bluuebunny/crossref_metadata_2025 huggingface dataset:
Solar and Convective Drying: Modeling, Color, Texture, Total Phenolic Content, and Antioxidant Activity of Peach (Prunus persica (L.) Batsch) Slices
Authors: García-Moreira Diana Paola , Hernández-Guzmán Harumi , Pacheco Neith , Cuevas-Bernardino Juan Carlos , Herrera-Pool Emanuel , Moreno Ivan , López-Vidaña Erick César
Published: April 2023

Peach is a fruit highly appreciated by consumers; however, it is highly perishable, so drying is an alternative to preserve its physical and chemical properties. In this study, the effect of drying in natural and forced convection at three different temperatures (40 °C, 45 °C and 50 °C) and solar drying with two air velocities (1 m/s and 3 m/s) on the color, texture, total phenol content and antioxidant capacity of peach (Prunus persica (L.) Batsch), were evaluated. The experimental data of the drying kinetics were adjusted to five different mathematical models (Page, Logarithmic, Two-term exponential, Wang and Singh, and Verma et al.). The model that best represented the experimental data in natural convection was the Wang and Singh model (r2 > 0.998; RMSE < 0.016; χ2 < 2.85 × 10−4); in forced convection (45 °C and 50 °C), it was the Verma et al. model (r2 > 0.997; RMSE < 0.025; χ2 < 8.12 × 10−4); and finally, for solar drying, it was the Logarithmic model at 3 m/s (r2 = 0.999; RMSE < 0.012; χ2 < 1.12 × 10−4) and Wang and Sing model (1 m/s) (r2 = 0.998; RMSE = 1.31 × 10−4; χ2 = 1.92 × 10−4). The highest color difference was in samples dried by the natural convection method. The highest values of hardness were obtained by the solar drying method. The value of chlorogenic acid increased with the temperature of natural convection, while the concentration of neochlorogenic acid increased with the temperature at forced convection. For solar drying, the values of chlorogenic acid were greater at 3 m/s; in contrast, the neochlorogenic acid was greater at 1 m/s.
Application of Artificial Neural Networks for Modelling and Control of Flux Decline in Cross-Flow Whey Ultrafiltration
Authors: Gaudio Maria Teresa , Curcio Stefano , Chakraborty Sudip , Calabrò Vincenza
Published: April 2023

This study is part of the re-valorisation of the dairy waste industry through the use of membrane ultrafiltration (UF), in order to recover whey proteins and remove as much water as possible from the permeate. This study aimed to predict and control the permeate flux decline in cross-flow whey UF through a step procedure, and to compare different Artificial Neural Networks (ANNs), followed by a genetic algorithm (GA), as the optimization strategy. Models were developed in Matlab® Neural Network Toolbox. ANNs of one or two hidden layers were trained and simulated. A trial-and-error procedure identified the best network based on its performance values. The networks were trained through a selected set of experimental data obtained for lab-scale hollow-fibre membrane modules used to re-value scotta, the final waste of the dairy industry. The operating conditions considered as the input of the ANN were: operating time (top), sampling time (tsample), cross-flow velocity (CFV) and transmembrane pressure (TMP), while the output of the network was exclusively the normalized permeate flux (Jn). GA optimization was carried out to the following range of operating conditions to reach the best performances and to manage the fouling effect: 225 < top < 300 min, 8.33 < tsample < 15.9 min, 6.25 < CFV < 8.33 L/min, and TMP equal to 1.33 bar, otherwise it can be ignored. In fact, it has been noted that the networks with only three inputs, without TMP, predict and control Jn output better. Moreover, considering the normalized flux, it was possible to ignore some other important operating conditions, such as the membrane geometry. Consequently, the proposed general solution could also be used for other kinds of membrane applications. Finally, a hybrid approach among the ANN networks and a theoretical model was also used to better predict the resistance trend. It also returned more evident correspondence results than the ANN simulation alone, especially in the initial drop of Jn. The use of the theoretical part in the hybrid approach acts as a filter and returned the following order of significance of the operational input conditions on the resistance: top, tsample, CFV and TMP.
Study and Optimization Defect Layer in Powder Mixed Electrical Discharge Machining of Titanium Alloy
Authors: Rodic Dragan , Gostimirovic Marin , Sekulic Milenko , Savkovic Borislav , Aleksic Andjelko
Published: April 2023

