bluuebunny/crossref_metadata_2025 huggingface dataset:
LoRaWAN Metering Infrastructure Planning in Smart Cities
Authors: Piechowiak Maciej , Zwierzykowski Piotr , Musznicki Bartosz
Published: July 2023

The planning of metering network infrastructure based on the concept of the Internet of Things primarily involves the choice of available radio technology. Then, regardless of the type and availability of power sources, energy conservation should be one of the main optimization criteria. For this reason, LPWANs operating in unlicensed ISM bands appear to be a suitable solution in urban environments due to their sub 1 GHz propagation properties. High signal penetration and coverage make them applicable in urban areas with buildings and various obstacles. Therefore, this article presents solutions developed to support the planning process of implementing a LoRaWAN network infrastructure aimed at monitoring and collecting electricity meter data in smart cities. To this end, an algorithm has been proposed to support the selection of the number of LoRaWAN gateways and their deployment, as well as the selection of transmission parameters at the measurement nodes with a particular focus on geographic data from real maps.
Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer’s Disease
Authors: Wang Zhiqiong , Lin Zican , Li Shuo , Wang Yibo , Zhong Weiying , Wang Xinlei , Xin Junchang
Published: July 2023

Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder that requires early diagnosis for timely treatment. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique for detecting brain activity. To improve the accuracy of Alzheimer’s disease diagnosis, we propose a new network architecture called Dynamic Multi-Task Graph Isomorphism Network (DMT-GIN). This approach uses fMRI images transformed into brain network structures to classify Alzheimer’s disease more effectively. In the DMT-GIN architecture, we integrate an attention mechanism with the Graph Isomorphism Network (GIN) to capture node features and topological structure information. To further enhance AD classification performance, we incorporate auxiliary tasks of gender and age classification prediction alongside the primary AD classification task in the network. This is achieved through sharing network parameters and adaptive weight adjustments for simultaneous task optimization. Additionally, we introduce a method called GradNorm for dynamically balancing gradient updates between tasks. Evaluation results demonstrate that the DMT-GIN model outperforms existing baseline methods on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, leading in various metrics with a prediction accuracy of 90.44%. This indicates that our DMT-GIN model effectively captures brain network features, providing a powerful auxiliary means for the early diagnosis of Alzheimer’s disease.
Bioactive Phenolic Compounds from Apples during Simulated In Vitro Gastrointestinal Digestion: Kinetics of Their Release
Authors: Jakobek Lidija , Ištuk Jozo , Barron Andrew R. , Matić Petra
Published: July 2023

Bioactive phenolic compounds in their natural form show beneficial effects on the gastrointestinal system. The kinetics of their release are important for understanding those effects. The aim was to study the kinetics of the release of phenolic compounds from apples during in vitro simulated gastrointestinal digestion by using modified equations of first- and second-order kinetics. 35% and 67% of total phenolic compounds were released in the gastric phase, and 26% and 27% in the intestinal phase (peel and flesh, respectively). Intensive release of anthocyanins, flavan-3-ols, dihydrochalcones, phenolic acids, and flavonols occurred in the first 10 min of gastric digestion. In intestinal digestion, flavan-3-ols and anthocyanins were not identified; the dihydrochalcone amount decreased, while phenolic acids and flavonols showed stability. Concentrations at the endpoint of the release (c∞) were determined with kinetic equations fitted well to the experimental data (cexp) (r2 = 0.9973 and 0.9946 for first- and second-order). The half-life for released phenolic subgroups in gastric digestion was up to 3.5 (first-order kinetics) or 2.5 min (second-order), or in intestinal digestion up to 20.9 (first-order) or 32.3 min (second-order). Modified equations reported here for the first time fit well with the experimental data.
Development of Biodegradable Films Produced from Residues of Nixtamalization of Popcorn
Authors: Rojas-Candelas Liliana Edith , Díaz-Ramírez Mayra , Rayas-Amor Adolfo Armando , Cruz-Monterrosa Rosy Gabriela , Méndez-Méndez Juan Vicente , Salgado-Cruz Ma. de la Paz , Calderón-Domínguez Georgina , Cortés-Sánchez Alejandro de Jesús , González-Vázquez Marcela
Published: July 2023

