bluuebunny/crossref_metadata_2025 huggingface dataset:
Rainfall Influences the Patterns of Diversity and Species Distribution in Sandy Beaches of the Amazon Coast
Authors: Checon Helio H. , Costa Hugo H. R. , Corte Guilherme N. , Souza Fernanda M. , Pombo Maíra
Published: March 2023

The Amazon region is one of the Earth’s hotspots of biodiversity and has a pivotal role in climate regulation. Yet, little is known about its coastal biodiversity. Here, we performed the first assessment of macrobenthic diversity and ecological patterns of sandy beaches north of the Amazon River delta, the world’s largest freshwater input into the oceans. By assessing spatial and temporal changes in the soft-bottom biodiversity and environmental variables of three beaches (Goiabal, Nazaré, and Sumaúma) in the northernmost stretch of the Brazilian coast, we found low richness (14 taxa, overall; Goiabal: 3.27 ± 1.78; Nazaré: 2.34 ± 1.29; Sumaúma: 2 ± 0.67) and diversity (Goiabal: 0.72 ± 0.52; Nazaré: 0.62 ± 0.46; Sumaúma: 0.55 ± 0.39) across 2949 individuals with great dominance of estuarine species (notably Nephthys fluviatis and Sphaeromopsis mourei). Abundance was higher during rainy periods, and the same pattern was observed for richness and diversity in comparison to transitional periods, at least on Nazaré Beach. Environmental heterogeneity was reduced during rainy periods, resulting in a higher abundance of dominant species and lower beta diversity. Most species presented aggregated distribution at the upper intertidal zone, and changes in macrobenthic assemblages were linked to variations in rainfall and organic matter content in the sediment. Given the ecological uniqueness and the severe erosional process affecting the northern coast of the Amazon region, our results provide essential baseline knowledge to better understand the patterns and processes influencing its understudied biodiversity. We advocate that further studies expand our findings to support the conservation of this region.
Structural Characterization of Geopolymers with the Addition of Eggshell Ash
Authors: Ivanović Marija , Knežević Sanja , Mirković Miljana M. , Kljajević Ljiljana , Bučevac Dušan , Pavlović Vladimir B. , Nenadović Miloš
Published: March 2023

It is well known that geopolymers are a new group of binder materials of alumosilicate origin. Geopolymers are made by the reaction of precursor aluminosilicate materials with alkaline activator solutions. The current research relates to a low-cost and eco-friendly procedure, suitable of being implemented in two easy steps. The first step is the production of a solid phase based on fly ash (Obrenovac, Serbia) and eggshell ash as waste materials rich in calcium. The second step is alkali activating the solid phase using an alkaline activator (a mixture of NaOH and Na2SiO3) and procedures in proper laboratory conditions. Four samples with different eggshell ash content were synthesized. The concentration of used NaOH was 12 mol dm−3. The structural properties of all investigated samples were analyzed by XRD (X-ray diffraction), DRIFT (diffuse reflectance infrared Fourier transform), SEM (scanning electron microscopy) and UV/Vis spectroscopy analysis. XRD determined the amorphous halo with the presence of quartz as the crystal phase in all of the investigated samples. These results were confirmed by DRIFT analysis. The morphology of the samples was determined by SEM analysis. UV/Vis showed that the material could be a potential adsorbent.
Support Control Design of Mining Roadway under Goaf of Close-Distance Coal Seam
Authors: Lu Guozhi , Ni Ping
Published: March 2023

Coal resources have always been the focus of attention in the field of sustainable development. Based on the problem of surrounding rock control in close-distance coal seams, first, a mechanical model of floor failure was established based on the 29204 working face in Dongqu Mine; the failure mechanism of the floor was revealed by the zero displacement line, and a method to judge the occurrence state of the roof in the lower coal seam was put forward. Furthermore, by FLAC3D numerical simulation software, the mechanical model is verified, and the optimal support parameters are optimized, and the optimal water–cement ratio and grouting pressure are determined to be 0.6 and 3 MPa respectively. The application shows that the roof displacement is reduced by about 73.48% compared with the control group. Compared with the control group, the cable stress decreased by about 50.68%, and the application effect is remarkable. The research results provide an effective solution to ensure the sustainable development of coal resources and disaster prevention.
Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint
Authors: Cao Guangmei , Wang Yuesen , Gao Honghu , Liu Hao , Liu Haibin , Song Zhigang , Fan Yuqing
Published: March 2023

