bluuebunny/crossref_metadata_2025 huggingface dataset:
Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice
Authors: Musa Sulaiman Muhammad , Haruna Usman Abubakar , Manirambona Emery , Eshun Gilbert , Ahmad Dalhatu Muhammad , Dada David Adelekan , Gololo Ahmed Adamu , Musa Shuaibu Saidu , Abdulkadir Abdulafeez Katibi , Lucero-Prisno III Don Eliseo
Published: August 2023

Background: Among the numerous challenges that Africa faces in improving its healthcare systems, the paucity of health data stands out as paramount. This study aims to examine the challenges related to the paucity of health data in Africa and its impact on the implementation of digital health and evidence-based practice. The findings of the study reveal that health data availability in Africa is both limited and frequently of poor quality. Several factors contribute to this concerning situation, encompassing inadequate infrastructure, a shortage of resources, and cultural barriers. Furthermore, the available data, despite its limitations, is often underutilized due to a lack of capacity and expertise in data analysis and interpretation.

Policy Options and Recommendations: To improve healthcare delivery in Africa, we recommend implementing novel strategies for data collection. It’s important to recognize that effective information technology service is crucial for enhancing healthcare delivery, and a holistic approach is necessary to achieve this.

Conclusion: This brief presents information to help policymakers develop long-term solutions to Africa’s health data poverty. Taking action based on this evidence can assist in addressing the problem.
Predicting Online Item-Choice Behavior: A Shape-Restricted Regression Approach
Authors: Nishimura Naoki , Sukegawa Noriyoshi , Takano Yuichi , Iwanaga Jiro
Published: August 2023

This paper examines the relationship between user pageview (PV) histories and their itemchoice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user–item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user’s previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.
Numerical Investigation of Hypersonic Flat-Plate Boundary Layer Transition Subjected to Bi-Frequency Synthetic Jet
Authors: Liu Xinyi , Luo Zhenbing , Liu Qiang , Cheng Pan , Zhou Yan
Published: August 2023

Transition delaying is of great importance for the drag and heat flux reduction of hypersonic flight vehicles. The first mode, with low frequency, and the second mode, with high frequency, exist simultaneously during the transition through the hypersonic boundary layer. This paper proposes a novel bi-frequency synthetic jet to suppress low- and high-frequency disturbances at the same time. Orthogonal table and variance analyses were used to compare the control effects of jets with different positions (USJ or DSJ), low frequencies (f1), high frequencies (f2), and amplitudes (a). Linear stability analysis results show that, in terms of the growth rate varying with the frequency of disturbance, an upstream synthetic jet (USJ) with a specific frequency and amplitude can hinder the growth of both the first and second modes, thereby delaying the transition. On the other hand, a downstream synthetic jet (DSJ), regardless of other parameters, increases flow instability and accelerates the transition, with higher frequencies and amplitudes resulting in greater growth rates for both modes. Low frequencies had a significant effect on the first mode, but a weak effect on the second mode, whereas high frequencies demonstrated a favorable impact on both the first and second modes. In terms of the growth rate varying with the spanwise wave number, the control rule of the same parameter under different spanwise wave numbers was different, resulting in a complex pattern. In order to obtain the optimal delay effect upon transition and improve the stability of the flow, the parameters of the bi-synthetic jet should be selected as follows: position it upstream, with f1 = 3.56 kHz, f2 = 89.9 kHz, a = 0.009, so that the maximum growth rate of the first mode is reduced by 9.06% and that of the second mode is reduced by 1.28% compared with the uncontrolled state, where flow field analysis revealed a weakening of the twin lattice structure of pressure pulsation.
Research on Optimization of the Bulb Form of the Bulb Tubular Pump Device for a Low-Head Agricultural Irrigation Pumping Station
Authors: Zhang Hongyin , Liu Jianlong , Wu Jinxin , Jiao Weixuan , Cheng Li , Yuan Mingbin
Published: August 2023

