Effective Resource Utilization Research Articles

THE QUALITATIVE AND QUANTITATIVE DEVELOPMENT OF EDUCATION IS A CRUCIAL FACTOR IN IMPROVING THE INNOVATION RANKING OF UZBEKISTAN

The qualitative and quantitative development of education is a crucial factor in improving the innovation ranking of Uzbekistan. Obstacles such as corruption, inefficient management, the lack of funds for research, and fund mismanagement hinder progress inthis regard. Corruption within the education sector undermines transparency and trust, necessitating stringent measures to combat this pervasive issue. Inefficient management practices impede the effective utilization of resources and hinder progress towards educational goals. The lack of adequate funding for research and development poses a significant obstacle to innovation, limiting the ability of educational institutions to conduct groundbreaking research and implement innovative projects. Additionally, mismanagement of available funds exacerbates the problem, necessitating a reevaluation of budget allocation mechanisms. To address these challenges, a comprehensive approach is required, including strengthening anti-corruption measures, implementing effective governance and management practices, and prioritizing investment in research and development by allocating sufficient funds and ensuring their judicious use. By overcoming these obstacles, Uzbekistan can foster a conducive environment for education and innovation, empowering the nation to thrive in an increasingly competitive global landscape.

Waste to worth: Facile preparation of CaO2 derived from oyster shells for highly efficient degradation of organic contaminants based on Fenton-like system

In recent years, CaO2 has been frequently used as a substitute for H2O2 to address the limitations of H2O2. However, commercially available CaO2 are facing the shortcomings of low purity and limited preparation conditions. Oyster shells can provide superior and abundant biomass calcium element with low cost for developing CaO2. In this work, highly purified CaO2 functional biomaterials were prepared via a facile approach derived from waste oyster shells, which were applied in a Fenton-like system to remove organic pollutants. Experimental results indicated that the oyster shells-derived CaO2 with 100% purity exhibited a greatly enhanced catalytic performance compared with other CaO2 materials in Fenton-like catalytic degradation of organic pollutants, which can be completely removed within 60 s. The high purity of CaO2 would facilitate the release of H2O2 during the Fenton-like catalytic reaction and promote the generation of more active species to oxidize pollutants. Besides, the abundant trace metal elements originated from oyster shells can further motivate the Fenton-like reaction and accelerate the degradation of pollutants, endowing oyster shells-derived CaO2 an excellent catalytic activity. Furthermore, the Fenton-like catalytic mechanism was proposed on basis of tests of radicals trapping and electron spin resonance, validating the vital roles of ⋅OH, ⋅O2– and 1O2 generated throughout the reaction process. This work not only provides a novel and low-cost approach to exploit CaO2 with highly efficient catalytic ability, but also realizes the effective resource utilization of waste shells.

Contribution and Prospect of Marine Fisheries in the Economy of Bangladesh and Sustainable Blue Economy Challenges: A Review

Marine resources of Bangladesh are abundant, both in terms of huge sea area and its biological richness. The marine water area supports a large number of commercially important fin fish, mollusks, crustaceans, and seaweeds, offering significant economic potential due to their diversified use and high demand in the international market. Therefore, the current review comprehensively examines both the contribution and potentials of marine fisheries resources to the economy, as well as the challenges involved in marine fisheries sectors in attaining a sustainable blue economy in Bangladesh. The study revealed that, the marine fisheries plays an important role in national economy and has huge prospect in employment generation and ensuring food security of Bangladesh. The expansion of mariculture and effective utilization of marine resources will open a new window for the economic development of Bangladesh. Marine fisheries production of Bangladesh has increased over the last two decades, but its relative share in total fisheries production has declined. Despite being rich in marine living resources, commercial mariculture has been developed only for shrimp, prawn, and mud crab. Challenges hindering mariculture expansion includes limited research, technological deficiencies, inadequate skilled workforce, underdeveloped domestic seafood markets, and financial constraints. Concerted efforts including exploring new fishing grounds, conducting stock assessments, implementing scientific management strategies, enacting a national marine fisheries policy, enforcing legislation, and establishing robust surveillance and monitoring mechanisms are required to foster a sustainable blue economy in Bangladesh.

