bluuebunny/crossref_metadata_2025 huggingface dataset:
Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective
Authors: Benbouzid Mohamed , Berghout Tarek
Published: January 2023

Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.
Dynamic Voltage Restorer Based on Integrated Energy Optimal Compensation
Authors: Li Shengwen , Chang Xiao , Wang Haoqing , Liu Ning , Liu Yizhao , Yang Yang , Duan Qing
Published: January 2023

This paper presents an advanced dynamic voltage restorer with integrated energy optimal compensation (EOC-DVR), which combines the advantages of pre-sag compensation and energy minimization compensation. The performance parameters of the EOC-DVR, such as the compensatory capability, loss of energy, and duration of compensation, can be balanced by this optimal compensation strategy. Additionally, the impact on the load is further reduced by the proposed method. By analyzing the working process of optimal comprehensive energy, the compensation limit of the strategy is discussed. After that, both the voltage and active power of the system for compensating are obtained by analyzing the compensation characteristics of the strategy. Therefore, the relations of the formula and curve between the related physical quantities can be obtained. Finally, simulation results based on MATLAB/Simulink are provided to verify the effectiveness of proposed compensation method.
A Novel Video Propagation Strategy Fusing User Interests and Social Influences Based on Assistance of Key Nodes in Social Networks
Authors: Jia Shijie , Wang Tianyin , Su Xiaoyan , Liang Liuke
Published: January 2023

Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead.
Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation
Authors: Dang Thai-Viet , Bui Ngoc-Tam
Published: January 2023

In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.
FIImap: Fast Incremental Inflate Mapping for Autonomous MAV Navigation
Authors: Li Yong , Wang Lihui , Ren Yuan , Chen Feipeng , Zhu Wenxing
Published: January 2023

Three-dimensional mapping is an essential component of autonomous Micro Aerial Vehicle (MAV) navigation. The paper focuses on the 3D spatial representation method of MAV to overcome the collision problem caused by soft constraints, control error, and planning with the center of mass by inflating the occupancy grid map. A fast incremental inflated map construction method is proposed, which reduces the time-consumption caused by the increase of map range and inflated size. The method focuses on areas of the map that occupied state changes and introduces two arrays that record newly appearing and disappearing obstacles. Then, a series of breadth-first search algorithms are used to traverse the parts of the inflated map that need local modification to update the inflated map. Moreover, a sliding map model is designed based on the MAV position, which is suitable for large-range autonomous flight. The effectiveness of the proposed approach is verified with simulated and actual flight data. The proposed method takes about 3 ms to construct the inflated map with a local update range of 16 m × 16 m × 6 m.
Observation of Large Threshold Voltage Shift Induced by Pre-applied Voltage to SiO2 Gate Dielectric in Organic Field-Effect Transistors
Authors: Guo Yifu , Deng Junyang , Niu Jiebin , Duan Chunhui , Long Shibing , Li Mengmeng , Li Ling
Published: January 2023

Field-effect transistors based on organic semiconducting materials (OFETs) have unique advantages of intrinsically mechanical flexibility, simple preparation process, low manufacturing cost, and large-area preparation. Through the innovation of new material design and device structures, the performance of device parameters such as mobility, on–off current ratio, and the threshold voltage (VTH) of OFETs continues to improve. However, the VTH shift of OFETs has always been an important problem restricting their practical applications. In this work, we observe that the VTH of polymer OFETs with the widely investigated device structure of a SiO2 bottom-gate dielectric is noticeably shifted by pre-applying a large gate voltage. Such a shift in VTH remains to a large extent, even after modifying the surface of the SiO2 dielectric using a hexamethyldisilazane (HMDS) self-assembled monolayer. This behavior of VTH can be ascribed to the charge trappings at the bulk of the SiO2. In addition, the generality of this observation is further proven by using two other conjugated polymers including p-type PDPP3T and n-type PTzNDI-2FT, and a similar trend is obtained.
Object Recognition System for the Visually Impaired: A Deep Learning Approach using Arabic Annotation
Authors: Alzahrani Nada , Al-Baity Heyam H.
Published: January 2023