A bulb tubular pump unit is a horizontal tubular pump unit composed of a water pump and bulb with an electric motor installed. Electric motors, transmission equipment, and bearings are usually placed in the bulb. The bulb is located in the flow channel and has a relatively narrow space. Therefore, the shape of the bulb has a significant influence on the flow pattern and pump efficiency in the flow channel. In this study, the CFX 19.2 software was employed to optimize the bulb hydraulically according to its geometry and parameters. The research results indicate that the flow pattern at the tail of the elliptical bulb was better, the hydraulic loss at the bulb section was small, and the device efficiency was higher than that at the tail, which was round. The streamlined support had small flow resistance, minimal hydraulic loss, and a high pump unit head and efficiency. Nine schemes were selected, and the geometrical characteristics and parameters of the bulb were determined as follows: the shape of the tail of the bulb was oval, the bulb ratio was 0.96, and the shape of the support parts was streamlined. The results hold important reference significance to improve efficiency and broaden the operating conditions of bulb tubular pump devices.
AI in Sustainable Pig Farming: IoT Insights into Stress and Gait
Authors: Neethirajan Suresh
Published: August 2023

This paper pioneers a novel exploration of environmental impacts in livestock farming, focusing on pig farming’s intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs aged 86 to 108 days using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores the potential detection of gait abnormalities, correlations between pigs’ age and their gait or activity patterns, and the evolution of pigs’ walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs’ physiological parameters. Sensor data are further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.
Vibration Control of Innovative Lightweight Thermoplastic Composite Material via Smart Actuators for Aerospace Applications
Authors: Angeletti Federica , Tortorici Daniele , Laurenzi Susanna , Gasbarri Paolo
Published: August 2023

Piezoelectric actuators and sensors can be incorporated into aerospace structures to suppress unwanted flexible oscillations. These devices need to interact with various passive structures, including innovative materials such as thermoplastic composites, which offer several advantages over traditional options. This study explores the application of a piezoelectric-based vibration control system on a lightweight carbon-reinforced thermoplastic material. Numerical and experimental investigations are conducted to assess the mechanical properties and damping behavior of the composite. As a case study, an equivalent orthotropic shell laminate is developed to facilitate finite element modeling of two composite solar panel structures equipped to a spacecraft. Moreover, an electro-mechanical formulation is implemented to integrate smart actuators and sensors onto the composite hosting structure. Finally, the efficiency of the active vibration control system is assessed when significant vibration perturbations are caused on the panels by rigid–flexible dynamics coupling during agile attitude maneuvers. The results demonstrate the damping factor of the material can be noticeably improved, making the proposed system a promising technological solution for further aerospace applications.
Hierarchical Spatial-Temporal Neural Network with Attention Mechanism for Traffic Flow Forecasting
Authors: Lian Qingyun , Sun Wei , Dong Wei
Published: August 2023

Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model’s prediction accuracy surpassed that of several benchmark methods.
Screw Dynamics of a Multibody System by a Schoenflies Manipulator
Authors: Zhao Jing-Shan , Sun Han-Lin , Li Hao-Yang , Zhao Dong-Jie
Published: August 2023

This paper presents a screw dynamics method for multi-rigid-body systems. It establishes the relationship between velocity screw (twist) and force screw (wrench) in the theorem of momentum screw. Then, the structure of a 2UPS + 2UPU parallel manipulator is introduced as an example of application. By analyzing the constraint wrench of each limb and the twist of the moving platform in the theorem of momentum screw, the dynamics equation for solving the driving force makes the method very convenient for computer programming. The dynamics equation can be reorganized into a non-homogeneous linear equation and establishes the relationship between the constraint wrench of each limb and the twist of the moving platform in screw coordinate. The outstanding advantage of the algorithm proposed in this paper is that the average calculation time is only 83.21% of that of the Newton–Euler method for the 2UPS + 2UPU parallel manipulator on the same computer. This methodology provides a convenient canonical form for the dynamics analysis of multi-rigid-body systems.
Usability of Certain Symbols Indicating Automobile Safety Status Based on Youth Assessment
Authors: Manojlovic Uros , Zunjic Aleksandar , Trifunović Aleksandar , Ivanišević Tijana , Duplakova Darina , Duplak Jan , Čičević Svetlana
Published: August 2023

