bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-angular radiation of electromagnetic waves acting on the site of targeted therapy based on high-bit metamaterials
Authors: Yang Qianke , Jing Xufeng
Published: May 2023

Encoding metasurface refers to a two-dimensional ultrathin planar array structure in which the unit structure is arranged in a periodic or aperiodic sequence, which can freely manipulate the amplitude, phase and polarization direction of the electromagnetic beam. Based on the generalized Snell’s theorem and the far-field scattering theorem, several reflective coding particles are designed to construct high-bit encoding metasurface sequences in the terahertz band. In order to realize the free regulation of the far-field scattering angle of the encoding metasurface, based on the Fourier convolution principle in digital signal processing, we performs addition and subtraction convolution operations on two different reflective encoding metasurface sequences to obtain a new encoding sequence. This coding sequence enables flexible regulation and continuous change of the scattering angle of the terahertz beam.
Broadband cross circularly polarized carpet invisibility cloak based on Pancharatnam–Berry phase reflective metasurface
Authors: Zhang Peng , Dong Yanyan
Published: May 2023

We present a broadband circularly polarized reflective metasurface carpet cloak. The metasurface unit cell is composed of two crossed H-shaped metal patterns. When the incident wave is left-handed circularly polarization (LCP), the metasurface can achieve high-efficiency cross-polarization conversion in the wide frequency range. The cross-polarization reflection efficiency is higher than 94%. By using the proposed cross-polarized reflection unit cell structure, the phase gradient metasurfaces based on the Pancharatnam–Berry phase principle is proposed, and a broadband reflective metasurface invisibility cloak is constructed. The simulation results show that when the LCP light is incident vertically, the proposed carpet cloak can significantly suppress the scattering and reconstruct the wavefront in the broadband frequency range.
Spatial dependent of the new generated light via Fano-interference in a quantum well nanostructure
Authors: Li Qiang , Yang Zhao
Published: May 2023

In this letter, we will study the spatially dependent of the new generated light via Fano interference in a quantum well nanostructure. We will show analytically that by Fano-type interference the new light can be generated when the weak probe light propagates through the medium. We will study the efficiency of the new generated light for different strengths of the Fano-interference. We will also consider the condition when the probe light becomes optical vortex light which can carry the orbital angular momentum (OAM). By controlling the OAM of light, we will study the spatial dependence of the absorption spectrum of the generated light. Our proposed model may have potential application in the future quantum information science based on semiconductor quantum well nanostructures.
Characterization of the collisional frequency shift in an atomic fountain clock using absorption imaging
Authors: Beattie Scott , Jian Bin
Published: May 2023

The collisional frequency shift is a dominant contribution to the uncertainty of many caesium fountain clocks. Minimizing the effect can be difficult, as lowering the atomic density comes at the cost of a reduced signal-to-noise ratio. Also, it is typically not atomic density, but total atom number that is measured in the experiment, which can lead to a potential measurement bias. In this paper, we describe a direct measurement of the atomic density using absorption imaging of the atomic cloud. For the caesium fountain clock, NRC-FCs2, at the National Research Council Canada, these measurements have led to a reduction in the uncertainty due to the collisional shift by a factor of 10.
Validated computational models predict vagus nerve stimulation thresholds in preclinical animals and humans
Authors: Musselman Eric D , Pelot Nicole A , Grill Warren M
Published: May 2023

Objective. We demonstrated how automated simulations to characterize electrical nerve thresholds, a recently published open-source software for modeling stimulation of peripheral nerves, can be applied to simulate accurately nerve responses to electrical stimulation. Approach. We simulated vagus nerve stimulation (VNS) for humans, pigs, and rats. We informed our models using histology from sample-specific or representative nerves, device design features (i.e. cuff, waveform), published material and tissue conductivities, and realistic fiber models. Main results. Despite large differences in nerve size, cuff geometry, and stimulation waveform, the models predicted accurate activation thresholds across species and myelinated fiber types. However, our C fiber model thresholds overestimated thresholds across pulse widths, suggesting that improved models of unmyelinated nerve fibers are needed. Our models of human VNS yielded accurate thresholds to activate laryngeal motor fibers and captured the inter-individual variability for both acute and chronic implants. For B fibers, our small-diameter fiber model underestimated threshold and saturation for pulse widths >0.25 ms. Our models of pig VNS consistently captured the range of in vivo thresholds across all measured nerve and physiological responses (i.e. heart rate, Aδ/B fibers, Aγ fibers, electromyography, and Aα fibers). In rats, our smallest diameter myelinated fibers accurately predicted fast fiber thresholds across short and intermediate pulse widths; slow unmyelinated fiber thresholds overestimated thresholds across shorter pulse widths, but there was overlap for pulse widths >0.3 ms. Significance. We elevated standards for models of peripheral nerve stimulation in populations of models across species, which enabled us to model accurately nerve responses, demonstrate that individual-specific differences in nerve morphology produce variability in neural and physiological responses, and predict mechanisms of VNS therapeutic and side effects.
A Path Planning Algorithm for Mobile Robot Based on MR-QL in the Unknown Scene
Authors: Fu Zongli , Liu Zhandong , Hu Chuangye , Ding Nan
Published: May 2023

