bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Power Generation Stability in Oscillating-Water-Column Wave Energy Converters through Deep-Learning-Based Time Delay Compensation
Authors: Roh Chan
Published: June 2023

Oscillating-water-column wave energy converters (OWC-WECs) are gaining attention for their high energy potential and environmental friendliness. However, their irregular input energy characteristics pose challenges to achieving stable power generation, particularly due to high peak power compared to average power. This study focuses on stable rating control to enable continuous power generation in the presence of irregular wave energy. It is difficult to precisely configure the existing rated power controllers due to physical time delays; this impacts system stability and utilization. To address this, we propose a rated power controller that compensates for system time delays using a deep learning algorithm. By predicting the valve control angle in advance and analyzing the input data for angle estimation, we successfully compensate for the physical time delay. The performance of the proposed rated power controller, incorporating the deep learning algorithm, is evaluated by analyzing the algorithm’s error rate. The results demonstrate that the proposed method improves power generation under various wave conditions by compensating for the unavoidable time delay of OWC-WECs, leading to a significant increase in annual power generation. In conclusion, the proposed method achieves approximately 31% higher annual power generation compared to the time delay controller.
Pd-M (M = Ni, Co) Bimetallic Catalysts with Tunable Composition for Highly Efficient Electrochemical Formic Acid Oxidation
Authors: Ding Qingwei , Luo Qing , Lin Liang , Yang Tianlun , Fu Xingping , Wang Laisen , Lei Caixia
Published: June 2023

Bimetallic Pd-based catalysts for formic acid oxidation (FAO) are one of the most promising anode materials for the next generation of direct formic acid fuel cells (DFAFC). It is imperative to develop a simple strategy for preparing efficient, stable, and clean nanoparticle catalysts. Herein, we prepared a series of Pd, PdNi, and PdCo nanoparticle catalysts using the nanoparticle beam composite deposition system, which revealed good catalytic activity and stability in the process of FAO. The incorporation of Ni or Co prevents the adsorption of active intermediates and the accumulation of toxic intermediates in the process of FAO. Therefore, more Pd active centers can be used to decompose formic acid directly by dehydrogenation. The results indicate that PdNi-2 (Pd0.9Ni0.1) and PdCo-3 (Pd0.89Co0.11) catalysts exhibit the optimal catalytic performance, with the mass activity of 1491.5 A g−1Pd and 1401.7 A g−1Pd, respectively, which is 2.1 and 2 times that of the pure Pd sample. By optimizing the rate of Pd to transition metal M (Ni, Co), a high-performance Pd-based catalyst was obtained through their synergistic effect, which provides a new approach for designing efficient anode catalysts for DFAFCs.
Is Regulatory Approval without Autonomous Operation for Natural Extract Manufacturing under Economic Competitiveness and Climate-Neutrality Demands Still Permissible?
Authors: Uhl Alexander , Knierim Larissa , Tegtmeier Martin , Schmidt Axel , Strube Jochen
Published: June 2023

Natural extracts are broadly utilized as remedies, nutrition additives, cosmetics or flavors as well as natural pesticides, fungicides or herbicides. Green manufacturing technologies are of added market value and are sustainable towards the climate neutrality politically demanded for 2045. The concept of digital twins involves experimentally distinct validated process models combined with process analytical technology that is to be adapted to the existing operations. This is a key technology for the autonomous operations in industry 4.0. This paper exemplifies this approach and evaluates the results of the application and implementation efforts of regulated industries. A conductivity sensor for the measurement of the dry residue content and/or Fourier-transformed infrared spectroscopy for marker/lead or reference substance concentration determination are the most feasible and straight forward solutions. Different process control concepts from simple PID controllers (proportional, integral and differential) to advanced process control using digital twin models are evaluated and discussed in terms of industrialization efforts and benefits. The global warming potential CO2 equivalent per kg of natural product could be decreased by a factor of 5–10 as well as the cost of goods, which makes the pay-out time for the industrialization investment less than 1 year and the approach highly competitive. The success rate of the extraction process under regulatory constraints can be raised to 100%, reducing waste, overall solvent consumption, personnel efforts and energy requirements to a minimum.
Phycoremediation of Synthetic Dyes Laden Textile Wastewater and Recovery of Bio-Based Pigments from Residual Biomass: An Approach towards Sustainable Wastewater Management
Authors: Nair Vivek Kumar , Selvaraju Koushalya , Samuchiwal Saurabh , Naaz Farah , Malik Anushree , Ghosh Pooja
Published: June 2023

