bluuebunny/crossref_metadata_2025 huggingface dataset:
Induced Relaxation Enhances the Cardiorespiratory Dynamics in COVID-19 Survivors
Authors: Sánchez-Solís Alejandra Margarita , Peláez-Hernández Viridiana , Santiago-Fuentes Laura Mercedes , Luna-Rodríguez Guadalupe Lizzbett , Reyes-Lagos José Javier , Orea-Tejeda Arturo
Published: May 2023

Most COVID-19 survivors report experiencing at least one persistent symptom after recovery, including sympathovagal imbalance. Relaxation techniques based on slow-paced breathing have proven to be beneficial for cardiovascular and respiratory dynamics in healthy subjects and patients with various diseases. Therefore, the present study aimed to explore the cardiorespiratory dynamics by linear and nonlinear analysis of photoplethysmographic and respiratory time series on COVID-19 survivors under a psychophysiological assessment that includes slow-paced breathing. We analyzed photoplethysmographic and respiratory signals of 49 COVID-19 survivors to assess breathing rate variability (BRV), pulse rate variability (PRV), and pulse–respiration quotient (PRQ) during a psychophysiological assessment. Additionally, a comorbidity-based analysis was conducted to evaluate group changes. Our results indicate that all BRV indices significantly differed when performing slow-paced breathing. Nonlinear parameters of PRV were more appropriate for identifying changes in breathing patterns than linear indices. Furthermore, the mean and standard deviation of PRQ exhibited a significant increase while sample and fuzzy entropies decreased during diaphragmatic breathing. Thus, our findings suggest that slow-paced breathing may improve the cardiorespiratory dynamics of COVID-19 survivors in the short term by enhancing cardiorespiratory coupling via increased vagal activity.
Exponential Consensus of Multi-Agent Systems under Event-Triggered Impulsive Control with Actuation Delays
Authors: Zhang Jian , Peng Shiguo
Published: May 2023

This paper investigates the exponential consensus problem for a class of nonlinear leader-following multi-agent systems using impulsive control, where impulses are generated by the event-triggered mechanism and are subjected to actuation delays. It is proved that Zeno behavior can be avoided, and by employing the linear matrix inequality technique, some sufficient conditions for realizing exponential consensus of the considered system are derived. Actuation delay is an important factor affecting the consensus of the system, and our results show that increasing the actuation delay can enlarge the lower bound of the triggering interval, while it harms the consensus. To demonstrate the validity of the obtained results, a numerical example is provided.
Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network
Authors: Hao Shufeng , Yao Jikun , Shi Chongyang , Zhou Yu , Xu Shuang , Li Dengao , Cheng Yinghan
Published: May 2023

Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features by using lexicon, n-gram, and pragmatic feature-based models. However, these methods ignore the diverse contextual clues that could provide more evidence of the sarcastic nature of sentences. In this work, we propose a Contextual Sarcasm Detection Model (CSDM) by modeling enhanced semantic representations with user profiling and forum topic information, where context-aware attention and a user-forum fusion network are used to obtain diverse representations from distinct aspects. In particular, we employ a Bi-LSTM encoder with context-aware attention to obtain a refined comment representation by capturing sentence composition information and the corresponding context situations. Then, we employ a user-forum fusion network to obtain the comprehensive context representation by capturing the corresponding sarcastic tendencies of the user and the background knowledge about the comments. Our proposed method achieves values of 0.69, 0.70, and 0.83 in terms of accuracy on the Main balanced, Pol balanced and Pol imbalanced datasets, respectively. The experimental results on a large Reddit corpus, SARC, demonstrate that our proposed method achieves a significant performance improvement over state-of-art textual sarcasm detection methods.
Detection of Respiratory Events during Sleep Based on Fusion Analysis and Entropy Features of Cardiopulmonary Signals
Authors: Yan Xinlei , Liu Juan , Wang Lin , Wang Shaochang , Zhang Senlin , Xin Yi
Published: May 2023

Sleep apnea hypopnea syndrome (SAHS) is a common sleep disorder with a high prevalence. The apnea hypopnea index (AHI) is an important indicator used to diagnose the severity of SAHS disorders. The calculation of the AHI is based on the accurate identification of various types of sleep respiratory events. In this paper, we proposed an automatic detection algorithm for respiratory events during sleep. In addition to the accurate recognition of normal breathing, hypopnea and apnea events using heart rate variability (HRV), entropy and other manual features, we also presented a fusion of ribcage and abdomen movement data combined with the long short-term memory (LSTM) framework to achieve the distinction between obstructive and central apnea events. While only using electrocardiogram (ECG) features, the accuracy, precision, sensitivity, and F1 score of the XGBoost model are 0.877, 0.877, 0.876, and 0.876, respectively, demonstrating that it performs better than other models. Moreover, the accuracy, sensitivity, and F1 score of the LSTM model for detecting obstructive and central apnea events were 0.866, 0.867, and 0.866, respectively. The research results of this paper can be used for the automatic recognition of sleep respiratory events as well as AHI calculation of polysomnography (PSG), which provide a theoretical basis and algorithm references for out-of-hospital sleep monitoring.
Autonomic Nervous System Influences on Cardiovascular Self-Organized Criticality
Authors: Fortrat Jacques-Olivier , Ravé Guillaume
Published: May 2023

