bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Synechocystis sp. CPCC 534 Motility during Different Stages of the Growth Period in Active Fluids
Authors: Samadi Zahra , Mehdizadeh Allaf Malihe , Vourc’h Thomas , DeGroot Christopher T. , Peerhossaini Hassan
Published: May 2023

The motility behavior of suspended microorganisms plays an essential role in the properties of active fluids. Despite the important progress in our understanding of microorganisms’ motility in recent years, there are still several open questions about the dynamics of cell motility in active suspensions. Of special interest is the relationship between cell motility and age. In this study, cyanobacterium Synechocystis sp. CPCC 534 was used as the model microorganism, and the cell trajectories were tracked for 78 days during the cell growth period. Results showed that the length of cell trajectories had substantially increased from the exponential growth phase to the stationary phase and had declined at the end of the stationary phase. Similar trends were observed for the cells’ mean squared displacement (MSD), the time-dependent diffusion coefficient of cell suspensions, and the cell displacement probability density function (PDF). These results suggest that the cellular age of microorganisms has a significant effect on various metrics of cell motility and, therefore, can impact the transport properties of active suspensions.
Preparation of Alginate Oligosaccharides from Laminaria japonica Biomass by a Novel Biofunctional Alginate Lyase with pH and Salt Tolerance
Authors: Song Lei , Guo Yan-Feng , Wang Yi-Lei , Wang Chuan-Bao , Liu Jing-Hua , Meng Guo-Qing , Wang Zhi-Peng
Published: May 2023

Enzymatic alginate oligosaccharides were proved with various biological activities. Developing robust alginate lyase with high production is essential for its industrial application. In this study, a novel alginate lyase gene, AL07, was successfully screened and expressed in Pichia pastoris, and its characteristics were characterized. The secreted alginate lyase has a molecular weight of approximately 40.0 kDa and an activity of 45.4 U/mL. AL07 exhibits superior biological activity at pH 7.0 and 40 °C, with a preference for polyG substrates. Notably, the enzyme exhibits more than 60% relative activity over a pH range of 5.0 to 10.0. AL07 is also independent of ions and exhibits salt tolerance properties. The main degradation product of AL07 is DP2 oligosaccharide. Using AL07-based enzyme cocktail, Laminaria japonica can be degraded within 120 min. Therefore, AL07 has been demonstrated to be an efficient tool for the preparation of alginate oligosaccharides and the degradation of L. japonica.
The Medium Is the Definer: Daily Journalism as a Tool for Forming Community: A Case Study—The Ultra-Orthodox Community in Israel
Authors: Friedman Efi , Rashi Tsuriel
Published: May 2023

There are many studies that deal with the role of media and the motives for their creation. The present article explores the background behind the development of Ultra-Orthodox journalism. It examines the establishment of Ultra-Orthodox daily newspapers in Israel at the end of the twentieth century and the beginning of the twenty-first by analyzing semistructured texts and ideas. The historical background and the way this journalism developed in general and specifically during those years reflect a strong social and censorial orientation. This study concludes that these newspapers can be seen as social definers that help preserve a community through censorship aimed at the general and other Ultra-Orthodox media. It helps communities and individuals self-define and can delineate an additional role for mass media as a social definer. The contention herein is that the establishment of Ultra-Orthodox newspapers in Israel serves as a mode for social definition; a definition that is arrived at by being part of the circle of the newspaper’s readers, and the newspaper, in turn, defines itself by its censor board.
A High-Precision Remote Sensing Identification Method on Saline-Alkaline Areas Using Multi-Sources Data
Authors: Yang Jingyi , Wang Qinjun , Chang Dingkun , Xu Wentao , Yuan Boqi
Published: May 2023

Soil salinization is a widespread and important environmental problem. We propose a high-precision remote sensing identification method for saline-alkaline areas using multi-source data, a method which is of some significance for improving ecological and environmental problems on a global scale which have been caused by soil salinization. Its principle is to identify saline-alkaline areas from remote sensing imagery by a decision tree model combining four spectral indices named NDSI34 (Normalized Difference Spectral Index of Band 3 and Band 4), NDSI25 (Normalized Difference Spectral Index of Band 2 and Band 5), NDSI237 (Normalized Difference Spectral Index of Band 3 and Band 4) and NDSInew (New Normalized Difference Salt Index) that can distinguish saline-alkaline areas from other features. In this method, the complementary information within the multi-source data is used to improve classification accuracy. The main steps of the method include multi-source data acquisition, adaptive feature fusion of multi-source data, feature identification and integrated expression of the saline-alkaline area from multi-source data, fine classification of the saline-alkaline area, and accuracy verification. Taking Minqin County, Gansu Province, China as the study area, we use the method to identify saline-alkaline areas based on GF-2, GF-6/WFV and DEM data. The results show that the overall accuracy of the method is 88.11%, which is 7.69% higher than that of the traditional methods, indicating that it could effectively identify the distribution of saline-alkaline areas, and thus provide a scientific technique for the quick identification of saline-alkaline areas in large regions.
PSI Spatially Constrained Clustering: The Sibari and Metaponto Coastal Plains
Authors: Amoroso Nicola , Cilli Roberto , Nitti Davide Oscar , Nutricato Raffaele , Iban Muzaffer Can , Maggipinto Tommaso , Tangaro Sabina , Monaco Alfonso , Bellotti Roberto
Published: May 2023