The textile industry is a growing sector worldwide and has immense opportunity in terms of providing employment and boosting a nation’s economy. However, there exist severe environmental risks associated with textile effluents that impact the surrounding ecosystem. This review offers an approach for sustainable water management using phycoremediation to treat dye-laden wastewater and recover bio-based pigments from the residual biomass. Microalgae such as Chlorella, Scenedesmus, Phormidium, and macroalgae like Sargassum, Enteromorpha, and Codium has been extensively used in several phycoremediation-based studies, and their residual biomass could be a potent source for extraction of bio-based pigments. This review also recommends studies involving the algal-bacterial consortia approach for treating dye-laden wastewater as an alternative to conventional, biobased methods. The outcome of this study will provide policymakers and researchers with new insight to manage water and wastewater resources sustainably. Furthermore, this review also enhances our understanding of nature-based decontamination approaches for treating dye-laden wastewater through algal-based technologies.
A New Form of Taoist Theurgy in the Qing Dynasty: Xizhu Doufa in the Taoist–Tantric Fusion Style
Authors: Wu Yuhao
Published: June 2023

The Longmen Xizhu Xinzong 龍門西竺心宗 was a Taoist sect that was active during the Qing Dynasty. The sect reportedly originated in India and has long been renowned for its Xizhu Doufa 西竺斗法. However, due to its secrecy and lack of literature, its true form remains a mystery. Examining the self-reported history of the Longmen Xizhu Xinzong, it can confidently be stated that Xizhu Doufa was often used in conjunction with Dharani and had deep roots in Tantric Buddhism. During the Sui and Tang Dynasties, Tantric Buddhism gained popularity in China and evolved into Tang Tantrism (Ch. Tangmi 唐密). There is a large amount of Dipper Method (Ch. Doufa 斗法)-related content in Tang Tantrism, which is a variant of China’s original Dipper Method observed in India. After being passed back to China, it was named “Western Transmission”. Many of the existing documents on the Dipper Method from the Ming and Qing Dynasties originated from Tang Tantrism. In terms of belief, they reflect the combination of Doumu 斗姆 and Marici; in terms of methods, they incorporate multiple elements, including the Taoist Thunder Method (Ch. Leifa 雷法) and Marici mantra. Overall, the ideas, lineage of transmission, and other aspects recorded in these documents are extremely similar to those emphasized by the Longmen Xizhu Xinzong, providing evidence for the origin of Xizhu Doufa. This evidence also provides a new reference for the direction in which Tang Tantrism developed after the Huichang Persecution of Buddhism 會昌毀佛.
Improvement in Intestinal-Failure-Associated Liver Disease by Using Parenteral Fish Oil as Monotherapy: Case-Based Review of the Literature
Authors: Fessatou Smaragdi , Kourti Afroditi , Zavras Nikolaos , Zouganeli Sofia , Kouna Niki , Stefos Eustathios , Kanavaki Ino
Published: June 2023

Intestinal-failure-associated liver disease (IFALD) is a common complication of prolonged parenteral nutrition (PN). Risk factors for IFALD include clinical features, as well as medical interventions, and its management was initially based on the decrease or interruption of parenteral nutrition while increasing enteral nutrition. However, the tolerance of full enteral nutrition in children with intestinal failure may require prolonged intestinal rehabilitation over a period of years. As a consequence, infants unable to wean from PN are prone to develop end-stage liver disease. We describe the case of an infant receiving long-term PN who was diagnosed with IFALD wherein we were able to reverse IFALD by switching lipid emulsions to fish oil monotherapy. A systemic review of case reports and case series on reversing IFALD using fish oil lipid emulsion follows the case description.
Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region
Authors: Kharakhashyan Artem , Maltseva Olga
Published: June 2023

Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both neural networks based on LSTM and GRU, and temporal convolutional networks. The set of methods included 10 architectures: TCN, modified LSTM-/GRU-based deep networks, including bidirectional ones, and BiTCN. The comparison of TEC forecasting accuracy is performed between individual architectures, as well as their bidirectional modifications, by means of MAE, MAPE, and RMSE estimates. The F10.7, 10 Kp, Np, Vsw, and Dst indices are used as predictors. The results are presented for the reference station Juliusruh, three stations along the meridian 30°E (Murmansk, Moscow, and Nicosia), and three years of different levels of solar activity (2015, 2020, and 2022). The MAE and RMSE values depend on the station latitude, following the solar activity. The conventional LSTM and GRU networks with the proposed modifications and the TCN provide results at the same level of accuracy. The use of bidirectional neural networks significantly improves forecast accuracy for all the architectures and all stations. The best results are provided by the BiTCN architecture, with MAE values less than 0.3 TECU, RMSE less than 0.6 TECU, and MAPE less than 5%.
Remote Sensing Data for Digital Soil Mapping in French Research—A Review
Authors: Richer-de-Forges Anne C. , Chen Qianqian , Baghdadi Nicolas , Chen Songchao , Gomez Cécile , Jacquemoud Stéphane , Martelet Guillaume , Mulder Vera L. , Urbina-Salazar Diego , Vaudour Emmanuelle , Weiss Marie , Wigneron Jean-Pierre , Arrouays Dominique
Published: June 2023

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.
Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning
Authors: Li Yun , He Xinqi , Lu Zhenkun , Jing Peiguang , Su Yishan
Published: June 2023

Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms.
A New Approach Combining a Multilayer Radiative Transfer Model with an Individual-Based Forest Model: Application to Boreal Forests in Finland
Authors: Henniger Hans , Bohn Friedrich J. , Schmidt Kim , Huth Andreas
Published: June 2023

To understand forest dynamics under today’s changing environmental conditions, it is important to analyze the state of forests at large scales. Forest inventories are not available for all regions, so it is important to use other additional methods, e.g., remote sensing observations. Increasingly, remotely sensed data based on optical instruments and airborne LIDAR are becoming widely available for forests. There is great potential in analyzing these measurements and gaining an understanding of forest states. In this work, we combine the new-generation radiative transfer model mScope with the individual-based forest model FORMIND to generate reflectance spectra for forests. Combining the two models allows us to account for species diversity at different height layers in the forest. We compare the generated reflectances for forest stands in Finland, in the region of North Karelia, with Sentinel-2 measurements. We investigate which level of forest representation gives the best results and explore the influence of different calculation methods of mean leaf parameters. For the majority of the forest stands, we generated good reflectances with all levels of forest representation compared to the measured reflectance. Good correlations were also found for the vegetation indices (especially NDVI with R2=0.62). This work provides a forward modeling approach for relating forest reflectance to forest characteristics. With this tool, it is possible to analyze a large set of forest stands with corresponding reflectances. This opens up the possibility to understand how reflectance is related to succession and different forest conditions.
Altimetry Method for an Interferometric Radar Altimeter Based on a Phase Quality Evaluation
Authors: Ha Jong-Soo , Hong Sung-Yong
Published: June 2023

A radar altimeter (RA) is useful to improve autonomous functions such as landing guidance or navigation control of an aircraft. To ensure more precise and safer flights by aircraft, an interferometric RA (IRA) capable of measuring the angle of a target is required. However, the phase-comparison monopulse (PCM) technique used in IRAs has a problem in that an angular ambiguity arises with respect to a target with multiple reflection points, such as terrain. In this paper, we propose an altimetry method for IRAs that reduces the angular ambiguity by evaluating the quality of the phase. The altimetry method as introduced here is sequentially described based on synthetic aperture radar, a delay/Doppler radar altimeter, and PCM techniques. Finally, a phase quality evaluation method is proposed for use in the azimuth estimation process. Aircraft captive flight test results are presented and analyzed, and the validity of the proposed method is examined.
UFO-Net: A Linear Attention-Based Network for Point Cloud Classification
Authors: He Sheng , Guo Peiyao , Tang Zeyu , Guo Dongxin , Wan Lingyu , Yao Huilu
Published: June 2023

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.
Paper-Based Electrodes Decorated with Silver and Zinc Oxide Nanocomposite for Electro-Chemical Sensing of Methamphetamine
Authors: Anzar Nigar , Suleman Shariq , Bano Husnara , Parvez Suhel , Khanuja Manika , Pilloton Roberto , Narang Jagriti
Published: June 2023

