bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection
Authors: Abdul Samad Saleem Raja , Balasubaramanian Sundarvadivazhagan , Al-Kaabi Amna Salim , Sharma Bhisham , Chowdhury Subrata , Mehbodniya Abolfazl , Webber Julian L. , Bostani Ali
Published: March 2023

Phishing leverages people’s tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2.
Credit Risk Prediction Model for Listed Companies Based on CNN-LSTM and Attention Mechanism
Authors: Li Jingyuan , Xu Caosen , Feng Bing , Zhao Hanyu
Published: March 2023

The financial market has been developing rapidly in recent years, and the issue of credit risk concerning listed companies has become increasingly prominent. Therefore, predicting the credit risk of listed companies is an urgent concern for banks, regulators and investors. The commonly used models are the Z-score, Logit (logistic regression model), the kernel-based virtual machine (KVM) and neural network models. However, the results achieved could be more satisfactory. This paper proposes a credit-risk-prediction model for listed companies based on a CNN-LSTM and an attention mechanism, Our approach is based on the benefits of the long short-term memory network (LSTM) model for long-term time-series prediction combined with a convolutional neural network (CNN) model. Furthermore, the advantages of being integrated into a CNN-LSTM model include reducing the complexity of the data, improving the calculation speed and training speed of the model and solving the possible lack of historical data in the long-term sequence prediction of the LSTM model, resulting in prediction accuracy. To reduce problems, we introduced an attention mechanism to assign weights independently and optimize the model. The results show that our model has distinct advantages compared with other CNNs, LSTMs, CNN-LSTMs and other models. The research on the credit-risk prediction of the listing formula has significant meaning.
Policy-Based Chameleon Hash with Black-Box Traceability for Redactable Blockchain in IoT
Authors: Duan Pengfei , Wang Jingyu , Zhang Yuqing , Ma Zhaofeng , Luo Shoushan
Published: March 2023

Blockchain has become an integral part of various IoT applications, and it has been successful in boosting performance in various aspects. Applying blockchain as a trust solution for Internet-of-Things is a viable approach. The immutability of blockchain is essential to prevent anyone from manipulating registered IoT data transactions for illegitimate benefits. However, the increasing abuse of blockchain storage negatively impacts the development of IoT blockchain and potential stakeholders are discouraged from joining the IoT data sharing as the IoT data recorded on the blockchain contains private information. Hence, it is crucial to find ways to redact data stored on the IoT blockchain, which is also supported by relevant laws and regulations. Policy-based chameleon hash is useful primitive for blockchain rewriting, allowing the modifier to rewrite the transaction if they possess enough rewriting privileges that satisfy the access policy. However, this approach lacks traceability, which can be exploited by malicious modifiers to grant unauthorized user modification privileges for personal gain. To overcome this deficiency, we introduce a new design of policy-based chameleon hash with black-box traceability (PCHT), which enables the authority to identify the set of producers responsible for generating the pirate decoder. Specifically, PCHT is constructed by practical attribute-based encryption with black-box traceability (ABET) and collision-resistant chameleon hash with ephemeral trapdoor (CHET). After modeling PCHT, we present its concrete instantiation and rigorous security proofs. Finally, a PCHT-based redactable transaction scheme for IoT blockchain is given. Compared to the state-of-the-art mutable blockchain solutions, our scheme provides fine-grained blockchain rewriting and black-box traceability. The evaluation results demonstrate that our scheme is efficient and practical while still ensuring that no computational overhead is placed on IoT devices with limited computing resources.
CMOS-Based Memristor Emulator Circuits for Low-Power Edge-Computing Applications
Authors: Ghosh Prosenjit Kumar , Riam Shah Zayed , Ahmed Md Sharif , Sundaravadivel Prabha
Published: March 2023

In this paper, an optimized memristor emulator circuit is designed, by using nine MOSFET transistors and a ground capacitor. Our area- and power-optimized emulator circuit can be used for basic data storage and processing at the monitoring edge, in real-time applications. The memristor shows a nonlinear voltage–current relationship, but no multiplier circuit provides the memristor’s nonlinear characteristics. As a result, the proposed memristor emulator has a very low chip area. The memristor circuit is designed in LTSpice, using 16 nm and 45 nm CMOS technology parameters, and the operating voltage is ±0.9 V. In this research, the theoretical derivations are validated using the simulated results of the memristor emulator circuit using different frequencies, capacitors, and input voltages in SPICE simulations.
A Delay-Optimal Task Scheduling Strategy for Vehicle Edge Computing Based on the Multi-Agent Deep Reinforcement Learning Approach
Authors: Nie Xuefang , Yan Yunhui , Zhou Tianqing , Chen Xingbang , Zhang Dingding
Published: March 2023