Moth Flame Optimisation for optimal power flow in a power system with Static var compensator

It consequently becomes imperative in the current transmission networks to make full use of existing resources and to rapidly transition to renewable energy alternatives. Electrical energy end users also benefit from effective resource utilisation since it lowers the price they pay for electricity. In this work, we offer a multi-objective optimum power flow (OPF) for an integrated gearbox network where FACTS devices are present. The paper's originality lies in the use of a multi-objective function. Minimising voltage variation, power loss, and negative social welfare (NSW) are all part of the objective function. The abatement of loss and NSW guarantees the lowering of per-unit pricing of power at the consumer end resulting in improved customer satisfaction. The problem was fixed using a FACTS Static var compensator. The theory was tested using an IEEE 30 bus network. The objective function has been optimised using the Mouth Flame Optimisation Algorithm. Detailed presentations, comparisons, and analyses of the collected findings have been made.

Prospects and challenges of agricultural financing in Nigeria: A review

This paper examined the prospects and challenges of agricultural financing in Nigeria. It was observed that agricultural financing is all about providing services to the farmers who are traditionally not served by the conventional financial institutions. Agricultural financing holds a lot of prospects for Nigerians especially the poor as it will enable them develop their skills, engage in production and many others. Among the problems identified are access to banking services in rural communities, lack of acceptable collateral/ security, perceived high agricultural risk, lack of access to credit information, high cost of credit administration, access to market, infrastructural challenges and subsistence nature of farming. The various prospects highlighted include; economic empowerment of farmers, poverty reduction, gains from international trade, employment generations, rural development, skill development, new innovations, effective utilization of local resources and output expansion. Consequent upon the problems noted, the paper recommends that the supervisory authorities should enforce standards so as to address the issue of poor management of funds meant for credit disbursement.

Short-term wind power forecasting through stacked and bi directional LSTM techniques.

Computational intelligence (CI) based prediction models increase the efficient and effective utilization of resources for wind prediction. However, the traditional recurrent neural networks (RNN) are difficult to train on data having long-term temporal dependencies, thus susceptible to an inherent problem of vanishing gradient. This work proposed a method based on an advanced version of RNN known as long short-term memory (LSTM) architecture, which updates recurrent weights to overcome the vanishing gradient problem. This, in turn, improves training performance. The RNN model is developed based on stack LSTM and bidirectional LSTM. The parameters like mean absolute error (MAE), standard deviation error (SDE), and root mean squared error (RMSE) are utilized as performance measures for comparison with recent state-of-the-art techniques. Results showed that the proposed technique outperformed the existing techniques in terms of RMSE and MAE against all the used wind farm datasets. Whereas, a reduction in SDE is observed for larger wind farm datasets. The proposed RNN approach performed better than the existing models despite fewer parameters. In addition, the approach requires minimum processing power to achieve compatible results.

Energy Efficient Business Management System for Improving QoS in Network Model

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.

A Hybrid Approach for the Ensembles of Neural Networks for Solar Power Forecasting

In this study, a unique hybrid method to solar power forecasting is presented. This strategy makes use of ensemble approaches, which include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs). A more accurate prediction of solar power production may be achieved using the hybrid ensemble, which takes advantage of the specific characteristics of each neural network design. Extensive trials conducted using datasets taken from the actual world demonstrate that the suggested technique is superior to both individual neural network models and conventional methods of predicting. Therefore, the ensemble is a feasible option for practical solar power forecasting applications, helping to the effective utilisation of renewable energy resources. Its flexibility to changing weather conditions, interpretability, and superior accuracy position it as a promising solution.