PSI data are extremely useful for monitoring on-ground displacements. In many cases, clustering algorithms are adopted to highlight the presence of homogeneous patterns; however, clustering algorithms can fail to consider spatial constraints and be poorly specific in revealing patterns at lower scales or possible anomalies. Hence, we proposed a novel framework which combines a spatially-constrained clustering algorithm (SKATER) with a hypothesis testing procedure which evaluates and establishes the presence of significant local spatial correlations, namely the LISA method. The designed workflow ensures the retrieval of homogeneous clusters and a reliable anomaly detection; to validate this workflow, we collected Sentinel-1 time series from the Sibari and Metaponto coastal plains in Italy, ranging from 2015 to 2021. This particular study area is interesting due to the presence of important industrial and agricultural settlements. The proposed workflow effectively outlines the presence of both subsidence and uplifting that deserve to be focused and continuous monitoring, both for environmental and infrastructural purposes.
Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery
Authors: Lv Yulong , Han Ning , Du Huaqiang
Published: May 2023

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.
Contrastive Learning with Prototype-Based Negative Mixing for Satellite Telemetry Anomaly Detection
Authors: Guo Guohang , Hu Tai , Zhou Taichun , Li Hu , Liu Yurong
Published: May 2023

Telemetry data are the most important basis for ground operators to assess the status of satellites in orbit, and telemetry data-based anomaly detection has become a key tool to improve the reliability and safety of spacecrafts. Recent research on anomaly detection focuses on constructing a normal profile of telemetry data using deep learning methods. However, these methods cannot effectively capture the complex correlations between the various dimensions of telemetry data, and thus cannot accurately model the normal profile of telemetry data, resulting in poor anomaly detection performance. This paper presents CLPNM-AD, contrastive learning with prototype-based negative mixing for correlation anomaly detection. The CLPNM-AD framework first employs an augmentation process with random feature corruption to generate augmented samples. Following that, a consistency strategy is employed to capture the prototype of samples, and then prototype-based negative mixing contrastive learning is used to build a normal profile. Finally, a prototype-based anomaly score function is proposed for anomaly decision-making. Experimental results on public datasets and datasets from the actual scientific satellite mission show that CLPNM-AD outperforms the baseline methods, achieves up to 11.5% improvement based on the standard F1 score and is more robust against noise.
LoRaWAN Transmissions in Salt Water for Superficial Marine Sensor Networking: Laboratory and Field Tests
Authors: Pozzebon Alessandro , Cappelli Irene , Campagnaro Filippo , Francescon Roberto , Zorzi Michele
Published: May 2023

In this paper, the authors present the results of a set of measurements carried out to analyze the transmission capabilities of the LoRaWAN technology for underwater to above water transmission in saline water. A theoretical analysis was used to model the link budget of the radio channel in the considered operative conditions and to estimate the electrical permittivity of salt water. Preliminary measurements were performed in the laboratory at different salinity levels to confirm the application boundaries of the technology, then field tests were conducted in the Venice lagoon. While these test are not focused on demonstrating the usability of LoRaWAN to collect data underwater, the achieved results demonstrate that LoRaWAN transmitters can be used in all those conditions when they are expected to be partially or totally submerged below a thin layer of marine water, in accordance with the prediction of the proposed theoretical model. This achievement paves the way for the deployment of superficial marine sensor networks in the Internet of Underwater Things (IoUT) context, as for the monitoring of bridges, harbor structures, water parameters and water sport athletes and for the realization of high-water or fill-level alarm systems.
An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs
Authors: Wu Haojie , Luktarhan Nurbol , Tian Gaoqi , Song Yangyang
Published: May 2023