Cloudlet-based vehicular networks are a promising paradigm to enhance computation services through a distributed computation method, where the vehicle edge computing (VEC) cloudlet are deployed in the vicinity of the vehicle. In order to further improve the computing efficiency and reduce the task processing delay, we present a parallel task scheduling strategy based on the multi-agent deep reinforcement learning (DRL) approach for delay-optimal VEC in vehicular networks, where multiple computation tasks select the target threads in a VEC server to execute the computing tasks. We model the target thread decision of computation tasks as a multi-agent reinforcement learning problem, which is further solved by using a task scheduling algorithm based on multi-agent DRL that is implemented in a distributed manner. The computation tasks, with each selection acting on the target thread acting as an agent, collectively interact with the VEC environment and receive observations with respect to a common reward and learn to reduce the task processing delay by updating the multi-agent deep Q network (MADQN) using the obtained experiences. The experimental results show that the proposed DRL-based scheduling algorithm can achieve significant performance improvement, reducing the task processing delay by 40% and increasing the processing probability of success for computation tasks by more than 30% compared with the traditional task scheduling algorithms.
Integrated Feature-Based Network Intrusion Detection System Using Incremental Feature Generation
Authors: Kim Taehoon , Pak Wooguil
Published: March 2023

Machine learning (ML)-based network intrusion detection systems (NIDSs) depend entirely on the performance of machine learning models. Therefore, many studies have been conducted to improve the performance of ML models. Nevertheless, relatively few studies have focused on the feature set, which significantly affects the performance of ML models. In addition, features are generated by analyzing data collected after the session ends, which requires a significant amount of memory and a long processing time. To solve this problem, this study presents a new session feature set to improve the existing NIDSs. Current session-feature-based NIDSs are largely classified into NIDSs using a single-host feature set and NIDSs using a multi-host feature set. This research merges two different session feature sets into an integrated feature set, which is used to train an ML model for the NIDS. In addition, an incremental feature generation approach is proposed to eliminate the delay between the session end time and the integrated feature creation time. The improved performance of the NIDS using integrated features was confirmed through experiments. Compared to a NIDS based on ML models using existing single-host feature sets and multi-host feature sets, the NIDS with the proposed integrated feature set improves the detection rate by 4.15% and 5.9% on average, respectively.
Recess-Free E-Mode AlGaN/GaN MIS-HFET with Crystalline PEALD AlN Passivation Process
Authors: Jang Won-Ho , Yim Jun-Hyeok , Kim Hyungtak , Cha Ho-Young
Published: March 2023

We utilized a plasma-enhanced atomic layer deposition (PEALD) process to deposit an AlN passivation layer on AlGaN/GaN surface to enhance the polarization effects, which enabled the fabrication of an enhancement-mode (E-mode) AlGaN/GaN metal-insulator-semiconductor heterojunction field-effect transistor (MIS-HFET) without the need for a gate recess process. The AlN film deposited by PEALD exhibited a crystalline structure, not an amorphous one. The enhanced polarization effect of introducing the PEALD AlN film on a thin AlGaN barrier was confirmed through electrical analysis. To fabricate the E-mode AlGaN/GaN MIS-HFET, the PEALD AlN film was deposited on a 4.5 nm AlGaN barrier layer and then a damage-free wet etching process was used to open the gate region. The MIS-gate structure was formed by depositing a 15 nm plasma-enhanced chemical vapor deposition (PECVD) silicon dioxide (SiO2) film. The fabricated thin-AlGaN/GaN MIS-HFET demonstrated successful E-mode operation, with a threshold voltage of 0.45 V, an on/off ratio of approximately 109, a specific on-resistance of 7.1 mΩ·cm2, and an off-state breakdown voltage exceeding 1100 V.
SMART: A Lightweight and Reliable Multi-Path Transmission Model against Website Fingerprinting Attacks
Authors: Liu Ling , Hu Ning , Shan Chun , Jiang Yu , Liu Xin
Published: March 2023

The rapid development of IoT technology has promoted the integration of physical space and cyberspace. At the same time, it has also increased the risk of privacy leakage of Internet users. A large number of research works have shown that attackers can infer Internet surfing privacy through traffic patterns without decryption. Most of the existing research work on anti-traffic analysis is based on a weakened experimental assumption, which is difficult to apply in the actual IoT network environment and seriously affects the user experience. This article proposes a novel lightweight and reliable defense—SMART, which can ensure the anonymity and security of network communication without sacrificing network transmission performance. SMART introduces a multi-path transmission model in the Tor network, and divides traffic at multiple Tor entry onion relays, preventing attackers from obtaining network traffic statistical characteristics. We theoretically proved that SMART can improve the uncertainty of website fingerprint analysis results. The experimental result shows that SMART is able to resist encrypted traffic analysis tools, reducing the accuracy of four state-of-the-art classifiers from 98% to less than 12%, without inducing any additional artificial delay or dummy traffic. In order to avoid the performance degradation caused by data reassembly, SMART proposes a redundant slice mechanism to ensure reliability. Even in the case of human interference, the communication success rate is still as high as 97%.
Application of Inter-Well Connectivity Analysis with a Data-Driven Method in the SAGD Development of Heavy Oil Reservoirs
Authors: Huang Suqi , Jia Ailin , Zhang Xialin , Wang Chenhui , Shi Xiaomin , Xu Tong
Published: March 2023