The intelligent transformation of logistics plays a significant role in meeting the diverse needs of customers, improving operational efficiency, and reducing carbon emissions in logistics activities. Therefore, to achieve sustainable development, logistics enterprises need to face the decision-making problem of intelligent logistics transformation. In this paper, we construct a Stackelberg game model between a financially constrained logistics-service provider (LSP) and a well-funded logistics-service integrator (LSI) and discuss the impact of the wholesale price contract, the cost-sharing contract, the revenue-sharing contract, the two-part tariff contract, and the hybrid cost-sharing and revenue-sharing contract on the intelligence level of logistics services, the profits of supply-chain members, and the channel for logistics-service demand. We found that the cost-sharing contract and the revenue-sharing contract cannot achieve Pareto improvement in the profits of supply-chain members. In addition, the increase in bank-loan interest rates would seriously weaken the level of intelligence and market demand for the entire logistics service. However, when consumers do not have high requirements for the intelligence of logistics services, the two-part power–price contract can create a win–win situation for supply-chain members and increase market demand within a certain range; on the contrary, a hybrid contract of cost sharing and revenue sharing is the best choice. Moreover, in the process of contract design for the intelligent transformation of logistics services, it is necessary to pay attention to the influence of the price-sensitivity coefficient on decision-making.
Spatial Water Consumption Test and Analysis of Various Typical Vegetation in the Sanjiangyuan Region
Authors: Qu Liuyan , Jia Shaofeng , Li Runjie
Published: March 2023

Vegetation water consumption in the Sanjiangyuan Region is of direct significance to the utilization of local water resources. To measure the actual evapotranspiration of various typical vegetation with different vegetation types in the Sanjiangyuan Region, a Lysimeter was used between November 2019 and October 2020. Additionally, the Penman–Monteith equation was used to estimate the condensation water of different vegetation types. Based on the measured data, this paper analyzes the spatial distribution of annual water consumption and annual runoff of various vegetation types. Furthermore, the spatial and temporal distribution of monthly water consumption of vegetation types on different underlying surfaces are discussed. To establish the relationship between the precipitation and runoff of various vegetation types, an artificial rainfall test was conducted. This study’s results reveal several key findings: (1) Condensation water is widespread and can be observed throughout the year. The annual condensation water volume ranges between 28.47 and 56.88 mm, which is particularly significant for the growth of alpine desert steppe and alpine steppe vegetation. (2) The annual water consumption in the Sanjiangyuan Region was higher in the south than in the north. Shrub water consumption was found to be 58.1–73.3 mm higher than that of grasses. Water consumption primarily occurred during the growing season, spanning from May to October. (3) The total water consumption in the growing season of the alpine meadow was less affected by precipitation compared to the non-growing season (from November to the next April). (4) The runoff yield can be ignored in the non-growing season when calculating water balance. However, during the growing season, the calculation of runoff cannot be ignored due to its significant impact on vegetation water consumption.
Photovoltaic Thermal Management by Combined Utilization of Thermoelectric Generator and Power-Law-Nanofluid-Assisted Cooling Channel
Authors: Selimefendigil Fatih , Okulu Damla , Öztop Hakan F.
Published: March 2023

In this study, two different cooling systems for the thermal management of a photovoltaic (PV) module were developed. A PV/thermoelectric generator (TEG) and PV/TEG-mini-channel cooling systems were considered; in the later system, water and water-based Al2O3 nanofluids were used in the cooling channel. The effective cooling of the PV module was achieved by using higher-loading nanoparticles in the base fluid, while the nanofluid exhibited a non-Newtonian behavior. The PV/TEG with a cooling channel system was numerically assessed with respect to various values of Reynolds numbers (between 5 and 250), inlet nanofluid temperatures (between 288.15 K and 303.15 K), and nanoparticle volume fractions in the base fluid (between 1% and 5%). Variations in average cell temperature, PV power, TEG power, and efficiencies were computed by varying the pertinent parameters of interest with Galerkin’s weighted residual finite element method. The most favorable case for cooling was obtained with TEG-cooling channel at φ = 5% and Re = 250. In this case, PV electrical power increased by about 8.1% and 49.2% compared to the PV/TEG and PV system without cooling, respectively. The TEG output power almost doubled when compared to the PV/TEG system for all channel models at Re = 250. The inlet temperature of the nanofluid has a profound impact on the overall efficiency and power increment of the PV module. The use of the PV/TEG-cooling channel with the lowest fluid inlet temperature (288.15 K) and nanofluid at the highest particle loading (φ = 5%) resulted in a PV efficiency increment of about 52% and 10% compared to the conventional PV system without cooling and the PV/TEG system. In this case, the TEG efficiency rises by about 51% in the PV/TEG nanofluid model compared to the PV/TEG model.
Study on the Dynamic Characteristics of Loess
Authors: Qiao Feng , Chang Chaoyu , Bo Jingshan , Wang Liang , Wang Jing
Published: March 2023