The Q-Learning (QL) algorithm is widely used for path planning. As the scene in which the mobile robot is located becomes complex, the algorithm suffers from the limitations of low convergence speed and long exploration paths. Therefore, a Max Reward-Q-learning (MR-QL) path planning algorithm based on maximum reward is proposed for complex unknown scenarios. The original algorithm’s discrete reward function and action selection strategy are improved, and a new reward function is designed to dynamically adjust the reward mechanism to heuristically guide the robot motion. The action selection strategy is also optimized to avoid invalid exploration and improve the algorithm’s convergence. Finally, three experimental environments with different complexity are constructed to demonstrate the feasibility of the proposed algorithm. The simulation results show that the MR-QL algorithm is about 50% of the original algorithm in terms of exploration step length and training time, and the convergence speed of the algorithm is better than the original algorithm.
Document-level Relation Extraction Based on Heterogeneous Graph Convolutional Network and Local Semantic Fusion
Authors: Qu Siyu , Chen Duanbing , Xu Dengyou
Published: May 2023

Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the whole document as an essential global feature while ignoring the discrimination of entity representation. Focusing on local semantic feature, a novel model named GCNLEF based on graph convolutional network is proposed in this paper. In the presented method, a heterogeneous graph containing mention nodes and sentence group nodes is constructed first. Then multi-hop path reasoning is presented to infer the relations between entities. Experimental results on DocRED show that the proposed model can achieve 61.45 F
1 score with 90 epochs, improved by 0.15 F
1 score compared with the state-of-the-art method ATLOP.
Nested Deep Feature Attention Module for Underwater Image Enhancement
Authors: Xiao Yeqing , Wang Qiang , Li Yupeng , Tang Yandong
Published: May 2023

The captured underwater images always suffer degradations because of absorption and light scattering in water. Thus, underwater image enhancement becomes indispensable as a precondition to carry out underwater tasks. Thus, we put forward an end-to-end structure to solve the problem of underwater image degradation. Our network uses continuously stacked deep-layer feature extraction modules to exploit more significant features. For purpose of enhancing the image contrast, we recover high-frequency information by hiring the nested residual attention groups in feature extraction modules, which can adaptively extract more deeper features. Moreover, channel attention module is hired in residual groups to emphasize important information. Comparing with other state-of-the-art approaches, our network performs best on quantitative and qualitative evaluations at datasets EUVP and UIEBD.
Document-level Event Extraction - A Survey of Methods and Applications
Authors: Liu Qi , Luan Zhen , Wang Kunlong , Zou Ye , Liu Bing , Zhang Yang
Published: May 2023

Event extraction aims to detect occurrences of specified types and extract corresponding event arguments from unstructured data input, which is an integral part of information extraction [1]. Many downstream tasks, such as text summarization, causal relationship identification, and event reasoning, make extensive use of event extraction. However, most of the existing research on event extraction remains at the sentence level. Document-level event extraction is still under exploration and lacks relevant review. Focusing on the task of document-level event extraction, this survey first divides the existing technologies into three categories and introduces the most representative models; Then, we list some commonly used datasets for document-level event extraction with their usage messages; Finally, we analyse the current research gaps of document-level event extraction and future research trends.
Novel Robust Span-based Model for Event Detection
Authors: Zhang Hang , Yuan Xiaoguang , Song Yingyi , Yang Yuting , Wang Rui , Liu Bing , Han Cuihong
Published: May 2023

