bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cardiovascular Disease Prediction Based on Soft Voting Ensemble Model
Authors: Miao Lei , Wang Weisheng 
Published: May 2023

Abstract
In recent years, cardiovascular disease has become a serious threat to the health and safety of people all over the world. Machine learning, deep learning and other artificial intelligence (AI) technologies used to assist medical diagnosis are becoming more and more popular. In order to improve the performance of cardiovascular disease prediction, this paper proposes an ALD soft voting ensemble model (ALD-SVE), which is composed of three individual learners, Attentional Factorization Machines (AFM) can make full use of the cross features of cardiovascular disease data and capture. The attention mechanism introduced in the AFM model gives different weights to cross features to enhance interpretation, and then uses the soft voting ensemble of Logistic Regression (LR) and Decision Tree (DT) to further enhance the stability and generalization of the model. Experiments show that the ALD-SVE model has excellent performance in cardiovascular disease data sets, and its AUC value reached 0.80306, better than the listed comparison model.
Video based behavior detection algorithm
Authors: Wang Yonghui , Wang Mengjie , Meng Qingquan 
Published: May 2023

Abstract
Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is conducive to reducing training costs, realizing efficient calculation, and enabling researchers to quickly apply it to practical scenarios.
Video Surveillance for Indoor Office Environment Based on Object-Level Anomaly Detection
Authors: Chen Haotian , Zhang Chao , Lin Zhouchen 
Published: May 2023

Abstract
Traditional methods of Abnormal Behavior Detection (ABD) process the surveillance video on frame-level, which ignores object-level abnormal behavior patterns. To address the problem, this paper presents Object-Level Anomaly Detection model (OLAD), which aims to model various normal behavior patterns of different objects and the normal interaction patterns between them. Specifically, OLAD introduces an encoding-embedding network to transform object-level information into the feature space. By integrating such information, OLAD processes both frame-level and object-level cues in the video for ABD. In addition, we construct our own dataset Northking2022 especially for office scenes because of the lack of public datasets for indoor office environments. Experimental results show that OLAD gains better performance on both the public benchmark and Northking2022.
CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image
Authors: Xiao Shuomin , Qu Aiping , He Penghui , Hong Han 
Published: May 2023

Abstract
Accurately classifying nuclei in histopathology images is essential for cancer diagnosis and prognosis. However, due to the touching nuclei, nucleus shape variation, background complexity, and image artifacts, end-to-end nucleus classification is still difficult and challenging. In this manuscript, we propose a context aggregation network (CA-Net) for nuclei classification by fusing global contextual information which is critical for classifying nuclei in histopathology images. Specifically, we propose a multi-level semantic supervision (MSS) module focusing on extracting multi-scale context information by varying three different kernel sizes, and dynamically aggregating the context information from high to low level. Furthermore, we employ the GPG and SAPF modules in encoder and decoder networks to exact and aggregate global context information. Finally, the proposed network is verified on a mainstream nuclei classification image datasets (PanNuke) and achieves an improved global accuracy of 0.816. Our proposed MSS module can be easily transferred into any UNet-liked architecture as a deep supervision mechanism.
Research on Character Trajectory Prediction Method with Fused Perspective Information in Virtual Scene
Authors: Bi Mingyang , Wang Xihan , Gao Quanli , Wang Yani , Jiao Zixun , Gao Rongxin , Li Jingcao , He Miao 
Published: May 2023

Abstract
Character trajectory prediction in virtual scene is conducive to improving the preloading of scene resources. Most state-of-the-art trajectory prediction methods are devoted to the pedestrian trajectory in the real world. These methods cannot work well in virtual scene when encountering simple role interaction, more random motion and limited types of accessible data. In this paper, we proposed a role trajectory prediction method based on viewpoint information (RIPA algorithm, Role Intent Prediction Algorithm), which includes role interest calculation module based on viewpoint information and a role trajectory prediction module based on information of intention. Firstly, the role interest calculation module based on view information is used to predict the role’s interest in the virtual object. Secondly, the role interest is integrated into the trajectory prediction model, and the attention mechanism is introduced to improve the prediction performance. The experimental results show that, compared with the traditional LSTM model, the role trajectory prediction method proposed in this paper can improve the prediction accuracy by about 2%, and can predict the future target scene and role target position at least 1s in advance.
Task Scheduling Scheme for Mobile Edge Computing via Bayesian Classifier
Authors: Wang Yu , Liu Shixi 
Published: May 2023