Electrical discharge machining (EDM) has recently become very popular for processing titanium alloys, but surface quality is a major problem. During machining, a defect layer inevitably forms on the surface, which can have a negative impact on surface quality. One of the ways to reduce the defect layer is to add powder to the dielectric. However, it is not yet completely clear which powder and in what quantity it should be added to reduce the defect layer. In this sense, the present study aims to investigate the effects of machining parameters on the defect layer in powder-mixed electrical discharge machining of titanium alloys. The main goal is to achieve the minimum thickness of the defect layer by optimally adjusting the input parameters. Experimental studies were performed using the Taguchi orthogonal array L9, considering discharge current, pulse duration, duty cycle, and graphite powder concentration as input parameters. Based on the Taguchi and ANOVA analyses, the discharge current was found to have the greatest effect on the defect layer. In addition, analysis of variance revealed that pulse duration was the second influential parameter, followed by graphite powder and duty cycle. The minimum thickness of the defect layer is obtained at a discharge current of 1.5 A, a pulse duration of 30 µs, a duty cycle of 50%, and a graphite powder concentration of 12 g/L. The results obtained in this study provided answers to some of the unresolved research questions and confirmed the findings that the proposed method can be applied in the industry.
Anisotropic Analysis of Etch Rates for Sapphire Based on a Layer-by-Layer Removal Model of Surface Atoms
Authors: Li Yang , Wu Guorong
Published: April 2023

In this paper, a layer-by-layer removal model of surface atoms (Al) is established according to the atomic structure of sapphire, which can accurately calculate etch rates of crystal planes and analyze the anisotropy of etch rates of sapphire. Firstly, etch rate distributions of sapphire are gained through different etching experiments of sapphire hemispheres, and the effect of concentrations of the etching solution on etch rate distributions are analyzed. Then, different types of surface atoms are classified based on the types of chemical bonds of surface atoms, the arrangement laws of surface atoms of different crystal planes are analyzed and a general formula for calculating etch rates of different planes is proposed. Finally, the effectiveness of the layer-by-layer removal model of surface atoms (Al) is proved by small errors between calculated rates of the model and experimental rates at different concentrations, and the factors affecting the anisotropy of etch rates of sapphire are summarized, which include: (1) the vertical distances between two adjacent layers of surface atoms of crystal planes; (2) the configurations of the types of surface atoms of crystal planes.
Combining XRF, Multispectral Imaging and SEM/EDS to Characterize a Contemporary Painting
Authors: Brocchieri Jessica , Scialla Elvira , D’Onofrio Antonio , Sabbarese Carlo
Published: April 2023

Diagnostic analyses on a contemporary painting on canvas were performed with X-ray fluorescence (XRF), multispectral imaging and scanning electron microscope/energy dispersive spectroscopy (SEM/EDS). The results of each method provided complementary information to deepen the knowledge of the pictorial technique. Multispectral imaging provided insight into the topmost layers. XRF analysis made it possible to characterize the chemical composition of some materials and pigments used by the artist. Additional information such as that relating to canvas preparation emerged with the SEM/EDS technique. The results reveal (i) the use of pre-treated industrial canvas; (ii) the preparatory layer consists of plaster covered with a primer with titanium white, zinc and lithopone; (iii) a layer of cadmium yellow ground was inserted to give depth and three-dimensionality to the painting; (iv) the absence of underlying design; (v) the characterized pigments are all contemporary and (vi) a fixative spray covers the paint.
Ólöf the Rich and a Cloth from Svalbarð, Iceland
Authors: Kristjánsdóttir Steinunn , Erlendsson Guðjón Þór
Published: April 2023

About fifteen illustrated cloths have been preserved from medieval times in Iceland. One of them is an antependium from Svalbarð church on Svalbarðsströnd. The embroidered iconography on it is believed to depict the story of John the Apostle, who was the patron saint of the Svalbarð church. Upon closer inspection, the cloth appears to have been cut from a larger cloth, most likely a wall-hanging, in order to be used as an antependium. Moreover, the story that is embroidered on it seems to be related to secular people, because none of those appearing on it have halos around their heads, but halos are generally used in iconography to differentiate sanctified people from seculars. In this article, this discovery is discussed, and a theory that the embroidery shows the story of the most prominent woman of medieval Iceland, Ólöf the Rich, is proposed.
Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network
Authors: Mwata-Velu Tat’y , Niyonsaba-Sebigunda Edson , Avina-Cervantes Juan Gabriel , Ruiz-Pinales Jose , Velu-A-Gulenga Narcisse , Alonso-Ramírez Adán Antonio
Published: April 2023

Nowadays, Brain–Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT’s public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems’ requirements, dealing with short processing times and reliable classification accuracy.
Distributed DRL-Based Computation Offloading Scheme for Improving QoE in Edge Computing Environments
Authors: Park Jinho , Chung Kwangsue
Published: April 2023

