Efficient Resource Utilization Research Articles

Improved Variable Scale Optimisation Algorithm in Water Saving Landscape Design and Its Application

Abstract With the acceleration of urbanisation, the problem of water resource shortage is becoming increasingly serious. Currently, the water use situation in Chinese cities is severe and the contradiction between the supply and demand of water resources is prominent, which has become one of the main factors restricting sustainable development of cities. Traditional research on water-saving landscape design focuses mainly on the analysis of the efficiency of water resource utilisation within a single area or city. The scope of the research is limited and the research methods are individual, which cannot fully meet the current needs of sustainable urban water-saving landscape design. The improved variable scale optimisation algorithm has become an indispensable tool in modern landscape design and would play a more important role in landscape design and ecological environment protection in the future. In response to the shortcomings of traditional variable-scale optimisation algorithms in solving the optimisation problem of water-saving landscape design for ecological sustainable development, this article would use an improved variable-scale optimisation algorithm to study the water-saving landscape design for ecological sustainable development in Community X, City B, Province A. The research results indicated that there were three experts who rated the effect of the water saving landscape design of the first group of designers as good or above, accounting for 37.5 %. The number of people who rated the effectiveness of water-saving landscape design in the second group as good or higher was 8, representing 100 %. The improved variable-scale optimisation algorithm could effectively improve the application of water-saving landscape design in ecological sustainable development.

Instantaneous growth: a compact measure of efficient carbon and nitrogen allocation in leaves and roots of C3 and C4 plants.

Elucidating plant functions and identifying crop productivity bottlenecks requires the accurate quantification of their performance. This task has been attained through photosynthetic models. However, their traditional focus on the leaf's capacity to uptake CO2 is becoming increasingly restrictive. Advanced bioengineering of C3 plants has made it possible to increase rates of CO2 assimilation by packing photosynthetic structures more densely within leaves. The operation of mechanisms that concentrate CO2 inside leaves can boost rates of assimilation while requiring a lower investment in carboxylating enzymes. Therefore, whether in the context of spontaneous plants or modern manipulation, considering trade-offs in resource utilization efficiency emerges as a critical necessity. I've developed a concise and versatile analytical model that simulates concurrent leaf and root growth by balancing instantaneous fluxes of carbon and nitrogen. Carbon is made available by leaf photosynthesis, encompassing all types of biochemistries, while nitrogen is either taken up by roots or remobilized after senescence. The allocation of leaf nitrogen between light or carbon reactions was determined using a fitting algorithm: growth maximisation was the only reliable fitting goal. Both the leaf nitrogen pool and the root-to-leaf ratio responded realistically to various environmental drivers (CO2 concentration, light intensity, soil nitrogen), replicating trends typically observed in plants. Furthermore, modifying the strength of CO2 concentrating mechanisms proved sufficient to alter the root-to-leaf ratio between C3 and C4 types. This direct and mechanistic one-to-one link convincingly demonstrates, for the first time, the functional dependence of a morphological trait on a single biochemical property.

Assess resource utilization efficiency in mustard cultivation on different size of sample farms

Assess resource utilization efficiency in mustard cultivation on different size of sample farms

Technology for a Food-secure Future: A Review of Technology Advances in Sustainable Agriculture