Multifunctional fish-skin collagen-based hydrogel sealant with dual-dynamic-bond cross-linked for rapid hemostasis and accelerated wound healing

Collagen (COL) is the most widespread functional protein. Designing and developing dual-dynamic-bond cross-linked COL adhesive hydrogel sealants with multifunctional is highly advantageous for achieving a superior wound closure effect and hemostasis. In this study, we developed hybrid hydrogels consisting of fish-skin COL, oxidized sodium alginate (OSA), borax and polyvinyl alcohol (PVA) to enhance full-thickness wound healing. The hydrogels were furnished with first-rate self-healing capabilities through the dual-dynamic-bond cross-linking of dynamic Schiff base bonds (COL-OSA) and diol boric acid bonds (OSA-borax) with reversible breakage and re-formation. Moreover, the incorporation of PVA stimulated the formation of hydrogen bonds in the system, bolstering the stability of the hydrogel framework. The prepared hydrogel manifests self-healing, injectability, multifunctional adhesiveness and biodegradability. In vivo assessment of the hemostatic capacity of COSP20 hydrogel was superior to gauze both in the mice liver injury model and mice tail amputation model. In addition, a full-thickness skin wound model in mice revealed that the COSP20 hydrogel facilitated faster wound closure by accelerating reepithelialization, COL deposition and angiogenesis. These findings illustrate the potential of hybrid fish-skin COL-based hydrogels to enhance wound healing and promote rapid tissue repair, and provide new possibilities for the effective utilization of marine fishery resources.

Implementation of Ministry of Agriculture Policy Number 64 of 2013 Concerning the Organic Agriculture System at the Food Security, Food Crops and Horticulture Office of North Sumatra Province

The implementation of the Regulation of the Minister of Agriculture Number 64 of 2013 concerning Organic Farming Systems is the focus of this study, with an emphasis on conditions in the Food Security, Food Crops, and Horticulture Office of North Sumatra Province. Through qualitative methods with primary data collection through interviews and secondary data from related sources, this study explores supporting and inhibiting factors in the implementation of these policies. The findings show that the implementation of this policy in the Food Security Office of North Sumatra Province has gone quite well. Community involvement, coordination between agencies, and effective utilization of resources have been carried out. The bureaucratic structure of the agency has facilitated the dissemination of this policy, while the attitude of the implementers indicates full compliance with the instructions of the central government

Multi-objective planning for time-cost trade-offs in multi-project parallel environment

PurposeProject managers bear the responsibility of selecting and developing resource scheduling methods that align with project requirements and organizational circumstances. This study focuses on resource-constrained project scheduling in multi-project environments. The research simplifies the problem by adopting a single-project perspective using gain coefficients.Design/methodology/approachIt employs uncertainty theory and multi-objective programming to construct a model. The optimal solution is identified using Matlab, while LINGO determines satisfactory alternatives. By combining these methods and considering actual construction project situations, a compromise solution closely approximating the optimal one is derived.FindingsThe study provides fresh insights into modeling and resolving resource-constrained project scheduling issues, supported by real-world examples that effectively illustrate its practical significance.Originality/valueThe research highlights three main contributions: effective resource utilization, project prioritization and conflict management, and addressing uncertainty. It offers decision support for project managers to balance resource allocation, resolve conflicts, and adapt to changing project demands.

The first investigation of an Iron Age shell midden in Oman: The Nafūn complex

The Nafūn shell midden complex is a cluster of eight shell mounds in al-Wusṭā Governorate (south-central Oman). Located 40 km north of Duqm, the shell midden is part of a unique cultural landscape encompassing a variety of archaeological features. With a total of 207 recorded archaeological sites, the Nafūn area emerges as a significant site for studying the Early and Late Iron Age communities in south-central Oman. The first investigations of the shell midden were conducted between 2019 and 2022. Radiocarbon dating of charcoal samples from the site provided a date range of 1012–536 BCE (cal. 2σ), aligning with the Iron Age II (1000–600 BCE) material culture. Together with stratigraphic observations, this indicates that the midden was intermittently occupied for at least 500 years. Additional evidence indicates that there was also activity in the area during the Late Iron Age, evidenced by the presence of a large rock art site, circular stone burial mounds and numerous clusters of trilith stone monuments along the wadi Nafūn route, with a source of drinking water 3 km from the coast. Such findings underscore the importance of Nafūn as a significant site for enduring socio-economic and cultural contacts, strategically located at the mouth of the wadi that connects with the Indian Ocean. The effective utilisation of marine resources by the local communities not only supported their economic and subsistence needs but also fostered strong economic, social and cultural connections with the inland regions. Furthermore, it facilitated their active engagement in long-distance trade and interactions along the coastal areas. The unique combination of sites and the fusion of elements from both northern Oman and Dhofar make Nafūn an intriguing and, so far, undocumented intersection along the long-distance routes connecting the northern and southern cultural circles.