The development of heavy oil reservoirs in China is of great significance to safeguard national energy security, but great challenges are faced due to the complex and heterogeneous reservoir properties. Inter-well connectivity analysis is critical to enhancing the development performance, as it is a good way to interpret fluid flow and provides a theoretical basis for injection-production optimization. Data-driven deep learning methods have been widely used in reservoir development and can be employed to develop surrogate models of injection and production and to infer inter-well connectivity. In this study, the model performance of a recurrent neural network (RNN) and its four variants were evaluated and compared in a temporal production prediction. The comparison results showed that bidirectional gated recurrent unit (Bi-GRU) is the optimal algorithm with the highest accuracy of 0.94. A surrogate model was established to simulate the inter-well connectivity of steam-assisted gravity drainage (SAGD) in the research area by utilizing the Bi-GRU algorithm. A global sensitivity analysis method, Fourier amplitude sensitivity testing (FAST), was introduced and combined with the surrogate model to explain the influence of the input variables on the output variables by quantitatively calculating the sensitivity of each variable. Quantitative results for the inter-well connectivity of SAGD were derived from the sensitivity analysis of the proposed method, which was effectively applied to typical linear patterns and five-spot patterns. Inter-well connectivity varied from 0.1 to 0.58 in test applications, and mutual corroboration with previous geological knowledge can further determine the distribution of the interlayer in the reservoir. The workflow proposed in this study provides a new direction for analyzing and inferring the inter-well connectivity of SAGD in Northeast China heavy oil reservoirs.
Operational Wind Turbine Blade Damage Evaluation Based on 10-min SCADA and 1 Hz Data
Authors: Chrétien Antoine , Tahan Antoine , Cambron Philippe , Oliveira-Filho Adaiton
Published: March 2023

This work aims to propose a method enabling the evaluation of wind turbine blade damage and fatigue related to a 1 Hz wind speed signal applied to a large period and based on standard 10-min SCADA data. Previous studies emphasize the need for sampling with a 1 Hz frequency when carrying out blade damage computation. However, such methods cannot be applied to evaluate the damage for a long period of time due to the complexity of computation and data availability. Moreover, 1 Hz SCADA data are not commonly used in the wind farm industry because they require a large data storage capacity. Applying such an approach, which is based on a 1 Hz wind speed signal, to current wind farms is not a trivial pursuit. The present work investigates the possibility of overcoming the preceding issues by estimating the equivalent 1 Hz wind speed damage over a 10-min period characterized by SCADA data in terms of measured mean wind speed and turbulence intensity. Then, a discussion is carried out regarding a method to estimate the uncertainty of the simulation, in a bid to come up with a tool facilitating decision-making by the operator. A statistical analysis of the damage assessed for different wind turbines is thus proposed to determine which one has sustained the most damage. Finally, the probability of reaching a critical damage level over time is then proposed, allowing the operator to optimize the operating and maintenance schedule.
Design and Optimization of Linear Permanent Magnet Vernier Generator for Direct Drive Wave Energy Converter
Authors: Zhao Mei , Kong Zhiquan , Tang Pingpeng , Zhang Zhentao , Yu Guodong , Zhang Huaqiang , Xu Yongxiang , Zou Jibin
Published: March 2023

A novel linear permanent magnet vernier generator (LPMVG) for small-power off-grid wave power generation systems is proposed in this paper. Firstly, in order to reduce the cogging force and the inherent edge effect of the linear generator, a staggered tooth modular structure is proposed. Secondly, in order to improve the output power and efficiency of the LPMVG and reduce the fluctuation coefficient of electromagnetic force, the relationship between the parameters of the generator is studied, and a method combining multi-objective optimization and single parameter scanning based on the response surface model and particle swarm optimization algorithm is proposed to obtain the optimal structural parameters of the generator. Thirdly, the output power and efficiency of the optimized generator are calculated and analyzed based on the two-dimensional finite element method, and the effectiveness of the multi-objective optimization design method based on the response surface model and particle swarm optimization algorithm is verified. Finally, a prototype is developed, and the calculated results and the measured results are shown to be in good agreement.
A Hybrid Approach of the Deep Learning Method and Rule-Based Method for Fault Diagnosis of Sucker Rod Pumping Wells
Authors: He Yanfeng , Guo Zhijie , Wang Xiang , Abdul Waheed
Published: March 2023