The smartphone has become an indispensable tool in our daily lives, and the Android operating system is widely installed on our smartphones. This makes Android smartphones a prime target for malware. In order to address threats posed by malware, many researchers have proposed different malware detection approaches, including using a function call graph (FCG). Although an FCG can capture the complete call–callee semantic relationship of a function, it will be represented as a huge graph structure. The presence of many nonsensical nodes affects the detection efficiency. At the same time, the characteristics of the graph neural networks (GNNs) make the important node features in the FCG tend toward similar nonsensical node features during the propagation process. In our work, we propose an Android malware detection approach to enhance node feature differences in an FCG. Firstly, we propose an API-based node feature by which we can visually analyze the behavioral properties of different functions in the app and determine whether their behavior is benign or malicious. Then, we extract the FCG and the features of each function from the decompiled APK file. Next, we calculate the API coefficient inspired by the idea of the TF–IDF algorithm and extract the sensitive function called subgraph (S-FCSG) based on API coefficient ranking. Finally, before feeding the S-FCSG and node features into the GCN model, we add the self-loop for each node of the S-FCSG. A 1-D convolutional neural network and fully connected layers are used for further feature extraction and classification, respectively. The experimental result shows that our approach enhances the node feature differences in an FCG, and the detection accuracy is greater than that of models using other features, suggesting that malware detection based on a graph structure and GNNs has a lot of space for future study.
A New Method for Spatial Estimation of Water Quality Using an Optimal Virtual Sensor Network and In Situ Observations: A Case Study of Chemical Oxygen Demand
Authors: Zhao Na
Published: May 2023

Accurate water quality estimation is important for water environment monitoring and water resource management and has emerged as a pivotal aspect of ecological rehabilitation and sustainable development. However, due to the strong spatial heterogeneity of water quality parameters, it is still challenging to obtain highly accurate spatial patterns of them. Taking chemical oxygen demand as an example, this study proposes a novel estimation method for generating highly accurate chemical oxygen demand fields in Poyang Lake. Specifically, based on the different water levels and monitoring sites in Poyang Lake, an optimal virtual sensor network was first established. A Taylor expansion-based method with integration of spatial correlation and spatial heterogeneity was developed by considering environmental factors, the optimal virtual sensor network, and existing monitoring stations. The proposed approach was evaluated and compared with other approaches using a leave-one cross-validation process. Results show that the proposed method exhibits good performance in estimating chemical oxygen demand fields in Poyang Lake, with mean absolute error improved by 8% and 33%, respectively, on average, when compared with classical interpolators and remote sensing methods. In addition, the applications of virtual sensors improve the performance of the proposed method, with mean absolute error and root mean squared error values reduced by 20% to 60% over 12 months. The proposed method provides an effective tool for estimating highly accurate spatial fields of chemical oxygen demand concentrations and could be applied to other water quality parameters.
Measurement of the Acoustic Relaxation Absorption Spectrum of CO2 Using a Distributed Bragg Reflector Fiber Laser
Authors: Shen Kun , Yuan Jixian , Li Min , Wen Xiaoyan , Lu Haifei
Published: May 2023

Reconstruction of the acoustic relaxation absorption curve is a powerful approach to ultrasonic gas sensing, but it requires knowledge of a series of ultrasonic absorptions at various frequencies around the effective relaxation frequency. An ultrasonic transducer is the most widely deployed sensor for ultrasonic wave propagation measurement and works only at a fixed frequency or in a specific environment like water, so a large number of ultrasonic transducers operating at various frequencies are required to recover an acoustic absorption curve with a relative large bandwidth, which cannot suit large-scale practical applications. This paper proposes a wideband ultrasonic sensor using a distributed Bragg reflector (DBR) fiber laser for gas concentration detection through acoustic relaxation absorption curve reconstruction. With a relative wide and flat frequency response, the DBR fiber laser sensor measures and restores a full acoustic relaxation absorption spectrum of CO2 using a decompression gas chamber between 0.1 and 1 atm to accommodate the main molecular relaxation processes, and interrogates with a non-equilibrium Mach-Zehnder interferometer (NE-MZI) to gain a sound pressure sensitivity of −45.4 dB. The measurement error of the acoustic relaxation absorption spectrum is less than 1.32%.
A Method for Detecting LDoS Attacks in SDWSN Based on Compressed Hilbert–Huang Transform and Convolutional Neural Networks
Authors: Liu Yazhi , Sun Ding , Zhang Rundong , Li Wei
Published: May 2023