Due to its special material composition, formation environment, and special structure, loess usually shows different engineering and dynamic characteristics from ordinary soil. Based on previous research results, this paper studies the Haiyuan loess. It is found that: (1) the variation coefficients of various physical properties (except for the liquid index) of loess in the region are relatively stable, but the mechanical properties are relatively discrete; (2) the correlation among natural water content, void ratio, and density is high, but the correlation between mechanical properties is poor; (3) the consolidation ratio, effective confining pressure, and dynamic shear modulus ratio change in direct proportion, while the damping ratio changes in the opposite direction with increasing shear strain; and (4) in this paper, the recommended model of shear wave velocity and burial depth of loess in the study area is given. Compared with the example, the error is smaller and more reliable. (5) The characteristic period of the seismic response spectrum of the loess soil layer is not sensitive to the change in the damping ratio, and the change amplitude is small.
Impacts of Soil Erosion on Soil Quality and Agricultural Sustainability in the North-Western Himalayan Region of India
Authors: Mandal D. , Patra S. , Sharma N. K. , Alam N. M. , Jana C. , Lal R.
Published: March 2023

Erosion by water reduces soil quality and hence crop yield. Understanding the response of crop yields to soil erosion is vital in assessing agriculture’s vulnerability to erosion. However, these effects are difficult to quantify. The study presents a quantitative relationship between soil erosion and soil quality and productivity of rainfed wheat (Triticum aestivum) by comparing field plots with different degrees of erosion in some sub-tropical alfisols in the Doon Valley region of India. By comparing the topsoil depth with the reference site as the control, erosion severity was classified into different phases such as slight, moderate, severe, and very severe. A quantitative, weighted additive model was used to evaluate soil quality for different phases of erosion using soil clay content, water holding capacity, soil aggregate, soil organic carbon, pH, CEC, total N, available P, and available K. The synthesis of long-term experimental data revealed that the mean soil erosion rate varied from 5.5 Mg ha−1 yr−1 in slightly eroded plots to 33.4 Mg ha−1 yr−1 in very severely eroded plots. Compared with the reference forest, the soil organic carbon (SOC) declined by 81.4% and water holding capacity by 31% in severely eroded soils. A substantial loss of total N, extractable P, and available K was also observed. Water stable aggregates (WSA) decreased from 86% to 12.6%, and the cation exchange capacity (CEC) from 25 to 12.6 c mol(+) kg−1. The soil quality index was 0.7 in slightly eroded compared with 0.4 in severely eroded soil. Similarly, the sustainable yield index for wheat was 0.9 and 0.6 for slightly and severely eroded soils, respectively. Thus, there is a strong need to identify land management systems that reduce erosion risks, restore eroded soils, and enhance soil quality.
Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan
Authors: Asad Rimsha , Altaf Saud , Ahmad Shafiq , Mahmoud Haitham , Huda Shamsul , Iqbal Sofia
Published: March 2023

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.
Research on the Evolution of Global Electronics Trade Network Structure since the 21st Century from the Chinese Perspective
Authors: Zhu Xiaodong , Liu Xin
Published: March 2023

With the development of technology and the widespread adoption of digital technology, the trade volume of electronic products keeps improving. For a country’s trade situation, it is important to study the global trade of electronic products. In this paper, the data on global trade in electronic products from 240–246 countries and regions from 2000 to 2021 are used to create complex network models. Characteristic indicators, such as the network density, average clustering coefficient, average path length, and centrality are used to analyze the evolution of the global electronic product trade network pattern. The results of the complex network analysis show the following: (1) Since 2000, global electronic products have shown a trend of fluctuating growth, showing a state of three-pole differentiation. In addition, the trade volume is unevenly distributed, with the United States and China in the leading positions. (2) The global electronics trade network has significant scale-free and small-world characteristics, with high network density and close ties between countries. (3) There are differences between the closeness centrality and the betweenness centrality of the global electronic product trade network. The core countries are mainly in Europe and North America, while the influence of Asian countries is rising. (4) The global electronic product trade network has a clear division of communities and undergoes dynamic evolution. (5) Global electronic product trade is influenced by natural resources, economic and technological strength, political culture, and other factors. Finally, three policy suggestions are made for the development of China’s electronics trade.
Microplastic Accumulation in Agricultural Soils with Different Mulching Histories in Xinjiang, China
Authors: Zhang Shan , Bao Anming , Lin Xiaoli , Jia Gongxu , Zhang Qingling
Published: March 2023