Cardiovascular self-organized criticality has recently been demonstrated. We studied a model of autonomic nervous system changes to better characterize heart rate variability self-organized criticality. The model included short and long-term autonomic changes associated with body position and physical training, respectively. Twelve professional soccer players took part in a 5-week training session divided into “Warm-up”, “Intensive”, and “Tapering” periods. A stand test was carried out at the beginning and end of each period. Heart rate variability was recorded beat by beat (Polar Team 2). Bradycardias, defined as successive heart rates with a decreasing value, were counted according to their length in number of heartbeat intervals. We checked whether bradycardias were distributed according to Zipf’s law, a feature of self-organized criticality. Zipf’s law draws a straight line when the rank of occurrence is plotted against the frequency of occurrence in a log–log graph. Bradycardias were distributed according to Zipf’s law, regardless of body position or training. Bradycardias were much longer in the standing position than the supine position and Zipf’s law was broken after a delay of four heartbeat intervals. Zipf’s law could also be broken in some subjects with curved long bradycardia distributions by training. Zipf’s law confirms the self-organized nature of heart rate variability and is strongly linked to autonomic standing adjustment. However, Zipf’s law could be broken, the significance of which remains unclear.
Multi-Level Protective Factors of Adolescent Smoking and Drinking
Authors: Dudok Réka , Piko Bettina F.
Published: May 2023

Adolescence is the most critical life stage for experimentation with substance use; however, this is also the most suitable period for strengthening protective factors and thus promoting adult physical and mental health. Since smoking and drinking still appear among the most frequent types of substance abuse in Europe, this study aims to examine the role of potential protective factors at multiple levels for adolescent smoking and drinking: psychological factors at the individual level, aspects of school attachment at the school level, social support variables at the social level, and measures of quality of life at the level of mental health. This cross-sectional survey involved a sample of adolescents (aged 11–18 years, N = 276) in Budapest and villages in its metropolitan area (Hungary). In addition to descriptive statistics, logistic regression analyses were used to detect odds for potential protective factors. There were no sex differences in adolescents’ substance use. Self-control seems to be a universal and most determining protective factor against substance use, while other potential protective factors (self-esteem, resilience, social support from family or significant others, school attachment, and mental well-being) may also contribute to prevention. However, age and friend support acted as risk factors. Findings suggest that a complex approach to prevention should receive consideration.
Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter
Authors: Deng Mingjun , Li Shuhang , Jiang Xueqing , Li Xiang
Published: May 2023

Vehicle motion trajectory prediction is the basis of vehicle collision early warning or vehicle conflict resolution. In order to improve the accuracy of trajectory prediction, a vehicle trajectory prediction method based on “current” statistical (CS) model and cubature Kalman filter (CKF) is proposed. This method considers the acceleration variation rules in the actual motion process of the vehicle in the state equation, so that the estimated value of the acceleration can be consistent with the real range. This condition overcomes the limitation of the general trajectory prediction model, which ignores the acceleration change, so it improved prediction accuracy. In addition, this method also avoids the large amount of computational resources required, being that some new methods describe the real acceleration fluctuations. The vehicle trajectory at the intersection that crossed by Yingbin Avenue and Qiche Avenue in Nanchang is selected to verify the tracking performance of Constant Acceleration-Unscented Kalman Filter (CA-UKF), Current Statistical-Unscented Kalman Filter (CS-UKF), and CS-CKF models. The results show that the CS-CKF model has superior prediction effectiveness than the CA-UKF and CS-UKF models, and it improves the accuracy of vehicle motion trajectory prediction.
Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning
Authors: Wang Jiakang , Liu Hui , Zheng Guangji , Li Ye , Yin Shi
Published: May 2023

Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning. The novelty of this study is as follows: firstly, the Hampel identifier (HI) is employed to correct outliers in the original data; secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to extract the waveform characteristics of the data fully; and, finally, the temporal convolutional network, extreme learning machine, and gate recurrent unit are selected as the basic learners for forecasting load power data. An ensemble reinforcement learning algorithm based on Q-learning was adopted to generate optimal ensemble weights, and the predictive results of the three basic learners are combined. The experimental results of the models for three real load power datasets show that: (a) the utilization of HI improves the model’s forecasting result; (b) CEEMDAN is superior to other decomposition algorithms in forecasting performance; and (c) the proposed ensemble method, based on the Q-learning algorithm, outperforms three single models in accuracy, and achieves smaller prediction errors.
Investigation of Thermo-Hydraulic Performances of Artificial Ribs Mounted in a Rectangular Duct
Authors: Kumar B. Varun , Kanna P. Rajesh , Manikandan G. , Taler Dawid , Taler Jan , Sobota Tomasz , Nowak-Ocłoń Marzena
Published: May 2023

This research investigates the fluid flow characterization and thermohydraulic performances (THP) of rib surfaces, using computational and experimental methods. ANSYS computational fluid dynamics (CFD) software was used to predict and validate the findings in the experimental setup. Artificial rib surfaces, including polygonal and forward trapezoidal-shaped ribs, were placed in the absorber plate at different relative pitch distances (p/e) = 6.7, 10, 13.4 and relative height (e/d) = 20, and the mass flow rate of air (working fluid) varied at Reynolds numbers ranging from 2000 to 20,000. According to the validation results, the RNG renormalization k-ε model was selected for the investigation. The results show that strong turbulence occured closer to the wall surface and behind the rib surface, enhancing thermal performances due to the sharp edge shape of the rib. A polygonal rib with a pitch distance of p/e = 6.7 achieved a higher Nusselt number (Nu) and thermohydraulic performance of 2.95 at Re 4000. An empirical correlation between the Nusselt number (Nu) and friction factor (f) was developed using linear regression analysis and was compared with the predicted values. The comparison results show a close range of ±8% between the experimental and predicted values.
Assessment and Commissioning of Electrical Substation Grid Testbed with a Real-Time Simulator and Protective Relays/Power Meters in the Loop
Authors: Piesciorovsky Emilio C. , Borges Hink Raymond , Werth Aaron , Hahn Gary , Lee Annabelle , Polsky Yarom
Published: May 2023

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small-measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible troubleshooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources.
Numerical Investigation of the Use of Electrically Conductive Concrete-Encased Electrodes as Potential Replacement for Substation Grounding Systems
Authors: Daadaa Mouna , Brettschneider Stephan , Volat Christophe , Simard Guy
Published: May 2023

This paper presents a numerical investigation regarding the possibility of using electrically conductive concrete (ECON) combined with concrete-encased electrode (CEE) technology to develop new substation grounding systems (SGSs) called ECON-EE as a replacement for conventional copper or galvanized steel grounding grids. In the first step, the validation of the commercial FEM software used to perform grounding system analysis was performed in terms of the grid resistance (RG), ground potential rise (GPR), and step and touch voltages, using a symmetrical 70 m × 70 m conventional copper SGS. Next, several numerical simulations of an ECON-EE grounding system with the same dimensions as the conventional copper grid used for FEM software validation were performed. Thus, several parameters of the ECON-EE grounding system were studied, such as the geometry, dimensions, and resistivity of ECON and the diameter of the rebar. The numerical results obtained permit us to demonstrate that ECON-EE grounding systems can perform better than conventional SGSs equipped with vertical rods, particularly in the case of high ground resistivity. Moreover, it was demonstrated that the two main ECON-EE parameters affecting the grounding resistance and the touch and step voltages are the section area and the resistivity of the ECON. As discussed in detail in this paper, the proposed ECON-EE grounding system can offer several advantages compared to conventional SGSs in terms of efficiency and durability, as well as in terms of simplicity of conception and implementation.
Fuzzy Control Algorithm Applied on Constant Airflow Controlling of Fans
Authors: Sun Wangsheng , Si Haiqing , Li Yao , Wang Haibo , Qiu Jingxuan , Li Gen
Published: May 2023

Keeping a certain constant airflow in a lot of applications is critical, such as certain airflow volumes in the cabin of an aircraft, submarines, tunnels, and buildings. All fans change their output airflow and air pressure by changing motor speed in real applications. In order to achieve the target airflow volume, normal operation detects the data first, then provides orders to engineers to adjust manually. Extra airflow volume sensors are equipped to measure the airflow so that people know what the current airflow is and how much needs to be adjusted. Based on normal technology, airflow volume sensors and motor speed inverters are necessary parts of a whole air supply system. This research tries to integrate a fuzzy control algorithm into a motor control technology to build a new kind of fan that can not only self-detect the airflow volume changes but also can self-adjust the airflow volumes automatically. This self-detecting and adjusting fan can lead air supply systems in devices and buildings to need no airflow volume sensors, make the air supply system building cost lower, and make the maintaining and operating cost lower.
Efficiency Enhancement of the Single Line Multi-Stage Gasification of Hungarian Low-Rank Coal: Effects of Gasification Temperature and Steam/Carbon (S/C) Ratio
Authors: Mai Thuan Duc , Koós Tamás , Sebe Emese , Siménfalvi Zoltán , Kállay András Arnold
Published: May 2023