The research presented in this paper refers to the possibility to understand the information presented by symbols, which indicate safety status and possible troubles regarding automobiles and driving. The testing included sixteen symbols, six of which were ISO-verified and recommended symbols. The study included 204 youth respondents. The study used a multidisciplinary, ergonomic approach, based on research of the usability of symbols. The basic task of subjects was to recognize the symbols and to rate their confidence on the five-point scale that they gave exact answers. For each symbol, hypotheses about the proportion of correct answers at the population level were tested, applying the inferential statistics technique. The ISO criterion of 67% successful symbol recognition was adopted as the limit value for the justification of the use of symbols. The test results showed that eight out of sixteen symbols did not meet this criterion. The results of this research indicate that it is necessary to take certain measures in order to improve the understanding of the function of symbols on vehicle dashboards. One of the proposed measures consists in the improvement of training courses in driving schools, which from a theoretical but also a practical aspect should include education about symbols on car dashboards, primarily those responsible for informing about the safety status. In addition, a redesign of the symbols that had the lowest recognition rate can be recommended.
Multi-Objective Optimization of Square Corrugation Multilayer Nested Structures
Authors: Zhang Honghao , Yu Dongtao , Li Tao , Wang Lingyu , Huang Zhongwei , Zhang Yong
Published: August 2023

Thin-walled structures, when used for high-speed railways, can effectively mitigate the irreversible destruction when a malfunction occurs. Nested thin-walled tubes, as energy-absorbing structures, possess excellent specific energy absorption (SEA) and crushing force efficiency (CFE). This paper conducts multi-objective optimization by focusing on a square corrugation nested structure with a double octagon inner wall, namely SCOD, to ameliorate the crashworthiness of the nested structure. The finite element model of the SCOD is constructed and validated by test data. A set of experimental design points with good spatial distribution are obtained using the optimal Latin hypercube (LHC) method. The polynomial response surface (PRS) method was applied to establish the fitting relationship between design variables and optimization objectives, and validation is accomplished. The DCNSGA-III algorithm is employed for optimization, resulting in a Pareto alternative solution set with good population diversity and convergence. In addition, to observe the optimized performance, a set of optimal solutions considering a single objective value is derived, and a comprehensive optimal solution is obtained by applying the minimum distance selection method (TMDSM). Finally, the proposed optimized system is analyzed and validated. According to the alternative reference solutions, the initial peak force (IPCF) reduces by 53.75% and CFE increases by 8.7%. This paper provides some reference for the optimization design in practical engineering.
Research on Visualization Technology of Production Process for Mechanical Manufacturing Workshop
Authors: Li Li , Wu Zhaoyun , Lu Liping
Published: August 2023

The visualization of workshop information can affect production management and efficiency. Information can be presented both graphically and non-graphically (for example, in the form of data lists or tables). Graphical representations are intuitive and clear, but currently, most of them are based on statistical data, which makes it difficult to convey logical linkages between information and cannot help managers make decisions effectively. With the aim of designing the workshop production system with visual processes in small-sized enterprises, the key visualization technologies of the process flow chart, including the visual design of process flow chart, process card management, process flow chart release, process control, and production schedule monitoring, were all addressed in detail. On this basis, the mechanical manufacturing workshop production management system was created using C#.NET as the programming language. The main contribution of the research is that the system designed used the process flow chart as the main line through all functional modules and integrated all process data on the process nodes of the process flow chart to realize the graphical monitoring of workshop production schedule. The visualization technology of the process flow chart makes the system simple to use and easy to understand, which significantly improves information management and work efficiency in the workshop. Additionally, it provides the technical foundation for flow-driven production information transfer in the workshop and can serve as a universal standard for the process module in workshop production management systems.
Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences
Authors: Suhaeni Cici , Yong Hwan-Seung
Published: August 2023

