bluuebunny/crossref_metadata_2025 huggingface dataset:
Physio-biochemical and molecular stress regulators and their crosstalk for low-temperature stress responses in fruit crops: A review
Authors: Goswami Amit Kumar , Maurya Naveen Kumar , Goswami Suneha , Bardhan Kirti , Singh Sanjay Kumar , Prakash Jai , Pradhan Satyabrata , Kumar Amarjeet , Chinnusamy Viswanathan , Kumar Prabhat , Sharma Radha Mohan , Sharma Stuti , Bisht Deepak Singh , Kumar Chavlesh
Published: December 2022

Low-temperature stress (LTS) drastically affects vegetative and reproductive growth in fruit crops leading to a gross reduction in the yield and loss in product quality. Among the fruit crops, temperate fruits, during the period of evolution, have developed the mechanism of tolerance, i.e., adaptive capability to chilling and freezing when exposed to LTS. However, tropical and sub-tropical fruit crops are most vulnerable to LTS. As a result, fruit crops respond to LTS by inducing the expression of LTS related genes, which is for climatic acclimatization. The activation of the stress-responsive gene leads to changes in physiological and biochemical mechanisms such as photosynthesis, chlorophyll biosynthesis, respiration, membrane composition changes, alteration in protein synthesis, increased antioxidant activity, altered levels of metabolites, and signaling pathways that enhance their tolerance/resistance and alleviate the damage caused due to LTS and chilling injury. The gene induction mechanism has been investigated extensively in the model crop Arabidopsis and several winter kinds of cereal. The ICE1 (inducer of C-repeat binding factor expression 1) and the CBF (C-repeat binding factor) transcriptional cascade are involved in transcriptional control. The functions of various CBFs and aquaporin genes were well studied in crop plants and their role in multiple stresses including cold stresses is deciphered. In addition, tissue nutrients and plant growth regulators like ABA, ethylene, jasmonic acid etc., also play a significant role in alleviating the LTS and chilling injury in fruit crops. However, these physiological, biochemical and molecular understanding of LTS tolerance/resistance are restricted to few of the temperate and tropical fruit crops. Therefore, a better understanding of cold tolerance’s underlying physio-biochemical and molecular components in fruit crops is required under open and simulated LTS. The understanding of LTS tolerance/resistance mechanism will lay the foundation for tailoring the novel fruit genotypes for successful crop production under erratic weather conditions.
Effect of different sport environments on proactive and reactive motor inhibition: A study on open- and closed-skilled athletes via mouse-tracking procedure
Authors: Bravi Riccardo , Gavazzi Gioele , Benedetti Viola , Giovannelli Fabio , Grasso Stefano , Panconi Giulia , Viggiano Maria Pia , Minciacchi Diego
Published: December 2022

This study aimed to investigate the effect of different sport environments (open-and closed-skill sports) on proactive and reactive inhibitory processes as two distinct components of motor inhibition. A mouse-tracking procedure was employed to compare behavioral performance among three groups of participants (tennis players, swimmers and non-athletes) in non-sport-specific cued Go/No-Go (GNG) and Stop Signal Task (SST), which mainly engage proactive and reactive inhibitory control, respectively. Reaction times (RTs), inhibitory failures, and Stop Signal Reaction Times (SSRTs) were measured. To investigate dynamic aspects of inhibitory control, movement trajectories classified as one-shot (absence of trajectory alteration reflected in a steep slope) or non-one-shot (non-linear/multipeaked trajectory, with one or multiple corrections) were analyzed and compared among groups. Results showed no group differences in RTs in Go/No-Go and Stop conditions. SSRTs were significant shorter for the athletes than non-athletes in SST, but no differences emerged for inhibitory failures in cued GNG. During inhibitory failures athletes showed higher proportion of non-one-shot movements than non-athletes. Higher proportion of non-one-shot profiles was observed in cued GNG compared to SST. Finally, no differences between open-and closed-skilled athletes were found in both tasks. Our findings suggest that both proactive and reactive inhibitory controls do benefit from sport practice, but open-and closed-skill sports do not differ in influencing inhibitory processes. Movement profile analysis could be a promising, complementary behavioral analysis to integrate for more fine-grained evaluation and differentiation of inhibitory motor control in athletes, specifically when using GNG tasks.
Risky sexual behavior among people living with HIV/AIDS in Andabet district, Ethiopia: Using a model of unsafe sexual behavior
Authors: Worede Jejaw Berihun , Mekonnen Alemayehu Gonie , Aynalem Seblewongiel , Amare Nakachew Sewnet
Published: December 2022