We present the development of an electrochemical paper-based analytical device (ePAD) for the detection of methamphetamine. Methamphetamine is a stimulant that young people use as an addictive narcotic, and it must be detected quickly since it may be hazardous. The suggested ePAD has the advantages of being simple, affordable, and recyclable. This ePAD was developed by immobilizing a methamphetamine-binding aptamer onto Ag-ZnO nanocomposite electrodes. The Ag-ZnO nanocomposites were synthesized via a chemical method and were further characterized via scanning electron microscopy, Fourier transform infrared spectroscopy, and UV-vis spectrometry in terms of their size, shape, and colloidal activity. The developed sensor showed a limit of detection of about 0.1 μg/mL, with an optimum response time of about 25 s, and its extensive linear range was between 0.01 and 6 μg/mL. The application of the sensor was recognized by spiking different beverages with methamphetamine. The developed sensor has a shelf life of about 30 days. This cost-effective and portable platform might prove to be highly successful in forensic diagnostic applications and will benefit those who cannot afford expensive medical tests.
Real-Time Automated Solubility Screening Method Using Deep Neural Networks with Handcrafted Features
Authors: Jeon Minwoo , Yu Geunhyeok , Choi Hyundo , Kim Gahee , Hwang Hyoseok
Published: June 2023

Solubility measurements are essential in various research and industrial fields. With the automation of processes, the importance of automatic and real-time solubility measurements has increased. Although end-to-end learning methods are commonly used for classification tasks, the use of handcrafted features is still important for specific tasks with the limited labeled images of solutions used in industrial settings. In this study, we propose a method that uses computer vision algorithms to extract nine handcrafted features from images and train a DNN-based classifier to automatically classify solutions based on their dissolution states. To validate the proposed method, a dataset was constructed using various solution images ranging from undissolved solutes in the form of fine particles to those completely covering the solution. Using the proposed method, the solubility status can be automatically screened in real time by using a display and camera on a tablet or mobile phone. Therefore, by combining an automatic solubility changing system with the proposed method, a fully automated process could be achieved without human intervention.
Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks
Authors: Saad Mohammed Ayad , Jaafar Rosmina , Chellappan Kalaivani
Published: June 2023

Data gathering in wireless sensor networks (WSNs) is vital for deploying and enabling WSNs with the Internet of Things (IoTs). In various applications, the network is deployed in a large-scale area, which affects the efficiency of the data collection, and the network is subject to multiple attacks that impact the reliability of the collected data. Hence, data collection should consider trust in sources and routing nodes. This makes trust an additional optimization objective of the data gathering in addition to energy consumption, traveling time, and cost. Joint optimization of the goals requires conducting multiobjective optimization. This article proposes a modified social class multiobjective particle swarm optimization (SC-MOPSO) method. The modified SC-MOPSO method is featured by application-dependent operators named interclass operators. In addition, it includes solution generation, adding and deleting rendezvous points, and moving to the upper and lower class. Considering that SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed one of the multicriteria decision-making (MCDM) methods, i.e., simple additive sum (SAW), for selecting one of the solutions from the Pareto front. The results show that both SC-MOPSO and SAW are superior in terms of domination. The set coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive performance with NSGA-III.
MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network
Authors: Zhang Feiqing , Yin Zhenyu , Xu Fulong , Li Yue , Xu Guangyuan
Published: June 2023

Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises. To address the above problems, this paper proposes an improved dilated-convolutional-neural network-based bearing fault diagnosis method in noisy environments named MAB-DrNet. First, a basic model called the dilated residual network (DrNet) was designed based on the residual block to enlarge the model’s perceptual field to better capture the features from bearing fault signals. Then, a max-average block (MAB) module was designed to improve the feature extraction capability of the model. In addition, the global residual block (GRB) module was introduced into MAB-DrNet to further improve the performance of the proposed model, enabling the model to better handle the global information of the input data and improve the classification accuracy of the model in noisy environments. Finally, the proposed method was tested on the CWRU dataset, and the results showed that the proposed method had good noise immunity; the accuracy was 95.57% when adding Gaussian white noises with a signal-to-noise ratio of −6 dB. The proposed method was also compared with existing advanced methods to further prove its high accuracy.
Research on Crossing-Pipe Support Structure Defect Detection of EMAT-Excited CSH Wave
Authors: Hu Yang , Zhou Jinjie , Yue Wenying
Published: June 2023