Object detection is an important computer vision technique that has increasingly attracted the attention of researchers in recent years. The literature to date in the field has introduced a range of object detection models. However, these models have largely been English-language-based, and there is only a limited number of published studies that have addressed how object detection can be implemented for the Arabic language. As far as we are aware, the generation of an Arabic text-to-speech engine to utter objects’ names and their positions in images to help Arabic-speaking visually impaired people has not been investigated previously. Therefore, in this study, we propose an object detection and segmentation model based on the Mask R-CNN algorithm that is capable of identifying and locating different objects in images, then uttering their names and positions in Arabic. The proposed model was trained on the Pascal VOC 2007 and 2012 datasets and evaluated on the Pascal VOC 2007 testing set. We believe that this is one of a few studies that uses these datasets to train and test the Mask R-CNN model. The performance of the proposed object detection model was evaluated and compared with previous object detection models in the literature, and the results demonstrated its superiority and ability to achieve an accuracy of 83.9%. Moreover, experiments were conducted to evaluate the performance of the incorporated translator and TTS engines, and the results showed that the proposed model could be effective in helping Arabic-speaking visually impaired people understand the content of digital images.
Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 1: Radial Turbine Modelling and Design
Authors: Pipitone Emiliano , Caltabellotta Salvatore , Sferlazza Antonino , Cirrincione Maurizio
Published: January 2023

The efficiency of Hybrid Electric Vehicles (HEVs) may be substantially increased if the energy of the exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered by means of a properly designed turbogenerator and employed for vehicle propulsion; previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% by employing a 20 kW turbine on a 100 HP rated power thermal unit. The innovative thermal unit here proposed is composed of a supercharged engine endowed with a properly designed turbogenerator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In these two parts, the realistic efficiency of the innovative thermal unit for hybrid vehicle is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors present a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, owing to some assumptions and approximations, allows a fast determination of the proper turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine considered is quite different from the ones commonly employed for turbocharging application; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In the Part 2 paper, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantage that could be achieved by the implementation of the turbogenerator on a hybrid propulsion system is evaluated through the performance prediction model under the different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbocompound system could gain vehicle efficiency improvement between 3.1% and 17.9%, depending on the output power level, while an average efficiency increment of 10.9% was determined for the whole operating range.
Hardware-in-the-Loop Testing for Protective Relays Using Real Time Digital Simulator (RTDS)
Authors: Yadav Gaurav , Liao Yuan , Burfield Austin D.
Published: January 2023

With the increasing size and complexity of power systems, it is crucial to have an effective protection system in place to ensure its reliability. One of the important components of the protection system are relays. It is important for a relay to operate dependably and securely so that any fault can be cleared in time to minimize damages to the power network. However, it is important to test a relay in a realistic environment before commissioning it to the network. Testing a relay in the actual network can be expensive with limited fault scenarios. Hence, Hardware-in-the-Loop (HIL) testing is an efficient method to perform closed-loop testing of a relay since numerous fault cases can be simulated to provide a realistic operating environment for the relay under test. This paper sheds light on the HIL testing done for protective relays using a sample distribution system using RTDS. Two SEL-351 relays have been used in this experiment, and proper settings for the relays are calculated for coordination. The paper also describes the procedure of configuring the relay and other RTDS components crucial for interfacing of the relay with RTDS. After test setup, a pre-fault, fault, and post-fault analysis was done for the system. The results obtained from these analyses are cross-checked with the event history of the two SEL-351 relays, obtained with AcSELerator Quickset software. This paper provides thorough information for researchers to replicate the presented study or to develop new HIL experiments. It can also help in developing a fundamental understanding of the HIL testing setup that can be further applied to a more complex power system.
New Optical System for Long Distance Control of Electrical Energy Flows
Authors: Davydov Vadim , Reznikov Bogdan , Dudkin Valentin
Published: January 2023