Currently, Low-Rate Denial of Service (LDoS) attacks are one of the main threats faced by Software-Defined Wireless Sensor Networks (SDWSNs). This type of attack uses a lot of low-rate requests to occupy network resources and hard to detect. An efficient detection method has been proposed for LDoS attacks with the features of small signals. The non-smooth small signals generated by LDoS attacks are analyzed employing the time–frequency analysis method based on Hilbert–Huang Transform (HHT). In this paper, redundant and similar Intrinsic Mode Functions (IMFs) are removed from standard HHT to save computational resources and to eliminate modal mixing. The compressed HHT transformed one-dimensional dataflow features into two-dimensional temporal–spectral features, which are further input into a Convolutional Neural Network (CNN) to detect LDoS attacks. To evaluate the detection performance of the method, various LDoS attacks are simulated in the Network Simulator-3 (NS-3) experimental environment. The experimental results show that the method has 99.8% detection accuracy for complex and diverse LDoS attacks.
Transgender Health between Barriers: A Scoping Review and Integrated Strategies
Authors: Costa Davide
Published: May 2023

Transgender people have garnered attention in recent years. They have different health problems; the fact, however, that they belong to a minority means that this is characterized by complex mechanisms of stigmatization. This paper aims to analyze the current literature on the barriers to health services encountered by transgender people. This scoping review is based on the following research questions: (1) What are the main barriers to health care encountered by transgender people? (2) Is it possible to organize these barriers according to a macro-, meso- and microanalysis approach? (3) What are the main characteristics of the barriers to health care encountered by transgender people? (4) Are there significant relations between the different types of barriers? The review was undertaken following the PRISMA extension for scoping reviews. In total, 32 studies were included from which three types of barriers with different subcategories were identified: health system barriers, social barriers, and individual barriers. In conclusion, due to the complexity of gender issues and barriers to health care, a multidisciplinary approach is necessary. In this regard, some integrated strategies to reduce barriers to health care for transgender people are proposed.
Research on Digital Political Communication: Electoral Campaigns, Disinformation, and Artificial Intelligence
Authors: López-López Paulo Carlos , Barredo-Ibáñez Daniel , Jaráiz-Gulías Erika
Published: May 2023

In recent years, political communication has emerged as one of the most prolific subfields within political science and the social sciences as a whole [...]
Comparison of Two Hydrological Models, the HEC-HMS and Nash Models, for Runoff Estimation in Michałówka River
Authors: Janicka Ewelina , Kanclerz Jolanta , Agaj Tropikë , Gizińska Katarzyna
Published: May 2023

Floods are among the most devastating natural disasters in small suburban catchments. These phenomena, causing loss of life and massive property damage, pose a serious threat to the economy. Hydrological modeling is extremely important in terms of climate change, and the use of appropriate modeling can be a useful tool for flood risk prevention and mitigation. Rainfall–runoff modeling requires the selection of an appropriate hydrological model in order to obtain satisfactory results. Hydrological models are used in water resource planning and management to estimate catchment runoff. Small uncontrolled catchments play a particularly important role in hydrological phenomena, since changes in them affect flows in the recipient. Hydrologists are particularly interested in developing hydrological models that can be made with a minimum of data and parameters. Nash models and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) are examples of simple and most practical hydrologic models. These models were used in this paper to study geographic and qualitative changes in precipitation runoff due to land cover changes. The modeling was carried out for two spatial aspects relating to the years 1940 and 2018. The model allowed for the simulation of the river flow that can occur under different rainfall probabilities. The analysis of the results was used to evaluate the hydrological models used. The hundred-year flow modeled with the Nash model for 1940 was 13.4 m3∙s−1, whereas the second model gave slightly lower flow values. In addition, modeling the flow for 2018 (after changing the land cover) highlighted the increase in the flow value for both models, where again the flow volume was slightly higher for the Nash model and amounted to about 19 m3∙s−1. The flow differences for individual models were not too large. This made it possible to conclude that the simulated outflow hydrographs are in good agreement, and this means that the models accurately reproduce the flow of the Michałówka River. The study showed that rapid urbanization adversely affects hydrological processes. In addition, the study showed that a well-distributed model can outperform a global flood forecasting model, especially in terms of magnitude, as in the current study example.
Sustainable Development and Implementation of Quality Management Excellence Models in Public Organizations: A Systematic Literature Review
Authors: Taraza Effrosyni , Anastasiadou Sofia , Masouras Andreas , Papademetriou Christos
Published: May 2023