A circumferential shear horizontal (CSH) guide wave-detection method using a periodic permanent magnet electromagnetic acoustic transducer (PPM EMAT) was proposed to solve the defect detection located at the inside of the pipe welded by supporting structures. Firstly, a low-frequency CSH0 mode was selected to establish a three-dimensional equivalent model for the defect detection to cross the pipe support, and the ability of the CSH0 guided wave to propagate through the support and weld structure was analyzed. Then, an experiment was used for the further exploration of the influence of different sizes and types of defects on detection after using the support, as well as the ability of detection mechanism to cross different pipe structures. The results show that both the experiment and the simulation received a good detection signal at 3 mm crack defects, which proves that the method can detect the defects by crossing the welded supporting structure. At the same time, the support structure shows a greater impact on the detection of small defects than the welded structure. The research in this paper can provide ideas for guide wave detection across the support structure in the future.
Sensitivity Enhancement for Separation-Based Analytical Techniques Utilizing Solid-Phase Enrichment Approaches and Analyte Derivatization for Trace Analysis in Various Matrices
Authors: Farouk Hanan , Ebrahim Hager , Sonbol Heba , Malak Monika , Kamal Maha , Ibrahim Noha , Shawky Ahmed , Zarad Walaa , Emad Ahmed , Emara Samy
Published: June 2023

Despite the fact that strong routine separation methodologies can give reliable specificity and validity at usual working pharmaceutical concentrations, they may fail at very low concentration levels. This poses considerable challenges for researchers investigating product purity and therapeutic drug monitoring. Sensitivity enhancement procedures are thus required to maximize the performance of separation techniques. Solid-phase extraction/solid-phase enrichment (SPE/SPEn) and pre-, post-, and in-column derivatization, as well as the use of sensitive detection devices, are the simplest strategies for improving sensitivity of separation-based analytical techniques. Large-volume injection of samples with online SPE/SPEn coupled with separation techniques increased sensitivity and improved detection as well as quantification limits without affecting peak shape and system performance. Although the primary purpose of derivatization is to improve sensitivity and selectivity, greener derivatization is growing in popularity and should be considered in analytical chemistry. In general, two strategies are essential for accomplishing greener derivatization goals. The first is the search for and use of ecologically acceptable derivatizing reagents, solvents, and reaction conditions. The second is miniaturization and automation of analytical methods. This review discusses significant advances in separation-based analytical techniques, specifically enrichment approaches and detector signal improvement for pharmaceutical quantification in various matrices at very low concentration levels. As a result of improved analytical systems setup in drug assays, the possibility of high-throughput analyses was also highlighted.
No Media, No Voters? The Relationship between News Deserts and Voting Abstention
Authors: Ramos Giovanni , Torre Luísa , Jerónimo Pedro
Published: June 2023

Local journalism has suffered major transformations as traditional business models collapse and habits of news consumption change. A lack of funding and successive economic crises have brought about, on a global scale, the shutdown of many news outlets in smaller territories. These areas are becoming “news deserts”, a phenomenon that has been mapped in Brazil and Portugal. Territories without news could see an uptick in social problems such as disinformation, populism, and democratic crises, especially because of voting abstention. Background: This paper aims to analyze the relationship between news deserts and democracy, focusing on how news deserts correlate with voting abstention rates in Brazil and Portugal. Methods: A literature review was carried out including data from news deserts in both countries. The abstention rates in this analysis concern national elections held in 2022. A correlation analysis using binary logistic regression was deployed comparing municipalities with the highest and the lowest abstention rates. Results: In both countries, it was not possible to assess whether there was a correlation between abstention rates and the existence of news deserts. Conclusions: While the absence of media outlets is not correlated with the mobilization of citizens to vote, other variables may be affecting voters’ abstention behaviors.
A Perspective on Management Myopia: The Impact of Digital Transformation on Carbon Emission Intensity
Authors: Ma Yu , Tao Pan
Published: June 2023