Introduction

Human immunodeficiency virus (HIV) infection continues to be a major public health problem in Ethiopia. Previous studies have described risky sexual behavior and associated factors among HIV–positive people. These studies, however, did not use a model of unsafe sexual behavior that could address both subjective and objective factors of sexual activity, and there is no study that examines the distal aspects of risky sexual behavior among people living with HIV/AIDS in Ethiopia. Therefore, this study aimed to examine the risky sexual behavior among people living with HIV/AIDS using a model of unsafe sexual behavior.

Methods

An institutional-based study was conducted from March to April 2022. The sample size was determined by using Sloven's formula. In this study, both quantitative and qualitative methods were employed. Study participants were selected using systematic sampling method. An interviewer-administered questionnaire was used to collect the data. Descriptive statistics and correlation tests were computed to analyze the data. The qualitative data was analyzed thematically.

Results

This study included a total of 181 PLWHA clients. The average score for participants' perception regarding the facts of HIV/AIDS was 48.7% (95% CI: 38.9, 58.4). Three months prior to the study, 46.3% of study participants had engaged in at least one risky sexual activity (95% CI: 33.8, 65.4). The correlation model revealed a positive correlation between living in a rural area and risky sexual behavior (p-value = 0.001). Furthermore, a poor perception of HIV risks was associated with risky sexual behavior (p-value = 0.003). Economic issues, stigma and discrimination, and usage of substances were also identified as contributing factors to unsafe sexual activity in the qualitative data.

Conclusions

A high proportion of PLWHA clients had engaged in at least one risky sexual activity in the 3 months prior to the study. It is not enough to be on ART; additional educational interventions that shape the sexual behavior of PLWHA clients must be considered.
A Proposal of Printed Table Digitization Algorithm with Image Processing
Authors: Shi Chenrui , Funabiki Nobuo , Huo Yuanzhi , Mentari Mustika , Suga Kohei , Toshida Takashi
Published: December 2022

Nowadays, digital transformation (DX) is the key concept to change and improve the operations in governments, companies, and schools. Therefore, any data should be digitized for processing by computers. Unfortunately, a lot of data and information are printed and handled on paper, although they may originally come from digital sources. Data on paper can be digitized using an optical character recognition (OCR) software. However, if the paper contains a table, it becomes difficult because of the separated characters by rows and columns there. It is necessary to solve the research question of “how to convert a printed table on paper into an Excel table while keeping the relationships between the cells?” In this paper, we propose a printed table digitization algorithm using image processing techniques and OCR software for it. First, the target paper is scanned into an image file. Second, each table is divided into a collection of cells where the topology information is obtained. Third, the characters in each cell are digitized by OCR software. Finally, the digitalized data are arranged in an Excel file using the topology information. We implement the algorithm on Python using OpenCV for the image processing library and Tesseract for the OCR software. For evaluations, we applied the proposal to 19 scanned and 17 screenshotted table images. The results show that for any image, the Excel file is generated with the correct structure, and some characters are misrecognized by OCR software. The improvement will be in future works.
Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
Authors: Patel Krishna , Bhatt Chintan , Mazzeo Pier Luigi
Published: December 2022

One of the most critical issues that the marine surveillance system has to address is the accuracy of its ship detection. Since it is responsible for identifying potential pirate threats, it has to be able to perform its duties efficiently. In this paper, we present a novel deep learning approach that combines the capabilities of a Graph Neural Network (GNN) and a You Only Look Once (YOLOv7) deep learning framework. The main idea of this method is to provide a better understanding of the ship’s presence in harbor areas. The three hyperparameters that are used in the development of this system are the learning rate, batch sizes, and optimization selection. The results of the experiments show that the Adam optimization achieves a 93.4% success rate when compared to the previous generation of the YOLOv7 algorithm. The High-Resolution Satellite Image Dataset (HRSID), which is a high-resolution image of a synthetic aperture radar, was used for the test. This method can be further improved by taking into account the various kinds of neural network architecture that are commonly used in deep learning.
Global Well-Posedness of the Dissipative Quasi-Geostrophic Equation with Dispersive Forcing
Authors: Sun Jinyi , Zou Lingjuan
Published: December 2022