Coal gasification is considered a promising solution for the production of synthetic fuels and eventually as a fuel for combined heat and power systems and heating buildings. There are several factors that affect the gasification efficiency and syngas quality, such as gasification parameters (temperature, pressure, etc.), reactants and their ratio, utilisation of catalysts, and gasifier design. The multi-stage gasifier is known as a promising approach in the enhancement of process efficiency, as well as the syngas quality. In this study, the Hungarian brown coal was gasified in a two-stage gasifier. The pyrolysis stage was kept at 600 °C. The gasification stage was conducted at 700, 800, and 900 °C. The steam per carbon (S/C) ratio was examined at 0.75, 1.00, and 1.25. The positive effects of increasing gasification temperature on char and dry gas yield were obviously shown at all S/C ratios. The increase in the S/C ratio did not show a positive effect at all temperature conditions, especially at 700 and 900 °C. The highest dry syngas yield was 1.14 Nm3/kgcoal obtained at 900 °C and the S/C ratio of 1.25. The increase in the gasification temperature also had a significant impact on the volume fraction of CO and CO2. Meanwhile, the syngas concentration varied slightly when the S/C ratio increased from 0.75 to 1.25. From a chemical utilization point of view, the gasification temperature at 900 °C and the S/C ratio of 1.25 resulted in the most promising H2/CO ratio of 1.99. In addition, the highest carbon conversion and cold gas efficiency were achieved at 900 °C and an S/C ratio of 1.00–1.25, respectively.
Neutronic Assessments towards a Novel First Wall Design for a Stellarator Fusion Reactor with Dual Coolant Lithium Lead Breeding Blanket
Authors: Sosa David , Palermo Iole
Published: May 2023

The Stellarator Power Plant Studies Prospective R&D Work Package in the Eurofusion Programme was settled to bring the stellarator engineering to maturity, so that stellarators and particularly the HELIAS (HELical-axis Advanced Stellarator) configuration could be a possible alternative to tokamaks. However, its complex geometry makes designing a Breeding Blanket (BB) that fully satisfies the requirements for such a HELIAS configuration, which is a difficult task. Taking advantage of the acquired experience in BB design for DEMO tokamak, CIEMAT is leading the development of a Dual Coolant Lithium Lead (DCLL) BB for a HELIAS configuration. To answer the specific HELIAS challenges, new and advanced solutions have been proposed, such as the use of fully detached First Wall (FW) based on liquid metal Capillary Porous Systems (CPS). The proposed solutions have been studied in a simplified 1D model that can help to estimate the relative variations in Tritium Breeding Ratio (TBR) and displacement per atom (dpa) to verify their effectiveness in simplifying the BB integration and improving the machine availability while keeping the main BB nuclear functions (i.e., tritium breeding, heat extraction and shielding). This preliminary study demonstrates that the use of FW CPS would drastically reduce the radiation damage received by the blanket by 29% in some of the selected configurations along with a small decrease of 4.9% in TBR. This could even be improved to just a 3.8% TBR reduction by using a graphite reflector. Such an impact on the TBR is considered affordable, and the results presented, although preliminary in essence, have shown the existence of margins for further development of the FW CPS concept for HELIAS, as they have been not found, at least to date, to be significant showstoppers for the use of this technological solution.
Employment and Income Effects of Investments Made Using the Act 13 Unconventional Natural Gas Impact Fee in Pennsylvania
Authors: Young Corey
Published: May 2023

Unconventional natural gas extraction presents numerous opportunities and risks for communities across the United States. To capture a portion of the revenue generated by the resource states tax unconventional natural gas development. While most states collect revenue via severance taxes, Pennsylvania took a novel approach and established an impact fee on the industry instead. Unlike severance taxes in other states, the fee is collected annually and distributed directly to municipalities. While reports show that municipalities use the funds to pay for critical infrastructure, no best practices on how to allocate the funds exist. Citing the literature on mineral resource extraction and infrastructure-led development in American communities, this study examined impact fee payments made to counties with unconventional natural gas wells. The study evaluated whether counties that used the funds to invest in infrastructure were better off in terms of employment and income than other shale-producing counties that did not. Panel fixed- and random-effects regressions suggested that no statistically significant employment or income effects existed. The results suggest that local infrastructural investments are not a successful way to overcome the resource curse issues identified in the literature.
Machine Learning Requirements for Energy-Efficient Virtual Network Embedding
Authors: Hesselbach Xavier , Escobar-Perez David
Published: May 2023