Digitalization has emerged as an indispensable pathway for enterprises aiming to achieve low-carbon development, demanding strategic implementation by managers who play a crucial role in shaping organizational outcomes. This study utilizes text mining and IPCC methods (based on The Intergovernmental Panel on Climate Change standards) to assess the level of digital transformation and enterprise carbon emission intensity among Shanghai and Shenzhen A-share listed companies from 2008 to 2015. This study also investigates the impact of digital transformation on enterprise carbon emission intensity and examines the influence of myopic characteristics among managers along with their underlying mechanisms. The results indicate that: (1) Digital transformation decreases enterprise carbon emission intensity, with robust results supported by instrumental variable test, the Oster test, confounding variable threshold impact test, etc. (2) Heterogeneity analysis demonstrates that digital transformation is particularly effective in reducing enterprise carbon emission intensity for companies located in cities without national carbon trading pilot policies, heavy industrial sectors, and those influenced by peer effects. (3) The study on mechanisms reveals that management myopia poses a barrier to the decarbonization process driven by digitalization. It further explores the moderating effects of green innovation, sustainable investment, and environmental awareness, revealing that management constrained by innovation myopia, investment myopia, and environmental responsibility myopia faces challenges in promoting decarbonization. By examining the internal aspects of management myopia, we provide valuable insights and recommendations for enterprises seeking to achieve decarbonization through digital transformation.
Interconnection between the Dynamic of Growing Renewable Energy Production and the Level of CO2 Emissions: A Multistage Approach for Modeling
Authors: Skowron Łukasz , Chygryn Olena , Gąsior Marcin , Koibichuk Vitaliia , Lyeonov Serhiy , Drozd Serhii , Dluhopolskyi Oleksandr
Published: June 2023

The global reduction of carbon dioxide emissions is one of the critical priorities for implementing the Sustainable Development Goals by 2030 and the Paris Agreement 2015. Therefore, it stimulates and increases the ability of countries to implement green imperatives in policies to force the anthropogenic environment, reduce use of fossil fuels, and simultaneously develop alternative energy. Thus, it is crucial to understand the impact of renewable energy development on the dynamic of CO2 pollution. Countries can increase or decrease the development of renewable energy depending on the effectiveness of its impact on the level of CO2 pollution. This paper aims to analyze the influence of the growth dynamics of renewable energy production in countries on CO2 emissions. The article uses Ward’s method to test the research hypothesis. Empirical results allowed us to conclude the interdependence of renewable energy production and CO2 emissions. The results indicate a strong relationship between the level of renewable energy production and carbon emissions in countries. For the global development of renewable energy technologies, governments must understand their impact on changing the scale of environmental pollution and expand the awareness of state leadership, the business sector, and society.
An Elementary Proof That Everett’s Quantum Multiverse Is Nonlocal: Bell-Locality and Branch-Symmetry in the Many-Worlds Interpretation
Authors: Drezet Aurélien
Published: June 2023

Everett’s many-worlds or multiverse theory is an attempt to find an alternative to the standard Copenhagen interpretation of quantum mechanics. Everett’s theory is often claimed to be local in the Bell sense. Here, we show that this is not the case and debunk the contradictions by analyzing in detail the Greenberger–Horne–Zeilinger (GHZ) nonlocality theorem. We discuss and compare different notions of locality often mixed in the Everettian literature and try to explain the nature of the confusion. We conclude with a discussion of probability and statistics in the many-worlds theory and stress that the strong symmetry existing between branches in the theory prohibits the definition of probability and that the theory cannot recover statistics. The only way out from this contradiction is to modify the theory by adding hidden variables à la Bohm and, as a consequence, the new theory is explicitly Bell-nonlocal.
Evaluation of Machine Learning Algorithms in Network-Based Intrusion Detection Using Progressive Dataset
Authors: Chua Tuan-Hong , Salam Iftekhar
Published: June 2023