The problems that arise during the transmission of control commands for key elements and data on their execution via communication systems over long distances at energy facilities are considered. The necessity of a new approach to solving this problem using a fiber-optic communication line (FOCL) is substantiated. A new design of analog FOCL has been developed for transmitting control commands for key elements of substations and switching data. A new method for forming an optical signal in a laser transmitting module using direct current modulation is proposed. The advantages of using a new method for generating commands for control signals and switching data using a sequence of command codes compared with digital signals currently used in control and monitoring systems are substantiated. The main parameters of the developed analog FOCL have been calculated. An experimental prototype of an analog FOCL was assembled, and research was conducted. The results of calculations and experimental data are compared. The directions for further modernization of the developed FOCL are determined to increase the distance for transmitting an optical signal up to 600 km without the use of servers, the maintenance of which in the power transmission line system in some cases is challenging, expensive, and not always possible.
Sliding Mode Control of Ship DC Microgrid Based on an Improved Reaching Law
Authors: Xiang Chuan , Cheng Qi , Zhu Yizheng , Zhao Hongge
Published: January 2023

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid.
An Overview of Flexible Current Control Strategies Applied to LVRT Capability for Grid-Connected Inverters
Authors: Rincon David J. , Mantilla Maria A. , Rey Juan M. , Garnica Miguel , Guilbert Damien
Published: January 2023

Distributed power generation plays a critical role in the stability and reliability of modern power systems. Due to the rapid growth of renewable energy generation, the requirements of the transmission and distribution system operators are becoming more stringent. Among these requirements, one of the most important is the Low-Voltage Ride-Through (LVRT) capability, which demands that the inverters remain connected to the grid and provide support during voltage sags. For this purpose, flexible current control algorithms stand out because they can manage unbalanced voltages and simultaneously achieve other control objectives. With the aim of presenting a concrete document focused on describing fundamental principles and discussing common design guidelines, this paper presents an overview of flexible current control strategies applied to LVRT capability. The operation features and design aspects of experiences reported in the literature are reviewed. Moreover, the paper proposes a general methodology to design LVRT flexible current control algorithms. Finally, current and future trends are discussed.
Data-Driven Dynamic Stability Assessment in Large-Scale Power Grid Based on Deep Transfer Learning
Authors: Wen Weijia , Ling Xiao , Sui Jianxin , Lin Junjie
Published: January 2023

For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked problem, this work develops a light-weight framework for DSA-oriented stability knowledge transfer from off-the-shelf test systems to practical power grids. A scale-free system feature learner is proposed to characterize system-wide features of various systems in a unified manner. Given a real-world power grid for DSA, selective stability knowledge transfer is intelligently carried out by comparing system similarities between it and the available test systems. Afterward, DSA model fine-tuning is performed to make the transferred knowledge adapt well to practical DSA contexts. Numerical test results on a realistic system, i.e., the provincial GD Power Grid in China, verify the effectiveness of the proposed framework.
The Effects of Crude Oil Price Surprises on National Income: Evidence from India
Authors: Kathiravan Chinnadurai , Selvam Murugesan , Maniam Balasundram , Dana Leo Paul , Babu Manivannan
Published: January 2023

The goal of this study is to look into how changes in crude oil prices affect GDP per capita and exchange rate fluctuations.to investigate the influence of crude oil price shocks on GDP per capita and exchange rate movements. This research employed yearly time series data for the price of crude oil, exchange rate (USD/INR), and GDP per capita, from 1990 to 2020. Arithmetical tools such as Descriptive, Unit Root, Granger Causality Test, and OLS Model were applied. The present study discovered a strong bi-directional Granger causality effect of Dubai crude oil prices on exchange rates, as well as a bi-directional Granger influence of exchange rates on WTI crude oil prices. The diagnostic tests were successfully passed by the estimated models. According to the OLS model, the exchange rate was driven only by the price of Dubai crude oil, although the price of WTI crude oil influenced both the GDP per capita and the exchange rate over the research period. The key policy recommendation derived from this analysis is that the Reserve Bank of India (RBI) must depreciate the rupee, first to restore much-needed exchange rate stability, then to stimulate domestic manufacturers, and finally, to attract foreign capital inflows.
Development of a Thermal-Hydraulic Model for the EU-DEMO Tokamak Building and LOCA Simulation
Authors: D’Onorio Matteo , Glingler Tommaso , Porfiri Maria Teresa , Dongiovanni Danilo Nicola , Ciattaglia Sergio , Gliss Curt , Elbez-Uzan Joëlle , Cortes Pierre , Caruso Gianfranco
Published: January 2023