Microplastics (MPs) in soils have been widely studied, yet very little is known about their distribution in agricultural soils and the impact of mulching history. In this study, soil samples were taken across 3 soil layers of 60 sites with varying years of mulching history (<5 years, 5–10 years, 10–20 years and >20 years) in agricultural film-mulched cotton fields of Xinjiang, China. Microplastics were obtained from the soils using oil separation combined with density separation. Stereomicroscopy and Fourier transform infrared spectroscopy (FTIR) were used for identification. The average microplastic abundance of the sites with different years of mulching history are 538, 1484, 5812 and 9708 pieces/kg, respectively. The microplastics with sizes 1000–5000 and 200–500 μm are dominant in soils with less than 10 years and over 10 years of continuous mulching history, respectively. The results show that the abundance of microplastics increases and the size of microplastics decreases gradually as the number of years of mulching history increases. In addition, the best polynomial fitting curves were found between microplastic abundance (y) and mulching years (x) in different soil layers, and the relationship in the topsoil layer can be fitted as the following equation: y = 20.6x2 = 41.39x + 198.65 (p < 0.01, R2 = 0.62). The results indicate that residual agricultural mulching film is the dominating source of microplastics in cotton fields. This study provides rationale for further research on microplastics prediction in agricultural film-mulched fields.
Environmental Care Attitudes and Intention to Purchase Green Products: Impact of Environmental Knowledge, Word of Mouth, and Green Marketing
Authors: Simanjuntak Megawati , Nafila Nidya Luthfi , Yuliati Lilik Noor , Johan Irni Rahmayani , Najib Mukhamad , Sabri Mohamad Fazli
Published: March 2023

The intention to purchase green products cannot be separated from environmental consciousness among people. This study analyzes the effect of environmental knowledge, word of mouth (WOM), and green marketing on environmental care attitudes and the intention to purchase green products. A total of 159 valid questionnaires were analyzed with structural equation modeling. The empirical results indicate that environmental knowledge significantly affects environmental care attitudes. In addition, green marketing and environmental concerns significantly and positively affect the intention to purchase green products. Meanwhile, WOM and green marketing do not significantly affect environmental care attitudes. Similarly, environmental knowledge and WOM do not significantly affect the intention to purchase green products. Finally, managerial implications for the government and business were formulated.
The Impact of the Use of Augmented Reality on Online Purchasing Behavior Sustainability: The Saudi Consumer as a Model
Authors: AL Hilal Norah Saud H.
Published: March 2023

This study aimed to examine the impact of augmented reality (AR) on the purchasing behavior of Saudi customers using analytic–descriptive methods and data from a snowball sample of 812 online buyers. Positive correlations were found between AR factors (hermeneutic, embodiment, and background) and dimensions (quality, fun, and creativity) and the purchase experience. Young women aged 17–26 mainly use AR for buying clothes and accessories, and the majority of the sample shops are available locally through mobile apps. The findings indicate that AR has a significant influence on buying decisions and suggest its potential use in marketing communications. The results also reveal that gender, social status, education level, and monthly income have an impact on participants’ responses to AR, with women and those who are married having more favorable views. Clothing and accessories were found to be the most frequently purchased products through AR. There were no significant differences based on age or the number of family members. Participants reported positively about their AR experience, and their concerns and anxiety did not affect their purchasing experience. Based on the main study’s results, a number of recommendations can be made: Saudi businesses need to use AR in their marketing communication strategies to meet consumer needs and trends. To maximize the benefits of brand awareness, they should use AR techniques and adopt this technology for products that depend on design in their production. When using AR in general and in light of the theories that have been studied, it is important to think about the cultural traits and dimensions of Saudi consumers and conduct further exploratory research before implementation.
Performance Evaluation and Comparison of Cooperative Frameworks for IoT-Based VDTN
Authors: Rehman Ghani Ur , Zubair Muhammad , Aly Wael Hosny Fouad , Farman Haleem , Mahmood Zafar , Hoxha Julian , Butt Naveed Anwer
Published: March 2023

The term “Internet of Things” (IoT) refers to an architecture in which digital objects have identification, sensing, connectivity, and processing capabilities that allow them to connect with other devices as well as perform tasks on the internet. There are many applications of IoT, among which Vehicle Delay-Tolerant Networks (VDTNs) are one of the best known. This new generation of vehicular networks can be applied in a variety of circumstances. For example, it can be employed to make data connections possible in densely crowded cities and as well as in remote and sparsely populated places with weak connectivity. These environments are characterized by frequent network partitioning, inconsistent connectivity, considerable propagation delays, high error rates, and short contact duration. Most of these behaviours are due to node selfishness. This task is crucial because selfish behaviour by nodes may make other nodes hesitant to cooperate. Selfish nodes have significant negative impacts on the effectiveness and efficiency of the network as a whole. To solve these issues, cooperative strategies that motivate nodes to share their resources must be considered. Important contributions to cooperation for vehicular networks are presented in this article, which investigates the effects of six different cooperative techniques on network performance and makes corresponding suggestions for their use in IoT-based VDTNs. Across all simulations, our results show that the studied strategies are all able to increase overall network performance by improving throughput and packet delivery probability, which in turn reduces average packet delivery time, energy consumption, overhead ratio, and the number of packets dropped.
Research on the Impact of Digital Empowerment on China’s Human Capital Accumulation and Human Capital Gap between Urban and Rural Areas
Authors: Sun Dapeng , Yu Bintong , Ma Jun
Published: March 2023