The dissipative quasi-geostrophic equation with dispersive forcing is considered. By striking new balances between the dispersive effects of the dispersive forcing and the smoothing effects of the viscous dissipation, we obtain the global well-posedness for Cauchy problem of the dissipative quasi-geostrophic equation with dispersive forcing for arbitrary initial data, provided that the dispersive parameter is large enough.
Bioprocessing by Decellularized Scaffold Biomaterials in Cultured Meat: A Review
Authors: Lu Hongyun , Ying Keqin , Shi Ying , Liu Donghong , Chen Qihe
Published: December 2022

As novel carrier biomaterials, decellularized scaffolds have promising potential in the development of cellular agriculture and edible cell-cultured meat applications. Decellularized scaffold biomaterials have characteristics of high biocompatibility, bio-degradation, biological safety and various bioactivities, which could potentially compensate for the shortcomings of synthetic bio-scaffold materials. They can provide suitable microstructure and mechanical support for cell adhesion, differentiation and proliferation. To our best knowledge, the preparation and application of plant and animal decellularized scaffolds have not been summarized. Herein, a comprehensive presentation of the principles, preparation methods and application progress of animal-derived and plant-derived decellularized scaffolds has been reported in detail. Additionally, their application in the culture of skeletal muscle, fat and connective tissue, which constitute the main components of edible cultured meat, have also been generally discussed. We also illustrate the potential applications and prospects of decellularized scaffold materials in future foods. This review of cultured meat and decellularized scaffold biomaterials provides new insight and great potential research prospects in food application and cellular agriculture.
Investigation on Enamel and Dentine of Tooth through 1D Photonic Structure to Identify the Caries in Human Teeth
Authors: Alam Mohammad Khursheed , Dhasarathan Vigneswaran , Aly Moustafa H. , Zaman Mahmud Uz , Ganji Kiran Kumar , Basri Rehana , Munisekhar Manay Srinivas , Nagarajappa Anil Kumar
Published: December 2022

In this research, a one-dimensional (1D) photonic structure was employed to study the nature of both enamel and dentine teeth at the signal of 1.8 THz. A simple three layer one-dimensional crystal is chosen to avoid fabrication intricacy. The materials and methods for sample preparations are discussed. The principle of investigation of caries in the teeth relies on the amount of reflected signal from the structure. Similarly, reflectance is a function of refractive indices and thickness of each layer, the nature of both substrate and infiltrated materials, and the configuration of the structure. Apart from this, the fabrication process of one-dimensional structure and experimental set-up was proposed in this article. The numerical treatment is explained here to obtain reflectance, and subsequently, the output potential. Comparison studies on output potential between enamel and dentine are also shown through graphical representation. The output result in terms of milli-Volt (mV) were obtained at the output end and collected at the photodiode. Interesting results were also observed at the photodetector. For example; the output potential of the reflected signal is around 0.18 mV for both enamel and dentine teeth whereas the potential is more than 0.26 mV and 0.31 mV for caries in dentine and enamel, respectively. Finally, it was inferred that the nature of teeth pertaining to the caries in the enamel and dentine teeth can be investigated by identifying the amount of potential at the output end.
Historical Biogeography of Earwigs
Authors: Fattorini Simone
Published: December 2022

The Dermaptera are an insect order exhibiting their highest diversity in the tropical areas of the southern hemisphere. This pattern has been considered a reflection of a Gondwanan origin. However, this hypothesis has not been tested through analytical methods. In this paper, the world distribution of earwigs was analysed by using the ‘Cladistic Analysis of Distributions and Endemism’ (CADE), a method which groups areas of endemism on the basis of shared distributions and phylogenetic relationships among taxa. In addition, clustering techniques were applied to depict biotic relationships based on similarity indices. Results of CADE support the idea that Gondwanan fragmentation exerted a crucial role in shaping the current distribution of the main clades of earwigs. However, the relationships between India with South East Asia suggested a biotic interchange occurred after the Indian collision with the Eurasian plate. The overall scenario emerging from cluster analyses revealed a strong influence of dispersal events. Overall, the distribution of earwig major clades indicates that their biogeographical history was mainly characterized by vicariance events (led by the break-up of Gondwana) followed by large scale dispersal processes constrained by the Himalayan orogenesis and the presence of colder temperatures, which have largely hampered the colonization of the northern hemisphere.
Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling
Authors: Grešová Katarína , Alexiou Panagiotis , Giassa Ilektra-Chara
Published: December 2022