Accurately obtaining the working status of the sucker rod pumping wells is a challenging problem for oil production. Sensors at the polished rod collect working data to form surface dynamometer cards for fault diagnosis. A prevalent method for recognizing these cards is the convolutional neural network (CNN). However, this approach has two problems: an unbalanced dataset due to varying fault frequencies and similar dynamometer card shapes that complicate recognition. This leads to a low accuracy of fault diagnosis in practice, which is unsatisfactory. Therefore, this paper proposes a hybrid approach of the deep learning method and rule-based method for fault diagnosis of sucker rod pumping wells. Specifically, when the CNN model alone fails to achieve satisfactory accuracy in the working status, historical monitoring data of the relevant wells can be collected, and expert rules can assist CNN to improve diagnostic accuracy. By analyzing time series data of factors such as the maximum and minimum loads, the area of the dynamometer card, and the load difference, a knowledgebase of expert rules can be created. When performing fault diagnosis, both the dynamometer cards and related time series data are used as inputs. The dynamometer cards are used for the CNN model to diagnose, and the related time series data are used for expert rules to diagnose. The diagnostic results and the confidence levels of the two methods are obtained and compared. When the two diagnostic results conflict, the one with higher confidence is preserved. Out of the 2066 wells and 7 fault statuses analyzed in field applications, the hybrid approach demonstrated a 21.25% increase in fault diagnosis accuracy compared with using only the CNN model. Additionally, the overall accuracy rate of the hybrid approach exceeded 95%, indicating its high effectiveness in diagnosing faults in sucker rod pumping wells.
3D Solar Harvesting and Energy Generation via Multilayers of Transparent Porphyrin and Iron Oxide Thin Films
Authors: Lin Jou , Lyu Mengyao , Shi Donglu
Published: March 2023

Photovoltaic solar cells have been extensively used for various applications and are considered one of the most efficient green energy sources. However, their 2D surface area solar harvesting has limitations, and there is an increasing need to explore the possibility of multiple layer solar harvest for enhanced energy density. To address this, we have developed spectral-selective transparent thin films based on porphyrin and iron oxide compounds that allow solar light to penetrate multiple layers, significantly increasing solar harvesting surface area and energy density. These thin films are designed as photovoltaic (PV) and photothermal (PT) panels that can convert photons into either electricity or thermal energy for various green energy applications, such as smart building skins and solar desalination. The advantages of this 3D solar harvesting system include enlarged solar light collecting surface area and increased energy density. The multilayer system transforms the current 2D to 3D solar harvesting, enabling efficient energy generation. This review discusses recent developments in the synthesis and characterization of PV and PT transparent thin films for solar harvesting and energy generation using multilayers. Major applications of the 3D solar harvesting system are reviewed, including thermal energy generation, multilayered DSSC PV system, and solar desalination. Some preliminary data on transparent multilayer DSSC PVs are presented.
Photovoltaic Systems through the Lens of Material-Energy-Water Nexus
Authors: Belongeay Megan , Shirkey Gabriela , Lunardi Marina Monteiro , Rodriguez-Garcia Gonzalo , Sinha Parikhit , Corkish Richard , Stewart Rodney A. , Anctil Annick , Chen Jiquan , Celik Ilke
Published: March 2023

Solar photovoltaics (PV) has emerged as one of the world’s most promising power-generation technologies, and it is essential to assess its applications from the perspective of a material-energy-water (MEW) nexus. We performed a life cycle assessment of the cradle-to-grave MEW for single-crystalline silicon (s-Si) and CdTe PV technologies by assuming both PV systems are recycled at end of life. We found that the MEW network was dominated by energy flows (>95%), while only minor impacts of materials and water flows were observed. Also, these MEW flows have pyramid-like distributions between the three tiers (i.e., primary, secondary/sub-secondary, and tertiary levels), with greater flows at the primary and lower flows at the tertiary levels. A more detailed analysis of materials’ circularity showed that glass layers are the most impactful component of recycling due to their considerable weight in both technologies. Our analysis also emphasized the positive impacts that increased power-conversion efficiency and the use of recycled feedstock have on the PV industry’s circularity rates. We found that a 25% increase in power-conversion efficiency and the use of fully recycled materials in PV panel feedstocks resulted in 91% and 86% material circularity for CdTe and s-Si PV systems, respectively.
Design and Analysis of Grid-Connected Solar Photovoltaic Systems for Sustainable Development of Remote Areas
Authors: Razmjoo Armin , Ghazanfari Arezoo , Østergaard Poul Alberg , Abedi Sepideh
Published: March 2023