Human capital is a key factor in the economic growth and sustainable development of a country. However, the impact of digitalization on human capital accumulation and the urban-rural human capital gap in China is seldom a focus in the literature. This paper establishes a three-sector economic growth model surrounding knowledge accumulation, endogenous technology, and labor mobility. It is designed to theoretically explain the influence of digital empowerment on human capital accumulation and its urban-rural gap in an economy. An empirical test is also conducted using CFPS panel data for 25 Chinese provinces from 2016 to 2018. The results show that: Firstly, digital empowerment significantly promotes the accumulation of human capital in rural and urban areas. The number of years of education increases by 1.37% in rural areas and 3.07% in urban areas for each index point of digitalization increase. Secondly, the impact of digitalization on the human capital gap between urban and rural areas demonstrates an inverted U-shaped curve. However, it is still within the Matthew Effect that the human capital gap between urban and rural areas is widening in tandem with digital development. Finally, the Matthew Effect in digitalization on the urban-rural human capital gap is relatively larger in the female group, higher education population, and larger family size.
Does the Digital Economy Promote Coordinated Urban–Rural Development? Evidence from China
Authors: Cheng Yue , Zheng Dong
Published: March 2023

Urban–rural coordination development is a key factor in achieving sustainable development. The research sample consisted of panel data for 30 provinces in China for the period from 2011 to 2020. Our aim was to investigate whether and how the digital economy affects coordinated urban–rural development by using a panel data model, a spatial Durbin model (SDM), and a mediating effects model. The results indicate that (1) the growth of the digital economy has increased the level of coordinated urban–rural development directly and indirectly; (2) the coordinated development of urban and rural areas and the spatial distribution of the digital economy are highly correlated, with eastern regions generally experiencing a high level of agglomeration and central and western regions having a low level of agglomeration; (3) the digital economy can promote coordinated urban–rural development by reducing the income gap between urban and rural areas; and (4) the direct and the spatial promotion effects of digital economy development on coordinated urban–rural development appear to be stronger in the eastern region, insignificant in the central region, and to have a significant direct inhibition, as well as a significant spatial spillover effect, in the western region. This study provides a reference for China and other developing countries similar to China on how to promote coordinated urban and rural development in the development process of the digital economy.
Study on Rural Ecological Resilience Measurement and Optimization Strategy Based on PSR-“Taking Weiyuan in Gansu Province as an Example”
Authors: Xie Xiaoling , Zhou Gaonan , Yu Shibao
Published: March 2023

Under the dual impact of urbanization and ecological crisis, rural ecological resilience research can improve the system’s level of resisting external pressure and restoring ecological balance and provide a new perspective for sustainable rural development. This study establishes a rural ecological resilience measurement system in Weiyuan based on the PSR framework, evaluates the level of rural ecological resilience in Weiyuan in 2021 using the entropy method and the GWR model and detects its driving factors. The results show that (1) the spatial characteristics of rural ecological resilience diverge significantly, with the ecological resilience level of the three southern forest farms being higher overall the high values of resilience in Qingyuan, Wuzhu and Xiacheng being distributed in the central villages, while other villages are at low and medium values (2) X5 and X7 have negative driving effects on village ecological resilience, and X1, X5, X9 and X10 have positive driving effects on village ecological resilience (3) the dominant drivers and characteristics, we construct a scheme on stressor repair, state adaptation transformation and response efficiency optimization to provide ideas for improving rural ecological resilience.
Valorization of Dredged Harbor Sediments through Lightweight Aggregate Production: Application of Waste Oyster Shells
Authors: Lim Yee Cheng , Chen Chih-Feng , Chen Chiu-Wen , Dong Cheng-Di
Published: March 2023