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA–RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.
Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence—I
Authors: Wei Dongqing , Peslherbe Gilles , Selvaraj Gurudeeban , Wang Yanjing
Published: December 2022

Artificial intelligence (AI) has emerged as a key player in modern healthcare, especially in the pharmaceutical industry for the development of new drugs and vaccine candidates [...]
Differential Mobilization of the Phospholipid and Triacylglycerol Pools of Arachidonic Acid in Murine Macrophages
Authors: Bermúdez Miguel A. , Rubio Julio M. , Balboa María A. , Balsinde Jesús
Published: December 2022

Innate immune cells such as monocytes and macrophages contain high levels of arachidonic acid (AA), part of which can be mobilized during cellular activation for the formation of a vast array of bioactive oxygenated metabolites. Monocytes and macrophages present in inflammatory foci typically incorporate large amounts of AA, not only in membrane phospholipids, but also in neutral lipids such as triacylglycerol. Thus, it was of interest to investigate the metabolic fate of these two AA pools in macrophages. Utilizing a variety of radiolabeling techniques to distinguish the phospholipid and triacylglycerol pools, we show in this paper that during an acute stimulation of the macrophages with yeast-derived zymosan, the membrane phospholipid AA pool acts as the major, if not the only, source of releasable AA. On the contrary, the AA pool in triacylglycerol appears to be used at a later stage, when the zymosan-stimulated response has declined, as a source to replenish the phospholipid pools that were consumed during the activation process. Thus, phospholipids and triacylglycerol play different in roles AA metabolism and dynamics during macrophage activation.
Design of a Felid-like Humanoid Foot for Stability Enhancement
Authors: Cai Zhaoyang , Chen Xuechao , Li Qingqing , Liu Huaxin , Yu Zhangguo
Published: December 2022

The foot is an important part of humanoid robot locomotion that can help with shock absorption while making contact with the ground. The mechanism of the foot directly affects walking stability. A novel foot mechanism inspired by the toes of felids is proposed. The foot has four bionic modules with soft pads and sharp claws installed at the four corners of a flat foot. This foot can reduce the impact experienced during foot landing and increase the time that the foot is in contact with the ground, which can improve the adaptability of the robot to different ground surface conditions with different levels of stiffness. The main structure of the bionic module is a four-bar linkage consisting of a slide way and a spring. Furthermore, the length of the four-bar linkage and the posture of the claw during insertion into soft ground are optimized to improve the stability and buffering performance. The validity of the proposed foot mechanism has been proved in simulations.
Design and Test of Tread-Pattern Structure of Biomimetic Goat-Sole Tires
Authors: Zhang Fu , Qiu Yubo , Teng Shuai , Cui Xiahua , Wang Xinyue , Sun Haoxun , Ali Shaukat , Guo Zhijun , Wang Jiajia , Fu Sanling
Published: December 2022

To solve the technical problem that wheeled vehicles are prone to skidding on complex ground, due to poor adhesion performance, a tire-tread-structure design method based on the bionic principle is proposed in this paper. The 3D model of a goat’s foot was obtained using reverse engineering technology, and the curve equation was fitted by extracting the contour data of its outer-hoof flap edge, which was applied to the tire-tread-structure design. The bionic and herringbone-pattern rubber samples were manufactured, and a soil-tank test was carried out using an electronic universal tensile-testing machine, in order to verify the simulation results. The results showed that the overall adhesion of the bionic tread-pattern was greater than that of the normal tread-pattern with the same load applied and the same height and angle of the tread-pattern structure, and the maximum adhesion was increased by 14.23%. This research will provide a reference for optimizing the pattern structure and thus improving the passing performance of wheeled vehicles.
Learning to Cycle: Is Velocity a Control Parameter for Children’s Cycle Patterns on the Balance Bike?
Authors: Mercê Cristiana , Cordovil Rita , Catela David , Galdino Flávia , Bernardino Mafalda , Altenburg Mirjam , António Gonçalo , Brígida Nancy , Branco Marco
Published: December 2022