Rare Earth Extraction from Phosphogypsum by Aspergillus niger Culture Broth.

The extraction of rare earth elements (REEs) from phosphogypsum (PG) is of great significance for the effective utilization of rare earth resources and enhancing the resource value of PG waste residues. This study used Aspergillus niger (A. niger) fungal culture filtrate as a leaching agent to investigate the behavior of extracting REEs from PG through direct and indirect contact methods. According to the ICP-MS results, direct leaching at a temperature of 30 °C, shaking speed of 150 rpm, and a solid-liquid ratio of 2:1, achieved an extraction rate of 74% for REEs, with the main elements being yttrium (Y), lanthanum (La), cerium (Ce), and neodymium (Nd). Under the same conditions, the extraction rate of REEs from phosphogypsum using an A. niger culture filtrate was 63.3% higher than that using the simulated organic acid-mixed solution prepared with the main organic acid components in the A. niger leachate. Moreover, the morphological changes observed in A. niger before and after leaching further suggest the direct involvement of A. niger's metabolic process in the extraction of REEs. When compared to using organic acids, A. niger culture filtrate exhibits higher leaching efficiency for extracting REEs from PG. Additionally, using A. niger culture filtrate is a more environmentally friendly method with the potential for industrial-scale applications than using inorganic acids for the leaching of REEs from PG.

Dynamic Load Balancing in Cloud Computing: A Review and a Novel Approach

In cloud computing, load balancing is essential since it guarantees effective resource utilisation and reduces response times. It effectively distributes incoming workload across available servers. Load balancing involves dividing tasks among multiple systems or resources over the internet. By distributing traffic and workloads, load balancing ensures that no server or computer is overloaded, underutilized, or idle in a cloud computing environment. To enhance overall performance in the cloud, it optimises a number of variables, including execution time, response time, and system stability. To increase the effectiveness and reliability of cloud services, load balancing evenly distributes traffic, workloads, and computing resources across the environment. The proposed method, Enhanced Dynamic Load Balancing for Cloud Computing, adjusts load distribution and maintains a balanced system by considering variables like server capacity, workload distribution, and system load. By incorporating these factors and employing adaptive threshold adjustment, this approach optimizes resource allocation and enhances system performance. Experimental research shows that the proposed new novel approach is more effective and efficient than current load balancing techniques. In this context of cloud computing, this ground-breaking method offers a workable substitute for dynamic load balancing.

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room.

Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30min or ±20% of the scheduled duration if the scheduled time is ≥ or <150min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p <.001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p <.001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p =.0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p =.0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p =.22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954hr). The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.

Integrating veterinary and poultry component with rice-based farming system for effective utilization of resources, environmental restoration and profitability

Integrating veterinary and poultry component with rice-based farming system for effective utilization of resources, environmental restoration and profitability