This study analyses the expansion of solar energy in Iran, considering political, economic, social, and technological factors. Due to the prolonged sanctions on Iran, the development of clean energy power plants has been either halted or significantly reduced. Hence, this study aims to identify barriers to the expansion of solar energy power plants and simulate solar power plants using PVsyst (Photovoltaic system) software. The study is unique in its approach of combining technical analysis with social sciences to facilitate the implementation of solar energy expansion in remote areas. This study focuses on two specific areas with high solar radiation, namely Darab and Meybod, which are located in Fars and Yazd provinces, respectively. Solar energy can be generated in these two areas due to their unique location with high levels of solar irritation. To achieve this goal, the technical analyses focuson simulating the performance of a 9 kWp (kilowatt ‘peak’ power output of a system) grid-connected polysilicon(poly-Si) photovoltaic plant for Darab and a 9.90 kWp plant for Meybod. The simulation is carried out to obtain maximum electricity production and evaluate parameters such as incident radiation, performance ratio, energy into the grid, energy output at the array, and losses. The produced energy for Darab was 20.40 MWh/year, with specific production of 2061 kWh/kWp/year, and the performance ratio (PR) was 81.26%. For Meybod, production was 20.70 MWh/year, with specific production of 2091 kWh/kWp/year, and the performance ratio (PR) was 80.88%. Through the PEST analysis, it is evident that strategic planning and appropriate actions are crucial at the provincial, national, and local levels for energy systems’ development. This indicates that both governments and citizens should play an active role in supporting the expansion of energy systems by planning and creating awareness among the public to embrace and adopt energy systems.
Roots, Litter, and Seasonal Drought Together Inhibit Plant Growth in the Herbaceous Layer in a Subtropical Moist Forest of Southwestern China
Authors: Liu Xianbin , Li Yun , Kong Lingqian , Lodge D. Jean , Hogan J. Aaron , Wang Chao
Published: March 2023

The mechanisms of the maintenance of plant diversity in forests have been extensively studied because of their ecological importance. The study of the regeneration and growth dynamics of herbaceous understory communities in forests is relatively more common than that of woody plant overstory and understory communities. To investigate which environmental factors (plant roots, forest litter, or both) control seedling survival, growth, and production in the herbaceous layer in the context of increasingly severe seasonal drought caused by global climate change, we performed a seedling growth experiment of the herbaceous layer influenced by the interaction of plant roots and forest litter through a manipulative complete block experiment, crossed with an irrigation experiment, in a montane subtropical moist evergreen broad-leaved forest of southwestern China. Within both the control and watered plots, we established four experimental subplots with plant roots and forest litter included (R+L+); plant roots included, but forest litter excluded (R+L−); plant roots excluded, but forest litter included (R−L+); and both plant roots and forest litter excluded (R−L−). After one year, the R+L+ treatment in the control plot had statistically less species richness and plant individuals, shorter mean and maximum seedling heights, and less dry biomass of plant seedlings than those in the other seven experimental treatments. Across all the experimental replicates, the pooled data showed that plant roots, forest litter, and seasonal drought, together, inhibited all the dependent herbaceous growth variables. Our study demonstrates how plant roots, forest litter, and seasonal drought synergistically regulate seedling establishment and the growth of the herbaceous layer in tropical and subtropical forest understory. This synergistic regulation changes plant physiological responses and forest evolution through controlling plant diversity and the individual richness of the herbaceous layer in the context of sustained global climate change.
Diurnal and Sex Ratio Flight Activity of Rare Cavity-Dweller Eucnemis capucina Ahrens, 1812 (Coleoptera: Eucnemidae) in Lowland Deciduous Forest: Case Study from Czech Republic
Authors: Nakládal Oto , Synek Jiří , Zumr Václav
Published: March 2023

The cavity-dwelling saproxylic beetles are very poorly studied because of their hidden way of life, and they are threatened by forest management. Eucnemis capucina is a rare and sporadic species in all of its distribution area. This species is associated with old hollow trees, which may be why it is on the red lists of many European countries; however, this association could be an indicator of diverse forest structure. We monitored diurnal and seasonal flight activity with intercept flight traps installed on massive ash trees during three seasons. The observed peak of seasonal activity was in May and June. Contrasting most eucnemids, E. capucina is clearly a daytime species, with flight activity between 8:00 and 20:00 and peaking at 14:20. The peak of flight activity is the same for both sexes, but males are more active at the beginning and end of the flight period compared to females. An unequal sex ratio 1.91:1 (F:M) of captured individuals was recorded. The increasing body size of females affected the number of eggs in the body of adult females. The average potential fecundity of a female was 54 eggs, 10 eggs per 1 mm of female body length. The eggs themselves were oval in shape and only the length of the eggs correlated to the body size of the females; the width did not change with body size and did not correlate with egg length, either. We also confirmed that even a single hollow tree can host high numbers or whole populations of rare species and, thus, have a high conservation value. Our study may help better understand the biology of cavity-dwelling beetles and their active life.
Exploring the Impact of Psychological Accessibility on the Restorative Perception in Urban Forests: A Case Study of Yuelu Mountain, Central China
Authors: Wang Yangyi , Luo Fen , Gazal Kathryn Arano , Wen Ying , Lei Haiqian , Xiao Zhijun
Published: March 2023