The balance bike (BB) has been pointed out as being the most efficient learning bicycle due to its inherent stimulation of balance. However, the process of acquiring the control of balance on the BB has not been explored. This study aimed to: (i) categorize the cycle patterns of children on the BB, (ii) compare the cycle patterns in different stages of learning (before and after six sessions of a BB practice program), and (iii) verify whether velocity is a control parameter leading to transitions between different cycle patterns on a BB. The data were collected during the Learning to Cycle program from 12 children aged 6.06 ± 1.25 years. The velocity was measured using an inertial sensor. Seven different movement patterns were captured and categorized through video analysis. After practice, there was an increase in the mean number of different patterns and in the global mean and maximum velocity. These were interpreted as an improvement of the motor competence in the use of the BB. The results obtained support the hypothesis that velocity is a control parameter which leads to the emergence of diverse patterns of behavior. As the speed increased, the amount of foot contact with the ground became less frequent and the locomotor modes that imply that longer flight phases began to emerge.
Teachers’ Knowledge and Attitudes toward Sustainable Inclusive Education for Students with Emotional and Behavioral Disorders
Authors: Alkahtani Keetam D. F.
Published: December 2022

With the growing number of students diagnosed with emotional and behavioral disorders (EBDs), there is a need to understand how teachers perceive those students. A mixed-method research design was used to determine whether there was a relationship between the level of general education teachers’ knowledge of emotional and behavioral disorders (EBDs) and their attitudes toward students with EBDs. The participants in the study were 782 certified elementary regular education teachers. Quantitative data were collected using two questionnaires, the Knowledge of Emotional and Behavioral Disorders Questionnaire (KEBDQ) and the General Educators’ Attitudes toward Emotional and Behavioral Disorders Questionnaire (GEAEBDQ). Descriptive analysis and Pearson correlation coefficients were used to analyze the data. The results of the quantitative data indicated that the teachers’ responses reflected both negative attitudes toward students with EBDs and poor knowledge of EBDs. A significant correlation was found between teachers’ level of knowledge and their attitudes toward students with EBDs. Qualitative data gathered from the interviews were analyzed using the thematic analysis approach. The qualitative findings are in line with the quantitative results. Implementation of professional development training to support general education teachers to acquire knowledge of EBDs may improve teachers’ perceptions of students with EBDs.
Predictors of Early Mandarin Chinese Character Reading Development
Authors: Yuan Han , Segers Eliane , Verhoeven Ludo
Published: December 2022

In the present study, we investigated the development of Chinese character reading and its predictors in 55 children from K3 (the last year of kindergarten) to G1 (first grade) in Mainland China. It was examined to what extent first graders’ Mandarin Chinese character reading was related to their phonological awareness, Pinyin letter knowledge, and visual perception skills in kindergarten. The results showed that phonological awareness, Pinyin letter knowledge, and Chinese character reading improved from kindergarten to Grade 1, with Pinyin showing ceiling effects in Grade 1. Children’s character reading in first grade was not predicted from phonological awareness in kindergarten. However, visual discrimination showed an indirect effect on Grade 1 character reading via its effect on kindergarten character reading. It can be concluded that both kindergarten visual discrimination and character reading ability facilitate first-grade reading ability for children in Mainland China.
Impact of Blood Loss on Renal Function and Interaction with Ischemia Duration after Nephron-Sparing Surgery
Authors: Buse Stephan , Mager René , Mazzone Elio , Mottrie Alexandre , Frees Sebastian , Haferkamp Axel
Published: December 2022

