Resource Utilization Research Articles

FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection

The identification and detection of microalgae are essential for the development and utilization of microalgae resources. Traditional methods for microalgae identification and detection have many limitations. Herein, a Feature-Enhanced YOLOv7 (FE-YOLO) model for microalgae cell identification and detection is proposed. Firstly, the feature extraction capability was enhanced by integrating the CAGS (Coordinate Attention Group Shuffle Convolution) attention module into the Neck section. Secondly, the SIoU (SCYLLA-IoU) algorithm was employed to replace the CIoU (Complete IoU) loss function in the original model, addressing the issues of unstable convergence. Finally, we captured and constructed a microalgae dataset containing 6300 images of seven species of microalgae, addressing the issue of a lack of microalgae cell datasets. Compared to the YOLOv7 model, the proposed method shows greatly improved average Precision, Recall, mAP@50, and mAP@95; our proposed algorithm achieved increases of 9.6%, 1.9%, 9.7%, and 6.9%, respectively. In addition, the average detection time of a single image was 0.0455 s, marking a 9.2% improvement.

Lunar Power Sources: An Opportunity to Experiment

This paper presents a systematic analysis of power generation technologies for a lunar outpost supporting six astronauts. Based on a detailed power budget analysis requiring 65 kWe for life support, scientific equipment, and in situ resource utilization (ISRU), a comparative analysis of solar and nuclear power solutions is conducted. Nuclear fission is identified as the most promising technology based on key criteria, including mass efficiency, reliability, and power density. A parametric study is then conducted to optimize the nuclear reactor design, with particular focus on radiation shielding using lunar regolith and its impact on safety distances. The analysis demonstrates that proper shielding can reduce the required safety distance from over 2.5 km to approximately 90 m while maintaining radiation exposure within acceptable limits. Finally, leveraging insights from existing reactor designs, an optimized configuration is proposed that combines multiple small reactors to meet the unique challenges of lunar power generation while ensuring crew safety and operational redundancy.

Examination of patients presenting to the emergency department as “apparently drunk”

Abstract Background Alcohol, a significant public health concern, contributes to a substantial burden on emergency services. Identifying avoidable causes of Emergency Department (ED) presentations may reduce hospital overcrowding and benefit public health. Aims This prevalence study aims to provide a detailed analysis of patients presenting to the ED at Mercy University Hospital (MUH) as “Apparently Drunk” in 2022 and 2023. Methods Data were sourced from the Integrated Patient Management Information System at MUH. All patients recorded as “Apparently Drunk” were included. Data collected included demographics, associated injuries or complaints, mode of arrival, admission status, and discharge status. Descriptive statistics were used to summarise the data and trends were examined by comparing 2022 and 2023 data. Results A total of 1662 presentations were categorised as “Apparently Drunk,” representing 3% of all ED visits. There was an 18% increase in such presentations from 2022 to 2023. The majority of patients were male (72%). The median age was 39 years. Notably, 23% of the presentations involved people experiencing homelessness, and 81% were conveyed by ambulance. Significant associated injuries included head injuries (7%), falls/collapses (6%), mental health issues (5%), and assaults (4%). Conclusion The findings indicate a growing trend in patients presenting to the ED as “Apparently Drunk”. The high incidence among people experiencing homelessness and the substantial resource utilisation underscores the need for targeted public health interventions and integrated services. Policymakers should consider these findings in the context of potential legislative changes that may impact alcohol availability.

A Hybrid Approach to Multi-Objective Task Scheduling in Cloud Computing: Merging Estimation of Distribution and Genetic Algorithms

In the evolving landscape of cloud computing, efficient task scheduling plays a pivotal role in optimizing resource utilization and enhancing performance. This paper presents a novel hybrid algorithm that combines the strengths of Estimation of Distribution Algorithms (EDA) and Genetic Algorithms (GA) to address multi-objective task scheduling challenges. The proposed approach aims to balance conflicting objectives such as minimizing execution time, energy consumption, and cost while maximizing resource utilization. By leveraging the probabilistic modeling capabilities of EDA and the evolutionary search efficiency of GA, the hybrid algorithm achieves a more diverse solution set and faster convergence compared to traditional methods. Extensive simulations demonstrate the effectiveness of the proposed algorithm across various cloud environments and task complexities. The results highlight significant improvements in achieving Pareto-optimal solutions, offering a robust framework for cloud service providers to enhance scheduling efficiency. This study underscores the potential of hybrid metaheuristic techniques in addressing the dynamic and complex nature of cloud computing task scheduling, paving the way for more resilient and adaptive cloud infrastructures. Keywords: Cloud Computing, Task Scheduling, Estimation of Distribution Algorithm, Genetic Algorithm, Multi-Objective Optimization, Load Balancing, Resource Utilization.

Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks.

Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility

Transportation mode selection is pivotal for navigating through cities plagued by heavy traffic congestion. This plays a crucial role in ensuring the efficient utilization of time and resources to achieve the desired objectives. Given the complex dynamics of urban mobility, strategically selecting a transportation mode can significantly mitigate delays and enhance overall productivity in densely populated areas. The objective of this study is to find the most efficient result among various transportation modes to make deliveries from different points on a university campus. To solve this problem, reinforcement learning was used and tested on the simulation environment SUMO. Traffic density was increased by using an equal number of different transportation modes, such as driving, cycling, motorbiking, and walking. Various traffic densities were generated, and different reward models were applied to select the best means of transportation. Various probability distributions were used as reward models to avoid the unfair distribution caused by how near or how far the road is when moving from random points to the destination region. As a result of the models created using the applied reward–penalty functions, it was determined that the best means of transportation in areas with a low traffic density is cycling, and in areas with high traffic density, the optimal mode of transportation is motorbiking.

Numerical Simulation Study on Hydrodynamic Characteristics of Offshore Floating Photovoltaics

With the development of renewable energy and the utilization of marine resources, large-scale offshore floating photovoltaics have gradually attracted widespread attention. In order to develop offshore floating photovoltaics and promote sustainable development, it has become necessary to explore the hydrodynamic characteristics of floating photovoltaic units and floating arrays. In this work, based on the viscous flow theory, the Computational Fluid Dynamics (CFD) and the discrete element method (DEM) methods are used to analyze the hydrodynamics of the floating body unit of offshore floating photovoltaics. The influencing factors include mooring length, mooring radius, and floating unit length. In addition, the hydrodynamic performance of the floating body unit and the floating body array under different wave heights and periods is also discussed to explore the influence of environmental loads on the floating body unit and the floating body array. The results indicate that the mooring tension exhibits an opposite trend with the surge and heave motions when the mooring line length and radius are varied. The motion is found to be more pronounced when the floating body unit length is 0.4 times the wavelength. The heave motion of the floating body unit exhibits a strong linear relationship with wave height, increasing by 0.01 m for every 0.015 m increase in wave height. The motion of the floating body units on both sides connected to the mooring lines decreases as the array length increases.

Quantifying and Mapping the Impact of Construction Land Expansion on Cultivated Land Fragmentation—A Case Study of Fuqing City, China

To ensure the sustainable utilization of cultivated land resources, it is essential to quantify and map the characteristics of construction land and cultivated land and analyze the mechanisms by which construction land expansion affects cultivated land. However, few studies have been conducted focusing on this issue. This study integrated morphological spatial pattern analysis, spillover effect analysis, landscape pattern analysis, and a land use transition monitoring method to investigate the characteristics of construction land expansion and cultivated land fragmentation. Fuqing City of China was selected as the case study area for demonstration. The results demonstrated that the expansion of construction land resulted in fragmented patterns within the cultivated land landscape: (1) The large core area of cultivated land was subdivided into smaller core areas during 2000–2020, while the construction land exhibited a tendency towards aggregation and a spillover effect; (2) The expansion rate of the construction land in the study area accelerated, while the extent of the cultivated land decreased; (3) Cultivated land fragmentation intensified as landscape aggregation weakened, leading to an expansion in the agglomeration of construction land. The highlights of this study are: (1) examining the characteristics of construction land expansion and cultivated land fragmentation from morphological and geospatial perspectives; (2) categorizing the core areas of cultivated land based on their size to facilitate the analysis of factors contributing to cultivated land fragmentation. The findings in this study can be used to develop models to predict future patterns of cultivated and construction land to provide suggestions for landscape planning.