Various edge collaboration schemes that rely on reinforcement learning (RL) have been proposed to improve the quality of experience (QoE). Deep RL (DRL) maximizes cumulative rewards through large-scale exploration and exploitation. However, the existing DRL schemes do not consider the temporal states using a fully connected layer. Moreover, they learn the offloading policy regardless of the importance of experience. They also do not learn enough because of their limited experiences in distributed environments. To solve these problems, we proposed a distributed DRL-based computation offloading scheme for improving the QoE in edge computing environments. The proposed scheme selects the offloading target by modeling the task service time and load balance. We implemented three methods to improve the learning performance. Firstly, the DRL scheme used the least absolute shrinkage and selection operator (LASSO) regression and attention layer to consider the temporal states. Secondly, we learned the optimal policy based on the importance of experience using the TD error and loss of the critic network. Finally, we adaptively shared the experience between agents, based on the strategy gradient, to solve the data sparsity problem. The simulation results showed that the proposed scheme achieved lower variation and higher rewards than the existing schemes.
Effect of Stocking Density on Sustainable Growth Performance and Water Quality of Nile Tilapia-Spinach in NFT Aquaponic System
Authors: Al-Zahrani Mohammed S. , Hassanien Hesham A. , Alsaade Fawaz W. , Wahsheh Heider A. M.
Published: April 2023

In Saudi Arabia, there is a scarcity of water used for agriculture and human consumption. Therefore, the aquaponic technique needs to be considered. Aquaponics is a modern, environmentally friendly agricultural technology that combines hydroponics and aquaculture into one system. However, the key to a successful aquaponic system is optimizing the stocking density for the target species. This study estimates the effect of three stocking densities—3 kg/m−3, 6 kg/m−3, and 9 kg/m−3—in five replicates on the growth performance and water quality of fingerling Nile tilapia (Oreochomis niloticus), as well as the yield of spinach (Spinacia oleracea) grown in the nutrient film technique (NFT) aquaponic system. As for the planting density, 36 spinach plants are planted per m−2 for each replicate. The experiment is set up for 8 weeks. The findings reveal that the average final body weight, weight gain, specific growth rate, and survival rate of Nile tilapia were significantly higher in the 3 kg/m−3 treatment. It is evident that the total yield of spinach increased as the stocking density increased (p < 0.05). Most of the water quality measurements are significant, pH values range from 6.74 to 7.47, dissolved oxygen is 4.33 to 6.35, ammonia is 0.13 to 0.17 mg/L−1, nitrite is 0.045 to 0.089, and nitrate is 2.44 to 3.35. Therefore, to maximize the productivity of spinach and tilapia while achieving the maximum benefit from fresh water, it can be recommended to use the stocking density of Nile tilapia fingerlings in the range of 6 kg/m−3 in the aquaponic system.
Aspergillus niger Enhances the Efficiency of Sewage Sludge Biochar as a Sustainable Phosphorus Source
Authors: Rossati Kamila Fernanda , Figueiredo Cícero Célio de , Mendes Gilberto de Oliveira
Published: April 2023

Phosphorus-rich biochar derived from sewage sludge (SS) could be a sustainable alternative P source for agriculture. However, most of biochar P is not readily available to plants. We evaluated the potential of P release from SS biochar into soil solution by Aspergillus niger. Additionally, we assessed the effect of SS biochar on the solubilization of phosphate minerals by the fungus. An incubation study was performed for 7 days in shaken flasks containing culture medium supplemented, or not, with a P-fixing soil. Biochar showed a dual role in phosphate solubilization by A. niger: (i) improved solubilization of AlPO4 and rock phosphate; and (ii) acted as a P source. Aspergillus niger solubilized up to 50% of the P contained in the biochar. A combined application of SS biochar and A. niger increased P availability by 500 times in a solution containing P-fixing soil. These results suggest that P-use efficiency from SS biochar could be improved by A. niger, allowing for the use of lower doses of this soil amendment. These findings contribute to a better understanding of the role of phosphate-solubilizing microorganisms in the interaction between soil and biochar. Furthermore, the results underpin the potential P fertilizer value of the SS biochar. Finally, our results present a win–win environmental benefit as it reduces SS accumulation and improves P-use efficiency.
The Effect of Tire Age and Anti-Lock Braking System on the Coefficient of Friction and Braking Distance
Authors: Lorenčič Vivien
Published: April 2023

A technical study of the effects of the use of ABS and the type of road surface on a vehicle’s coefficient of friction when braking at maximum force is presented in this article. It was found that, with ABS, the coefficient of friction was on average 13% higher on dry surfaces and 30% higher on wet surfaces, resulting in a 14.3% reduction in stopping distance on dry surfaces and a 37% reduction on wet surfaces. Measurements were taken with the Vericom VC4000DAQ performance computer, which is a simple method used to measure braking performance on different surfaces with different tires. It was also observed that the friction coefficient decreases with tire age, with a moderate to high correlation between the two variables (age and friction coefficient). However, the model used in the study only explains a moderate amount of variability in the data. An estimate is presented in the article regarding tire wear, indicating that tires used for from five to eight years and driven at 12,000 km per year would wear 0.79–1.33 mm in one year, assuming that the original tread depth of the new tire is about 8–9 mm and the legal minimum tread depth in most European countries for all summer tires is 1.6 mm. The research recommends changing summer tires after six years of use if the driver travels an average of 12,000 km per year, as the tires’ weight decreases by 1.3 kg on average, the tread height decreases by 6 mm on average, and the friction coefficient decreases with age, leading to longer braking distances.
The Resilience of a Resettled Flood-Prone Community: An Application of the RABIT Framework in Pasig City, Metro Manila
Authors: Ner Nikko Torres , Okyere Seth Asare , Abunyewah Matthew , Frimpong Louis Kusi , Kita Michihiro
Published: April 2023