Network virtualization is a technology proven to be a key enabling a family of strategies in different targets, such as energy efficiency, economic revenue, network usage, adaptability or failure protection. Network virtualization allows us to adapt the needs of a network to new circumstances, resulting in greater flexibility. The allocation decisions of the demands onto the physical network resources impact the costs and the benefits. Therefore it is one of the major current problems, called virtual network embedding (VNE). Many algorithms have been proposed recently in the literature to solve the VNE problem for different targets. Due to the current successful rise of artificial intelligence, it has been widely used recently to solve technological problems. In this context, this paper investigates the requirements and analyses the use of the Q-learning algorithm for energy-efficient VNE. The results achieved validate the strategy and show clear improvements in terms of cost/revenue and energy savings, compared to traditional algorithms.
Exploring the Spatial Dynamics of Endangered Nothofagus alesandrii and Its Relationship with Geomorphometric Variables and Native Tree Species in a Stand of Its Northern Distribution
Authors: Ovando-Mena Soledad , Mora-Poblete Freddy , Santelices-Moya Rómulo , Palacios-Rojas Carlos , Cabrera-Ariza Antonio M.
Published: May 2023

Nothofagus alessandrii is an endangered species with limited and fragmented distribution in the Maule coastal forest of central Chile. Understanding the factors and processes that influence the natural growth of this species is crucial for mitigating its ecological vulnerability. The primary objective of this research is to determine the spatial distribution pattern of N. alessandrii and its association with geomorphometric variables (slope, elevation, and exposure), as well as its association with other tree species in a representative forest located in the northernmost natural distribution range of the species. To achieve this, the coordinates (x, y, z) of all N. alessandrii individuals and accompanying tree species, along with their slope, elevation, and exposure, were obtained using a total station. A spatial analysis tool based on distance indices (SADIE) was used to quantify the spatial pattern of N. alessandrii and detect local aggregates, as well as determine the degree of spatial association between pairs of variables. The results showed that N. alessandrii trees had a random distribution pattern and a significant spatial association with the studied geomorphometric variables. An additional significant finding was the lack of spatial association observed between N. alessandrii and the accompanying species. In conclusion, our study provides valuable information on the spatial distribution and ecological correlates of the endangered N. alessandrii in a fragmented forest ecosystem of central Chile. The results highlight the importance of geomorphometric variables in shaping the distribution pattern of the species, which can be used to guide restoration and conservation efforts.
GWAS on the Attack by Aspen Borer Saperda calcarata on Black Cottonwood Trees Reveals a Response Mechanism Involving Secondary Metabolism and Independence of Tree Architecture
Authors: Sepúlveda Sebastián L. , Neale David B. , Holliday Jason A. , Famula Randi , Fiehn Oliver , Stanton Brian J. , Guerra Fernando P.
Published: May 2023

Black cottonwood (Populus trichocarpa) is a species of economic interest and an outstanding study model. The aspen borer (Saperda calcarata) causes irreversible damage to poplars and other riparian species in North America. The insect can produce multiple effects ranging from the presence of some galleries in the stem to tree death. Despite the ecological and commercial importance of this tree–insect interaction, the genetic mechanisms underlying the response of P. trichocarpa to S. calcarata are scarcely understood. In this study, a common garden trial of P. trichocarpa provenances, established in Davis, California, was assessed at the second year of growth, regarding the infestation of S. calcarata from a natural outbreak. A genome-wide association study (GWAS) was conducted using 629k of exonic SNPs to assess the relationship between genomic variation and insect attack. Tree architecture, in terms of stem number per plant, and the wood metabolome were also included. Insect attack was independent of the number of stems per tree. The performed GWAS identified three significantly associated SNP markers (q-value < 0.2) belonging to the same number of gene models, encoding proteins involved in signal transduction mechanisms and secondary metabolite production, including that of R-mandelonitrile lyase, Chromodomain-helicase-DNA-binding family protein, and Leucine-rich repeat protein. These results are aligned with the current knowledge of defensive pathways in plants and trees, helping to expand the understanding of the defensive response mechanisms of black cottonwood against wood borer insects.
Detecting Spatiotemporal Dynamics and Driving Patterns in Forest Fragmentation with a Forest Fragmentation Comprehensive Index (FFCI): Taking an Area with Active Forest Cover Change as a Case Study
Authors: Zhen Shiyong , Zhao Qing , Liu Shuang , Wu Zhilong , Lin Sen , Li Jian , Hu Xisheng
Published: May 2023