Aquaponics Advancements: A Comprehensive Exploration of Sustainable Aqua-Agriculture Practices in the Indian Context

Aquaponics, an innovative and symbiotic approach to agricultural practices, has gained increasing attention as a potential pathway to enhance the sustainability of ‘Aqua-Agri’ farming in India. This review comprehensively explores the current state of aquaponics in the Indian context, examining its overview, suitability, challenges, and potentiality. The analysis encompasses the ecological dynamics of aquaponic systems, emphasizing the efficient utilization of resources in Indian context, minimal environmental impact, and the potential for increased yields in both fish and plant production. Additionally, it explores successful case studies, showcasing the viability and adaptability of aquaponics across diverse regions in India. The review concludes by outlining future research directions and policy recommendations to foster the widespread adoption of aquaponics as a transformative and sustainable method within the aquaculture sector in India.

Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities

Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.

An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks

Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol’s ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments.

Generative artificial intelligence and sustainable higher education: Mapping the potential

Generative artificial intelligence (GAI) becomes widespread in higher education, and it creates new educational possibilities, with a potential to transform the educational process and promote sustainability. This study aims to explore the potential of GAI tools such as ChatGPT in promoting sustainable higher education. ChatGPT was utilized to aid the investigation at the initial stage, while the output generated was reviewed and edited by the researcher. It is indicated that GAI’s integration into higher education can lead to advancements in sustainability, such as enhancing educational practices (e.g., personalized learning, automated assessment and feedback, and educators’ professional development), optimizing resource utilization (e.g., digital learning resources, and efficient energy use), supporting inclusive and accessible education, and promoting environmental awareness and sustainability. Through these contributions, GAI can assist in the creation of more efficient, inclusive, and sustainable educational environments. It is suggested that higher education policies are modified and re-formulated to serve sustainable development, while empirical research on GAI implementation in higher education is a necessity (most publications are theoretical/conceptual). Limitations and ethical considerations should also be addressed. The study contributes to the ongoing debate on the role of GAI for sustainability in higher education.

After the Johannesburg II Declaration, What Next for Ecological Sustainability in BRICS? Perspectives on Green Innovations and Green Transitions

ABSTRACTAdvancing ecological sustainability, a major focus of the SDGs, necessitates evaluating the influence of green energy, sustainable technology, and resource utilization on ecological load capacity factors—an area that has remained underexplored in existing research. Also, the LCF method, which elucidates the unique features of the supply and demand dynamics in the natural environment, is still not used by BRICS environmental modelers. This study examined panel data of pertinent LCF predictors spanning from 1980 to 2021. The analysis utilized several advanced methodologies, and a new viewpoint was presented through the application of the panel‐frequency domain Granger causality test. The results demonstrate that (i) green transitions notably enhance LCF across various quantile distributions, and (ii) green technologies exert a moderately positive influence on LCF. The analysis suggests that ongoing shifts towards clean energy and the encouragement of eco‐friendly technological innovations necessitate well‐planned policies to enhance LCF within the BRICS nations.

Progress of treatment and resource utilization for high salinity printing and dyeing wastewater based on bibliometric study

The treatment or resource utilization of high-salinity wastewater from printing and dyeing has received great attention in recent decades due to their negative impacts on the surrounding environment. To improve the direction of research, we analyzed the published papers in the online version of the Science Citation Index Expanded database for 1991–2022—a total of 1090 articles or reviews—using VOSviewer, Python, origin 2023, and the statistical software R. The results showed that the published papers in this field have increased exponentially in the past 17 years. The Journal of Membrane Science, Desalination, the Chemical Engineering Journal, and the Journal of Hazardous Materials are the most important for publishing relevant research. Van Der Bruggen B was the most productive author. Katholieke University Leuven and Donghua University were the main research institutions. There were additional needs to enhance collaboration relationships between different authors, institutions, and countries. At present, research focused on methods that are closer to “real” field or ground conditions, “photocatalysis,” “loose nanofiltration membranes”, and “dye/salt separation” are the research frontier. The present study determines the trends of this field of research by visualizing the current landscape to facilitate future collaborative research.