In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naïve Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research.
Analysis of the Characteristics of Uneven Spatio-Temporal Distribution in Wujiang River Basin over the Last 60 Years
Authors: Wang Junchao , Peng Tao , Xiang Yiheng , Yin Zhiyuan , Qi Haixia
Published: August 2023

Exploring the characteristics of uneven temporal and spatial distribution of precipitation in mountain watersheds can provide a reference for regional agricultural development and resource utilization, and contribute to the protection of the ecological environment. Based on the daily precipitation observation data of 40 meteorological stations in the Wujiang River Basin from 1963 to 2021, the temporal- and spatial-variation characteristics of the precipitation concentration degree (PCD) and precipitation concentration period (PCP) were analyzed using the Randall S analysis method, Mann–Kendall test method, Pettitt method, wavelet analysis and empirical orthogonal function (EOF). The results showed that the fluctuation range of PCD in Wujiang River Basin from 1963 to 2021 was 0.34–0.59, with a multi-year average of 0.47, which was obviously higher than the national average level and is showing a trend of slowly increasing. The fluctuation range of PCP was between 17.1 and 21.5 days, with a multi-year average of 19.0. The annual precipitation was mostly concentrated around the middle of July and showed a slowly decreasing trend. The abrupt change in PCD and PCP occurred around 1983 and 2001, respectively. There is an obvious Hearst phenomenon in PCP. In the future, the trend of precipitation concentration in the middle period will remain in advance, and the degree of precipitation concentration will continue to increase. The maximum precipitation in the flood season will continue to be delayed. The spatial pattern of the first mode of PCD and PCP in the Wujiang River basin was consistent and showed an opposite pattern between the upper reaches and the middle-lower reaches of the basin, which reflects the influence of the topography of the basin on the spatial distribution of precipitation. The distribution of precipitation is affected by topography. The elevation change in the basin was complex, and the leeward slope varied a lot. Therefore, it has a significant impact on precipitation. Areas with less precipitation are at higher elevations and on mountain leeward slopes, with a lack of moist air flow. The area with more precipitation was the summer monsoon mountain windward slope, and the topography blocks the increase in precipitation. The elevation of the central region is relatively uniform and the terrain is flat. Therefore, the distribution of precipitation is more uniform.
Nonlinear Trend and Multiscale Variability of Dry Spells in Senegal (1951–2010)
Authors: Agbazo Noukpo M. , Tall Moustapha , Sylla Mouhamadou Bamba
Published: August 2023

Dry spells occurring during the rainy season have significant implications for agricultural productivity and socioeconomic development, particularly in rainfed agricultural countries such as Senegal. This study employs various chaos-theory-based tools, including the lacunarity method, rescaled analysis, and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, to investigate the distribution, predictability, and multiscale properties of the annual series of maximum dry spell length (AMDSL) in Senegal during the rainy season. The analysis focuses on 29 stations across Senegal, spanning the period from 1951 to 2010. The findings reveal persistent behavior in the AMDSL across nearly all stations, indicating that predictive models based on extrapolating past time trends could enhance AMDSL forecasting. Furthermore, a well-defined spatial distribution of the lacunarity exponent β is observed, which exhibits a discernible relationship with rainfall patterns in Senegal. Notably, the lacunarity exponent displays a south-to-north gradient for all thresholds, suggesting its potential for distinguishing between different drought regimes and zones while aiding in the understanding of spatiotemporal rainfall variability patterns. Moreover, the analysis identifies five significant intrinsic mode functions (IMFs) characterized by different periods, including interannual, interdecadal, and multidecadal oscillations. These IMFs, along with a nonlinear trend, are identified as the driving forces behind AMDSL variations in Senegal. Among the inter-annual oscillations, a 3-year quasi-period emerges as the primary contributor and main component influencing AMDSL variability. Additionally, four distinct morphological types of nonlinear trends in AMDSL variations are identified, with increasing–decreasing and increasing trends being the most prevalent. These findings contribute to a better understanding of the variability in annual maximum dry spell lengths, particularly in the context of climate change, and provide valuable insights for improving AMDSL forecasting. Overall, this study enhances our comprehension of the complex dynamics underlying dry spell occurrences during the rainy season and presents potential avenues for predicting and managing the AMDSL in Senegal.
Designing a Secure Mechanism for Image Transferring System Based on Uncertain Fractional Order Chaotic Systems and NLFPID Sliding Mode Controller
Authors: Rasouli Mohammad , Zare Assef , Yaghoubi Hassan , Alizadehsani Roohallah
Published: August 2023