Context-attentive robot reconfiguration for human-machine space missions

With humankind’s aspiration of extraterrestrial inhabitation, the need for robotic support is eminent. To master the emerging challenge of providing astronauts with assistive space robots possessing the required adaptability to new situations and tasks under the constraints of severely limited supply chains in extraterrestrial missions, the utilization of existing hardware to its fullest is required. We propose the combination of a concept with an architecture to jointly support context-aware collaboration between humans and robotic systems for implementing effective resource utilization in space missions. The underlying framework is an implementation of a flexible architecture that enables context awareness for robotic systems. It supports processing nodes for retrieval of context information from the raw sensor values as well as further inferences using the output of nodes. Context information spans across three entities and describes the current state of the environment, the astronaut, and the robot itself. This information then can be used to infer the human’s current intention as well as influence the behaviour to be executed next. Additionally, dynamic changes in the data processing chains are handled by the framework to facilitate an adequate adaptation of the system to situational events. We combine this architecture with the concept of space robots as a composition from building blocks, which are supported and made accessible by a software toolkit. The modular design approach enables online self-reconfiguration of robotic hardware and software components. In combination with dynamic mission planning, based on ontological descriptions of available resources and functionalities, robots are able to adapt their physical and computational appearance dynamically during a mission according to different tasks and goals. By incorporating these two developments in a joint deployment we envision raising the efficiency of robotic systems in human-machine interaction through the usage of self-reconfiguration as a reactive behavior in order to adapt to a specific task, recognized or derived from a human’s intention. Transfer of the proposed idea back to Earth may help to abate resource dissipation caused by deploying specialized monolithic systems with a narrow range of capabilities through utilizing the adaptivity of reconfigurable robots.

Provision and Utilization of Physical Resources for Effective Teaching and Learning Effectiveness in Public Universities in Rivers State

The paper explores the role of physical resources in effective teaching and learning in public universities in Rivers State. It emphasizes the importance of managing resources such as money, computers, teaching technology, and internet facilities to achieve educational goals. The paper is based on Wernerfelt's Resource-Based Theory, which suggests that organizations can achieve a sustainable competitive advantage through strategic and efficient use of physical resources. The study concludes that effective provision and utilization of physical resources are crucial for the success of teaching and learning in public universities. It suggests that a multifaceted approach, including infrastructure development, technological integration, library enrichment, and accommodation considerations, can create an environment conducive to academic excellence and a holistic learning experience. The paper also suggests that the state government should establish mechanisms to improve transparency and accountability in the use of physical resources to ensure efficient allocation of funds towards educational priorities.

The Effect of Artificial Intelligence Adoption, Demand Prediction, and Production Planning on Operational Efficiency in the Textile Industry in Jakarta

This research investigates the impact of Artificial Intelligence (AI) adoption, demand prediction, and production planning on operational efficiency within the textile industry in Jakarta. A quantitative approach, employing surveys and statistical analysis, was undertaken with a diverse sample of 150 participants representing various company sizes and industry tenures. The study reveals a moderate level of AI adoption, with machine learning algorithms and predictive analytics being prevalent. While perceived benefits include improved production efficiency and enhanced quality control, challenges such as initial investment costs and the need for skilled personnel underscore the nuanced landscape of AI integration. The effectiveness of demand prediction is moderate, with traditional methods prevailing but advanced analytics demonstrating higher efficacy. Production planning strategies exhibit a positive correlation with Industry 4.0 principles, showcasing their role in enhancing operational efficiency. Participants perceive operational efficiency positively, with significant correlations identified between AI adoption, demand prediction, production planning, and perceived efficiency. Key factors contributing to operational efficiency include streamlined processes, effective resource utilization, and adaptive production planning. The findings provide actionable insights for industry stakeholders, emphasizing the importance of a holistic approach to technology adoption and strategic planning.

Tea saponin-derived porous carbon bearing rich oxygen-containing groups towards high efficient CO2 fixation

The effective utilization of biomass resources can bring significantly environmental and economic benefits. In this context, tea saponin, which can be easily and extensively extracted from the oil cake as the biomass waste during the seed oil production from camellia seeds, is explored as the precursor to synthesize porous carbon bearing abundant –OH and –COOH groups via a simple carbonization process. Owing to the obtained porous structure and existence of rich oxygen-containing groups serving as Lewis acidic sites, the carbonized tea saponin (CTS) can display impressive catalytic activities for CO2 cycloaddition reaction with both high selectivity (> 99%) and high yield of target cyclic carbonate under either thermally driven (80 °C and 0.1 MPa CO2) or photo-thermally driven (UV-Vis light irradiation with the light intensity of 350 mW/cm2) conditions. In addition, benefiting from the good chemical stability, CTS can be reused for many cycles with almost no catalytic activity loss, making CTS a promising biomass-based catalyst for CO2 fixation under mild conditions.