The treatment and valorization of wastes such as dredged harbor sediments and oyster shells have become critical for environmental management. In order to promote waste valorization and resource sustainability, this study evaluated the feasibility of using harbor sediments and oyster shells for lightweight aggregate (LWA) production. The effects of the oyster shell content and sintering temperature on the sediment-based LWA properties, including particle density (PD), water absorption (WA), and crushing strength (CS), were investigated. The engineering applicability of the sediment-based LWAs was also assessed. The results showed that it was feasible to use harbor sediments admixed with oyster shells to produce LWAs that were suitable for engineering applications. The LWA properties were highly varied according to the sintering temperature and oyster shell content. Remarkably, the LWAs prepared with a 5–15% oyster shell content and sintered at 1125 °C were suitable for structural lightweight concrete (PD: 1.73–1.83 g/cm3, WA: 12.2–15.1%, CS: 7.2–10.4 MPa). The open porosity was a key factor affecting the particle density, water absorption, and crushing strength of the LWAs. Moreover, the leachability of toxic metals and chloride ions in the LWAs complied with the regulations for building materials in Taiwan. Waste oyster shells could be an excellent additive to lower the optimal sintering temperature required for sediment-based LWA production.
Photosynthetic Microorganisms: Cultivation and Application
Authors: Touloupakis Eleftherios
Published: March 2023

Photosynthetic microorganisms such as microalgae, cyanobacteria, and purple bacteria have attracted much interest as promising platforms for the sustainable production of high-value products and bioenergy [...]
[k]-Roman Domination in Digraphs
Authors: Zhang Xinhong , Song Xin , Li Ruijuan
Published: March 2023

Let D=(V(D),A(D)) be a finite, simple digraph and k a positive integer. A function f:V(D)→{0,1,2,…,k+1} is called a [k]-Roman dominating function (for short, [k]-RDF) if f(AN−[v])≥|AN−(v)|+k for any vertex v∈V(D), where AN−(v)={u∈N−(v):f(u)≥1} and AN−[v]=AN−(v)∪{v}. The weight of a [k]-RDF f is ω(f)=∑v∈V(D)f(v). The minimum weight of any [k]-RDF on D is the [k]-Roman domination number, denoted by γ[kR](D). For k=2 and k=3, we call them the double Roman domination number and the triple Roman domination number, respectively. In this paper, we presented some general bounds and the Nordhaus–Gaddum bound on the [k]-Roman domination number and we also determined the bounds on the [k]-Roman domination number related to other domination parameters, such as domination number and signed domination number. Additionally, we give the exact values of γ[kR](Pn) and γ[kR](Cn) for the directed path Pn and directed cycle Cn.
A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark
Authors: Saghari Asad , Budinská Ivana , Hosseinimehr Masoud , Rahmani Shima
Published: March 2023

The automotive family design is known as one of the most complex engineering design problems with multiple groups of stakeholders involved from different domains of interest and contradictory attributes. Taking into account all stakeholders’ preferences, which are generally symmetrical, non-deterministic distributions around a mean value, and determining the right value of attributes for each alternative are two basic challenges for these types of decision-making problems. In this research, the possibility to achieve a robust-reliable decision by focusing on the two aforementioned challenges is explored. In the proposed methodology, a random simulation technique is used to elicit stakeholders’ preferences and determine the relative importance of attributes. The decision space and values of attributes are determined using the Knowledge Discovery in Databases (KDD) technique, and to achieve a robust-reliable decision, statistical and sensitivity analyses are performed. By implementing this methodology, the decision-maker is assured that the preferences of all stakeholders are taken into account and the determined values for attributes are reliable with the least degree of uncertainty. The proposed methodology aims to select benchmark platforms for the development of an automotive family. The decision space includes 546 automobiles in 11 different segments based on 34 platforms. There are 6223 unique possible states of stakeholders’ preferences. As a result, five platforms with the highest degree of desirability and robustness to diversity and uncertainty in the stakeholders’ preferences are selected. The presented methodology can be implemented in complex decision-making problems, including a large and diverse number of stakeholders and multiple attributes. In addition, this methodology is compatible with many Multi-Attribute Decision-Making (MADM) techniques, including SAW, AHP, SWARA, and TOPSIS.
Comparing the Min–Max–Median/IQR Approach with the Min–Max Approach, Logistic Regression and XGBoost, Maximising the Youden Index
Authors: Aznar-Gimeno Rocío , Esteban Luis M. , Sanz Gerardo , del-Hoyo-Alonso Rafael
Published: March 2023