Purpose: The purpose of this study was to determine the effects of the European Foundation for Quality Management Excellence Model and Six Sigma and Lean Six Sigma approaches in public organizations. Design/methodology/approach: A systematic literature review was conducted based on articles from three academic publishers (Emerald, Elsevier/Science Direct and Taylor & Francis). The 88 selected journal articles were published between 2004 and 2022 and documented the results of the quality tools. Findings: The effects of applying the models in the public sector are presented. From the literature review, specific findings were identified regarding the motivations of all areas of education and services and the challenges they face in applying the qualitative tool methodologies. The main topics discussed are the human factors involved in implementing quality tools. Research limitations/implications: An important limitation is that data were drawn from only three major journals and the authors did not always have access to all databases and peer-reviewed journals or to any review articles in languages other than English. Multiple keywords limited the article search, as qualitative tools have been widely used in the private sector but less so in the public sector. Practical implications: The results and limitations detailed in the study and presentation of the 88 articles will motivate academic researchers to further study the application of qualitative tools in the public sector and fill the knowledge gap caused by the limited publications on this topic. Originality/value: The European Foundation for Quality Management Excellence Model and the Six Sigma and Lean Six Sigma approaches have not been widely implemented in the public sector, and literature reviews are limited despite the increasing trend of their use in the sector in recent years. More future research in public administration is needed to determine the effects and limitations of implementing qualitative tools.
Biorefinery for the Sustainable Biochemicals Production: Process Design and Technological Advances
Authors: Bhatia Shashi Kant
Published: May 2023

To fulfill the demands of an ever-increasing population and ensure sustainable development, the implementation of a bio-economy based on renewable resources is necessary [...]
Proposing Dynamic Pricing as an Alternative to Improve Technical and Economic Conditions in Rural Electrification: A Case Study from Colombia
Authors: López García Dahiana , Beltrán Gallego José David , Carvajal Quintero Sandra Ximena
Published: May 2023

Electricity access in rural areas is a critical challenge for global electrification. Most countries have focused on increasing electricity coverage without assessing the long-term sustainability of such solutions. To achieve sustainability in rural electrification solutions, it is necessary to consider five dimensions: technical, environmental, economic, social, and institutional. This paper reviews the state of rural electrification worldwide and proposes a dynamic tariff scheme that increases the technical and economic conditions of implemented solutions over an extended period. The proposed time-of-use (TOU) pricing methodology aims to flatten the system demand curve and utilize on-site renewable energy potentials. For the methodology’s evaluation, we analyzed a case study focused on electrification in isolated areas of Colombia, conducting a sensitivity analysis of user-behavior to the proposed tariff scheme using the concept of price elasticity of demand. We also evaluated the effect of the achieved demand curve flattening on the system frequency. The identified benefits highlight that an accurate pricing scheme can reduce the variation range in the system frequency. Furthermore, the evaluation results show that the implementation of the proposed tariff scheme has the potential to significantly flatten the demand curve and encourage the connection of non-conventional renewable sources to improve network conditions.
Spatial Analysis of Tertiary Industry Evolution Based on Gravity Model: A Case of Central Plains Economic Region in China
Authors: Li Qian , Wang Jianping , Gao Guohong , Lv Yingying , Li Zhiyu , Chen Guanglan
Published: May 2023

The service and knowledge economy has emerged as the primary drivers of national economic growth, with the tertiary industry leading the way. To promote growth in the central plain region and achieve structural convergence of industries for sustainable, rapid, and healthy economic development, conducting a spatial structural analysis of the evolution of the industrial structure is essential. However, traditional statistical analysis needs to keep pace with the dynamic and non-linear relationships within and outside industries. In this paper, we propose a novel spatial analysis method based on the gravity model to examine the tertiary industry in China’s central plains economic region (CPER). By analyzing statistical data from 2018 to 2022, our results reveal that the economic linkage intensity (F-value) of the tertiary industry in CPER has increased over the years. Specifically, F-values for the high-end, general, and other service industries are 2815, 9882, and 14,826, respectively. The network density (D-value) indicates a single-core structure, with D-values of 0.71, 0.74, and 0.82 for high-end, general, and other service industries. Our findings suggest that the economic linkage network of the tertiary industry in CPER has matured, with solid industry integration and convergence of the regional financial industry structure. There is a high correlation (p < 0.01) among various network structures in the service industry, and a stable and cohesive subgroup with multicentre characteristics is beginning to emerge. These findings provide valuable insights for the development of the tertiary industry in CPER during the later stage of the 14th Five-Year Plan in China.
Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model
Authors: Alzain Elham , Al-Otaibi Shaha , Aldhyani Theyazn H. H. , Alshebami Ali Saleh , Almaiah Mohammed Amin , Jadhav Mukti E.
Published: May 2023