Harnessing hardware acceleration in high-energy physics through high-level synthesis techniques

At the Large Hadron Collider, the vast amount of data from experiments demands not only sophisticated algorithms but also substantial computational power for efficient processing. This paper introduces hardware acceleration as an essential advancement for high-energy physics data analysis, focusing specifically on the application of High-Level Synthesis (HLS) to bridge the gap between complex software algorithms and their hardware implementation. We will explore how HLS facilitates the direct implementation of software algorithms into hardware platforms such as FPGAs, enhancing processing speeds and enabling real-time data analysis. This will be highlighted through the case study of a track-finding algorithm for muon reconstruction with the CMS experiment, demonstrating HLS’s role in translating computational tasks into high-speed, low-latency hardware solutions for particle detection and reconstruction. Key techniques in HLS, including parallel processing, pipelining, and memory optimization, will be discussed, illustrating how they contribute to the efficient acceleration of algorithms in high-energy physics. We will also cover design methodologies and iterative processes in HLS to optimize performance and resource utilization, alongside a brief mention of additional techniques like algorithm approximation and hardware/software co-design. In short, this paper will underscore the potential of hardware acceleration in high-energy physics research, emphasizing HLS as a powerful tool for physicists to enhance computational efficiency and foster groundbreaking discoveries.

Self-Foaming Expanded Ceramsites Prepared from Electrolytic Manganese Residue, Red Mud and Waste Soil

In this study, in order to solve the problems of resource utilization of electrolytic manganese residue and the destruction of natural resources by the over-exploitation of raw materials of traditional ceramics, electrolytic manganese residue (EMR), red mud (RM), and waste soil (WS) were used to prepare self-foaming expanded ceramsite (SEC), and different firing temperatures and four groups with different mixing ratios of these three raw materials were considered. Water absorption, porosity, heavy metal ion leaching, and compressive strength in the cylinder of SEC were evaluated. The chemical composition and microscopic morphology of SEC were investigated by XRD and SEM. The mechanism behind the reaction among EMR, RM, and WS and self-foaming was discussed. The results showed that both the temperature and mixing ratio significantly influenced the basic performance of SEC. With the temperature lower than 1200 °C, sphere appearance could be maintained in all of these four groups; however, the density, porosity, and compressive strength in the cylinder seemed unacceptable. When the temperature rose up to 1220 °C, sphere appearance could be only found in the group whose mixing ratio of EMR, RM, and WS was 2:2.5:0.5. Under this condition, the excellent performance of SEC was observed, with a porosity of 46.7%, bulk density of 0.61 g/cm3, and 3 d compressive strength in a cylinder of 26.82 MPa. The mechanism behind the reaction among EMR, RM, and WS could be described: when the temperature is up to 1180 °C, an obvious chemical reaction took place, followed by the liquid phase being produced and the gas released by the decomposition of Fe2O3 in RM and gypsum in EMR. When the temperature is up to 1200 °C, the viscosity of the liquid phase and the rate of gas generation achieved the balance, and the liquid phase encapsulated the gas and anorthite (CaAl2Si2O8) began to grow slowly. As time passed, self-foaming expanded ceramsite was prepared. The results of this study are of great significance in the field of artificial lightweight aggregate and industrial solid waste resource utilization.

A Scenario-Based Simulation Study for Economic Viability and Widespread Impact Analysis of Consumption-Side Energy Storage Systems

This study investigates energy storage within the contexts of production-side and consumption-side energy storage concepts. The theoretical advantages of consumption-side energy storage over production-side systems are initially explored. The analysis is supported by a scenario-based simulation, with results presented to assess the feasibility and applicability of consumption-side energy storage under varying conditions. The simulation examines multiple scenarios, incorporating economic assessments to evaluate the viability of such systems. Additionally, the study explores the broader impact of consumption-side energy storage when adopted by 5 million, 10 million, 20 million, and 40 million residential consumers across separate scenarios. The analysis emphasizes the potential for shifting peak-period energy consumption to nighttime usage and assesses the corresponding reduction in energy generation requirements and transmission line loads, alongside the economic benefits derived from postponing energy infrastructure investments. The study focuses exclusively on residential consumers, with the energy storage systems referred to as residential energy storage systems (RESS). These systems are assumed to be organized and managed by energy provider companies rather than individual consumers. The research also considers the potential costs associated with implementing RESS. The simulation-based findings reveal significant benefits, including reduced reliance on new power plants, decreased risk of transmission line overload, increased utilization of renewable energy resources, financial advantages for both energy providers and consumers, and positive environmental impacts. These results provide valuable insights with implications for shaping future energy policies, particularly in the United States.