Although linearly combining multiple variables can provide adequate diagnostic performance, certain algorithms have the limitation of being computationally demanding when the number of variables is sufficiently high. Liu et al. proposed the min–max approach that linearly combines the minimum and maximum values of biomarkers, which is computationally tractable and has been shown to be optimal in certain scenarios. We developed the Min–Max–Median/IQR algorithm under Youden index optimisation which, although more computationally intensive, is still approachable and includes more information. The aim of this work is to compare the performance of these algorithms with well-known Machine Learning algorithms, namely logistic regression and XGBoost, which have proven to be efficient in various fields of applications, particularly in the health sector. This comparison is performed on a wide range of different scenarios of simulated symmetric or asymmetric data, as well as on real clinical diagnosis data sets. The results provide useful information for binary classification problems of better algorithms in terms of performance depending on the scenario.
A Malware Detection Approach Based on Deep Learning and Memory Forensics
Authors: Zhang Shuhui , Hu Changdong , Wang Lianhai , Mihaljevic Miodrag , Xu Shujiang , Lan Tian
Published: March 2023

As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory forensics. As the malware has many symmetric features, the saved training model can detect malicious code with symmetric features. The method includes collecting executable static malicious and benign samples, running the collected samples in a sandbox, and building a dataset of portable executables in memory through memory forensics. When a process is running, not all the program content is loaded into memory, so binary fragments are utilized for malware analysis instead of the entire portable executable (PE) files. PE file fragments are selected with different lengths and locations. We conducted several experiments on the produced dataset to test our model. The PE file with 4096 bytes of header fragment has the highest accuracy. We achieved a prediction accuracy of up to 97.48%. Moreover, an example of fileless attack is illustrated at the end of the paper. The results show that the proposed method can detect malicious codes effectively, especially the fileless attack. Its accuracy is better than that of common machine learning methods.
Asymmetry of Movement and Postural Balance and Underlying Functions in Humans
Authors: Paillard Thierry
Published: March 2023

Human movements and posture often show lateral asymmetries. Although symmetry [...]
Complexity Analysis of Benes Network and Its Derived Classes via Information Functional Based Entropies
Authors: Yang Jun , Fahad Asfand , Mukhtar Muzammil , Anees Muhammad , Shahzad Amir , Iqbal Zahid
Published: March 2023

The use of information–theoretical methodologies to assess graph-based systems has received a significant amount of attention. Evaluating a graph’s structural information content is a classic issue in fields such as cybernetics, pattern recognition, mathematical chemistry, and computational physics. Therefore, conventional methods for determining a graph’s structural information content rely heavily on determining a specific partitioning of the vertex set to obtain a probability distribution. A network’s entropy based on such a probability distribution is obtained from vertex partitioning. These entropies produce the numeric information about complexity and information processing which, as a consequence, increases the understanding of the network. In this paper, we study the Benes network and its novel-derived classes via different entropy measures, which are based on information functionals. We construct different partitions of vertices of the Benes network and its novel-derived classes to compute information functional dependent entropies. Further, we present the numerical applications of our findings in understanding network complexity. We also classify information functionals which describe the networks more appropriately and may be applied to other networks.
A New Technique to Uniquely Identify the Edges of a Graph
Authors: Ikhlaq Hafiz Muhammad , Ismail Rashad , Siddiqui Hafiz Muhammad Afzal , Nadeem Muhammad Faisal
Published: March 2023

Graphs are useful for analysing the structure models in computer science, operations research, and sociology. The word metric dimension is the basis of the distance function, which has a symmetric property. Moreover, finding the resolving set of a graph is NP-complete, and the possibilities of finding the resolving set are reduced due to the symmetric behaviour of the graph. In this paper, we introduce the idea of the edge-multiset dimension of graphs. A representation of an edge is defined as the multiset of distances between it and the vertices of a set, B⊆V(Γ). If the representation of two different edges is unequal, then B is an edge-multiset resolving a set of Γ. The least possible cardinality of the edge-multiset resolving a set is referred to as the edge-multiset dimension of Γ. This article presents preliminary results, special conditions, and bounds on the edge-multiset dimension of certain graphs. This research provides new insights into structure models in computer science, operations research, and sociology. They could have implications for developing computer algorithms, aircraft scheduling, and species movement between regions.
How to Bell the Cat? A Theoretical Review of Generative Artificial Intelligence towards Digital Disruption in All Walks of Life
Authors: Mondal Subhra , Das Subhankar , Vrana Vasiliki G.
Published: March 2023