Photovoltaic (PV) power production systems throughout the world struggle with inconsistency in the distribution of PV generation. Accurate PV power forecasting is essential for grid-connected PV systems in case the surrounding environmental conditions experience unfavourable shifts. PV power production forecasting requires the consideration of critical elements, such as grid energy management, grid operation and scheduling. In the present investigation, multilayer perceptron and adaptive network-based fuzzy inference system models were used to forecast PV power production. The developed forecasting model was educated using historical data from October 2011 to February 2022. The outputs of the proposed model were checked for accuracy and compared by considering the dataset from a PV power-producing station. Three different error measurements were used—mean square error, root-mean-square error, and Pearson’s correlation coefficient—to determine the robustness of the suggested method. The suggested method was found to provide better results than the most recent and cutting-edge models. The MLP and ANFIS models achieved the highest performance (R = 100%), with less prediction errors (MSE = 1.1116 × 10−8) and (MSE = 1.3521 × 10−8) with respect to MLP and ANFIS models. The study also predicts future PV power generation values using previously collected PV power production data. The ultimate goal of this work is to produce a model predictive control technique to achieve a balance between the supply and demand of energy.
Global Value Chains, Digital Economy, and Upgrading of China’s Manufacturing Industry
Authors: Li Wenqi , Li Qi , Chen Ming , Su Yutong , Zhu Jianhua
Published: May 2023

This study utilized panel data from 31 provinces in China from 2006 to 2020 to investigate the impact of the digital economy on the upgrading of the manufacturing industry’s global value chain. Two types of spatial weighting matrices were used to construct SAR, SEM, SAC, and SDM models. The results revealed that technological innovation plays a direct mediating role in the upgrading of the manufacturing industry, and the global value chain has a positive regulatory effect on the relationship between the digital economy and the manufacturing industry’s upgrading. Under the economic distance spatial weighting matrix, the spatial spillover effect of the digital economy on the manufacturing industry’s global value chain is not significant, whereas, under the geographic distance spatial weighting matrix, the digital economy has a positive and significant spatial spillover effect. The SDM model showed the best explanatory effect. This implies that geographic spatial dependence has a significant impact on the upgrading of the manufacturing industry’s industrial structure, and it is positively influenced by nearby provinces. Understanding the impact mechanism and spatial spillover effects of the digital economy on the manufacturing industry’s upgrading can help promote efficient, fair, and balanced regional development. It can also aid in constructing a new domestic and international “dual circulation” development pattern that evolves with the global manufacturing value chain, sharing the dividends of the digital economy’s impact on the global value chain’s development.
Organizing Emotions throughout Disenfranchised Grief: Virtual Support Group Sensemaking through Emotion Discourses
Authors: Coker Michael Cody , Riforgiate Sarah E.
Published: May 2023

Online support groups provide members a space to express emotions and gain emotional support, contributing to individual and organizational sustainability. Communication in these virtual spaces organizes and is simultaneously organized by member interactions and emotion expressions. To better understand how communication contributes to emotion, organizing, and meaning making, this study draws on Weick’s communication sensemaking theory and uses qualitative netnographic methods to analyze interactions in an online pet loss support group. Following pet loss, many American caregivers share their grief over the loss of a pet through online support groups, which help bereaved individuals acquire support, make sense of their experiences, and support similar others. Importantly, existing research indicates that virtual support groups provide members a safe space to engage their emotions. However, competing communication discourses uphold restrictive emotion rules across organized settings and can challenge how individuals perform their emotions. This study uses qualitative netnographic data gathered over 5 months from 106 participants, to better understand how virtual support group members used communication to understand, resist, reify, and reimagine emotions. We found that organizational members grappled with their grief at work and at home, often regulating their negative emotions in pursuit of advancing their workplace productivity and deferring to others’ expectations. Furthermore, although grieving members used communication processes to legitimize the virtual support group as an organizational safe space for displaying authentic emotions, site members controlled their emotions and reinforced managerialist discourses in their communication, demonstrating that emotion discourses are far-reaching and can contribute to or distract from sustainable healing practices. We offer implications regarding how online experiences complicate emotion rules, how safe spaces reinforce professionalism and managerialism, and how organized spaces can promote sustainable practices to support members.
Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode
Authors: Omotoso Hammed Olabisi , Al-Shamma’a Abdullrahman A. , Alharbi Mohammed , Farh Hassan M. Hussein , Alkuhayli Abdulaziz , Abdurraqeeb Akram M. , Alsaif Faisal , Bawah Umar , Addoweesh Khaled E.
Published: May 2023

This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage at the point of common coupling (PCC). The aim is to ensure seamless transitions between different modes of operation and maintain the load demand while distributing it among the available sources. To validate the performance of the proposed approach, the paper compares it to a traditional proportional integral (PI) controller for controlling the dynamic response of the frequency and voltage at the PCC. The simulation experiments conducted in MATLAB/Simulink show the effectiveness of the regression tree machine learning algorithm over the PI controller, in terms of the step response and harmonic distortion of the system. The results of the study demonstrate that the proposed approach offers an improved stability and efficiency for the system, making it a promising solution for microgrid operations.
Hyperchaotic Maps and the Single Neuron Model: A Novel Framework for Chaos-Based Image Encryption
Authors: Alexan Wassim , Chen Yen-Lin , Por Lip Yee , Gabr Mohamed
Published: May 2023