Event detection (ED) aims to identify events and their types from the given text, which is the vital subtask of event extraction. However, poor quality training corpus can seriously affect the validity of current dominating deep learning-based techniques. In order to improve the robustness of the model, this paper converts the ED task into the named entity recognition (NER) task, and uses the span-based NER framework to identify and classify event triggers. In addition, we ameliorate the framework through data cleaning strategy, hierarchical learning rate strategy, sentence-level loss function and model ensemble strategy. The experimental results suggest that our approach achieves state-of-the-art performance on the widely used Chinese dataset. Moreover, the deep analysis has confirmed the effectiveness of the improvement strategy in resisting data pollution.
Densely stacked rebar counting based on image semantic segmentation in wide scene
Authors: Wang Yi , Chen Duanbing , Li Wenrui , Xue Yu , Deng Xiang , Zhou Junlin , Ren Junsong
Published: May 2023

To accurately count densely stacked rebars belongs to semantic segmentation in image. Various methods have been proposed to segment the objects in single image. However, widely and densely stacking scene such as warehouse or vehicle stacking of rebars is always faced. For these scenes, clear image of end faces of rebars cannot be obtained through a single camera. Generally, multiple cameras are often used to take clear images to count rebars. One strategy is to stitch multiple images into a single image at pixel level first, and then count rebars in this stitched image. Various methods have been proposed to stitch images, but it is extremely difficult to stitch two images at pixel level accurately for parallax distortion or partially hidden. A topological stitching strategy is proposed in this paper to count the rebars in whole scene. In the method, rebars in each image are identified and counted first, and then the identification and counting results in the adjacent images are stitched so as to esitmate the total number of rebars. The perfomance of the method is evaluated on real datasets, and the counting accuracy exceeds 99%. Experiments show that the proposed method is effective on widely and densely stacked rebars identification.
Object Detection from Point Clouds for Driverless Vehicles
Authors: Wu Sidong , Zhong Zhuonan , Jiang Tao , Ren Liuquan , Yuan Jianying , Duan Cuiping
Published: May 2023

Point clouds offer the advantage of providing direct access to 3D data. So, utilizing solely point clouds, we present an object detection technique for driverless vehicles in this study. In this method, a graph is first constructed using the k-nearest neighbour (KNN) method, and then a graph neural network module is proposed for local information extraction. Then, depending on the coordinates of points, we utilize a pillar-based projection approach to project the locally informative feature into bird’s-eye-view (BEV). After that, residual-based networks with attention mechanisms are used for the BEV features processing. The attention system is capable of assigning appropriate weights to several nearby points, hence improving detection performance. The proposed work achieves 58.49 percent and 67.90 percent mAP (mean Average Precision) for 3D Object detection and BEV detection, respectively, on the KITTI 3D object detection benchmark. We also tested the proposed method on our driverless vehicle in our campus, and we compared it to the prior method we utilized. The experimental results reveal that the proposed method outperforms the existing methods (about 2% higher than PointPillars in BEV detection) and gives more accurate information.
2-D Omnidirectional Human Body Attribute Fusion Recognition System
Authors: Bu Deqing , Jiang Niande , Yang Yang , Gao Xiaohui , Hu Min , Zhu Hongqin , Wang Yaobin
Published: May 2023

Traditional face or body attribute recognition can solely train and understand the specific attributes marked according to the marked content of the dataset. Most recognition systems on the market integrate specific attribute recognition algorithms. The lack of corresponding attribute data fusion leads to the problem of high diversity but poor recognition accuracy. This paper designs a recognition system based on data fusion and attribute fusion of two-dimensional images, which can fuse subtle data during training, and fuse the recognition results of overlapping attributes, to achieve multi-directional recognition of human subtle attributes, make up for the shortcomings of single recognition algorithm, and obtain more comprehensive and accurate recognition results. The experimental results show that the recognition system. Firstly, the overall recognition accuracy is above 83.5%. Secondly, it can make up for the problems of high accuracy but poor diversity of single recognition algorithm and poor accuracy of multi-feature recognition algorithm.
Optical Image Change Detection Method Based On Depth Convolution Difference Map Fusion
Authors: Li Yujing , Wang Longchao , Chen Xi , Li Chuan , Xiang Haibing
Published: May 2023