The recreational and restorative functions of urban forests are gaining increasing attention as people focus more on having a higher quality of life. While the rationale behind the spatial layout of urban forest parks is important, the real psychological perceptions of users cannot be ignored. This study has developed a structural equation model to examine the relationship between environmental restoration and psychological accessibility. Specifically, an empirical study was conducted on a random sample of visitors of the Yuelu Mountain, Central China, to investigate the effect of visitors’ psychological accessibility on their environmental restorative perception. The results show that psychological accessibility and the quality of experience have a significant positive effect on the perceived effectiveness of environmental restoration. Environmental attitudes indirectly influence the effect of environmental restorative perceptions through psychological accessibility and the quality of experience. Finally, comments on the development of green space planning in urban forest parks are also presented in this paper.
A Novel Scheme of the ARA Transform for Solving Systems of Partial Fractional Differential Equations
Authors: Burqan Aliaa
Published: March 2023

In this article, a new analytical scheme of the ARA transform is introduced to solve systems of fractional partial differential equations. The principle of the proposed technique is based on combining the ARA transform with the residual power series method to create an approximate series solution for a system of partial differential equations of fractional order on the form of a rapid convergent series. To illustrate the effectiveness, accuracy, and validity of the suggested technique, an Attractive physical system, the fractional neutron diffusion equation with one delayed neutrons group, is discussed and solved. Two different neutron flux initial conditions are presented numerically to clarify various cases in order to ensure the theoretical results. The necessary Mathematica codes are run using vital nuclear reactor cross-section data, and the results for various values of time are tabulated and graphically represented.
A Systematic Review of the Heterogenous Gene Expression Patterns Associated with Multidrug Chemoresistance in Conventional Osteosarcoma
Authors: Mthethwa Phakamani Goodman , Marais Leonard Charles , Ramsuran Veron , Aldous Collen Michelle
Published: March 2023

Multidrug chemoresistance (MDR) remains the most significant obstacle to improving survival in osteosarcoma patients. Heterogeneous genetic alterations characterise the tumour microenvironment, and host molecular markers have been associated with MDR. This systematic review examines the genetic alterations of molecular biomarkers associated with multidrug chemotherapy resistance in genome-wide analysis of central high-grade conventional osteosarcoma (COS). We systematically searched MEDLINE, EMBASE, Web of Science, Wiley online library and Scopus. Only human studies involving genome-wide analysis were included, while candidate gene, in vitro and animal studies were excluded. The risk of bias of the studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. The systematic search identified 1355 records. Following the screening, six studies were included in the qualitative analysis. There were 473 differentially expressed genes (DEGs) associated with chemotherapy response in COS. Fifty-seven of those were associated with MDR in osteosarcoma. The heterogeneous gene expressions were related to the mechanism of MDR in osteosarcoma. The mechanisms include drug-related sensitivity genes, bone remodelling and signal transduction. Complex, variable and heterogenous gene expression patterns underpin MDR in osteosarcoma. Further research is needed to identify the most relevant alterations for prognostication and to guide the development of possible therapeutic targets.
A Novel LncRNA MSTRG.310246.1 Promotes Differentiation and Thermogenesis in Goat Brown Adipocytes
Authors: Tang Jing , Liu Xin , Su Duo , Jiang Tingting , Zhan Siyuan , Zhong Tao , Guo Jiazhong , Cao Jiaxue , Li Li , Zhang Hongping , Wang Linjie
Published: March 2023

Brown adipose tissue (BAT) plays a critical role in maintaining the body temperature in newborn lamb due to its unique non-shivering thermogenesis. Previous studies have found that BAT thermogenesis is regulated by several long non-coding RNAs (lncRNAs). Here, we identified a novel lncRNA, MSTRG.310246.1, which was enriched in BAT. MSTRG.310246.1 was localized in both the nuclear and cytoplasmic compartments. In addition, MSTRG.310246.1 expression was upregulated during brown adipocyte differentiation. Overexpression of MSTRG.310246.1 increased the differentiation and thermogenesis of goat brown adipocytes. On the contrary, the knockdown of MSTRG.310246.1 inhibited the differentiation and thermogenesis of goat brown adipocytes. However, MSTRG.310246.1 had no effect on goat white adipocyte differentiation and thermogenesis. Our results show that MSTRG.310246.1 is a BAT-enriched LncRNA that improves the differentiation and thermogenesis of goat brown adipocytes.
Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
Authors: Wang Jingyu , Zhou Fujie , Li Cheng , Yin Ning , Liu Huiming , Zhuang Binxian , Huang Qingyu , Wen Yongxian
Published: March 2023