Cybersecurity has become one of the focuses of organisations. The number of cyberattacks keeps increasing as Internet usage continues to grow. As new types of cyberattacks continue to emerge, researchers focus on developing machine learning (ML)-based intrusion detection systems (IDS) to detect zero-day attacks. They usually remove some or all attack samples from the training dataset and only include them in the testing dataset when evaluating the performance. This method may detect unknown attacks; however, it does not reflect the long-term performance of the IDS as it only shows the changes in the type of attacks. In this work, we focused on evaluating the long-term performance of ML-based IDS. To achieve this goal, we proposed evaluating the ML-based IDS using a dataset created later than the training dataset. The proposed method can better assess the long-term performance as the testing dataset reflects the changes in the attack type and network infrastructure changes over time. We have implemented six of the most popular ML models, including decision tree (DT), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), and deep neural network (DNN). These models are trained and tested with a pair of datasets with symmetrical classes. Our experiments using the CIC-IDS2017 and the CSE-CIC-IDS2018 datasets show that SVM and ANN are most resistant to overfitting. Our experiments also indicate that DT and RF suffer the most from overfitting, although they perform well on the training dataset. On the other hand, our experiments using the LUFlow dataset have shown that all models can perform well when the difference between the training and testing datasets is small.
Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses
Authors: Muhammad Zia , Anwar Zahid , Javed Abdul Rehman , Saleem Bilal , Abbas Sidra , Gadekallu Thippa Reddy
Published: June 2023

There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.
Reemergence of Visceral Leishmaniasis in Henan Province, China
Authors: Yang Chengyun , Li Suhua , Lu Deling , He Zhiquan , Wang Dan , Qian Dan , Liu Ying , Zhou Ruimin , Ji Penghui , Chen Jun-Hu , Zhang Hongwei
Published: June 2023

Visceral leishmaniasis (VL) was widely prevalent in Henan Province in the 1950s. Through active efforts by the government, there were no local cases reported from 1984 to 2015. In 2016, local VL cases reoccurred, and there was an increasing trend of VL cases in Henan Province. To provide a scientific control of VL, an investigation was conducted in Henan Province from 2016 to 2021. The data from VL cases were obtained from the Disease Surveillance Reporting System of the Chinese Center for Disease Control and Prevention. The rK39 immunochromatographic test (ICT) and PCR assay were performed among high-risk residents and all dogs in the patients’ village. ITS1 was amplified, sequenced, and subjected to phylogenetic analyses. A total of 47 VL cases were reported in Henan Province during 2016–2021. Of the cases, 35 were local, and they were distributed in Zhengzhou, Luoyang, and Anyang. The annual average incidence was 0.008/100,000, showing an upward trend year by year (χ2 = 3.987, p = 0.046). Their ages ranged from 7 months to 71 years, with 44.68% (21/47) in the age group of 0–3 years and 46.81% (22/47) in the age group ≥15 years. The cases occurred throughout the year. The high-risk populations were infants and young children (age ≤3), accounting for 51.06% (24/47), followed by farmers at 36.17% (17/47). The ratio of males to females was 2.13:1. The positive rates of rK39 ICT and PCR were 0.35% (4/1130) and 0.21% (1/468) in the residents. The positive rates of rK39 ICT and PCR were 18.79% (440/2342) and 14.92% (139/929) in the dogs. The ITS1 amplification products in the patients and positive dogs were sequenced. The homology between the target sequence and Leishmania infantum was more than 98%. The phylogenetic analysis indicated that the patients and the positive dogs were infected by the same type of Leishmania, which was consistent with the strains in the hilly endemic areas in China. This paper showed that patients and domestic dogs were infected by the same type of L. infantum and that the positive rate in dogs was relatively high in Henan Province. Because the measures of patient treatment and culling of infected dogs were not effective in reducing VL incidence in Henan Province, it is urgent to develop new approaches for the control of VL, such as wearing insecticide-impregnated collars on dogs, treating the positive dogs, spraying insecticide for sandflies control, and improving residents’ self-protection awareness to prevent the further spread of VL in Henan Province.
Morbidity of Returning Travelers Seen in Community Urgent Care Centers throughout Israel
Authors: Itzkowitz Eyal , Alpert Evan A. , Farojeh Abdulhadi Z. , Zimmerman Deena R. , Schwartz Eli , Lachish Tamar
Published: June 2023