Abstract
The enormous computing tasks in the Mobile Edge Computing (MEC) beget challenges correlated with cost, energy consumption and quality of service. However, existing studies are not optimized enough for MEC in terms of the selection of task classification and scheduling schemes. Inspired by spam classification idea, a novel task scheduling scheme is proposed for MEC via Bayesian Classifier. The MEC tasks are first categorized into three priority categories using Naive Bayes. In addition, the priority-based classification task scheduling (PCTS) scheme is proposed to schedule the classified tasks with different priorities. Finally, the experimental evaluation is conducted using three metrics that include latency, processing time, and energy consumption. Simulation results show that the proposed scheme outperforms the baseline schemes in accordance with completion time and throughout rate.
Camera-irrelevant Feature Invariance Learning for Unsupervised Person Re-identification
Authors: Li Mengyao , Zhang Rumeng , Lv Xueshuai , Gao Ling 
Published: May 2023

Abstract
Unsupervised person re-identification (re-ID) aims to match pictures of target pedestrians from some other cameras without any labels. The existing methods usually adopt a cluster-based pipeline to generate false labels for training. However, because of the differences in camera configuration, taking pictures Angle, illumination intensity and pedestrian background, unlabeled pedestrian training samples captured by different cameras are hard to classify into a cluster. To solve this problem, the paper proposes a camera-irrelevant feature learning framework, which learns camera-irrelevant features and camera-irrelevant distances through network training and clustering. Specifically, during training, images of other camera styles are generated, which enriches cross-camera learning. During clustering, we propose a camera-irrelevant distance matrix, which reduces the inter-class variance caused by camera variance.
A GNN-based Network for Tissue Semantic Segmentation in Histopathology Image
Authors: He PengHui , Qu AiPing , Xiao ShuoMin , Ding MeiDan 
Published: May 2023

Abstract
Segmentation of different tissue regions in pathological images hold an significant position diagnosis and prognosis of cancer. Although the convolutional neural network(CNN) and transformer which treat the image as a grid or sequence structure have been widely employed in this task, which is difficult to capture irregular and complex targets flexibly. So it is still a challenging task. At this paper, we employ a GNN-based segmentation method for pathological images which adopts the encoding-decoding structure. We first represent the input image as a graph structure which consists of a number of patches viewed as nodes and connect the nearest neighbors to build a graph. We also introduce ViG block to build a hierarchical pyramid architecture which consists of grapher module with graph convolution and FFN module with two linear layers. In addition, we utilize a pyramid CNN architecture decoder to aggregate graph information with multi-scales. The proposed method reaches 75.68% and 87.72% mean Dice on BCSS and LAUD-HistoSeg datasets respectively demonstrate the effectiveness.
Correlation Study of Temporal Climate Networks Based on Weighted Cumulative Node Evaluation
Authors: Luo Guilan , Wang Caikui , Hu Anshun , Ma Xin , Fang Lianbiao , Liu Xuan 
Published: May 2023

Abstract
In order to systematically analyze the characteristics of climate change in China in recent years, this paper selects the data of 6 climate factors from China meteorological stations during 1984-2016 for 33 years. With the help of complex network theory, the representative climate nodes in China are determined by node importance evaluation method with the power to accumulate intermediate centrality. Multiple single-factor temporal static and temporal dynamic climate network models are constructed to explore the correlation of representative node networks of Climate in China. The results show that the temperature network has the strongest correlation. The temporal static network has the weakest correlation with precipitation network, while the temporal dynamic network has the weakest correlation with wind velocity network and shows a weak fluctuation downward trend.
A reverse duality for the ASEP with open boundaries
Authors: Schütz G M 
Published: May 2023