Resilience measurement is an emerging topic in the field of disaster risk reduction. However, its application in Global South cities has proven to be a challenge due to the uniqueness of southern urbanisms and data challenges. As a result, the Resilience Benchmarking Assessment and Impact Toolkit (RABIT) framework has recently been developed to support resilience assessment in informal, marginalized, and disaster-prone contexts of southern cities. This paper asserts the relevance of the RABIT framework and uses it to assess the resilience of Manggahan residences, a resettled marginalized community in Pasig City, Metro Manila. Drawing on a quantitative approach and using exploratory factor analysis (EFA), the study revealed that scale, robustness, and learning attributes of the RABIT framework are strong contributors to the community’s resilience. Self-organization, diversity, and redundancy have similar levels of contribution. Equality and rapidity were found to have the weakest relative contribution. The study findings emphasize the need to view resilience in resettled communities holistically and adopt an integrated and comprehensive approach that considers the multiple aspects of everyday life to proactively build adaptive and future resilient capacities.
Expert Opinion on the Key Influencing Factors of Cost Control for Water Engineering Contractors
Authors: Ding Zhenhan , Liu Xun , Xue Zhiyuan , Li Xiaobo
Published: April 2023

There are many unpredictable circumstances during the implementation process of a water conservancy project, which often cause financial loss, increased construction costs and schedule delays. This paper investigates the influence factors for water conservancy project cost control. The present study used the factor analysis method to extract the major cost control influence factors, and performed a correlation analysis to clarify the relationship between these cost control influence factors and the sub-factors under each factor. Several water conservancy project practitioners were invited to analyze the sub-factors of the cost control influence factors and to provide some strategic suggestions in terms of minimizing the impact of the influence factors. The results of the study illustrate that in construction, water engineering contractors who want to reduce costs need to focus on the lack of a clear definition for the scope of works, subcontractors’ insufficient ability to perform the work, site construction conditions and the escalation of the construction material cost. The correlation analysis demonstrated that unreasonable requirements from the supervision unit and unfair standpoints of the supervision unit are highly correlated; the insufficient mobilization ability and lack of management capability of the subcontractor are medium correlated; the site construction conditions and the lack of a clear definition for the scope of works are medium correlated; and the escalation of the construction material cost and shortage of construction materials are medium correlated. This facilitates future water works contractors to identify the underlying causes of cost increases.
Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis
Authors: Wei Ze , Liu Hui , Tao Xuewen , Pan Kai , Huang Rui , Ji Wenjing , Wang Jianhai
Published: April 2023

Risk assessment is of great significance in industrial production and sustainable development. Great potential is attributed to machine learning in industrial risk assessment as a promising technology in the fields of computer science and the internet. To better understand the role of machine learning in this field and to investigate the current research status, we selected 3116 papers from the SCIE and SSCI databases of the WOS retrieval platform between 1991 and 2022 as our data sample. The VOSviewer, Bibliometrix R, and CiteSpace software were used to perform co-occurrence analysis, clustering analysis, and dual-map overlay analysis of keywords. The results indicate that the development trend of machine learning in industrial risk assessment can be divided into three stages: initial exploration, stable development, and high-speed development. Machine learning algorithm design, applications in biomedicine, risk monitoring in construction and machinery, and environmental protection are the knowledge base of this study. There are three research hotspots in the application of machine learning to industrial risk assessment: the study of machine learning algorithms, the risk assessment of machine learning in the Industry 4.0 system, and the application of machine learning in autonomous driving. At present, the basic theories and structural systems related to this research have been established, and there are numerous research directions and extensive frontier branches. “Random Forest”, “Industry 4.0”, “supply chain risk assessment”, and “Internet of Things” are at the forefront of the research.
Study on the Location-Routing Problem in Network-Type Tractor-and-Trailer Transportation Mode
Authors: Wang Qingbin , Liu Xiaolin , Li Gang , Zheng Jianfeng
Published: April 2023