There is no doubt that advanced technologies play a very important and crucial role in driving the agricultural sustainable practices in order to satisfy the increasing demand for food production as well as to minimize environmental impacts. This review article aims to provide comprehensive points of view regarding a current state as well as the future advances in smart farming techniques for developing an agricultural sustainability. The digital sensors are considered as key techniques for agricultural transformation which provide an accurate managing and monitoring the various agricultural activities such as crop production, soil moisture, micro climate data, and others. Valuable detailed data are acquired by these sensors to be delivered to the farmers in order to make suitable decisions, ensuring the sustainable and efficient utilization of the agricultural resources. Moreover, managing irrigation using the advanced technologies is benefited wireless monitoring remotely and controlling systems. These technologies and advanced tools help the farmers to reduce water consumption and increase their benefits as well as promotes sustainable management practices of the water resources. Additionally, the drones such as unmanned aerial vehicles (UAVs) can be attached with several kinds of sensors or cameras for capturing detailed information of crop health, soil status, required fertilizers, irrigation, and pesticides. Besides these benefits of drones, they provide a function of an early detection of agricultural problems which allow the decision makers for taking suitable actions. Furthermore, the biotechnology advances such as CRISPR gene editing, and transgenic animals’ developing are very beneficial for enhancing the crop yield, disease resistance, and nutrients availability, and agricultural sustainability. The precision agriculture (PA) integration with geographic information system (GIS) and remote sensing (RS) provides site-specific management and its decisions in order to optimize the inputs utilization, waste reduce, and sustainable practices promotion. Also, advanced technologies’ application (i.e. artificial intelligence ‘AI’, machine learning ‘ML’, and the Internet of Things ‘IoT’) has been found to be a promising for achieving an agricultural sustainability. Therefore, by using these powerful technologies, the farmers can enhance an agricultural production, decrease an environmental effect and achieve the food security.

Enhanced Hydroponic Farming with Crop Suitability Prediction

Hydroponics, a soil-less cultivation technique, offers efficient resource utilization and enhanced crop yields. However, precise water quality control is essential to ensure optimal nutrient absorption and successful crop growth. This research paper presents a comprehensive IoT-based water quality monitoring system specifically designed for hydroponics, incorporating pH, turbidity, and temperature sensors. The system can acquire data from sensors, data processing, and data visualization in real time. The sensor data is seamlessly transmitted from Arduino to ESP8266 and securely stored on the ThingSpeak cloud platform. Utilizing web scraping techniques, the collected data is extracted from ThingSpeak and seamlessly integrated into a user-friendly website. On the website, a machine learning (ML) model, trained on required data, automatically processes the realtime sensor readings and performs crop suitability predictions. The system enables continuous and automated monitoring of key water quality parameters, ensuring optimal conditions for hydroponic crop growth. The deployed ML model demonstrates remarkable performance in predicting water suitability for different crops. The model provides real-time predictions on the website by utilizing the data, empowering hydroponic farmers with data-driven insights to optimize crop selection based on water quality. To evaluate the system’s efficacy, extensive experimentation, and validation have been conducted using hydroponic setups with various crop types.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Determining the efficiency of techniques for optimizing the number of tags in modern human-machine interfaces under conditions of limited resources

The object of this study is modern Human-Machine Interfaces (HMI) and SCADA systems in the industry. The subject of research is techniques for optimizing the number of tags (variables) in the SCADA/HMI environment to enhance resource utilization efficiency. One of the challenges in creating SCADA/HMI-based solutions can be the number of tags (variables) in the runtime environment. A large number of tags can lead to a problem of limited available resources. The technique presented here allow for the optimization of the number of tags used in Human-Machine Interface systems built with SCADA software and operator panels in combination with Programmable Logic Controllers (PLCs). An evaluation of the efficiency of techniques for reducing the number of HMI tags was conducted on an experimental configuration consisting of objects such as discrete input/output, analog input/output, actuators such as valves with discrete/analog control, and drives with frequency converters. The optimization coefficient, defined as the ratio of the number of input/output tags used directly to the number of tags after applying the optimization principle, was used as the efficiency criterion. Depending on the techniques and their combinations, the criterion values reached orders of 4, 10, and in one case even more than 100. These values are explained by the application of multiplexing approaches and various packing techniques. The advantages and disadvantages of the reported techniques, as well as their application limitations, have been identified. Some techniques are suitable only for specific tasks. These techniques could be applied in practical implementation when designing modern high-efficiency Human-Machine Interfaces under conditions of limited resources.