A control method for the robust synchronization of a class of chaotic systems with unknown time delay, unknown uncertainty, and unknown disturbance is presented. The robust controller was designed using a nonlinear fractional order PID sliding surface. The Lyapunov method was used to determine the update laws, prove the stability of the proposed mechanism, and guarantee the convergence of the synchronization errors to zero. The simulation was performed using MATLAB software to evaluate the performance of the proposed mechanism, and the results showed that it was efficient. Finally, the proposed method was combined with a secure communication application to encrypt images, and the results obtained were favorable regarding the standard criteria of correlation, NPCR, PSNR, and information entropy.
The Stability Analysis of Linear Systems with Cauchy—Polynomial-Vandermonde Matrices
Authors: Rehman Mutti-Ur , Alzabut Jehad , Fatima Nahid , Rasulov Tulkin H.
Published: August 2023

The numerical approximation of both eigenvalues and singular values corresponding to a class of totally positive Bernstein–Vandermonde matrices, Bernstein–Bezoutian structured matrices, Cauchy—polynomial-Vandermonde structured matrices, and quasi-rational Bernstein–Vandermonde structured matrices are well studied and investigated in the literature. We aim to present some new results for the numerical approximation of the largest singular values corresponding to Bernstein–Vandermonde, Bernstein–Bezoutian, Cauchy—polynomial-Vandermonde and quasi-rational Bernstein–Vandermonde structured matrices. The numerical approximation for the reciprocal of the largest singular values returns the structured singular values. The new results for the numerical approximation of bounds from below for structured singular values are accomplished by computing the largest singular values of totally positive Bernstein–Vandermonde structured matrices, Bernstein–Bezoutian structured matrices, Cauchy—polynomial-Vandermonde structured matrices, and quasi-rational Bernstein–Vandermonde structured matrices. Furthermore, we present the spectral properties of totally positive Bernstein–Vandermonde structured matrices, Bernstein–Bezoutian structured matrices, Cauchy—polynomial-Vandermonde structured matrices, and structured quasi-rational Bernstein–Vandermonde matrices by computing the eigenvalues, singular values, structured singular values and its lower and upper bounds and condition numbers.
Modified Inertial-Type Subgradient Extragradient Methods for Variational Inequalities and Fixed Points of Finite Bregman Relatively Nonexpansive and Demicontractive Mappings
Authors: Wang Cong-Shan , Ceng Lu-Chuan , Li Bing , Cao Sheng-Long , Hu Hui-Ying , Liang Yun-Shui
Published: August 2023

In this paper, we design two inertial-type subgradient extragradient algorithms with the linear-search process for resolving the two pseudomonotone variational inequality problems (VIPs) of and the common fixed point problem (CFPP) of finite Bregman relatively nonexpansive operators and Bregman relatively demicontractive operators in Banach spaces of both p-uniform convexity and uniform smoothness, which are more general than Hilbert ones. By the aid of suitable restrictions, it is shown that the sequences fabricated by the suggested schemes converge weakly and strongly to a solution of a pair of VIPs with a CFPP constraint, respectively. Additionally, the illustrative instance is furnished to back up the practicability and implementability of the suggested methods. This paper reveals the competitive advantage of the proposed algorithms over the existing algorithms; that is, the existing hybrid projection method for a single VIP with an FPP constraint is extended to develop the modified inertial-type subgradient extragradient method for a pair of VIPs with an CFPP constraint.
Existence and Asymptotic Stability of the Solution for the Timoshenko Transmission System with Distributed Delay
Authors: Braik A. , Zennir Kh. , Hassan E. I. , Alfedeel A. H. A. , M. Mirgani Safa
Published: August 2023