Under the trend of developing green transportation in China, tractor-and-trailer transportation has received more attention. This paper focuses on the network-type tractor-and-trailer transportation mode in the port hinterland, aiming to tackle the problems of low efficiency and customer satisfaction in the existing transportation network. The authors recommend considering opening several alternative depots and making vehicle scheduling decisions simultaneous in order to optimize the existing transportation network. Therefore, this paper constructs a bi-level programming model with a generalized total cost minimization as the objective function. The solution to the original problem is divided into two stages: the location-allocation problem and vehicle scheduling; a two-stage hybrid heuristic algorithm is designed to solve the problem. Through the continuous iteration of the upper genetic algorithm and the lower hybrid particle swarm algorithm, the overall optimization of the problem is achieved. Finally, a specific example verifies the model and the algorithm’s effectiveness. The results show that the method proposed in this paper can significantly improve customer satisfaction and reduce transportation costs to a certain extent. It can also provide effective theoretical decision support for logistics enterprises to carry out tractor-and-trailer transportation business and develop green transportation.
Elevating Children’s Play Experience: A Design Intervention to Enhance Children’s Social Interaction in Park Playgrounds
Authors: Yang Jhu-Ting , Chen Ching-I , Zheng Meng-Cong
Published: April 2023

Peer interaction through play is one approach to stimulating preschool children’s growth. The outdoor playground facilities in parks are ideal places for children to practice their social skills. This study utilized nonparticipant observation to observe and record children’s play behaviors and interactions with others to ascertain whether outdoor playground facilities favor peer interaction. We summarized the design elements of peer-interaction-promoting playground facilities to optimize the facilities by determining the types of environments and facilities that trigger peer interaction. This study discovered that children spent most of their time in solo play and the least in peer interaction. Such interaction occurred only in spaces in which children stopped briefly. After installing a new bubble machine designed to increase peer interaction, solo play behaviors and parent–child interactions became less frequent for children younger than six years old, whereas peer interaction became more frequent. During the peer interaction of children aged 3 to 6, the frequency of level one, three, and four interactions increased. They also displayed level five behaviors, which were not observed before the installation. The new facility triggered higher-level behaviors, such as cooperation and playing together, enhancing peer interaction between different age groups.
Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems
Authors: Alkahtani Hasan , Aldhyani Theyazn H. H. , Alsubari Saleh Nagi
Published: April 2023

Solar power is an excellent alternative power source that can significantly cut our dependency on nonrenewable and destructive fossil fuels. Solar radiation (SR) can be predicted with great precision, and it may be possible to drastically minimize the impact cost associated with the development of solar energy. To successfully implement solar power, all projects using solar energy must have access to reliable sun radiation data. However, the deployment, administration, and performance of photovoltaic or thermal systems may be severely impacted by the lack of access to and the ambiguity of this data. Methods for estimating and predicting solar radiation can help solve these problems. Prediction techniques can be put to use in the real world to, for example, keep the power grid functioning smoothly and ensure that the supply of electricity exactly matches the demand at all times. Recently developed forecasting methods include the deep learning convolutional neural networks combined with long short-term memory (CNN-LSTM) model. This study provides a comprehensive examination of meteorological data, along with the CNN-LSTM methods, in order to design and train the most accurate SR forecasting artificial neural network model possible. Weather data was collected from a NASA meteorological station that included details such as the current temperature, the relative humidity, and the speed of the wind. This research revealed that SR is highly correlated with both temperature and radiation. Furthermore, the findings demonstrated that the CNN-LSTM algorithm outperformed the other algorithm-trained models, as evidenced by the performance score of the respective models, with a maximum coefficient determination (R²) > 95% and a minimum mean square error (MSE) of 0.000987 at the testing step. In comparison with different existing artificial intelligence models, the CNN-LSTM model outperformed the other models. These scenarios demonstrated that a basic implementation of CNN-LSTM can be used to supplement conventional methods for predicting SR, provide possibilities to monitor radiation at a low cost, and encourage the adoption of data-driven management.
Exergoeconomic and Exergetic Sustainability Analysis of a Combined Dual-Pressure Organic Rankine Cycle and Vapor Compression Refrigeration Cycle
Authors: Özdemir Küçük Esra , Kılıç Muhsin
Published: April 2023