The EU-DEMO must demonstrate the possibility of generating electricity through nuclear fusion reactions. Moreover, it must denote the necessary technologies to control a powerful plasma with adequate availability and to meet the safety requirements for plant licensing. However, the extensive radioactive materials inventory, the complexity of the plant, and the presence of massive energy sources require a rigorous safety approach to fully realize fusion power’s environmental advantages. The Tokamak building barrier design must address two main issues: radioactive mass transport hazards and energy-related or pressure/vacuum hazards. Safety studies are performed in the frame of the EUROfusion Safety And Environment (SAE) work package to support design improvement and evaluate the thermal-hydraulic behavior of confinement building environments during accident conditions in addition to source term mobilization. This paper focuses on developing a thermal-hydraulic model of the EU-DEMO Tokamak building. A preliminary model of the heat ventilation and air conditioning system and vent detritiation system is developed. A loss-of-coolant accident is studied by investigating the Tokamak building pressurization, source term mobilization, and release. Different nodalizations were compared, highlighting their effects on source term estimation. Results suggest that the building design should be improved to maintain the pressure below safety limits; some mitigative systems are preliminarily investigated for this purpose.
GaAs/Si Tandem Solar Cells with an Optically Transparent InAlAs/GaAs Strained Layer Superlattices Dislocation Filter Layer
Authors: Kim Yeonhwa , Madarang May Angelu , Ju Eunkyo , Laryn Tsimafei , Chu Rafael Jumar , Kim Tae Soo , Ahn Dae-Hwan , Kim Taehee , Lee In-Hwan , Choi Won Jun , Jung Daehwan
Published: January 2023

Epitaxial growth of III–V materials on Si is a promising approach for large-scale, relatively low-cost, and high-efficiency Si-based multi-junction solar cells. Several micron-thick III–V compositionally graded buffers are typically grown to reduce the high threading dislocation density that arises due to the lattice mismatch between III–V and Si. Here, we show that optically transparent n-In0.1Al0.9As/n-GaAs strained layer superlattice dislocation filter layers can be used to reduce the threading dislocation density in the GaAs buffer on Si while maintaining the GaAs buffer thickness below 2 μm. Electron channeling contrast imaging measurements on the 2 μm n-GaAs/Si template revealed a threading dislocation density of 6 × 107 cm−2 owing to the effective n-In0.1Al0.9As/n-GaAs superlattice filter layers. Our GaAs/Si tandem cell showed an open-circuit voltage of 1.28 V, Si bottom cell limited short-circuit current of 7.2 mA/cm2, and an efficiency of 7.5%. This result paves the way toward monolithically integrated triple-junction solar cells on Si substrates.
Band-to-Band Transitions in InAs/GaSb Multi-Quantum-Well Structures Using k.p Theory: Effects of Well/Barrier Width and Temperature
Authors: Seyedein Ardebili S. Bahareh , Kim Jong Su , Ha Jaedu , Kang Tae In , Zeinalvand Farzin Behnam , Kim Yeongho , Lee Sang Jun
Published: January 2023

We investigated the conduction- and valence-confined energy levels and first band-to-band transition energies of a type-II InAs/GaSb multi-quantum-well at 77 K and room temperature for various well and barrier thicknesses. We calculated the electron and hole confined energies based on Kane’s eight-band k.p formalism. We also explored the effect of the barrier width on the wells’ interactions, which was negligible for wells with a width wider than 30 nm. Moreover, we proposed a single exponential function to predict the first transition energies without considering the complex approach of k.p theory. Then, we measured the photoluminescence spectra of the manufactured samples, including thin wells (1, 2, and 3 monolayers) and wide barriers (50 nm). Finally, we made comparisons between the theoretical band-to-band transition energies for kz=0 and experimental results from the photoluminescence spectra for different well thicknesses at 77 and 300 K.
Design and Conceptual Development of a Novel Hybrid Intelligent Decision Support System Applied towards the Prevention and Early Detection of Forest Fires
Authors: Casal-Guisande Manuel , Bouza-Rodríguez José-Benito , Cerqueiro-Pequeño Jorge , Comesaña-Campos Alberto
Published: January 2023

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.
New Prospects to Systematically Improve the Particulate Matter Removal Efficiency of Urban Green Spaces at Multi-Scales
Authors: Zhang Rui , Ma Keming
Published: January 2023