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. The main advantages of this work are (1) vast key space and (2) high encryption efficiency. The superior key space of 22551 is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of using the SNM as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite. Average achieved values for the metrics include MSE of 9626, PSNR of 8.3 dB, MAE of 80.99, entropy of 7.999, NPCR of 99.6% and UACI of 31.49%.
Extreme Sample Imbalance Classification Model Based on Sample Skewness Self-Adaptation
Authors: Xue Jie , Ma Jinwei
Published: May 2023

This paper aims to solve the asymmetric problem of sample classification recognition in extreme class imbalance. Inspired by Krawczyk (2016)’s improvement direction of extreme sample imbalance classification, this paper adopts the AdaBoost model framework to optimize the sample weight update function in each iteration. This weight update not only takes into account the sampling weights of misclassified samples, but also pays more attention to the classification effect of misclassified minority sample classes. Thus, it makes the model more adaptable to imbalanced sample class distribution and the situation of extreme imbalance and make the weight adjustment in hard classification samples more adaptive as well as to generate a symmetry between the minority and majority samples in the imbalanced datasets by adjusting class distribution of the datasets. Based on this, the imbalance boosting model, the Imbalance AdaBoost (ImAdaBoost) model is constructed. In the experimental design stage, ImAdaBoost model is compared with the original model and the mainstream imbalance classification model based on imbalanced datasets with different ratio, including extreme imbalanced dataset. The results show that the ImAdaBoost model has good minority class recognition recall ability in the weakly extreme and general class imbalance sets. In addition, the average recall rate of minority class of the mainstream imbalance classification models is 7% lower than that of ImAdaBoost model in the weakly extreme imbalance set. The ImAdaBoost model ensures that the recall rate of the minority class is at the middle level of the comparison model, and the F1-score comprehensive index performs well, demonstrating the strong stability of the minority class classification in extreme imbalanced dataset.
Mercury Biogeochemical Cycle in Yanwuping Hg Mine and Source Apportionment by Hg Isotopes
Authors: Jin Xingang , Yan Junyao , Ali Muhammad Ubaid , Li Qiuhua , Li Ping
Published: May 2023

Although mercury (Hg) mining activities in the Wanshan area have ceased, mine wastes remain the primary source of Hg pollution in the local environment. To prevent and control Hg pollution, it is crucial to estimate the contribution of Hg contamination from mine wastes. This study aimed to investigate Hg pollution in the mine wastes, river water, air, and paddy fields around the Yanwuping Mine and to quantify the pollution sources using the Hg isotopes approach. The Hg contamination at the study site was still severe, and the total Hg concentrations in the mine wastes ranged from 1.60 to 358 mg/kg. The binary mixing model showed that, concerning the relative contributions of the mine wastes to the river water, dissolved Hg and particulate Hg were 48.6% and 90.5%, respectively. The mine wastes directly contributed 89.3% to the river water Hg contamination, which was the main Hg pollution source in the surface water. The ternary mixing model showed that the contribution was highest from the river water to paddy soil and that the mean contribution was 46.3%. In addition to mine wastes, paddy soil is also impacted by domestic sources, with a boundary of 5.5 km to the river source. This study demonstrated that Hg isotopes can be used as an effective tool for tracing environmental Hg contamination in typical Hg-polluted areas.
Chemical Survey and Risk Assessment of 56 Pesticides in the Sado River Estuary (Iberian Peninsula, Portugal)
Authors: Rocha Maria João , Rocha Eduardo
Published: May 2023

The Sado basin (~8000 km2) is an area where intensive agriculture occurs. However, this region still has few data about the water levels of priority pesticides such as fungicides, herbicides, and insecticides. Therefore, water samples were collected every two months at nine sites along the Sado River Estuary and analyzed by GC-MS/MS to determine the influx of pesticides in that ecosystem. More than 87% of the pesticides were quantified, and 42% and 72% were above the maximum totals established by the European Directives 98/83/EC and 2013/39/EU, respectively. Fungicides (91%), herbicides (87%), and insecticides (85%) attained average annual amounts of ≈3.2 µg/L, ≈1.0 µg/L, and ≈12.8 µg/L, respectively. A mathematical approach was used to evaluate the hazard of the pesticide mixture at the maximum concentrations found in this area. The assessment identified invertebrates as the most at-risk trophic level and identified two chemicals (chlorpyriphos and cyfluthrin) as the primary culprits. This assumption was supported by acute in vivo assays using Daphnia magna. These observations, and the high concentrations of phosphates, indicate that the status of the Sado waters poses environmental and potential human health risks.
Functional Characterization of a New Degradation Peptide BmTX4-P1 from Traditional Chinese Scorpion Medicinal Material
Authors: Qin Chenhu , Yang Xuhua , Zhang Yuanyuan , Deng Gang , Huang Xin , Zuo Zheng , Sun Fang , Cao Zhijian , Chen Zongyun , Wu Yingliang
Published: May 2023