Forests play an irreplaceable role in preserving soil and water, as well as realizing carbon neutrality. However, logging and urban expansion have caused widespread forest fragmentation globally, resulting in biodiversity loss and carbon emissions. Therefore, it is a prerequisite to develop a comprehensive index for evaluating the degree of forest fragmentation to propose effective policies for forest protection and restoration. In this study, a forest fragmentation comprehensive index (FFCI) was constructed through principal component analysis (PCA) based on land-use data from 2000 to 2020 in Fujian Province, composed of five commonly used landscape metrics: patch density (PD), largest patch index (LPI), mean patch area (MPA), aggregation index (AI), and division. Then, the semivariogram function and moving windows method were employed to explore the scale effect and spatiotemporal variations of FFCI. The spatial autocorrelation analysis was used to distinguish the spatial relationship of forest fragmentation, while the driving mechanisms were explored using the geographic detector (GD). The results show that the optimal scale to reflect forest fragmentation based on the semivariogram and moving window method was 3500 m. The proposed FFCI could explain more than 85% of the information for all landscape metrics, and the effectivity of FFCI was validated by urban–rural gradient and transect analysis. We also found that, despite having the highest forest coverage in China, Fujian Province has experienced severe forest fragmentation. High and medium fragmentation accounted for over 50% of all types of fragmentation, with decreasing trends in low and very low fragmentation and increasing trends in high fragmentation over time, indicating that the degree of forest fragmentation in the study area was aggravated over time. Moreover, the spatial distribution pattern of FFCI was mainly high–high clusters and low–low clusters, showing a decreasing trend year by year. The areas with high fragmentation were mainly distributed in the urban center of coastal cities, while the internal cities in western and central regions had a relatively low degree of fragmentation. Additionally, the spatial differentiation in the variation in FFCI was mainly influenced by elevation, slope, and nighttime light intensity. The superimposed impact of two factors on the variation in FFCI was greater than the impact of individual factors. These results provide an effective approach for assessing the degree of forest fragmentation and offer scientific support for mitigating forest fragmentation.
Synchronizing Many Filesystems in Near Linear Time
Authors: Csirmaz Elod P. , Csirmaz Laszlo
Published: May 2023

Finding a provably correct subquadratic synchronization algorithm for many filesystem replicas is one of the main theoretical problems in operational transformation (OT) and conflict-free replicated data types (CRDT) frameworks. Based on the algebraic theory of filesystems, which incorporates non-commutative filesystem commands natively, we developed and built a proof-of-concept implementation of an algorithm suite which synchronizes an arbitrary number of replicas. The result is provably correct, and the synchronized system is created in linear space and time after an initial sorting phase. It works by identifying conflicting command pairs and requesting one of the commands to be removed. The method can be guided to reach any of the theoretically possible synchronized states. The algorithm also allows asynchronous usage. After the client sends a synchronization request, the local replica remains available for further modifications. When the synchronization instructions arrive, they can be merged with the changes made since the synchronization request. The suite also works on filesystems with a directed acyclic graph-based path structure in place of the traditional tree-like arrangement. Consequently, our algorithms apply to filesystems with hard or soft links as long as the links create no loops.
Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
Authors: Ismail Shereen , Dawoud Diana W. , Reza Hassan
Published: May 2023

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.
PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks
Authors: Muñoz Ernesto Cadena , Chica Pedraza Gustavo , Cubillos-Sánchez Rafael , Aponte-Moreno Alexander , Buitrago Mónica Espinosa
Published: May 2023

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.
A Cotangent Fractional Derivative with the Application
Authors: Sadek Lakhlifa
Published: May 2023

In this work, we present a new type of fractional derivatives (FD) involving exponential cotangent function in their kernels called Riemann–Liouville Dσ,γ and Caputo cotangent fractional derivatives CDσ,γ, respectively, and their corresponding integral Iσ,γ. The advantage of the new fractional derivatives is that they achieve a semi-group property, and we have special cases; if γ=1 we obtain the Riemann–Liouville FD (RL-FD), Caputo FD (C-FD), and Riemann–Liouville fractional integral (RL-FI). We give some theorems and lemmas, and we give solutions to linear cotangent fractional differential equations using the Laplace transform of the Dσ,γ, CDσ,γ and Iσ,γ. Finally, we give the application of this new type on the SIR model. This new type of fractional calculus can help other researchers who still work on the actual subject.
Correlated Equilibrium and Evolutionary Stability in 3-Player Rock-Paper-Scissors
Authors: Grant William C.
Published: May 2023