Functional linear regression models have been widely used in the gene association analysis of complex traits. These models retain all the genetic information in the data and take full advantage of spatial information in genetic variation data, which leads to brilliant detection power. However, the significant association signals identified by the high-power methods are not all the real causal SNPs, because it is easy to regard noise information as significant association signals, leading to a false association. In this paper, a method based on the sparse functional data association test (SFDAT) of gene region association analysis is developed based on a functional linear regression model with local sparse estimation. The evaluation indicators CSR and DL are defined to evaluate the feasibility and performance of the proposed method with other indicators. Simulation studies show that: (1) SFDAT performs well under both linkage equilibrium and linkage disequilibrium simulation; (2) SFDAT performs successfully for gene regions (including common variants, low-frequency variants, rare variants and mix variants); (3) With power and type I error rates comparable to OLS and Smooth, SFDAT has a better ability to handle the zero regions. The Oryza sativa data set is analyzed by SFDAT. It is shown that SFDAT can better perform gene association analysis and eliminate the false positive of gene localization. This study showed that SFDAT can lower the interference caused by noise while maintaining high power. SFDAT provides a new method for the association analysis between gene regions and phenotypic quantitative traits.
SLC38A10 Deficiency in Mice Affects Plasma Levels of Threonine and Histidine in Males but Not in Females: A Preliminary Characterization Study of SLC38A10−/− Mice
Authors: Lindberg Frida A. , Nordenankar Karin , Forsberg Erica C. , Fredriksson Robert
Published: March 2023

Solute carriers belong to the biggest group of transporters in the human genome, but more knowledge is needed to fully understand their function and possible role as therapeutic targets. SLC38A10, a poorly characterized solute carrier, is preliminary characterized here. By using a knockout mouse model, we studied the biological effects of SLC38A10 deficiency in vivo. We performed a transcriptomic analysis of the whole brain and found seven differentially expressed genes in SLC38A10-deficient mice (Gm48159, Nr4a1, Tuba1c, Lrrc56, mt-Tp, Hbb-bt and Snord116/9). By measuring amino acids in plasma, we found lower levels of threonine and histidine in knockout males, whereas no amino acid levels were affected in females, suggesting that SLC38A10−/− might affect sexes differently. Using RT-qPCR, we investigated the effect of SLC38A10 deficiency on mRNA expression of other SLC38 members, Mtor and Rps6kb1 in the brain, liver, lung, muscle, and kidney, but no differences were found. Relative telomere length measurement was also taken, as a marker for cellular age, but no differences were found between the genotypes. We conclude that SLC38A10 might be important for keeping amino acid homeostasis in plasma, at least in males, but no major effects were seen on transcriptomic expression or telomere length in the whole brain.
vwa1 Knockout in Zebrafish Causes Abnormal Craniofacial Chondrogenesis by Regulating FGF Pathway
Authors: Niu Xiaomin , Zhang Fuyu , Ping Lu , Wang Yibei , Zhang Bo , Wang Jian , Chen Xiaowei
Published: March 2023

Hemifacial microsomia (HFM), a rare disorder of first- and second-pharyngeal arch development, has been linked to a point mutation in VWA1 (von Willebrand factor A domain containing 1), encoding the protein WARP in a five-generation pedigree. However, how the VWA1 mutation relates to the pathogenesis of HFM is largely unknown. Here, we sought to elucidate the effects of the VWA1 mutation at the molecular level by generating a vwa1-knockout zebrafish line using CRISPR/Cas9. Mutants and crispants showed cartilage dysmorphologies, including hypoplastic Meckel’s cartilage and palatoquadrate cartilage, malformed ceratohyal with widened angle, and deformed or absent ceratobranchial cartilages. Chondrocytes exhibited a smaller size and aspect ratio and were aligned irregularly. In situ hybridization and RT-qPCR showed a decrease in barx1 and col2a1a expression, indicating abnormal cranial neural crest cell (CNCC) condensation and differentiation. CNCC proliferation and survival were also impaired in the mutants. Expression of FGF pathway components, including fgf8a, fgfr1, fgfr2, fgfr3, fgfr4, and runx2a, was decreased, implying a role for VWA1 in regulating FGF signaling. Our results demonstrate that VWA1 is essential for zebrafish chondrogenesis through effects on condensation, differentiation, proliferation, and apoptosis of CNCCs, and likely impacts chondrogenesis through regulation of the FGF pathway.
Changes in Loneliness, BDNF, and Biological Aging Predict Trajectories in a Blood-Based Epigenetic Measure of Cortical Aging: A Study of Older Black Americans
Authors: Simons Ronald L. , Ong Mei Ling , Lei Man-Kit , Beach Steven R. H. , Zhang Yue , Philibert Robert , Mielke Michelle M.
Published: March 2023