Objectives: Nephron-sparing surgery (NSS) exposes the kidney to ischemia–reperfusion injury. Blood loss and hypotension are also associated with kidney injury. We aimed to test the hypothesis that, during NSS, both ischemia duration and blood loss significantly affect postoperative renal function and that their effects interact. Methods: Consecutive patients undergoing NSS were enrolled. The primary endpoint was renal function expressed as the absolute delta between preoperative and postoperative peak creatinine. We developed a generalized linear model with the ischemia duration and absolute hemoglobin difference as independent variables, their interaction term, and the RENAL score. The model was than expanded to include a history of hypertension (as a proxy for hypotension susceptibility) and related interaction terms. Further, we described the perioperative and mid-term oncological outcomes. Results: A total of 478 patients underwent NSS, and 209 (43.7%) required ischemia for a mean of 10.9 min (SD 8). Both the ischemia duration (partial eta 0.842, p = 0.006) and hemoglobin difference (partial eta 0.933, p = 0.029) significantly affected postoperative renal function, albeit without evidence of a significant interaction (p = 0.525). The RENAL score also significantly influenced postoperative renal function (p = 0.023). After the addition of a previous history of hypertension, the effects persisted, with a significant interaction between blood loss and a history of hypertension (p = 0.02). Conclusions: Ischemia duration and blood loss had a similar impact on postoperative renal function, albeit without potentiating each other. While the surgical technique and ischemia minimization remain crucial to postoperative kidney function, increased awareness of conscious hemodynamic management appears warranted.
Identifying and Remediating Soil Microbial Legacy Effects of Invasive Grasses for Restoring California Coastal Sage Scrub Ecosystems
Authors: Pickett Brooke , Irvine Irina C. , Arogyaswamy Keshav , Maltz Mia R. , Shulman Hannah , Aronson Emma L.
Published: December 2022

As invasive grasses encroach upon native ecosystems, they have the potential to transform intact California coastal sage scrub (CSS) into non-native grasslands. This occurs—in part—due to legacy effects: changes in soil microbial composition elicited by grass invasions with long-term impacts on soil and native vegetation. Along with direct effects on CSS vegetation, legacy effects may alter microbial communities which may in turn impact native plant growth. To tease apart these factors, we monitored growth in three CSS species inoculated with either uninvaded soil or sterilized uninvaded soil that were subsequently planted into a site characterized by observed microbial legacy effects. Our findings indicate native plant growth can be explained by changes in soil microorganisms. Specifically, native Artemisia californica and Baccharis pilularis grew larger in plots with unsterilized uninvaded soil inoculum, which contained a larger abundance of Gemmatimonadetes and Glomus, compared to the sterilized soil inoculum plots. Therefore, microbial augmentation may not only improve restoration in post-invasive sites but long-term changes in soil microbial communities may be linked to native plant growth. Furthermore, adding uninvaded soil replete with native microbes has the potential to support restoration of invaded sites by promoting native plant survival and establishment in these restored ecosystems.
Rare but Not Gone: A Relict Population of the Black Sea Ship Sturgeon Acipenser nudiventris Persists in the Rioni River, Georgia
Authors: Beridze Tamar , Scheele Fleur , Edisherashvili Tamari , Anderson Cort
Published: December 2022

Historically, the ship sturgeon (Acipenser nudiventris) occurred in the Aral, Caspian, Azov, and Black Sea basins. However, its numbers decreased dramatically during the 20th century. It is now considered extirpated from the Aral, Azov, and Black Seas, and has almost disappeared in the Caspian Sea. A. nudiventris is listed as Critically Endangered on the IUCN Red List and, in Georgia, the species has been undetected for the last three decades. We collected 22 sightings, including nine genetic samples taken from fin clips of ship sturgeon from the Rioni River in Georgia during 2020–2022. For the genetic samples, the mitochondrial DNA control region was used for species identification. Because cases of sturgeon inter-species hybridization have been reported in the Rioni River, we used species-specific diagnostic markers and ship sturgeon-specific microsatellite markers for detecting hybridization with other sturgeon species. In addition, we used a sex-specific marker for sex identification. Based on the maternal identification, all nine individuals are identified as ship sturgeon, representing one haplotype, and the haplotype is different from all other A. nudiventris haplotypes available in GenBank. Based on genetic analysis, the specimens did not show signs of hybridization with other locally occurring species. We conclude that ship sturgeon still live in the Rioni River, and are a remnant of an older, preexisting Black Sea ship sturgeon population.
A Blockchain-Based Regulatory Framework for mHealth
Authors: Marbouh Dounia , Simsekler Mecit Can Emre , Salah Khaled , Jayaraman Raja , Ellahham Samer
Published: December 2022