Abstract
We prove a duality between the asymmetric simple exclusion process (ASEP) with non-conservative open boundary conditions and an asymmetric exclusion process with particle-dependent hopping rates and conservative reflecting boundaries. This is a reverse duality in the sense that the duality function relates the measures of the dual processes rather than expectations. Specifically, for a certain parameter manifold of the boundary parameters of the open ASEP this duality expresses the time evolution of a family of shock product measures with N microscopic shocks in terms of the time evolution of N particles in the dual process. The reverse duality also elucidates some so far poorly understood properties of the stationary matrix product measures of the open ASEP given by finite-dimensional matrices.
Temporal witnesses of non-classicality and conservation laws
Authors: Di Pietra Giuseppe , Marletto Chiara 
Published: May 2023

Abstract
A general entanglement-based witness of non-classicality has recently been proposed, which can be applied to testing quantum effects in gravity. This witness is based on generating entanglement between two quantum probes via a mediator. In this paper we provide a ‘temporal’ variant of this witness, using a single quantum probe to assess the non-classicality of the mediator. Within the formalism of quantum theory, we show that if a system M is capable of inducing a coherent dynamical evolution of a quantum system Q, in the presence of a conservation law, then M must be non-classical. This argument supports witnesses of non-classicality relying on a single quantum probe, which can be applied to a number of open issues, notably in quantum gravity or quantum biology.
Review on advanced nanomaterials for hydrogen production
Authors: Nkele A C , Offiah S U , Chime C P , Ezema F I 
Published: May 2023

Abstract
Global fuel consumption and harmful gaseous emissions diverted energy sources to alternative means. Solar water splitting amidst other solar conversion methods is the most clean and efficient means of hydrogen production. 21st century technologies have delved into adopting nanomaterials of high efficiency to treat environmental pollution and produce hydrogen through electrochemical, photocatalytic, or electrophotocatalytic processes due to their outstanding properties. We reviewed diverse means of producing hydrogen through the use of advanced nanomaterials like carbon nanomaterials, solid inorganic-organic hybrids, metallic oxides/sulfides, quantum dots, composite heterostructures, microbial electrolysis cells etc. Overview on hydrogen production, ways of generating hydrogen, advanced nanomaterials for hydrogen production, and recent progress in hydrogen-producing nanomaterials have been discussed.
A preliminary investigation of absorber embedded waste crown corks on the performance of passive solar still
Authors: Akubue G. U. , Okeke I. D. , Ubulom A. E. , Obiora J. C. , Uzoigwe K. U. , Akubue S. U. , Anyanso J. A. 
Published: May 2023

Abstract
Solar still is the main cost-effective methods of purifying water in remote areas with no access to grid connected electricity, and with expensive water piping network. One of the major drawbacks of this technology has been detected as low distillate output when compared to modern but expensive purification methods. In this work, a sensible heat storage material (waste crown corks) which are freely available and causing nuisance in the environment is investigated for the productivity enhancement. To achieve this objective, two solar stills of similar materials and dimensions were designed and fabricated. One of the stills is without modification (conventional type) and the other is modified by embedding waste crown corks on the absorber plate. The results evidenced that incorporating waste crown corks can improve the efficiency and productivity by 25.64% and 42.96% respectively, when compared to the conventional type. It showed that waste plastic crown corks can be used as sensible heat storage material when enhancing productivity of passive solar still than any other expensive materials.
Design and analysis of an electronic control device for speed compensation and direction realignment in wind turbine
Authors: Kindama N , Ibrahim A G , Oyedum O D 
Published: May 2023