Information regarding post-travel morbidity is usually reported via dedicated post-travel clinics and mainly relates to travelers returning from low–middle-income countries (LMIC), however, the spectrum of morbidity seen within the community setting is scarcely reported. This prospective observational study among visitors to 17 community Urgent Care Centers (UCC) was designed to evaluate the reasons for post-travel community clinic visits and to compare travelers returning from LMIC to high-income countries (HIC). All visitors within one-month post-travel to all destinations were included. A total of 1580 post-travel visits were analyzed during 25 months. Travelers to LMICs were younger (mean 36.8 years old vs. 41.4 in the HIC group) and stayed longer periods abroad (30.1 ± 41.2 vs. 10.0 ± 10.6 in the HIC group) but more of them had pre-travel vaccines (35.5% vs. 6.6%). Travel-related morbidity was significantly more common in the LMIC group 58.3% (253/434) vs. 34.1% (391/1146) in the HIC group, (p < 0.001). Acute diarrhea was the leading cause of morbidity after visiting LMIC (28.8%) and was significantly more common than in the HIC (6.6%, p < 0.001). Other common morbidities in the LMIC cohort were respiratory (23.3%), cutaneous (15.8%), and injuries (9.9%). In the HIC group, the common morbidities were respiratory (37.3%), and diarrhea composed only 6.6% of the complaints. Our study group represents a less biased sample of travelers to LMIC as well as HIC, therefore, data from the UCC setting and at the specialized travel clinics complete each other in understanding the true extent of morbidity in travelers.
Spatiotemporal Analysis of Emergency Calls during the COVID-19 Pandemic: Case of the City of Vaughan
Authors: Asgary Ali , Solis Adriano O. , Khan Nawar , Wimaladasa Janithra , Shafiei Sabet Maryam
Published: June 2023

Cities have experienced different realities during the COVID-19 pandemic due to its impacts and public health measures undertaken to respond to and manage the pandemic. These measures revealed significant implications for municipal functions, particularly emergency services. The aim of this study is to examine the spatiotemporal distribution of emergency calls during different stages/periods of the pandemic in the City of Vaughan, Canada, using spatial density and the emerging hotspot analysis. The Vaughan Fire and Rescue Service (VFRS) provided the dataset of all emergency calls responded to within the City of Vaughan for the period of 1 January 2017 to 15 July 2021. The dataset was divided according to 11 periods during the pandemic, each period associated with certain levels of public health restrictions. A spatial analysis was carried out by converting the data into shapefiles using geographic coordinates of each call. Study findings show significant spatiotemporal changes in patterns of emergency calls during the pandemic, particularly during more stringent public health measures such as lockdowns and closures of nonessential businesses. The results could provide useful information for both resource management in emergency services as well as understanding the underlying causes of such patterns.
Correlation between Clinical Characteristics and Antibody Levels in COVID-19 Convalescent Plasma Donor Candidates
Authors: Uzun Günalp , Müller Rebecca , Althaus Karina , Becker Matthias , Marsall Patrick , Junker Daniel , Nowak-Harnau Stefanie , Schneiderhan-Marra Nicole , Klüter Harald , Schrezenmeier Hubert , Bugert Peter , Bakchoul Tamam
Published: June 2023

COVID-19 convalescent plasma (CCP) with high neutralizing antibodies has been suggested in preventing disease progression in COVID-19. In this study, we investigated the relationship between clinical donor characteristics and neutralizing anti-SARS-CoV-2 antibodies in CCP donors. COVID-19 convalescent plasma donors were included into the study. Clinical parameters were recorded and anti-SARS-CoV-2 antibody levels (Spike Trimer, Receptor Binding Domain (RBD), S1, S2 and nucleocapsid protein) as well as ACE2 binding inhibition were measured. An ACE2 binding inhibition < 20% was defined as an inadequate neutralization capacity. Univariate and multivariable logistic regression analysis was used to detect the predictors of inadequate neutralization capacity. Ninety-one CCP donors (56 female; 61%) were analyzed. A robust correlation between all SARS-CoV-2 IgG antibodies and ACE2 binding inhibition, as well as a positive correlation between donor age, body mass index, and a negative correlation between time since symptom onset and antibody levels were found. We identified time since symptom onset, normal body mass index (BMI), and the absence of high fever as independent predictors of inadequate neutralization capacity. Gender, duration of symptoms, and number of symptoms were not associated with SARS-CoV-2 IgG antibody levels or neutralization. Neutralizing capacity was correlated with SARS-CoV-2 IgG antibodies and associated with time since symptom onset, BMI, and fever. These clinical parameters can be easily incorporated into the preselection of CCP donors.