Mobile health (mHealth) is playing a key role in facilitating health services for patients. Such services may include remote diagnostics and monitoring, chronic conditions management, preventive medicine, and health promotion. While mHealth has gained significant traction during the COVID-19 pandemic, they may pose safety risks to patients. This entails regulations and monitoring of shared data and management of potential safety risks of all mHealth applications continuously and systematically. In this study, we propose a blockchain-based framework for regulating mHealth apps and governing their safe use. We systematically identify the needs, stakeholders, and requirements of the current mHealth practices and regulations that may benefit from blockchain features. Further, we exemplify our framework on a diabetes mHealth app that supports safety risk assessment and incident reporting functions. Blockchain technology can offer a solution to achieve this goal by providing improved security, transparency, accountability, and traceability of data among stakeholders. Blockchain has the potential to alleviate existing mHealth problems related to data centralization, poor data quality, lack of trust, and the absence of robust governance. In the paper, we present a discussion on the security aspects of our proposed blockchain-based framework, including limitations and challenges.
An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality
Authors: Jiang Wenxin , Zhu Guochang , Shen Yiyun , Xie Qian , Ji Min , Yu Yongtao
Published: December 2022

Air quality has a significant influence on people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner.
Adaptive Backward/Forward Sweep for Solving Power Flow of Islanded Microgrids
Authors: Kumar Abhimanyu , Kumar Abhishek , Mallipeddi Rammohan , Lee Dong-Gyu
Published: December 2022

This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurate, and easy to implement, and it can be considered an effective tool for planning and analyzing islanded DRACMs.
Modeling and Simulation of a Wave Energy Converter: Multibody System Coupled to Fluid-Film Lubrication Model and Thermal Analysis
Authors: Senatore Adolfo , De Simone Alex
Published: December 2022

Sea wave energy is being increasingly regarded as one of the most promising sources of renewable energy. This paper deals with the modeling and simulation of an onshore wave energy converter system designed by UMBRA GROUP SpA. Several topics are addressed. Starting from the multibody modeling strategy, this paper delves more deeply into the mechanical efficiency evaluation of the ball-screw in the elastohydrodynamic lubrication regime, the core of the energy conversion process, as well as the thermal characterization of the power take-off module, based on the lumped-parameter and finite element method models. High values of ball-screw indirect efficiency have been observed, ranging from 73% to 97%; these results appear even more encouraging when compared to the performance of alternative energy-consuming technologies. Thermal analysis, on the other hand, provided a maximum temperature increase of 40 °C, allowing for the aversion of any structural collapse and the realistic identification of the lubrication regime, which turned out to be mostly mixed. Finally, an inverse multibody dynamic analysis is performed, and the most interesting simulation results are collected to prove the effectiveness of the proposed approach.
Energy Harvesting in the Crane-Hoisting Mechanism
Authors: Haniszewski Tomasz , Cieśla Maria
Published: December 2022

The subject of the model research contained in this paper is an application of a motion energy–harvesting device on a crane-hoisting mechanism to power independent measurement devices. Numerical experiments focused on the selected motion energy–harvesting device (M-EHS) and its configuration properties in the context of energy-harvesting efficiency in the case of using it on a crane. The results of the computer simulations were limited to the initial specified conditions for the harvester and the movement of the conditions of the crane-hoisting mechanism. The article compares the energy efficiency for the selected construction and parameters of the harvester for specific hoisting speed and the arm length of the motion conversion system. For this purpose, the initial conditions for the crane and the configuration of parameters of the energy harvester were assumed. The results are visualized on the diagram of RMS voltage induced on piezoelectric elements, showing the impact of individual solutions of the proposed motion energy–harvesting device on the efficiency of energy harvesting. The results of the efficiency of the simulations show that the motion harvester ranges from 0.44 V to 14.22 V, depending on the speed of the crane-hoisting mechanism and the length of the arm of the motion conversion system. Still, the design allows for an adjustment to the given conditions by tuning up the M-EHS to a specified excitation frequency and working conditions.
A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties
Authors: Jaramillo Manuel , Carrión Diego , Muñoz Jorge
Published: December 2022