In this paper, an exergoeconomic and exergetic sustainability analysis of a dual-pressure organic Rankine cycle (ORC) and vapor compression refrigeration cycle (VCRC) driven by waste heat is performed for power generation and cooling production. In addition, the most suitable fluid couple among the thirty-five different fluid pairs was investigated for the proposed combined system. The results indicate that the highest energy utilization factor, exergy efficiency, the system coefficient of performance, and net power are calculated for the R123-R141b fluid pair. In terms of exergetic sustainability indicators, the best performance results are obtained for the R123-R141b fluid combination. The minimum unit electricity generation cost and the shortest payback period are calculated as 0.0664 $/kWh and 2.5 years, respectively, for the R123-R290 fluid pair. The system component with the highest exergy destruction is the boiler, with 21.67%. The result of the parametric analysis showed that the thermodynamic performance parameters increase with the increment of the ORC’s boiling temperature. In addition, with the increasing boiling temperature, the environmental effect factor of the system decreases, while the exergetic sustainability index increases. Additionally, as the boiling temperature increases, the total system cost increases, while the unit electricity production cost and payback period decrease. It is suggested to use a R123-R141b fluid couple among fluid pairs created as a result of thermodynamic, exergoeconomic and sustainability analysis.
Field Investigation and Finite Element Analysis of Landslide-Triggering Factors of a Cut Slope Composed of Granite Residual Soil: A Case Study of Chongtou Town, Lishui City, China
Authors: Yan Tiesheng , Xiong Jun , Ye Longjian , Gao Jiajun , Xu Hui
Published: April 2023

Landslides caused by excavations and precipitation events are widespread types of slope failures in southwest Zhejiang, China, in areas with granite residual soil. Investigations of the effect of high precipitation on the hydrological response, stability, and evolutionary mechanism of cut slopes in granite soil areas are lacking. The characteristics of historical landslides in Chongtou Town in southwestern Zhejiang were summarized, and a typical slope was selected for analysis. The hydraulic and mechanical properties of the residual soil and fully weathered granite were tested, and the surface displacements on the slope were monitored. Geo-studio was utilized to establish a coupled seepage-deformation model to validate the numerical method and investigate the landslide-triggering factors of the cut slope. The results showed nearly all historical landslides in Chongtou Town were triggered by precipitation events, and the slide bodies consisted of residual soil and fully weathered granite with similar geotechnical properties. The simulated and measured horizontal displacements were in good agreement, indicating the reliability of the established model and parameters. The stability coefficient decreased with an increase in the gradient or height of the cut slope. The critical height values were 5.3 m, 5.5 m, 5.7 m, 6.0 m, and 6.3 m at slopes of 60°, 65°, 70°, 75°, and 80°, respectively. Long-term torrential rain and short-term high-intensity precipitation events are likely to trigger landslides when the precipitation event lasts longer than 26 h and 78 h, respectively. The landslide formation includes four stages: slope evolution, formation of unloading zone at slope foot, migration and loss of soil particles, and instability of the cut slope. The findings can be used to prevent and manage landslides on cut slopes in areas with granite residual soil.
Evaluation of Effective Factors in Achieving Ecological City in the Direction of Sustainable Development
Authors: Hu Liansheng , Xi Yongqin
Published: April 2023

Diverse ways have been presented for achieving urban balance and sustainability. The ecological city is one of these approaches. Consequently, the primary objective of this study is to identify and assess the effective variables in attaining an ecological city in the direction of sustainable development in the city of Jingzhou. Separating the effective criteria and variables for creating the ecological city and comparing the perspectives of professionals and citizens regarding the priority of these factors are additional objectives of this study. The current study has an applied objective and a descriptive data gathering strategy. In this study, a two-part questionnaire was developed in which Friedman’s test was used to prioritize the effective criteria for attaining an ecological city from the people’ perspective. The Analysis Hierarchy Process (AHP) was used to prioritize the aforementioned variables from the experts’ perspective. The statistical population of the study consists of residents of the city of Jingzhou. A total of 384 individuals were selected as the sample. The analysis of data was performed using SPSS statistical software. According to the results, there is no substantial difference in ways in which residents and specialists prioritize the effective components for building an ecological city. Except for the sub-factors of the “physical” section (where the priorities are completely different among the groups) and the two “environmental” sections (where the priorities of the two groups are slightly different), the priority of the sub-factors is the same in the remaining sections from the perspective of both groups. According to the findings of this study, “independent and self-sufficient local economy” and “use of renewable energy” are the most beneficial components for creating an ecological city from the perspective of both inhabitants and professionals. In addition, among the indicators of Jingzhou city, the economic index is in a better position for the city to be ecological.
Leveraging Social Media for SMEs: Findings from a Bibliometric Review
Authors: Pellegrino Alfonso , Abe Masato
Published: April 2023

This bibliometric review explores the role of social media in small- and medium-sized enterprises (SMEs) by examining the current literature and identifying research gaps. A comprehensive analysis of 293 Scopus-indexed journal articles published between 2007 and 2022 was conducted using VOSviewer software. The study’s objective is to understand the impact of social media on SMEs’ growth and development, with a focus on customer engagement, return on investment (ROI) metrics, and marketing strategies. The analysis uncovers three primary research clusters: (i) social media customer engagement, (ii) social media ROI metrics, and (iii) social media marketing strategies. These findings offer valuable insights for researchers and practitioners aiming to comprehend the existing knowledge landscape and inform future studies regarding the practical implementation of social media in SMEs.
An Improved Photovoltaic Module Array Global Maximum Power Tracker Combining a Genetic Algorithm and Ant Colony Optimization
Authors: Huang Kuo-Hua , Chao Kuei-Hsiang , Lee Ting-Wei
Published: April 2023