In the present paper, a transmission problem of the Timoshenko beam in the presence of distributed delay is considered. Under appropriate assumptions, we prove the well-posedness by using the semi-group theory. Furthermore, we study the asymptotic behavior of solutions using the multiplier method. We investigate the techniques and ideas used by the second author to extend the recent results.
Recent Progress in Flame-Retardant Polymer Electrolytes for Solid-State Lithium Metal Batteries
Authors: Liao Yubin , Xu Xijun , Luo Xiongwei , Ji Shaomin , Zhao Jingwei , Liu Jun , Huo Yanping
Published: August 2023

Lithium-ion batteries (LIBs) have been widely applied in our daily life due to their high energy density, long cycle life, and lack of memory effect. However, the current commercialized LIBs still face the threat of flammable electrolytes and lithium dendrites. Solid-state electrolytes emerge as an answer to suppress the growth of lithium dendrites and avoid the problem of electrolyte leakage. Among them, polymer electrolytes with excellent flexibility, light weight, easy processing, and good interfacial compatibility with electrodes are the most promising for practical applications. Nevertheless, most of the polymer electrolytes are flammable. It is urgent to develop flame-retardant solid polymer electrolytes. This review introduces the latest advances in emerging flame-retardant solid polymer electrolytes, including Polyethylene oxide (PEO), polyacrylonitrile (PAN), Poly (ethylene glycol) diacrylate (PEGDA), polyvinylidene fluoride (PVDF), and so on. The electrochemical properties, flame retardancy, and flame-retardant mechanisms of these polymer electrolytes with different flame retardants are systematically discussed. Finally, the future development of flame-retardant solid polymer electrolytes is pointed out. It is anticipated that this review will guide the development of flame-retardant polymer electrolytes for solid-state LIBs.
A Greater Increase in Complement C5a Receptor 1 Level at Onset and a Smaller Decrease in Immunoglobulin G Level after Recovery in Severer Coronavirus Disease 2019 Patients: A New Analysis of Existing Data with a New Two-Tailed t-Test
Authors: Ishida Torao , Takagi Ken , Wang Guifeng , Tanahashi Nobuyuki , Kawanokuchi Jun , Takagi Hisayo , Guo Yi , Ma Ning
Published: August 2023

(1) Background: It is our purpose to identify the differences in the changes in Complement C5a receptor 1 (C5aR1) levels showing the degree of inflammation at onset and Immunoglobulin G (IgG) levels showing the extent of survival of the virus fragments after recovery between coronavirus disease 2019 (COVID-19) and pneumonia coronavirus disease (non-COVID-19) for saving patients’ lives. (2) Methods: First, the studies showing these markers’ levels in individual patients before and after the passage of time were selected from the PubMed Central® databases with the keywords (((COVID-19) AND individual) NOT review) AND C5a/IgG. Then, no changes in these markers’ levels with conventional analyses were selected from the studies. Finally, the no changes were reexamined with our new two-tailed t-test using the values on the regression line between initial levels and changed levels instead of the mean or median of changed levels as the expected values of changed levels. (3) Results: Not conventional analyses but our new t-test suggested a greater increase in C5aR1-levels at onset and a smaller decrease in IgG-levels after recovery in COVID-19 patients than non-COVID-19 patients. (4) Conclusion: Our new t-test also should be used in clinics for COVID-19 patients.
World Spread of Tropical Soda Apple (Solanum viarum) under Global Change: Historical Reconstruction, Niche Shift, and Potential Geographic Distribution
Authors: Qi Yuhan , Xian Xiaoqing , Zhao Haoxiang , Yang Ming , Zhang Yu , Yu Wentao , Liu Wanxue
Published: August 2023