Aiming at the problem of low change detection accuracy caused by the difference in the brightness of optical remote sensing images, an optical This method borrows the idea of convolution learning features in deep learning, performs convolution operations on two images, and obtains the convolutional map fusion. features in deep learning, performs convolution operations on two images, and obtains the convolutional difference map and logarithmic ratio map; and then performs multi-layer convolution on the two difference maps respectively. The feature maps after each layer of convolution are combined to obtain a The feature maps after each layer of convolution are combined to obtain a deep convolution difference map; two deep convolution difference maps are fused to obtain a deep convolution difference fusion map; finally the maximum This article uses LANDSAT satellite images to verify and compares the maximum variance automatic threshold between classes is used for segmentation to obtain change detection result graph. The experimental results show that the difference map fusion enhances the detailed information of the In terms of the detection effect, the anti-noise performance is enhanced. The detection accuracy is improved.
Cardiovascular Disease Prediction Based on Soft Voting Ensemble Model
Authors: Miao Lei , Wang Weisheng
Published: May 2023

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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

The 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

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

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.
Characterization of Cashew Nut Shell Pyrolysis Liquid: As a Source of Sustainable Energy and a Precursor in many Chemical Industries
Authors: Fombu A H , Ochonogor A E , Olayide O E
Published: May 2023

20 g cashew nut shell (CNS) was pyrolyzed in a fixed bed pyrolysis reactor at a temperature of 700 °C, heating rate of 100 °C/min, and time of 45 minutes. The cashew nut shell liquid (CNSL) obtained was 61.3 %. Proximate and ultimate analyses, viscosity (cp), density (g/ml), pH, FT-IR, and GC-MS were carried out on the liquid. The results from the GC-MS and FT-IR analyses showed that the liquid consist mainly of aliphatic and aromatic hydrocarbons. The GC-MS analysis showed the different compounds present in this liquid and their relative abundance. From analyses, the liquid had an ash content of 0.06 %, sulphur content of 0.30 %, density of 0.992 g/ml, and a pH of 4.3.
The effects of catalyst and co-digestion on the performance of biogas yield
Authors: Ofomatah A C , Okoro G I , Ezekoye V A , Agbogu A , Ezekoye D
Published: May 2023

This study presented the effects of catalyst and co-digestion on the performance of biogas production. Rabbit dung and plantain peels were used as feedstock materials while clay, KOH and NaOH were used as catalysts. The experiment was divided into five setups. The slurry stayed in the digester for 45days. The digester temperature fluctuated around 35-47°C while the pH varied from 6.8-7.8. The result of the analysis revealed that the gas produced has high methane content values from 69.75 – 72.38% with low CO2 concentration values from 7.16 - 15.41%. Total volume of biogas were noted to be 74L, 31.9L, 125.5L, 112.4L and 55.8L for each sample. The physicochemical properties revealed that the TS varied from 3.63% - 4.20%, while VS varied from 3.17% - 3.78%. The TOC ranged from 2.18% - 4.79% while the nitrogen content ranged within 0.195% - 0.352%, Crude fibre ranged from 3.15 – 3.81%. Ash content ranged within 3.21 – 4.50% and moisture content varied between 82.47 – 90.05%. The COD and BOD were also obtained. The calorific values of the samples obtained ranged from 17,600 – 20,184KJ/kg. The phosphorus content was between 0.25% and 0.40% while potassium was between 0.15 and 0.37% on average.
Utilization of agricultural waste in the production of charcoal briquette blend as alternative source of energy
Authors: Nwankwo C I , Onuegbu T U , Okafor I F , Igwenagu P C
Published: May 2023

This study assessed the utilization of agricultural wastes in the production of charcoal briquette blend as alternative source of energy by using mixture of waste materials. The briquettes were produced by mixing varying compositions of carbonized biomass (cassava stem charcoal) and uncarbonized biomasses (pawpaw straw and cashew leaves) at the ratios of 0:100, 20:80, 80:20 and 100:0 using cassava starch binder. Proximate analyses, elemental analysis, combustion and gas emission analysis of the briquettes were determined using standard methods. The results obtained are in the following ranges: volatile matter (47.60 to59.54%), the ash content (3.70 to 23.89%), moisture content (31.80 to 45.56%), fixed carbon (0.11 to 1.01%), calorific values (16,059 to 21,012 kJ/kg), and the amounts of fuel elements ranging from (38.6 to 64.8% carbon, and 0.1 to 1.1% Sulphur). The CO and CO2 ranged from 0.01 to 0.02 g/MJ and 0.01 to 0.28g/MJ respectively. It was observed from the results that the production of briquettes from agricultural wastes is economical because it involves conversion of waste to wealth, providing an alternative and renewable source of energy to man and at the same time minimizing the environmental nuisance which makes the environment unclean.
Doping FePSe3 with Mg and Ca ions: A Density Functional Theory Study
Authors: Ouserigha Collins E. , Benjamin Ayibapreye K.
Published: May 2023