Previous studies on the removal of airborne particulate matter (PM) by plants have mostly focused on the individual scale, hence there is a lack of systematic understanding of how to improve the PM removal effect of green spaces (GS) at multi-scales. We provide new insights into an integrated model, which integrates the utilization efficiency of vertical space and time into the multi-cycle PM removal model developed in our previous study. By analyzing the variabilities of the influencing factors at different scales, directions to improve this function at multiple scales can be proposed. According to the planning of urban GS, five scales were divided. At the species scale, plants should not only have the characteristics to match the local climate, but also a high utilization efficiency of time and space. At the community scale, increasing the hierarchy and structural complexity can help improve the utilization of vertical space. At the patch and landscape scales, the factor affecting the PM removal efficiency of GS lie in precipitation frequency, and large/small green patches with low/high landscape fragmentation in climates with low/high precipitation frequency are recommended. At the urban scale, it is necessary to increase the degree of temporal and spatial distribution matching between PM and GS. These findings can improve urban GS planning to contribute to the removal of airborne PM.
Acknowledgment to the Reviewers of Fishes in 2022
Authors:   Fishes Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Acknowledgment to the Reviewers of Foods in 2022
Authors:   Foods Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
On the Estimation of the Persistence Exponent for a Fractionally Integrated Brownian Motion by Numerical Simulations
Authors: Abundo Mario , Pirozzi Enrica
Published: January 2023

For a fractionally integrated Brownian motion (FIBM) of order α∈(0,1],Xα(t), we investigate the decaying rate of P(τSα>t) as t→+∞, where τSα=inf{t>0:Xα(t)≥S} is the first-passage time (FPT) of Xα(t) through the barrier S>0. Precisely, we study the so-called persistent exponent θ=θ(α) of the FPT tail, such that P(τSα>t)=t−θ+o(1), as t→+∞, and by means of numerical simulation of long enough trajectories of the process Xα(t), we are able to estimate θ(α) and to show that it is a non-increasing function of α∈(0,1], with 1/4≤θ(α)≤1/2. In particular, we are able to validate numerically a new conjecture about the analytical expression of the function θ=θ(α), for α∈(0,1]. Such a numerical validation is carried out in two ways: in the first one, we estimate θ(α), by using the simulated FPT density, obtained for any α∈(0,1]; in the second one, we estimate the persistent exponent by directly calculating Pmax0≤s≤tXα(s)<1. Both ways confirm our conclusions within the limit of numerical approximation. Finally, we investigate the self-similarity property of Xα(t) and we find the upper bound of its covariance function.
Selected SNPs of FCN2 Associated with Chronic Tonsillitis in the Polish Adult Population
Authors: Gaździcka Jadwiga , Gołąbek Karolina , Hudy Dorota , Miśkiewicz-Orczyk Katarzyna , Zięba Natalia , Tynior Wojciech , Asman Marek , Misiołek Maciej , Strzelczyk Joanna Katarzyna
Published: January 2023

Chronic tonsillitis is a problem related to bacterial and viral infections. Ficolins play a key role in the defence against various pathogens. In the present study, we investigated the associations between the selected single nucleotide polymorphisms (SNPs) of the FCN2 gene and chronic tonsillitis in the Polish population. The study included 101 patients with chronic tonsillitis and 101 healthy individuals. The selected SNPs of FCN2 (rs3124953, rs17514136 and rs3124954) were genotyped using TaqMan SNP Genotyping Assays (Applied Biosystem, Foster City, CA, USA). The analysis of rs17514136 and rs3124953 showed no significant differences in genotype frequencies between the chronic tonsillitis patients and controls (p > 0.01). The CT genotype of rs3124954 was significantly more frequent, while the CC genotype was less frequent in chronic tonsillitis patients (p = 0.003 and p = 0.001, respectively). The frequency of the A/G/T haplotype (rs17514136/rs3124953/rs3124954) was significantly more common in chronic tonsillitis patients (p = 0.0011). Moreover, the FCN2 CT genotype of rs3124954 was associated with a higher risk of chronic tonsillitis, while the CC genotype of rs3124954 decreased this risk. Our findings demonstrate that FCN2 rs3124954 may be associated with chronic tonsillitis in the Polish adult population.
Gene Expression and Epigenetic Regulation in the Prefrontal Cortex of Schizophrenia
Authors: Bilecki Wiktor , Maćkowiak Marzena
Published: January 2023