In the game of rock-paper-scissors with three players, this paper identifies conditions for a correlated equilibrium that differs from the mixed strategy Nash equilibrium and is evolutionarily stable. For this to occur, the correlation device attaches more probability to three-way ties and solo-winner outcomes than would result from the Nash equilibrium. The correlated equilibrium is evolutionarily stable because any mutant fares worse than a signal-following player when facing two players who follow their own correlated signals. The critical quality of the correlation device is to make this true both for potential mutants who would disobey their signal and instead choose the action which would beat the action signaled to the player, as well as for potential mutants who would deviate to the action that would be beaten by what the device signals to the player. These findings reveal how a strict correlated equilibrium can produce evolutionarily stable strategies for rock-paper-scissors with three players.
Prediction of Tumor Development and Urine-Based Liquid Biopsy for Molecule-Targeted Therapy of Gliomas
Authors: Kurimoto Michihiro , Rockenbach Yumi , Kato Akira , Natsume Atsushi
Published: May 2023

The timing of the acquisition of tumor-specific gene mutations and the systems by which these gene mutations are acquired during tumorigenesis were clarified. Advances in our understanding of tumorigenesis are being made every day, and therapies targeting fundamental genetic alterations have great potential for cancer treatment. Moreover, our research team successfully estimated tumor progression using mathematical modeling and attempted early diagnosis of brain tumors. We developed a nanodevice that enables urinary genetic diagnosis in a simple and noninvasive manner. Mainly on the basis of our research and experience, this review article presents novel therapies being developed for central nervous system cancers and six molecules, which upon mutation cause tumorigenesis and tumor progression. Further understanding of the genetic characteristics of brain tumors will lead to the development of precise drugs and improve individual treatment outcomes.
Characterization and Phylogenetic Analysis of the Complete Mitochondrial Genome of Aythya marila
Authors: Zhang Lei , Xia Tian , Gao Xiaodong , Yang Xiufeng , Sun Guolei , Zhao Chao , Liu Guangshuai , Zhang Honghai
Published: May 2023

Aythya marila is a large diving duck belonging to the family Anatidae. However, the phylogenetic relationship among these Aythya species remains unclear due to the presence of extensive interspecific hybridization events within the Aythya genus. Here, we sequenced and annotated the complete mitochondrial genome of A. marila, which contained 22 tRNAs, 13 protein-coding genes (PCGs), 2 ribosomal RNAs, and 1 D-loop, with a length of 16,617 bp. The sizes of the PCGs ranged from 297 to 1824 bp and were all, except for ND6, located on the heavy chain (H). ATG and TAA were the most common start and termination codons of the 13 PCGs, respectively. The fastest- and slowest-evolving genes were ATP8 and COI, respectively. Codon usage analysis indicated that CUA, AUC, GCC, UUC, CUC, and ACC were the six most frequent codons. The nucleotide diversity values indicated a high level of genetic diversity in A. marila. FST analysis suggested a widespread gene exchange between A. baeri and A. nyroca. Moreover, phylogenetic reconstructions using the mitochondrial genomes of all available Anatidae species showed that, in addition to A. marila, four major clades among the Anatidae (Dendrocygninae, Oxyurinae, Anserinae, and Anatinae) were closely related to A. fuligula. Overall, this study provides valuable information on the evolution of A. marila and new insights into the phylogeny of Anatidae.
Determinants of Future Physical Activity Participation in New Zealand Adolescents across Sociodemographic Groups: A Descriptive Study
Authors: Bergen Tom , Kim Alice Hyun Min , Mizdrak Anja , Signal Louise , Kira Geoff , Richards Justin
Published: May 2023

This cross-sectional study aimed to explore various determinants of future physical activity (PA) participation in adolescents across sociodemographic groups. Sociodemographic characteristics (age, gender, ethnicity, deprivation status, physical disability status) were assessed in a national sample (n = 6906) of adolescents (12–17 years old) between 2017 and 2020 in New Zealand. The determinants of future PA participation chosen for analysis included current indicators of PA participation (i.e., total time, number of types, number of settings). We also examined widely recognised modifiable intrapersonal (i.e., physical literacy) and interpersonal (i.e., social support) determinants of current and future PA behaviour, along with indicators of PA availability issues. Older adolescents scored worse across all determinants of future PA than younger adolescents, with a key transition point appearing at 14–15 years of age. Māori and Pacific ethnicities scored best across each determinant category on average, with Asian populations scoring the worst. Gender diverse adolescents scored substantially worse than male and female adolescents across every determinant. Physically disabled adolescents scored worse than non-disabled across all determinants. Adolescents from medium and high deprivation neighbourhoods scored similarly across most determinants of future PA participation and both tended to score worse than people from low deprivation neighbourhoods. A particular focus on the improvement of future PA determinants is warranted within adolescents who are older, Asian, gender diverse, physically disabled, and from medium to high deprivation neighbourhoods. Future investigation should prioritise the longitudinal tracking of PA behaviours over time and develop interventions that affect multiple future PA determinants across a range of sociodemographic backgrounds.
Dental Status and Oral Health Behaviors of Selected 45–74-Year-Old Men from Northeastern Poland
Authors: Pawinska Malgorzata , Kondrat Anna , Jamiolkowski Jacek , Paszynska Elzbieta
Published: May 2023