Solanum viarum has become extensively invasive owing to international trade, climate change, and land–use change. As it is classified as a quarantine weed by countries such as the U.S. and Mexico, it is critical to understand the prevailing historical dispersal, ecological niche dynamics, and distribution patterns. We reconstructed the historical invasion countries and analyzed the ecological niche shift of S. viarum. Using MaxEnt based on the conservativeness of ecological niches, we studied variations in the potential geographical distributions (PGDs) of S. viarum in ecosystems and variations in suitability probabilities along latitudinal gradients. The invasion history in six continents involved three phases: lag (before 1980), spread (1980–2010), and equilibrium (2010–present). The ecological niche remains conserved. The area of S. viarum PGDs had increased by 259 km2; the PGDs will expand to reach a maximum in the 2050s, SSP5–8.5. The PGDs of S. viarum will migrate to higher latitudes under the same future climate scenarios. The latitudes subject to high threats range from 20° to 30° in forest and cropland ecosystems, 15.5° to 27.5° (northern hemisphere) and 33.1° to 42.8° (southern hemisphere) in grassland ecosystems, and 20° to 35° in urban ecosystems. Global change has led to an increased threat of S. viarum at high latitudes. These findings provide a theoretical basis to monitor and control S. viarum.
From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies
Authors: Ottaiano Alessandro , Ianniello Monica , Santorsola Mariachiara , Ruggiero Raffaella , Sirica Roberto , Sabbatino Francesco , Perri Francesco , Cascella Marco , Di Marzo Massimiliano , Berretta Massimiliano , Caraglia Michele , Nasti Guglielmo , Savarese Giovanni
Published: August 2023

Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.
Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology
Authors: Timakova Anna , Ananev Vladislav , Fayzullin Alexey , Makarov Vladimir , Ivanova Elena , Shekhter Anatoly , Timashev Peter
Published: August 2023

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.
Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks
Authors: Azam Humera , Tariq Humera , Shehzad Danish , Akbar Saad , Shah Habib , Khan Zamin Ali
Published: August 2023

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).
Experiment and Prediction of Pressure Drop in a Fiber–Powder Composite Material with Porous Structure for Energy Wheels and Air Cleaners
Authors: Gao Han , Li Zhenhai , Zhou Xigang , Yin Xiaolong , Shan Mengmeng
Published: August 2023

Energy wheels and air cleaners play crucial roles in building air conditioning systems. The former is essential for conserving energy in air conditioning systems, while the latter is necessary for ensuring the quality of indoor air. Pressure drop is a crucial parameter for both energy wheels and air cleaners, and it is essential to conduct theoretical and experimental investigations to aid in their design. In this study, we focused on the study of pressure drop in a fiber–powder composite material which can be used for both total heat exchange and air purification. Experimental tests were initially conducted to examine the impact of different parameters on the pressure drop in the material. Subsequently, based on the special fiber–powder structure of the material, two pressure drop prediction methods with different prediction strategies were proposed. The two prediction strategies were compared by analyzing the prediction accuracy of the two methods. As tested by experimental data, for both methods, the absolute prediction error was less than ±6 Pa when the pressure drop was below 50 Pa, and the relative prediction error was less than ±8% for most data sets when the pressure drop was greater than 50 Pa. Moreover, the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of prediction for both methods were less than 4 Pa and 7% respectively. The test results show that although the prediction strategies are different, both prediction methods can obtain acceptable prediction results, and both methods are practical. This study is intended to serve as a valuable reference for the design of energy wheels and air cleaners.
The Development of Immunotherapy for the Treatment of Recurrent Glioblastoma
Authors: Liu Xudong , Zhao Zihui , Dai Wufei , Liao Kuo , Sun Qi , Chen Dongjiang , Pan Xingxin , Feng Lishuang , Ding Ying , Wei Shiyou
Published: August 2023