In this paper, a hybrid optimization controller that combines a genetic algorithm (GA) and ant colony optimization (ACO) called GA-ACO algorithm is proposed. It is applied to a photovoltaic module array (PVMA) to carry out maximum power point tracking (MPPT). This way, under the condition that the PVMA is partially shaded and that multiple peaks are produced in the power-voltage (P-V) characteristic curve, the system can still operate at the global maximum power point (GMPP). This solves the problem seen in general traditional MPPT controllers where the PVMA works at the local maximum power point (LMPP). The improved MPPT controller that combines GA and ACO uses the slope of the P-V characteristic curve at the PVMA work point to dynamically adjust the iteration parameters of ACO. The simulation results prove that the improved GA-ACO MPPT controller is able to quickly track GMPP when the output P-V characteristic curve of PVMA shows the phenomenon of multiple peaks. Comparing the time required for tracking to MPP with different MPPT approaches for the PVMA under five different shading levels, it was observed that the improved GA-ACO algorithm requires 19.5~35.9% (average 29.2%) fewer iterations to complete tracking than the mentioned GA-ACO algorithm. Compared with the ACO algorithm, it requires 74.9~79.7% (average 78.2%) fewer iterations, and 75.0~92.5% (average 81.0%) fewer than the conventional P&O method. Therefore, it is proved that by selecting properly adjusted values of the Pheromone evaporation rate and the Gaussian standard deviation of the proposed GA-ACO algorithm based on the slope scope of the P-V characteristic curves, a better response performance of MPPT is obtained.
Tebuconazole Induces ER-Stress-Mediated Cell Death in Bovine Mammary Epithelial Cell Lines
Authors: Lee Won-Young , Lee Ran , Park Hyun-Jung
Published: April 2023

Tebuconazole (TEB) is a triazole fungicide used to increase crop production by controlling fungi, insects, and weeds. Despite their extensive use, people are concerned about the health risks associated with pesticides and fungicides. Numerous studies have defined the cellular toxicity of triazole groups in pesticides, but the mechanisms of TEB toxicity in bovine mammary gland epithelial cells (MAC-T cells) have not yet been studied. Damage to the mammary glands of dairy cows directly affects milk production. This study investigated the toxicological effects of TEB on MAC-T cells. We found that TEB decreases both cell viability and proliferation and activates apoptotic cell death via the upregulation of pro-apoptotic proteins, such as cleaved caspases 3 and 8 and BAX. TEB also induced endoplasmic reticulum (ER) stress via the upregulation of Bip/GRP78; PDI; ATF4; CHOP; and ERO1-Lα. We found that TEB induced mitochondria-mediated apoptotic MAC-T cell death by activating ER stress. This cell damage eventually led to a dramatic reduction in the expression levels of the milk-protein-synthesis-related genes LGB; LALA; CSN1S1; CSN1S2; and CSNK in MAC-T cells. Our data suggest that the exposure of dairy cows to TEB may negatively affect milk production by damaging the mammary glands.
Assessment of Dams’ Failure and Flood Wave Hazards on the Downstream Countries: A Case Study of the Grand Ethiopian Renaissance Dam (GERD)
Authors: Eldeeb Hazem M. , Ibrahim Ali , Mowafy Magdy H. , Zeleňáková Martina , Abd-Elhamid Hany F. , Pietrucha-Urbanik Katarzyna , Ghonim Mahmoud T.
Published: April 2023

Dams are built for different purposes, but failure of a dam could result in extreme disasters such as floods. The Grand Ethiopian Renaissance Dam (GERD) was mainly built for power generation, but concerns about its safety, location and site conditions have led the downstream countries to investigate the GERD’s stability. This paper aims to investigate the impact of the failure of the dam on the downstream countries using the Hydrologic Engineering Center River Analysis System (HEC-RAS). Outflow hydrographs and flood inundation maps were provided through a hypothetical dam break scenario. An unsteady flow hydrodynamic routing with a 2D model was used to simulate the failure of the dam. A sensitivity analysis study of the output’s findings against breach parameters was also performed. The breach outflow discharge increases as the breach dimension increases. However, the peak discharge decreases with increasing breach formation time. Moreover, to prepare emergency action plans, it is important to predict the inundation depth, levels, arrival of flood waves, flood coverage area and water velocity. Furthermore, the results showed that Khartoum would turn into lakes within about 10 days and flood water depth would exceed 11 m at some locations in residential areas. Moreover, the flood waves overtopped the Roseires, Sennar and Merowe dams by 11, 7 and 20 m, respectively. In addition, the level of Lake Nasser would reach 188 m above sea level and the Aswan High Dam might be in great danger.
Influence of Biological Manganese Oxides on the Removal of Organic Matter and Ammonia in Micro-Polluted Source Water
Authors: Nengzi Lichao , Jiang Ying , Fang Zhirong , Hu Qiyuan , Qiu Guanglei , Li Haitao
Published: April 2023