Generative Artificial Intelligence (GAI) has brought revolutionary changes to the world, enabling businesses to create new experiences by combining virtual and physical worlds. As the use of GAI grows along with the Metaverse, it is explored by academics, researchers, and industry communities for its endless possibilities. From ChatGPT by OpenAI to Bard AI by Google, GAI is a leading technology in physical and virtual business platforms. This paper focuses on GAI’s economic and societal impact and the challenges it poses. Businesses must rethink their operations and strategies to create hybrid physical and virtual experiences using GAI. This study proposes a framework that can help business managers develop effective strategies to enhance their operations. It analyzes the initial applications of GAI in multiple sectors to promote the development of future customer solutions and explores how GAI can help businesses create new value propositions and experiences for their customers, and the possibilities of digital communication and information technology. A research agenda is proposed for developing GAI for business management to enhance organizational efficiency. The results highlight a healthy conversation on the potential of GAI in various business sectors to improve customer experience.
Characterization of Effects of Compressed Sensing on High Spectral and Spatial Resolution (HiSS) MRI with Comparison to SENSE
Authors: Medved Milica , Vicari Marco , Karczmar Gregory S.
Published: March 2023

High Spectral and Spatial resolution (HiSS) MRI shows high diagnostic performance in the breast. Acceleration methods based on k-space undersampling could allow stronger T2*-based image contrast and/or higher spectral resolution, potentially increasing diagnostic performance. An agar/oil phantom was prepared with water-fat boundaries perpendicular to the readout and phase encoding directions in a breast coil. HiSS MRI was acquired at 3T, at sensitivity encoding (SENSE) acceleration factors R of up to 10, and the R = 1 dataset was used to simulate corresponding compressed sensing (CS) accelerations. Image quality was evaluated by quantifying noise and artifact levels. Effective spatial resolution was determined via modulation transfer function analysis. Dispersion vs. absorption (DISPA) analysis and full width at half maximum (FWHM) quantified spectral lineshape changes. Noise levels remained constant with R for CS but amplified with SENSE. SENSE preserved the spatial resolution of HiSS MRI, while CS reduced it in the phase encoding direction. SENSE showed no effect on FWHM or DISPA markers, while CS increased FWHM. Thus, CS might perform better in noise-limited or geometrically constrained applications, but in geometric configurations specific to breast MRI, spectral analysis might be compromised, decreasing the diagnostic performance of HiSS MRI.
Monitoring the Health of Coastal Environments in the Pacific Region—A Review
Authors: Tremblay Louis A. , Chariton Anthony A. , Li Meng-Shuo , Zhang Yong , Horiguchi Toshihiro , Ellis Joanne I.
Published: March 2023

Coastal areas provide important ecological services to populations accessing, for example, tourism services, fisheries, minerals and petroleum. Coastal zones worldwide are exposed to multiple stressors that threaten the sustainability of receiving environments. Assessing the health of these valuable ecosystems remains a top priority for environmental managers to ensure the key stressor sources are identified and their impacts minimized. The objective of this review was to provide an overview of current coastal environmental monitoring frameworks in the Asia-Pacific region. This large geographical area includes many countries with a range of climate types, population densities and land uses. Traditionally, environmental monitoring frameworks have been based on chemical criteria set against guideline threshold levels. However, regulatory organizations are increasingly promoting the incorporation of biological effects-based data in their decision-making processes. Using a range of examples drawn from across the region, we provide a synthesis of the major approaches currently being applied to examine coastal health in China, Japan, Australia and New Zealand. In addition, we discuss some of the challenges and investigate potential solutions for improving traditional lines of evidence, including the coordination of regional monitoring programs, the implementation of ecosystem-based management and the inclusion of indigenous knowledge and participatory processes in decision-making.
Evaluation of Apical and Molecular Effects of Algae Pseudokirchneriella subcapitata to Cerium Oxide Nanoparticles
Authors: Mahaye Ntombikayise , Musee Ndeke
Published: March 2023

Cerium oxide engineered nanoparticles (nCeO2) are widely used in various applications and are, also, increasingly being detected in different environmental matrixes. However, their impacts on the aquatic environment remain poorly quantified. Hence, there is a need to investigate their effects on non-target aquatic organisms. Here, we evaluated the cytotoxic and genotoxic effects of <25 nm uncoated-nCeO2 on algae Pseudokirchneriella subcapitata. Apical (growth and chlorophyll a (Chl a) content) and genotoxic effects were investigated at 62.5–1000 µg/L after 72 and 168 h. Results demonstrated that nCeO2 induced significant growth inhibition after 72 h and promotion post 96–168 h. Conversely, nCeO2 induced enhanced Chl a content post 72 h, but no significant changes were observed between nCeO2–exposed and control samples after 168 h. Hence, the results indicate P. subcapitata photosynthetic system recovery ability to nCeO2 effects under chronic-exposure conditions. RAPD-PCR profiles showed the appearance and/or disappearance of normal bands relative to controls; indicative of DNA damage and/or DNA mutation. Unlike cell recovery observed post 96 h, DNA damage persisted over 168 h. Thus, sub-lethal nCeO2-induced toxicological effects may pose a more serious threat to algae than at present anticipated.