Overlooked pyrite-mediated heterogeneous Fenton processes: Mechanisms of surface hydroxyl radical generation and associated decontamination performance

Pyrite-based Fenton-like processes have been extensively studied for wastewater decontamination; however, most relevant studies placed excessive emphasis on the homogeneous Fenton reaction mediated by aqueous Fe2+, resulting in the proposed technologies facing issues such as additional acid requirements for pH adjustment and excessive iron sludge production. Herein, through in situ shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), custom dual-chamber reactor experiments, and a series of control experiments, significant hydroxyl radical generation was identified during the pyrite/H2O2 process, while the dominant reactive iron species was verified to the structural Fe sites on the pyrite surface, rather than structural Fe(II) in secondary iron minerals and surface adsorbed Fe2+. Consequently, even with significant suppression of the homogeneous Fenton pathway, the pyrite/H2O2 process exhibited significant degradation efficiency for sulfamethoxazole (SMX) at pH 4. Moreover, the pyrite/H2O2 process was found to selectively remove 50 μM of pollutants with high affinity for pyrite (bisphenol A, carbamazepine, nitrobenzene, and SMX), even in the presence of 50–100 mM methanol. Compared to the typical iron-based reductive catalyst (zero-valent iron, ZVI), pyrite mediated a Fenton process with greater potential for practical applications at pH 4, achieving a 43.75-fold reduction in iron sludge production and almost doubling the H2O2 utilization efficiency. Additionally, in contrast to ZVI, minimal iron oxide formed on the pyrite surface during the oxidation process. Thus, after seven cycles of degradation experiments, the decontamination efficiency of the pyrite/H2O2 process remained stable. These findings are crucial for understanding the complex environmental behavior of pyrite in both natural and engineering processes and provide a new perspective for the efficient utilization of pyrite resources as well.

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Effects of Water and Nitrogen Regulation on Apple Tree Growth, Yield, Quality, and Their Water and Nitrogen Utilization Efficiency.

Apple tree productivity is influenced by the quantity of water and nutrients that are supplied during planting. To enhance resource utilization efficiency and optimize yields, a suitable strategy for supplying water and nitrogen must be established. A field experiment was conducted using a randomized block group design on five-year-old apple trees in Ningxia, with two irrigation lower limit levels (55%FC (W1) and 75%FC (W2)) and four N application levels (0 (N1), 120 (N2), 240 (N3), and 360 (N4) kg·ha-1). Our findings showed that leaf N content increased with a higher irrigation lower limit, but the difference was not statistically significant. However, the leaf N content significantly increased with increasing N application. The growth pattern of new shoots followed logistic curve characteristics, with the maximum new shoot growth rate and time of new shoot growth being delayed under high water and high nitrogen treatments. Apple yield and yield components (weight per fruit and number of fruits per plant) were enhanced under N application compared to no N application. The maximum apple yields were 19,405.3 kg·ha-1 (2022) and 29,607 kg·ha-1 (2023) at the N3 level. A parabolic relationship was observed between apple yield and N application level, with the optimal range of N application being 230-260 kg⸱ha-1. Apple quality indicators were not significantly affected by the irrigation lower limit but were significantly influenced by N application levels. The lower limit of irrigation did not have a significant impact on the quality indicators of the apples. Water and N utilization efficiencies improved with the W2 treatment at the same N application level. A negative relationship was observed between the amount of nitrogen applied and the biased productivity of nitrogen fertilizer. The utilization of nitrogen fertilizer was 127.6 kg·kg-1 (2022) and 200.3 kg·kg-1 (2023) in the W2N2 treatment. The apple yield was sustained, the quality of the fruit improved, and a substantial increase in water productivity was achieved with the W2N3 treatment. The findings of this study can be used as a reference for accurate field irrigation.

Enhancing diagnostic precision in thyroid nodule assessment: evaluating the efficacy of a novel cell preservation technique in fine-needle aspiration cytology.