A recent epigenetic measure of aging has developed based on human cortex tissue. This cortical clock (CC) dramatically outperformed extant blood-based epigenetic clocks in predicting brain age and neurological degeneration. Unfortunately, measures that require brain tissue are of limited utility to investigators striving to identify everyday risk factors for dementia. The present study investigated the utility of using the CpG sites included in the CC to formulate a peripheral blood-based cortical measure of brain age (CC-Bd). To establish the utility of CC-Bd, we used growth curves with individually varying time points and longitudinal data from a sample of 694 aging African Americans. We examined whether three risk factors that have been linked to cognitive decline—loneliness, depression, and BDNFm—predicted CC-Bd after controlling for several factors, including three new-generation epigenetic clocks. Our findings showed that two clocks—DunedinPACE and PoAm—predicted CC-BD, but that increases in loneliness and BDNFm continued to be robust predictors of accelerated CC-Bd even after taking these effects into account. This suggests that CC-Bd is assessing something more than the pan-tissue epigenetic clocks but that, at least in part, brain health is also associated with the general aging of the organism.
Damage-Free Shortening of Telomeres Is a Potential Strategy Supporting Blind Mole-Rat Longevity
Authors: Adwan Shekhidem Huda , Sharvit Lital , Huffman Derek M. , Manov Irena , Atzmon Gil , Shams Imad
Published: March 2023

Telomere shortening or loss of shelterin components activates DNA damage response (DDR) pathways, leading to a replicative senescence that is usually coupled with a senescence-associated secretory phenotype (SASP). Recent studies suggested that telomere aberration that activates DDR may occur, irrespective of telomere length or loss of shelterin complex. The blind mole-rat (Spalax) is a subterranean rodent with exceptional longevity, and its cells demonstrate an uncoupling of senescence and SASP inflammatory components. Herein, we evaluated Spalax relative telomere length, telomerase activity, and shelterin expression, along with telomere-associated DNA damage foci (TAFs) levels with cell passage. We show that telomeres shorten in Spalax fibroblasts similar to the process in rats, and that the telomerase activity is lower. Moreover, we found lower DNA damage foci at the telomeres and a decline in the mRNA expression of two shelterin proteins, known as ATM/ATR repressors. Although additional studies are required for understanding the underling mechanism, our present results imply that Spalax genome protection strategies include effective telomere maintenance, preventing early cellular senescence induced by persistent DDR, thereby contributing to its longevity and healthy aging.
Sequencing of the Pituitary Transcriptome after GnRH Treatment Uncovers the Involvement of lncRNA-m23b/miR-23b-3p/CAMK2D in FSH Synthesis and Secretion
Authors: Wang Tian , Zhao Guokun , Yu Song , Zheng Yi , Guo Haixiang , Wang Haoqi , Zhao Peisen , Xie Wenyin , Ren Wenzhi , Yuan Bao
Published: March 2023

The pituitary gland is a key participant in the hypothalamic–pituitary–gonadal axis, as it secretes a variety of hormones and plays an important role in mammalian reproduction. Gonadotrophin-releasing hormone(GnRH) signaling molecules can bind to GnRH receptors on the surfaces of adenohypophysis gonadotropin cells and regulate the expression of follicle-stimulating hormone(FSH) and luteinizing hormone(LH) through various pathways. An increasing number of studies have shown that noncoding RNAs mediate the regulation of GnRH signaling molecules in the adenohypophysis. However, the expression changes and underlying mechanisms of genes and noncoding RNAs in the adenohypophysis under the action of GnRH remain unclear. In the present study, we performed RNA sequencing (RNA-seq) of the rat adenohypophysis before and after GnRH treatment to identify differentially expressed mRNAs, lncRNAs, and miRNAs. We found 385 mRNAs, 704 lncRNAs, and 20 miRNAs that were significantly differentially expressed in the rat adenohypophysis. Then, we used a software to predict the regulatory roles of lncRNAs as molecular sponges that compete with mRNAs to bind miRNAs, and construct a GnRH-mediated ceRNA regulatory network. Finally, we enriched the differentially expressed mRNAs, lncRNA target genes, and ceRNA regulatory networks to analyze their potential roles. Based on the sequencing results, we verified that GnRH could affect FSH synthesis and secretion by promoting the competitive binding of lncRNA-m23b to miR-23b-3p to regulate the expression of Calcium/Calmodulin Dependent Protein Kinase II Delta(CAMK2D). Our findings provide strong data to support exploration of the physiological processes in the rat adenohypophysis under the action of GnRH. Furthermore, our profile of lncRNA expression in the rat adenohypophysis provides a theoretical basis for research on the roles of lncRNAs in the adenohypophysis.
Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach
Authors: Karpov Oleg E. , Pitsik Elena N. , Kurkin Semen A. , Maksimenko Vladimir A. , Gusev Alexander V. , Shusharina Natali N. , Hramov Alexander E.
Published: March 2023

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.