Recurrent glioblastoma (rGBM) is a highly aggressive form of brain cancer that poses a significant challenge for treatment in neuro-oncology, and the survival status of patients after relapse usually means rapid deterioration, thus becoming the leading cause of death among patients. In recent years, immunotherapy has emerged as a promising strategy for the treatment of recurrent glioblastoma by stimulating the body’s immune system to recognize and attack cancer cells, which could be used in combination with other treatments such as surgery, radiation, and chemotherapy to improve outcomes for patients with recurrent glioblastoma. This therapy combines several key methods such as the use of monoclonal antibodies, chimeric antigen receptor T cell (CAR-T) therapy, checkpoint inhibitors, oncolytic viral therapy cancer vaccines, and combination strategies. In this review, we mainly document the latest immunotherapies for the treatment of glioblastoma and especially focus on rGBM.
Influence of the Human Field of View on Visual and Non-Visual Quantities in Indoor Environments
Authors: Zauner Johannes , Broszio Kai , Bieske Karin
Published: August 2023

The visual and non-visual effectiveness of light is often determined by measuring the spectrally weighed irradiance on the corneal plane. This is typically achieved using spectral irradiance or illuminance measurements, captured in a hemispheric (2π) geometry with a diffuser. However, the human binocular field of view (FOV) is not a perfect hemisphere, as it is occluded both upward and downward. Previous research on FOV-restricted measurements is limited, leaving the error from using hemispheric measurements for non-visual quantities undefined. In our study, we tackled this issue by designing and 3D printing FOV occlusions as attachments to spectral measurement devices. We took measurements with and without the occlusion in various laboratory (light from different directions) and real-world lighting situations (light typically from above). Our findings reveal a reduction of visual and melanopic values due to the FOV occlusion. These ranged from negligible to more than 60% in realistic scenarios. Interestingly, the reduction was consistent for both visual and melanopic parameters, as the distribution of light in the FOV was generally spectrally homogeneous. An exception occurred in a specific artificial laboratory situation, where the melanopic daylight (D65) efficacy ratio changed by more than a factor of 2 solely because of the FOV occlusion. Additionally, we observed that head orientation had a marked effect on all quantities measured. In conclusion, our results highlight the potential for substantial errors when solely relying on vertical, hemispheric measurements in experiments and non-visual lighting design projects. We encourage the (additional) use of FOV occlusion in eye-level measurements for typical viewing directions, and we are providing open-source 3D-print files to facilitate this practice.
Recent Advances in Endodontic Diagnosis and Modern Treatment Plans
Authors: Iandolo Alfredo
Published: August 2023

Generally, to achieve success in endodontics, it is essential to perform all stages of treatment with cautiousness and excellence [...]
Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey
Authors: Zhang Rui , Wu Yichao , Jin Wei , Meng Xiaoman
Published: August 2023

With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.
Condition Monitoring Method for the Gearboxes of Offshore Wind Turbines Based on Oil Temperature Prediction
Authors: Fu Zhixin , Zhou Zihao , Zhu Junpeng , Yuan Yue
Published: August 2023

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.
Analytical Study for the Determination of the Energy Use Potential of Forest Dendromass in the Czech Republic
Authors: Březina David , Michal Jakub , Hlaváčková Petra
Published: August 2023

The European Union’s current pressure on the Member States to adopt both the Green Deal and the Fit for 55 package is leading to an accelerated drive to put in place measures to meet the 2030 climate targets. At the same time, many discussions at the international bilateral meetings of EU country representatives raise the question of the realism of setting climate targets, and therefore, the ability to meet these commitments. The results elaborated in this analytical study offer a realistic picture of the possibilities of meeting strategic climate targets using the example of the use of forest dendromass. The data assess the possibility of achieving a 22% share of renewable energy sources (RES) in gross final energy consumption by 2030 in the Czech Republic. The study points out that at present, the use of forest dendromass from primary production is at its maximum and meeting the climate targets for increasing the share of RES in the energy mix represents a major problem in the long term. The findings published in this study also point to the objective threat of the increased use of dendromass in the energy sector to the maintenance of sustainable forest management and the preservation of forest quality.