This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution).
Effect Quantification of BESS Providing Frequency Response on Penetration Limit of VER in Power Systems
Authors: Choi Woo Yeong , Kook Kyung Soo , Yoo Hyeong-Jun
Published: December 2022

Increasing the penetration of variable energy resources (VER) can reduce the inertia and frequency response performance of power systems supported by replacement synchronous power generation. Therefore, it is necessary to manage the VER penetration limit in power systems for stable operation and to increase the operability to the desired level. This study proposes a method to evaluate and quantify the effect of increasing the penetration limit of VER by controlling a battery energy storage system (BESS). The BESS can provide a fast response, but frequency response performance varies depending on the operating conditions. In the proposed quantification method, various control methods of a BESS, operating conditions of the power system, and penetration conditions of additional VER were analyzed, and the effect of the BESS on increasing the penetration limit of VER was evaluated. This evaluation and analysis enabled the selection of the BESS operating conditions to achieve the target VER capacity in the power system. The proposed quantification method was analyzed through simulations based on the Korean power system model. Therefore, it can contribute to estimating the required performance of the BESS for each system operating condition required to achieve the VER target.
Pore-Scale Simulation of Gas and Water Two-Phase Flow in Rough-Walled Fractures Using the Volume of Fluid Method
Authors: Zhu Qingzhong , Yang Yanhui , Zhang Xueying , Wang Sanshuai , Yang Jinzhao , Zhang Jiyuan
Published: December 2022

The gas and water flow behavior in rough-walled hydrophilic fractures at the pore scale is crucial for understanding the gas production characteristics of naturally fractured formations. This paper presents a systematic analysis of the gas and water flow characteristics in both the single-fracture and Y-shaped junction fracture models using the volume of fluid (VOF) method. Numerical simulations showed that the gas/water rate ratio is the most significant factor influencing gas bubble/slug geometry, phase distribution, and saturation. The effect of fracture roughness and tortuosity is less significant than the gas/water ratio, whereas the total fluid rate has a negligible effect. For Y-shaped junction models, the phase distribution and referential pathways are predominantly controlled only by the channel aperture ratio, whereas the effect of the intersecting angle and fluid flow rate can be neglected.
Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning
Authors: Rodrigues João Antunes , Martins Alexandre , Mendes Mateus , Farinha José Torres , Mateus Ricardo J. G. , Cardoso Antonio J. Marques
Published: December 2022

Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the Elbow Method, cover almost all the variance of the data under study. Knowing the importance that the quality of the lubricants has in the functioning and classification of the state of machines, a lubricant classification algorithm was developed using Neural Networks. The lubricant classifier results were 98% accurate compared to human expert classifications. The main gap identified in the research is that the found classification works only carried out classifications of present, short-term, or mid-term failures. To close this gap, the work presented in this paper conducts a long-term classification.
A Review of Torque Ripple Reduction Design Methods for Radial Flux PM Motors
Authors: Suriano-Sánchez Sergio I. , Ponce-Silva Mario , Olivares-Peregrino Víctor H. , De León-Aldaco Susana E.
Published: December 2022

Nowadays the efficient use of energy is a major issue in applications such as electric vehicles. However, there are some phenomena that affect electric vehicle performance. One of those phenomena is the torque ripple of electric motors, which interferes with traction and the suspension system (causing vibration that stresses this system), and it can also introduce electric current harmonics into the battery, reducing its life, since torque ripple is partly a consequence of non-sinusoidal back EMF. For those reasons this is a topic worth investigating. The torque ripple of permanent magnets (PM) motors can be reduced in design or through control. Since control techniques have been reviewed and design methods have not, this paper presents a comparison of several design techniques to reduce torque ripple by different approaches and categorize them to show the characteristics of each group. A discussion is then made about the advantages and disadvantages of the designs, in general, as well as some comments about the missing, but important information, in the papers, such as the effects on efficiency. The study shows that a combination of methods provide the best results, although it complicates fabrication and suggests that this is a promising line of future investigation on torque ripple reduction methods.
Implementation of the Canny Edge Detector Using a Spiking Neural Network
Authors: Vemuru Krishnamurthy V.
Published: December 2022

Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.