This study aimed to evaluate the effectiveness of thyroid fine needle aspiration cytology (FNAC) using a novel-cell preserving matrix called Cytomatrix in improving diagnostic accuracy for thyroid nodules. Fifty patients undergoing thyroidectomy were enrolled and FNAC was performed on the excised thyroid glands, with the collected sample being placed on the Cytomatrix. The results were compared with histopathological analysis, and diagnostic performance was assessed statistically. Cytomatrix demonstrated an accuracy of 96%, sensitivity of 84.61%, and specificity of 100%. Concordance between cytological and histopathological findings highlighted Cytomatrix's potential to enhance thyroid FNAC accuracy. FNAC using Cytomatrix shows promise in improving diagnostic accuracy for thyroid nodules. Its application, marked by faster processing and efficient resource utilization, coupled with the preservation of cellular architecture, holds considerable potential in enhancing cytological diagnosis, thus optimizing patient management strategies.

Diversity analysis of panicle traits in Chinese prickly ash germplasm resources and their influence on its systematic classification

This study aimed to reveal the diversity and variation in panicle traits of the Chinese prickly ash and clarify their influence on the its systematic classification to provide a theoretical basis and technical support for the efficient utilization of Chinese prickly ash germplasm resources and breeding. Sixteen panicle traits were identified from 35 Chinese prickly ash germplasm resources from 2021 to 2022. The diversity of these panicle traits and their role in the plant’s systematic classification were studied using variance, correlation, cluster, and principal component analyses. Cluster analysis showed that the 35 Chinese prickly ash germplasm resources could be divided into two groups with Euclidean distances of 25. Further analysis showed that yield traits such as panicle length, panicle width, primary branching, grain number per panicle, and grain weight per panicle were significantly positively correlated with grain chlorophyll content, while grain anthocyanin content was negatively correlated with both panicle (panicle length, panicle width, panicle length to width ratio, primary branching, grain number per panicle, and grain weight per panicle) and grain characteristics (single grain weight, thousand-grain weight, grain length, grain width and fruit shape index). In conclusion, Chinese prickly ash germplasms have diverse panicle traits. Z. armatum has dark green grains, long and wide panicles, a long conical shape, many primary branches, high grain weight, and high grain number per panicle. In contrast, Z. bungeanum has bright red seeds, a panicle width larger than its length, short and conical panicles, a small number of primary branches, and low grain weight per panicle and number of grains per panicle. Overall, Z. armatum had a significant yield advantage over Z. bungeanum.

Innovation in Education Management and Talent Cultivation Models for New Quality Productivity: Based on Data Driven and Intelligent Teaching Management

The rapid development of information technology and the emergence of new quality productivity, the education management and talent cultivation models that meet the needs of modern society have to be innovated. Data-driven education management combined with intelligence, is an important innovation direction that improves the utilization of teaching resources, optimizes the teaching process, and provides customized learning support for students by leveraging data analysis and intelligent algorithms, has become an important innovation direction. This paper elaborated the key concepts and principles of data-driven, intelligent-based teaching management mode, and analyzed the realization and roles of data-driven, intelligent-based teaching management mode in terms of education management, and talent cultivation. The application of intelligent teaching management model based on data has achieved positive results. Teachers have given a positive evaluation of the model proposing that it can improve the utilization efficiency of teaching resources, optimize the teaching process, and personalize the support of student learning.

Microorganisms usage in enhanced oil recovery: Mechanisms, applications, benefits, and limitations

AbstractIn today's world, where the oil and gas industry faces challenges such as declining production and the increasing need for efficient resource utilization, microbial enhanced oil recovery (MEOR) is introduced as a biological solution. This method, based on mechanisms like surfactant production, reduction of oil viscosity, and improvement of reservoir chemical properties, can increase oil recovery by 15%–20%, reduce operational costs by up to 30%, and is highly environmentally friendly. This study reviews various MEOR methods, including stimulating existing microbial activity in reservoirs or injecting microbes and nutrients. It presents successful examples of this technology in different oil fields, showing how MEOR can be a sustainable alternative to traditional methods. However, challenges such as the need for further research, control of biological processes, and advanced technology usage are also emphasized.