Abstract
This work incorporated the solutions to the technical challenges of output fluctuations and intermittencies of wind turbines due to an everchanging wind speed and wind direction. The design comprises of wind sensing, electronic control and electric motor sections. Microcontroller technology using ATMEGA 2560 and electronic technology was applied to equip the device with both wind-direction harvesting and wind-speed reinforcement capabilities. Upon implementation on a turbine, the available wind was harvested, irrespective of the originating direction for channeling into attaining maximum turbine blade rotation and to compensate for drop in wind speed through generation of appropriate rotor speed reinforcement needed for sufficient Revolution Per Minute (RPM) of the alternator.
Extraction and purification of Lawsonia inermis (laali) dyes for applications in dye-sensitized solar cell
Authors: Akwolu R A , Nwakanma O , Offiah S U , Agbogu A , Ekechukwu O V , Okafor I F , Ugwuoke P E 
Published: May 2023

Abstract
This study explores the extraction of dyes from Lawsonia inermis (Laali) and the effects of purification on the dyes for solar cell applications. The TiO2 thin films were synthesized through Doctor Blade’s technique while the dye was applied on the thin film using a simple drop-casting method. Characterization of the doctor-blade deposited TiO2 thin-film using XRD confirmed the successful deposition of anatase crystallite structure with a mesoporous morphology confirmed using SEM analysis. The UV-Vis, FTIR, and EIS characterizations of the dyes showed increase in band gap, adequate functional groups and lower charge resistance respectively after the purification, which could contribute positively to enhancing the efficiency of solar cell devices.
A review on the inference of doping TiO2 with metals/non-metals to improve its photocatalytic activities
Authors: Orizu G E , Ugwuoke P E , Asogwa P U , Offiah S U 
Published: May 2023

Abstract
The band-gap energy of TiO2 nanoparticles has been modified using different methods. The study reviewed the effects of doping and co-doing TiO2 nanotubes with different metals and nonmetals to modify its band gap and improves the photocatalytic activity of the nanoparticles. From our findings reviewing different articles, co-doing TiO2 using dissimilar metal ions like Cerium (Ce) together with nitrogen (N) ions or chromium (Cr) and iron (Fe) ions will decrease particle size, broaden the area of the surface, and as well modifies the particle’s band-gap for visible light to energize an electron causing its movement from the valance band to the conduction band. The studies also revealed that co-doping TiO2 with chromium and iron metal ion demonstrated twice the photocatalytic activity for photodecomposition of gaseous-isopropyl alcohol when compared with single (Cr/Fe) doped TiO2 nanoparticles when exposed to visible light. The reviewed work estimated the optimal amount of Ce for Ce/N co-doped TiO2 at 0.05 grams. Different works of literature reviewed show that doping TiO2 with different transition metals reduces the band-gap for easy absorption of visible light and improvement in the photocatalytic activities of the nanoparticles.
Decomposition effects of energy and co2 intensities in the Nigerian economy: A logarithmic mean weight divisia index (LMDI) analysis.
Authors: Nnaji C E , Okafor I F , Akubue G U , Uzoma C C , Onyekaozuru E F , Adagba C I 
Published: May 2023

Abstract
Abstract This paper investigates CO2 intensity changes from energy use in the Nigerian economy for a period of 39 years (1981-2019) using the Logarithmic Mean weight Divisia Index (LMDI). The respective contribution and the impacts of different factors such as activity effects, conversion efficiency, intensity of energy, fuel mix, among others on the aggregate carbon emission changes in the economy has been examined bearing in mind the poor electricity supply experienced over the years. The investigation was undertaken in four periods and the outcomes revealed that energy use and CO2 emission have increased to some extent. The analysis shows that carbon emissions decreased most between 1981 and 1989 as a result of a decrease in energy intensity and substitution/fuel mix changes. There was considerable increase in intensities from 1990 upwards, thereby neutralising the benefits experienced in the first period. Increased energy intensity, inadequate electricity supply as well as a switch over to high carbon-emitting fuels were principal factors that fuelled the increase. Given the trends in the decomposition, it is important to promote cleaner fuels and improve energy efficiency in order to decrease Nigeria’s energy intensity and carbon emissions overall contributions in the coming years.
Theoretical investigation of the performance of benzimidazole derivatives as mild steel organic corrosion inhibitors in acidic environments.
Authors: Ojo Francis K. , Alum Ogechi L. , Akpomie Kovo G. , Ofomatah Anthony C. , Chukwuemeka-Okorie Helen O. , Ani Julius U. , Agbo Sunday C. , Odewole Olufemi A. , Ogunyemi Babatunde T. 
Published: May 2023