The aim of this study was to assess the dental status and oral health behaviors of selected 45–74-year-old men from northeastern Poland. A total of 419 men were included. A questionnaire on demographic data, socioeconomic status and oral health behaviors was conducted. Dental caries experience (DMFT index), oral hygiene (AP index) and a number of edentulous subjects were evaluated clinically. More than half of the respondents (53.2%) brushed their teeth once a day. Nearly half of respondents (45.6%) reported for check-up visits more rarely than once in two years. Active nicotinism affected 26.7% of males. The prevalence of decay, the mean DMFT, the mean API and the prevalence of edentulism were, respectively, 100%, 21.4 ± 5.5, 77% and 10.3%. Increased DMFT values and MT were significantly correlated with older age (p < 0.001). Subjects of high educational status showed significantly lower values of DMFT and MT (p < 0.001). An increase in per capita family income was accompanied by a significant decrease in the API (p = 0.024), and an increase in DMFT (p = 0.031). This study demonstrated low health awareness and unsatisfactory dental status among the examined males. Dental and oral hygiene status were associated with sociodemographic and behavioral determinants. The poor oral health condition of the study population indicates the need to intensify pro-health education among seniors about oral care.
Psychometric Properties and Cross-Cultural Invariance of the Beck Depression Inventory-II and Beck Anxiety Inventory among a Representative Sample of Spanish, Portuguese, and Brazilian Undergraduate Students
Authors: do Nascimento Rodrigo , Fajardo-Bullon Fernando , Santos Eduardo , Landeira-Fernandez J. , Anunciação Luis
Published: May 2023

Clinical psychologists often use the Beck Depression Inventory, 2nd edition (BDI-II), and Beck Anxiety Inventory (BAI) to aid in the diagnosis of mental health issues and verify the effectiveness of treatments. Despite this common practice, studies that implement a cross-cultural design to check psychometric properties and the invariance of these scales are still scarce in the literature, which can lead to biased results that prevent comparisons among different groups. The present study investigated the internal structure of both tools and their level of invariance. From a representative sample of undergraduate students from Spain (n = 1216), Portugal (n = 426), and Brazil (n = 315), Confirmatory Factor Analysis and Multigroup Confirmatory Factor Analysis were performed. The results revealed suitable fit indices for the two-factor structure of the BDI-II and BAI, assessed by Confirmatory Factor Analysis procedures. Additionally, the two-factor model of the BDI-II reached invariant properties at three levels, whereas the structural model of the BAI did not. Altogether, these results suggest using the BDI-II in this group in these three countries and imply that BAI scores should be interpreted cautiously.
An Agenda for Advancing Research and Prevention at the Nexus of Work Organization, Occupational Stress, and Mental Health and Well-Being
Authors: Lemke Michael K. , Hege Adam , Crizzle Alexander M.
Published: May 2023

Work characteristics and worker well-being are inextricably connected. In particular, the characteristics of work organization shape and perpetuate occupational stress, which contributes to worker mental health and well-being outcomes. Consequently, the importance of understanding and addressing connections between work organization, occupational stress, and mental health and well-being—the focus of this Special Issue—increasingly demand attention from those affected by these issues. Thus, focusing on these issues in the long-haul truck driver (LHTD) sector as an illustrative example, the purpose of this commentary is as follows: (1) to outline current research approaches and the extant knowledge base regarding the connections between work organization, occupational stress, and mental health; (2) to provide an overview of current intervention strategies and public policy solutions associated with the current knowledge base to protect and promote worker mental health and well-being; and (3) to propose a two-pronged agenda for advancing research and prevention for workers during the 21st century. It is anticipated that this commentary, and this Special Issue more broadly, will both echo numerous other calls for building knowledge and engaging in this area and motivate further research within complementary current and novel research frameworks.
Winner Strategies in a Simulated Stock Market
Authors: Taherizadeh Ali , Zamani Shiva
Published: May 2023

In this study, we explore the dynamics of the stock market using an agent-based simulation platform. Our approach involves creating a multi-strategy market where each agent considers both fundamental and technical factors when determining their strategy. The agents vary in their approach to these factors and the time interval they use for technical analysis. Our findings indicate that investing heavily in reducing the value–price gap was a successful strategy, even in markets where there were no trading forces to reduce this gap. Furthermore, our results remain consistent across various modifications to the simulation’s structure.