In order to improve the removal efficiency of refractory organic matters in micro-polluted source water, biological manganese oxides (BMOs) were generated in situ in the biological aerated filter (BAF) (BAF 2#), which could oxidize the refractory organic matters into biodegradable organic matters. CODMn and NH4+-N in the effluent of BAF 2# both stabilized on the 39th day, while CODMn and NH4+-N in the effluent of the control BAF (BAF 1#) stabilized on the 38th and 42nd days, respectively. In the steady phase, the removal rates of CODMn and NH4+-N in BAF 1# were 41.51% and 94.79%, respectively, while in BAF 2#, they were 54.52% and 95.55%, respectively. BMOs generated in BAF 2# evidently improved the efficiency of CODMn removal. With the increase in the influent Mn2+ in BAF 2#, the rate of CODMn removal was gradually improved to 63.60%, while the efficiency of NH4+-N removal was slightly improved, CODMn was evidently removed in each section of the filter layer, and ammonia was mainly removed in the 0~0.8 m layer of the filter. CODMn was evidently removed in each section of the filter layer, and NH4+-N was mainly removed in the 0~0.8 m layers of the filter. Biological CODMn, Mn2+, and NH4+-N removal all followed the first-order kinetic reaction. As the influent Mn2+ gradually increased from 0 to about 0.5, 1, and 2 mg/L, the efficiency of CODMn removal along the filter layer was significantly improved, but the efficiency of NH4+-N removal was slightly improved. The kinetic constant k of biological CODMn removal significantly increased, while the kinetic constant k of biological Mn2+ and NH4+-N removal gradually increased.
Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China
Authors: Luo Lan , Zhang Yanjun , Dong Wenxun , Zhang Jinglin , Zhang Liping
Published: April 2023

Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD–LSTM was developed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3-N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3-N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD–LSTM for NH3-N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the developed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD–LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.
Cultura organizacional y la calidad del servicio del hospital Universitario de Guayaquil en el 2022
Authors: Checa Calderón Vicente Napoleón
Published: April 2023

La investigación “Cultura organizacional y la calidad del servicio del hospital Universitario de Guayaquil en el 2022” responde al problema de investigación ¿Cómo influye la cultura organizacional en la calidad de servicio médico que ofrece el Hospital Universitario de Guayaquil, en el año 2022? Se utilizó como variable independiente “cultura organizacional” y su relación con la variable dependiente, “calidad del servicio médico”, con la intención de manifestar mejoraras estratégicamente y continúas en el Hospital Universitario de Guayaquil. El tipo de investigación fue descriptiva – correlacional. El marco teórico se fundamentó en aristas como recursos humanos, clima organizacional y trabajo en equipo. La población de estudio son los clientes internos y externos del hospital universitario de Guayaquil, del cual se establecen muestras representativas de la población; como técnica para recolectar datos se estableció la técnica de encuestas, aplicando cuestionarios con preguntas cerradas y de opción múltiple y soportadas con un nivel de confianza del 95% y un margen de error del 5%. Los resultados se expresan en tablas de distribución de frecuencias y gráficas estadísticas para el respectivo análisis; los resultados más relevantes demostraron una cultura organizacional muy buena en el entorno de trabajo, el cual influye positivamente en el servicio médico que ofrece el personal en el Hospital, lo recomendable es el mejoramiento continuo de metodologías y procesos de trabajo para optimizar la perspectiva de los usuarios que se atienden den el Hospital.
Análisis bibliométrico de la relación arte y diseño gráfico
Authors: Llerena Paz Manuel Andrés , Arévalo Avecillas Manuel Esteban
Published: April 2023

Si bien es cierto cuando escuchamos términos como arte y diseño pensamos que estamos hablando de lo mismo, el presente estudio se enfoca en contextualizar cada área de estudio para tener en consideración sus principales diferencias. El objetivo de la investigación se centra en conocer la relación existente entre arte y diseño gráfico durante el periodo 2010 – 2022. La metodología es de carácter cuantitativo proporcionado por Scopus y Web of Science en métodos de producción científica bibliométrica. Los principales resultados se esquematizan en autores más productivos, idioma de procedencia de las investigaciones, genero de los investigadores, países de procedencia de las publicaciones y palabras claves. Finalmente, como futuras investigaciones se plantea realizar una analisis de interacción entre las cátedras artísticas y el potencial que estas otorgan a los estudiantes de diseño de las diferentes instituciones de educación superior.