AbstractThe efficiency of various benzimidazole derivatives as mild steel corrosion inhibitors in HCl and H2SO4medium was evaluated using quantum chemical methods. The AM1 semi-empirical method and Density Functional Theory (DFT) at the B3LYP/6-31G* level were used. The orbital energies (EHOMOand ELUMO), Separation Energy (ELUMO-EHOMO), Dipole moment (µ), Log P, Polarizability, molecular volume, molecular weight, Hardness (η) and Softness (S) are the computed quantum chemical characteristics linked to inhibitory efficiency. There was a strong link between quantum chemical parameters and experimental inhibition efficiency. The findings revealed that the Inhibition Efficiency was linked to a number of quantum chemical factors. The 2-mercaptobenzimidazole derivative had a higher Inhibition Efficiency (IE) than the other benzimidazole derivatives, according to the results.
Production of a Biomass Briquette as a Boiler Fuel for Sustainable Steam Power Generation
Authors: Okafor I F , Bappah U A , Ugwu J C , Ugwuiyi C P 
Published: May 2023

Abstract
This work focused on the production of biomass briquette as a boiler fuel for sustainable steam power plant for electricity generation. Most existing power plants rely on fossil fuels, which are nonrenewable and hazardous to the environment. Biomass briquettes made with biomass materials, which are renewable can serve as a sustainable source of fuel for firing a boiler of a power plant. Producing biomass briquettes with improved characteristics is still a big challenge due to varying characteristics of the biomass materials. In this work, the physical, chemical (volatiles matter, Sulphur and ash contents), mechanical and combustion (energy values and burning rate etc.) characteristics of the un-carbonized and carbonized biomass briquettes produced with ground shells, sawdust and waste paper materials were determined to compare their suitability for use as a sustainable boiler fuel for steam production for power plants. The results indicated that the produced briquettes can serve as a viable source of fuel for firing the boiler of a power plant. The energy values of the produced briquettes are higher compared to others available biomass briquettes in the literature. The low Sulphur, moisture and ash contents of the produced briquettes also indicated their suitability for use as a boiler fuel.
Potentials of Biomass Waste Resources with Respect to their Calorific Value, Proximate and Ultimate Analysis for Energy Utilization
Authors: Onochie U P , Ofomatah A C , Onwurah C , Tyopine A A , Akingba O O , Kubeynje B F , Aluma C C , Alozie C 
Published: May 2023

Abstract
The increase in the prices and demand for cooking fuel has necessitated the deployment of an alternative fuel (biomass wastes), which is efficient and relatively inexpensive. Biomass wastes are abundant and underutilized for energy purposes in Nigeria. The conversion of these wastes into briquettes can help to mitigate the over-dependency of wood for cooking and deforestation. In this study, biomass wastes such as rice husks (RH), banana peels (BP), coconut husk (CH), groundnut shell (GS) and saw dust (SD) were collected in various dumpsites in Abakaliki, Ebonyi State and sun dried for a week for ease of grinding. Briquettes were produced mechanically from all samples, including a mixture of all the samples (MIX) using the same ratio. Waste paper was used as a binder. Characterization analyses (i.e. calorific value, proximate and ultimate analyses) were conducted using ASTM standards. The results revealed that the MIX briquette has an average calorific value of 26346kJ/kg, 12.35% moisture, 8.99% ash, 0.05% sulphur and 34.7% carbon. Essentially, the findings revealed that the combination of the biomass wastes have a positive effect with respect to the physical and chemical properties of the MIX briquette. This fuel briquette can replace wood for cooking and mitigate deforestation.