Thermally processed Buthus martensii Karsch scorpion is an important traditional Chinese medical material that has been widely used to treat various diseases in China for over one thousand years. Our recent work showed that thermally processed Buthus martensii Karsch scorpions contain many degraded peptides; however, the pharmacological activities of these peptides remain to be studied. Here, a new degraded peptide, BmTX4-P1, was identified from processed Buthus martensii Karsch scorpions. Compared with the venom-derived wild-type toxin peptide BmTX4, BmTX4-P1 missed some amino acids at the N-terminal and C-terminal regions, while containing six conserved cysteine residues, which could be used to form disulfide bond-stabilized α-helical and β-sheet motifs. Two methods (chemical synthesis and recombinant expression) were used to obtain the BmTX4-P1 peptide, named sBmTX4-P1 and rBmTX4-P1. Electrophysiological experimental results showed that sBmTX4-P1 and rBmTX4-P1 exhibited similar activities to inhibit the currents of hKv1.2 and hKv1.3 channels. In addition, the experimental electrophysiological results of recombinant mutant peptides of BmTX4-P1 indicated that the two residues of BmTX4-P1 (Lys22 and Tyr31) were the key residues for its potassium channel inhibitory activity. In addition to identifying a new degraded peptide, BmTX4-P1, from traditional Chinese scorpion medicinal material with high inhibitory activities against the hKv1.2 and hKv1.3 channels, this study also provided a useful method to obtain the detailed degraded peptides from processed Buthus martensii Karsch scorpions. Thus, the study laid a solid foundation for further research on the medicinal function of these degraded peptides.
The Hawking Radiation in Massive Gravity: Path Integral and the Bogoliubov Method
Authors: Arraut Ivan , Segovia Carlos , Rosado Wilson
Published: May 2023

We prove the consistency of the different approaches for deriving the black hole radiation for the spherically symmetric case inside the theory of Massive Gravity. By comparing the results obtained by using the Bogoliubov transformations with those obtained by using the Path Integral formulation, we find that in both cases, the presence of the extra-degrees of freedom creates the effect of extra-particles creation due to the distortions on the definitions of time defined by the different observers at large scales. This, however, does not mean extra-particle creation at the horizon level. Instead, the apparent additional particles perceived at large scales emerge from how distant observers define their time coordinate, which is distorted due to the existence of extra-degrees of freedom.
Real-World Safety and Efficacy Clinical Data of an Improved Allergen-Specific Immunotherapy Product for the Treatment of Bee Venom Allergy
Authors: Gonzalez Guzman Luis Alfredo , García Robaina Jose Carlos , Barrios Recio Javier , Escudero Arias Elena , Liñares Mata Tania , Cervera Aznar Raquel , De La Roca Pinzón Federico , Miguel Polo Lissette del Carmen , Arenas Villarroel Luis , López Couso Verónica P. , Alcover Diaz Javier , Rodriguez Gil David , Pelaez Ricardo Palacios , Carballada Gonzalez Francisco Javier
Published: May 2023

The aim of this study is to explore the safety and efficacy of bee venom immunotherapy without HSA, in real-life patients. Methods: This is an observational retrospective study developed in seven hospitals in Spain, where patients treated with this immunotherapy were included. They gathered the protocol used to initiate the immunotherapy, adverse reactions, field re-stings, and the patient clinical data (clinical history, biomarkers, and skin prick test). Results: A total of 108 patients were included. In total, 4 protocols were used (5 weeks reaching 200 μg, and 4, 3, and 2 weeks reaching 100 μg). An incidence of systemic adverse reactions for each 100 injections of 1.5, 1.7, 0, and 0.58, respectively, was found. The demographic data showed not to directly affect the appearance of adverse reactions, except for those having a grade 2 systemic reaction with immunotherapy previously had a grade 4 systemic reaction; the IgE to Apis mellifera was 3 times higher in patients with systemic reactions of grade 1 than in the general group, and other specific IgEs were lower in those with systemic reactions. Most of the patients recognized Api m 1 followed by Api m 10. In the sample, 32% experienced spontaneous re-stings, without presenting systemic reactions, after a year of treatment.