Parametric studies on energy utilization of the Chinese medicine residues: Preparation and properties of densified pellet

The depletion of fossil fuels has fueled an increased interest in biomass resources usage for heat and electricity generation. As an important biomass resource, Chinese medicine residues have great potential in substituting fossil fuels. However, that is basically limited by its poor properties, including low bulk density, high moisture content, and inhomogeneous structure. Herein, a safe and sustainable strategy was reported to prepare a high-quality densified pellet derived from Chinese medicine residues to address these worries. In this process, mixed and simple size materials were densified under various moisture content and pressure using a laboratory electronic tablet press machine equipped a single pellet mold. Results showed that higher pressure, ideal moisture content (∼6.5 %), and mixed particle size could densify better quality pellets. These findings pave the way for the safely and efficient resource utilization of Chinese medicine residues, as well as providing theoretical guidance and technical support for the household heating.

Mechanical Design and Manufacturing and its Automation Development Strategy Based on the Concept of Green Manufacturing

Under the background of increasing global environmental pressure, mechanical design and manufacturing and its automation industry are faced with major challenges of energy conservation, emission reduction and efficient utilization of resources. Although the concept of green design is gradually popularized, it still faces problems such as technical bottlenecks and imperfect system in the implementation process. This paper proposes to strengthen the theoretical research of green design, promote the innovation of green manufacturing process, build green supply chain management system and cultivate green manufacturing talents, aiming at technological innovation and management system optimization, and realize the green transformation of the whole process of mechanical design and manufacturing.

Softwarized and containerized microservices-based network management analysis with MSN

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

MODERN PRINCIPLES AND MEANS OF AUTOMATION IN ACCOUNTING, MANAGEMENT, AND MARKETING

Automation of auxiliary business processes (specifically accounting, management, and marketing) has long ceased to be an experiment and has become necessary. This necessity is a direct result of the impact of using automated devices and technical means, as well as computing technology based on mathematical methods, on the speed of decision-making and task execution, labor cost levels, resource utilization efficiency, the number of errors, and the quality of products or services. The article outlines the modern principles and tools of automation in accounting, management, and marketing. The research highlights important aspects of business process automation and identifies some key points. Based on the results obtained, a deep analysis of the interaction between humans and machines in automated accounting, management, and marketing systems can be conducted.

Indoor Environment Design and Energy Saving Analysis of Sustainable Development Integrated with Green Building

Modern architectural design has gradually distanced itself from the actual living needs of urban residents in the process of pursuing the integration of architecture and nature. To achieve the perfect combination of sustainable ecological concepts and effective energy utilization, green buildings have become an actively pursued design concept. Unlike relying solely on high-tech means, the core goal of green buildings is to build an environmentally friendly building environment by achieving sustainable and efficient utilization of energy and resources. In the design phase, green buildings should focus on functionality and economy, and be tailored to the local economic conditions, regional characteristics, natural resources, and climate conditions of the building's location to create green buildings with unique architectural and regional characteristics. This article endeavors to furnish both theoretical underpinnings and practical directives for the indoor environment design and energy conservation efforts of green buildings. By delving into the pivotal role of indoor environments as an essential architectural component, it integrates sustainable development principles with green building practices and delves into the intricate relationship between indoor environment design and energy efficiency, thus offering valuable insights and guidance for achieving sustainable and energy-efficient indoor spaces. In this process, this article examines the energy and resource utilization benefits that green building design can achieve, as well as the specific impact of indoor environment improvement on the quality of life of residents. Through an in-depth analysis of these aspects, this article aims to provide substantial contributions to achieving the sustainable development goals of green buildings. The experimental comparison results show that in terms of natural ventilation design, compared to mechanical ventilation, the proposed scheme can achieve an annual energy cost savings of 20%.