Schizophrenia pathogenesis remains challenging to define; however, there is strong evidence that the interaction of genetic and environmental factors causes the disorder. This paper focuses on transcriptional abnormalities in the prefrontal cortex (PFC), a key anatomical structure that determines functional outcomes in schizophrenia. This review summarises genetic and epigenetic data from human studies to understand the etiological and clinical heterogeneity of schizophrenia. Gene expression studies using microarray and sequencing technologies reported the aberrant transcription of numerous genes in the PFC in patients with schizophrenia. Altered gene expression in schizophrenia is related to several biological pathways and networks (synaptic function, neurotransmission, signalling, myelination, immune/inflammatory mechanisms, energy production and response to oxidative stress). Studies investigating mechanisms driving these transcriptional abnormalities focused on alternations in transcription factors, gene promoter elements, DNA methylation, posttranslational histone modifications or posttranscriptional regulation of gene expression mediated by non-coding RNAs.
Acknowledgment to the Reviewers of Horticulturae in 2022
Authors:   Horticulturae Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Influences of Urban Discharges and Urban Heat Effects on Stream Temperature
Authors: Kolath Anja Svane , Egemose Sara
Published: January 2023

Urban areas with dark and impermeable surfaces are known to have a heating effect on air and still water compared to surrounding areas, called the urban heat island effect (UHI). UHI and stormwater discharges’ collective impact on stream temperature, especially regarding seasonal changes, is a less-studied field. In this study, the temperature effect of the urban village Aarslev on Stream Vindinge in Southern Denmark was examined. Loggers (ID A–L) were placed in Stream Vindinge in 2020–2021, measuring temperature (°C) and pressure (kPa). Outlets were analyzed with respect to origin: Direct stormwater outlets (rain ÷ basin), stormwater delayed by ponds (rain + basin), common overflow, and common sewage from WWTP. Data showed the stream temperature rise through Aarslev village in all months (except March) with 0.3–1.9 °C, most notably in the summer months. A one-way ANOVA confirmed that the upstream station A and downstream station K were significantly different (p-values < 0.001). No significant difference in temperatures between the different outlet types was found. An increase in stream temperature was observed in response to rain events, followed by a temperature decrease. This was assumed to be a “first heat flush”. This was speculated to mean less optimal conditions for trout and sensitive macroinvertebrates not because of heat shock, but rather to lower O2 concentrations and higher mineralization. River and lake temperatures are projected to increase, and this effect might become more pronounced. A decrease in stream temperature was observed after the village (station L). Therefore, it was concluded that the rise in temperature through the village was due to UHI.
The Role of Religions in the COVID-19 Pandemic: A Narrative Review
Authors: Sisti Leuconoe Grazia , Buonsenso Danilo , Moscato Umberto , Costanzo Gianfranco , Malorni Walter
Published: January 2023

Culture, religion and health are closely intertwined, profoundly affecting people’s attitudes and behaviors as well as their conception and experience of illness and disease. In order to analyze the impact of religion in the current COVID-19 pandemic, we performed a literature review investigating both the scientific and grey literature on the topic. COVID-19 outbreaks reported in pilgrimages and religious ceremonies around the world—especially in the first wave of the pandemic wave—and the role played by religion in conveying culturally sensitive information about COVID-19 are some of the evidence we reviewed. Our research highlights how religions have represented, on the one hand, a risk for the spread of the virus and, on the other, a precious opportunity to engage people, and in particular minorities, in fighting the pandemic. To overcome this pandemic and to be prepared for similar ones in the future, scientists, politicians and health professionals should acknowledge the role that culture and religion play in people’s lives and how it can assist in tackling complex health challenges.
Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software
Authors: Chen Yi , Chen Yinrong , Chen Kun , Liu Min
Published: January 2023