The electronic and structural characteristics of FePSe3 doped with Mg and Ca were investigated using density functional theory (DFT) computations as implemented in the Quantum Espresso simulation package. The electronic conductivities of several Mg doped FePSe3 and Ca doped FePSe3 alloys were assessed in terms of density of states (DOS) and band structure. At the Fermi level, the electronic states of alloys such as, Fe1Mg1P2Se6, Fe2P2Se5Mg1, Fe1Ca1P2Se6 and Fe2P2Se5Ca1 suggest that they are non-metallic. While the electronic states of Fe2P1Mg1Se6 and Fe2P1Ca1Se6 are metallic. The alloys Fe1Mg1P2Se6 and Fe1Ca1P2Se6 have robust diffusion, according to structural analysis. Because of its shorter bond length of 2.67 Å, the Fe1Mg1P2Se6 alloy has the strongest ion diffusion into FePSe3. It also has the highest band gap of 1.62 eV, followed by Fe1Ca1P2Se6 at 1.50 eV. As seen in the alloys Fe1Mg1P2Se6 and Fe1Ca1P2Se6, the divalent ions Mg2+/Ca2+ prefer insertion into the Fe site of FePSe3. These alloys are potential materials for Magnesium ion batteries (MIB) and Calcium ion batteries (CIB) due to their stability and electronic conductivity.
Waste to energy conversion: a perspective from adult learners in South-East Nigeria
Authors: Ugwuoke N J , Obiozor E E , Osagie A O , Chuke N U , Anyanwu-Amunde A E , Nkemjika O
Published: May 2023

The study identified waste categories that can be turned into energy, energy conversion techniques, and ways that these techniques could support a circular economy in South East Nigeria from the viewpoint of adult learners. In the study, a descriptive survey design was employed. The study was directed by three research questions and two hypotheses. There were 67731 adult learners in the population. The study included a sample size of 605 participants. Mean, t-test, and Pearson correlation were used to examine the data, which was collected using questionnaires. The results showed that wood, sawdust leaves and bark of trees, paper and cardboard, food remains like vegetable stock, yam peels, among others, were the types of waste that can be converted to energy. The study found out that combustion technology, gasified technology, and anaerobic digestion, among others, were some of the waste to energy conversion techniques. The study also noted that waste to conversion techniques foster circular economy. The paper recommends that the government should display political will by designing and enacting laws that facilitate the development of indigenous waste-to-energy conversion technology. This way, even at the household level, these technologies will be inexpensive and accessible.
Development of refractories for the pyro-processing (heat-based) industry in Nigeria through the evaluation of mixtures of Enugu Iva-Pottery clay, Nsu-clay and palm kernel shell waste
Authors: Agbo Sunday C , Odewole Olufemi A , Ojo Francis K , Alum Ogechi L , Akpomie Kovo G , Ofomatah Anthony C , Chukwuemeka-Okorie Helen O , Ani Julius U , Onu Caius C
Published: May 2023

The energy demand is on the global increase as the world economy is increasing steadily. Hence, the need to develop energy-saving technology with local raw materials and abundant waste materials becomes paramount. The mixtures of Enugu Iva-pottery clay, Nsu clay and PKS were evaluated to improve the quality of locally produced refractory for pyro-processing industries. The chemical and mineralogical composition of the PKS and the clay were determined. Eight recipes were made by varying PKS with quartz from 5 to 35% and produced by the wet pressing technique, then fired to 1200 °C and 1300°C respectively. The mechanical properties were determined by the Modulus of Rupture test, also, the bulk density, linear shrinkage, total shrinkage, apparent porosity, water absorption and thermal efficiency tests of the produced refractories were ascertained.

The recipe with 15% PKS sintered well at 1300°C and gave the best optimum results of 1.76g/cm3 bulk density, 6.49% linear shrinkage, 21.384% apparent porosity, 20 cycles of spalling tests at 1300 °C. The results comply well with the standards of refractory properties. The PKS additives have shown significant improvement in the properties of the locally produced refractories and waste control; hence, can promote sustainable development of the local pyro-processing industry.