Study on the synergistic hydration mechanism of granulated blast furnace slag-carbide slag-based cementitious materials and the properties of full-solid waste backfill materials

The synergistic utilization of multiple solid waste is an effective means of achieving green filling and resource utilization of solid waste in mines. In this paper, the synergistic effects of solid waste granulated blast furnace slag (GS) and carbide slag (CS) as cementitious materials (GCCM) are investigated, along with their preliminary feasibility in combination with coal gangue (CG) and furnace bottom slag (FBS) for the preparation of backfill materials. The synergistic hydration mechanism, mechanical properties, working performance of GCCM and GBC were studied, and the environmental impact and cost-effectiveness of GBC were evaluated. The results indicate that when the molar ratio of n(CaO)/n(SiO2 + Al2O3) is 0.635 in GCCM, CS has a significant alkaline excitation effect on GS. In addition, the hydration products mainly composed of C-(A)-S-H gel, AFm and hydrotalcite. However, when the concentration of OH- is high in GCCM, Ca(OH)2 preferentially precipitates, leading to a reduction in the amount of main hydration product C-(A)-S-H gel. Moreover, GBC has better competitive strength performance than ordinary Portland cement (OPC). The optimal compressive strength of GBC at 28 days is 19.699 MPa, with a standard slump is 228 mm, fully meeting the strength and transportation requirements for mine filling. The heavy metal leaching rate at 28 days meets the requirements of GB 8978, demonstrating potential for carbon reduction and cost savings. The full-solid waste GBC developed in this study can replace cement as a backfill material, which is of significant importance for achieving green mining, the synergistic utilization of solid waste resources, reducing filling costs, and enhancing environmental benefits.

Steel surface defect detection based on improved YOLOv8

Abstract Addressing the issue of imbalance between detection accuracy and speed in current steel surface defect
detection methods, we propose an improved YOLOv8-based algorithm, named YOLO-SSD, for steel surface defect
detection. This algorithm replaces the downsampling module with a SPD-Conv module in the backbone network and
introduces the CBAM attention module to enhance feature extraction capability and small object detection ability.
Additionally, in the neck network, the C2f module is replaced with a C2f-Ghost module, which not only lightweightens
the neck module but also improves the utilization of computational resources. Experimental results on the NEU DET dataset demonstrate that compared to the original YOLOv8 algorithm, the average detection accuracy of the
YOLO-SSD algorithm for steel surface defect detection has been improved by 2.8%, with a frame per second (FPS)
reaching 96. Compared to current mainstream steel surface defect detection algorithms, the improved detection
algorithm can enhance the detection accuracy of the defect detection model without compromising detection speed.

Nectar Replenishment Dynamics and its Influence on Pollination by Tetragonula iridipennis (Smith) in Greenhouse-Grown Cucumis sativus L

It is well known that nectar is the primary pollinator attractant, little is known about whether nectar replenishes after being removed. To comprehend how nectar availability affects pollinators behavior and the long-term persistence of floral resources, it is imperative to look into the replenishment process. Our findings highlight the dynamic nature of nectar availability suggest that nectar is not entirely depleted in a single pollinator visit, with measurable amounts remaining or replenished over time, but the amount of nectar varies across time points, according to all four test statistics Pillai's Trace = 0.692, F = 20.769, p < 0.001; Wilks' Lambda = 0.308, F = 20.769, p < 0.001; Hotelling's Trace = 2.245, F = 20.769, p < 0.001; Roy's Largest Root = 2.245, F = 20.769, p < 0.001). This reoccurrence of nectar may be the reason for the increased visits of bees to flower, as increase in the amount of honey in the bee box was observed before and after the experiment. These patterns underscore the adaptive significance of nectar recurrence, supporting sustained pollinator activity and enhancing floral resource utility.