Residential carbon emissions are one of the critical causes of climate problems such as global warming. It is significant to explore the development and evolution trend of residential carbon emissions research for mitigating global climate change. However, there have been no studies that comprehensively review this research field. Based on the research papers on residential carbon emissions included in the Web of Science core database and China National Knowledge Infrastructure database, the CiteSpace bibliometric analysis software was used in this paper to draw the visual knowledge map of residential carbon emissions research and reveal its research status, research hotspots, and development trend. We found that residential carbon emissions research has gone through the stage of “emergence–initiation–rapid development”, and the research in the United States and the United Kingdom has played a fundamental role in developing this research field. Research hotspots mainly focus on analyzing energy demand, quantitative measurement, and impact mechanisms of residents’ direct and indirect carbon emissions and low-carbon consumption willingness. The focus of research has gradually shifted from qualitative analysis based on relevant policies to the analysis of quantitative spatiotemporal measurements and drive mechanisms of direct and indirect carbon emissions from residential buildings, transportation, and tourism based on mathematical models and geographic information system technologies. Modern intelligent means such as remote sensing technology and artificial intelligence technology can improve the dynamics and accuracy of this research, but there are few related types of research at present. Based on these research status and trends, we proposed that the future research direction of residential carbon emissions should focus more on spatial analysis and trend prediction based on intelligent methods under a low-carbon background.
How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China
Authors: Ma Xiang-Fei , Zhang Ru , Ruan Yi-Fan
Published: January 2023

Evaluating the level of green development is of great significance to better implement the concept of green development. By constructing an evaluation index system for green development, this paper comprehensively uses the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and coefficient of variation method to evaluate the green development level of 30 provinces in China from 2010 to 2019 and analyzes the regional differences of green development in China. The research findings are as follows: First, the level of green development in China is low but shows a slow rise trend, from 2010 to 2019; China’s green development level rises from 0.274 to 0.317, an increase of 15.7%. Secondly, regional differences of green development in China are obvious, with the level ranking from high to low as eastern, western, and central regions. Third, regional differences in China’s green development first widen and then narrow, with the variation coefficient of green development in 30 provinces and eastern, central, and western regions of China showing an inverted U-shaped trend of first increasing and then decreasing. Fourth, the regional difference of green development in eastern China is largest, followed by western China, and the smallest is central China. Finally, based on research findings, relevant policy recommendations are put forward.
Cumulative Ecological Risk and Academic Burnout in Chinese College Students: A Moderated Mediation Model
Authors: Liu Yan , Zhang Yan , Peng Cong , Li Yaojin , Tan Qianbao
Published: January 2023

This study explored the relationship between cumulative ecological risk exposure and academic burnout among Chinese college students and the mediating and moderating effects of neuroticism and gender, respectively. A total of 580 college students were selected as participants. They completed a battery of questionnaires that measured cumulative ecological risk, neuroticism, and academic burnout. The results showed that: (1) cumulative ecological risk was positively related to neuroticism and academic burnout; (2) cumulative ecological risk positively predicted academic burnout; (3) neuroticism partly mediated the relationship between cumulative ecological risk and academic burnout; and (4) gender moderated the effect of cumulative ecological risk and academic burnout. A high level of cumulative ecological risk had a greater impact on neuroticism among women, compared to men. These findings advance our current knowledge of the specific effects of cumulative ecological risk on academic burnout and the underlying internal mechanisms of this relationship. Furthermore, this study provides a constructive perspective on preventing and reducing academic burnout among college students.
Impact of Population Aging on Carbon Emissions in China: An Empirical Study Based on a Kaya Model
Authors: Xiang Hua , Zeng Xueting , Han Hongfang , An Xianjuan
Published: January 2023

As the world’s largest developing country, China is facing the serious challenge of reducing carbon emissions. The objective of this study is to investigate how China’s aging population affects carbon emissions from the production and consumption sides based on an improved Kaya model. The advantage of the Kaya model is that it links economic development to carbon dioxide generated by human activities, which makes it possible to effectively analyze carbon emissions in relation to the structure of energy consumption and human activities. Based on different energy consumption structures and technological innovation levels, a threshold effect model is constructed. The results show that: (1) There is an inverted U-shaped curve relationship between population aging and carbon emissions in China. (2) Energy consumption structure and technological innovation thresholds can be derived for the impact of population aging on carbon emissions, with thresholds of 3.275 and 8.904 identified, respectively. (3) Population aging can reduce carbon emissions when the energy consumption structure does not exceed the threshold value. (4) There is no significant intervention effect of technological innovation on the relationship between population aging and carbon emissions. Based on the research results, some countermeasures and suggestions to reduce carbon emissions are proposed.
Counseling for Health: How Psychological Distance Influences Continuance Intention towards Mobile Medical Consultation
Authors: Lu Fuyong , Wang Xintao , Huang Xian
Published: January 2023

