negative impact of artisanl gold mining on woody species diversity and structure. The study provides essential information for decision-making on sustainable forest management in the context of mining boom in Burkina Faso's.

The factors contributing to better workplaces for farmers on pasture-based dairy farms

Herd size expansion, combined with the reduced availability of people to work on farms, has led to an increased focus on techniques that can improve dairy farm social sustainability. Effective work organization is one such entity, which could influence farm social sustainability; focusing on having a productive, flexible and standardized farm workload. The objective of this study was to examine the factors that contribute to better workplaces for the farmer using a survey of representative pasture-based dairy farms in Ireland. Potential contributing factors to better workplaces for farmers were identified, namely; farm and farmer characteristics, working day structure, farmer attitudes, farm facilities, labor efficient practices and human resource management practices. A survey was completed by 313 Irish dairy farmers between 20 November and 3 January 2019 to capture relevant information. One proxy indicator was selected to represent each of productivity, flexibility and standardization within the workplace, and each of the 313 farms were categorized into quartiles based on their ranking for these 3 indicators (1 = most effective quartile to 4 = least effective quartile). The average farmer that completed the survey was 51 years old, milked 125 cows, reported to work 69.6 h/ week, take 10.3 d of holidays/ year and had a finish time of 19:52 in spring. The quartile of farms with the most effective farmer workplace reported reduced hours worked per week (58.6 v 82.6 h per week), more holiday days (16.6 v 5.1 d) and weekends off (8.3 v 2.4) per year, and earlier finish times (18:41 v 21:14 in spring) compared with the least effective quartile. Similarly, the most effective farms reported better facilities, and greater implementation of labor efficient and human resource management practices compared with the least effective farms. The most effective quartile for farmer workplace effectiveness were more positive about the industry's potential to offer an effective work-life balance, would be more likely to encourage young people to pursue careers in dairy, and had more positive attitudes toward attracting and retaining workers compared with the least effective quartile. The study highlighted the range of factors contributing to more effective workplaces for farmers, indicating scope for improvement on many farms, and challenges across all farms when compared with other industries in the case of some indicators (e.g., time-off). The results can support the continued extension of concepts regarding work organization to assist farms in alleviating social sustainability challenges; highlighting the differentiating factors between the most and least effective farmer workplaces.

A critical review of physics-informed machine learning applications in subsurface energy systems

Machine learning has emerged as a powerful tool in various fields, including computer vision, natural language processing, and speech recognition. It can unravel hidden patterns within large data sets and reveal unparalleled insights, revolutionizing many industries and disciplines. However, machine and deep learning models lack interpretability and limited domain-specific knowledge, especially in applications such as physics and engineering. Alternatively, physics-informed machine learning (PIML) techniques integrate physics principles into data-driven models. By combining deep learning with domain knowledge, PIML improves the generalization of the model, abidance by the governing physical laws, and interpretability. This paper comprehensively reviews PIML applications related to subsurface energy systems, mainly within the context of the oil and gas industry. The review highlights the successful utilization of PIML for tasks such as seismic applications, reservoir and fluid flow simulation, and hydrocarbons production forecasting. Additionally, it demonstrates PIML’s capabilities to revolutionize the oil and gas industry and other emerging areas of interest, such as carbon and hydrogen storage, by providing more accurate and reliable predictions for resource management and operational efficiency.