As mobile healthcare services entered the public sight with high frequency during the COVID-19 pandemic, patients are increasingly recognizing the effectiveness of mobile medical consultation (MMC). Earlier studies have investigated what influences continuance intention (CI) towards MMC, but few studies have scrutinized it from the perspective of patients’ psychological distance. We formulated a framework to examine the psychological factors influencing CI towards MMC by integrating the information systems continuance model and psychological distance theory. The framework was validated using the partial least squares structural equation modeling (PLS-SEM) approach and data from 475 MMC users in China. The empirical results revealed that immediacy, telepresence, intimacy, and substitutability were significant predictors of CI, while satisfaction mediated these pathways. Pandemic-induced anxiety positively moderated the effect of immediacy on satisfaction and the effect of satisfaction on CI. Practical implementations for MMC healthcare practitioners, designers, and marketers are drawn.
Not Getting Vaccinated? It Is a Matter of Problem-Solving Abilities and Socio-Cognitive Polarization
Authors: Cancer Alice , Salvi Carola , Antonietti Alessandro , Iannello Paola
Published: January 2023

The anti-COVID-19 vaccination campaign in the United States provided a significant contribution to the control of the virus spread. Despite the recommendations by public health institutions, vaccine skepticism and hesitancy contributed to low vaccine uptake, thus possibly disrupting the management of preventable diseases associated with the COVID-19 infection. The process that led individuals to accept COVID-19 vaccines required the ability to gather, synthesize, and weigh-up information within a novel, dynamically changing, complex, and ambiguous context. To deal with such complexity, we hypothesized that both the ability of reflection and flexible adaptation played a fundamental role. Based on previous research on cognitive predictors of vaccine refusal, we decided to investigate the combined role of two constructs, namely, problem-solving skills and socio-cognitive polarization (SCP), on vaccine acceptance and uptake. Two-hundred-seventy-seven US participants completed an online survey aimed to measure problem-solving ability, through a rebus puzzles task, and SCP, through a composite measure of absolutist thinking, political conservatism, and xenophobia. Mediation analyses indicated that SCP mediated the association between problem-solving ability and vaccine acceptance, so lower problem-solving abilities associated with higher polarization predicted vaccine rejection. Thus, our findings suggested that low problem-solving skills may represent a risk factor for COVID-19 vaccine refusal, with cognitive and social rigidity playing a crucial role in undermining the anti-COVID-19 vaccine uptake.
Single-Cell Transcriptomics Analysis Reveals a Cell Atlas and Cell Communication in Yak Ovary
Authors: Pei Jie , Xiong Lin , Guo Shaoke , Wang Xingdong , La Yongfu , Chu Min , Liang Chunnian , Yan Ping , Guo Xian
Published: January 2023

Yaks (Bos grunniens) are the only bovine species that adapt well to the harsh high-altitude environment in the Qinghai-Tibetan plateau. However, the reproductive adaptation to the climate of the high elevation remains to be elucidated. Cell composition and molecular characteristics are the foundation of normal ovary function which determines reproductive performance. So, delineating ovarian characteristics at a cellular molecular level is conducive to elucidating the mechanism underlying the reproductive adaption of yaks. Here, the single-cell RNA-sequencing (scRNA-seq) was employed to depict an atlas containing different cell types with specific molecular signatures in the yak ovary. The cell types were identified on the basis of their specifically expressed genes and biological functions. As a result, a cellular atlas of yak ovary was established successfully containing theca cells, stromal cells, endothelial cells, smooth muscle cells, natural killer cells, macrophages, and proliferating cells. A cell-to-cell communication network between the distinct cell types was constructed. The theca cells were clustered into five subtypes based on their biological functions. Further, CYP11A1 was confirmed as a marker gene for the theca cells by immunofluorescence staining. Our work reveals an ovarian atlas at the cellular molecular level and contributes to providing insights into reproductive adaption in yaks.