Nejayote and pericarp derived from nixtamalization are an environmental problem. Therefore, there is research interest in using these residues as new compostable and environmentally friendly materials. This work aimed to create, characterize (color, thickness, water solubility, water adsorption capacity, microstructure, and degradability), and apply biodegradable films using residues of nejayote and pericarp of nixtamalized popcorn. Three types of films were compared, pericarp (P), nejayote–pericarp (NP), and nejayote (N), and were applied to avocado stored at room temperature. Results showed that the P film was the best because it was significantly (p < 0.05) more transparent (L = 94.75 ± 2.21) and thicker (0.27 ± 0.02 mm). It had higher water adsorption capacity (162.60 ± 14.05%) and higher elastic modulus and toughness (0.004 ± 0.001 MPa, 2.25 ± 0.25 J/m3) values than the N and NP films; in addition, its microstructure showed a roughness value (Ra) of 6.59 ± 0.57 nm that was intermediate compared to other films. Moreover, the P coating slowed down the maturing process of avocado and showed a visual effect against fungal infection. All films were generally soft and had a degradation time between 11 and 12 days. The novelty of this study is to provide the alternative of using nejayote and pericarp in a film that is compostable and maintains the lifespan of avocados.
A Data Feature Extraction Method Based on the NOTEARS Causal Inference Algorithm
Authors: Wang Hairui , Li Junming , Zhu Guifu
Published: July 2023

Extracting effective features from high-dimensional datasets is crucial for determining the accuracy of regression and classification models. Model predictions based on causality are known for their robustness. Thus, this paper introduces causality into feature selection and utilizes Feature Selection based on NOTEARS causal discovery (FSNT) for effective feature extraction. This method transforms the structural learning algorithm into a numerical optimization problem, enabling the rapid identification of the globally optimal causality diagram between features and the target variable. To assess the effectiveness of the FSNT algorithm, this paper evaluates its performance by employing 10 regression algorithms and 8 classification algorithms for regression and classification predictions on six real datasets from diverse fields. These results are then compared with three mainstream feature selection algorithms. The results indicate a significant average decline of 54.02% in regression prediction achieved by the FSNT algorithm. Furthermore, the algorithm exhibits exceptional performance in classification prediction, leading to an enhancement in the precision value. These findings highlight the effectiveness of FSNT in eliminating redundant features and significantly improving the accuracy of model predictions.
Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
Authors: Lei Yanmin , Pan Dong , Feng Zhibin , Qian Junru
Published: July 2023

With the development of deep learning technology, more and more researchers are interested in ear recognition. Human ear recognition is a biometric identification technology based on human ear feature information and it is often used for authentication and intelligent monitoring field, etc. In order to make ear recognition better applied to practical application, real time and accuracy have always been very important and challenging topics. Therefore, focusing on the problem that the mAP@0.5 value of the YOLOv5s-MG method is lower than that of the YOLOv5s method on the EarVN1.0 human ear dataset with low resolution, small target, rotation, brightness change, earrings, glasses and other occlusion, a lightweight ear recognition method is proposed based on an attention mechanism and feature fusion. This method mainly includes the following several steps: First, the CBAM attention mechanism is added to the connection between the backbone network and the neck network of the lightweight human ear recognition method YOLOv5s-MG, and the YOLOv5s-MG-CBAM human ear recognition network is constructed, which can improve the accuracy of the method. Second, the SPPF layer and cross-regional feature fusion are added to construct the YOLOv5s-MG-CBAM-F human ear recognition method, which further improves the accuracy. Three distinctive human ear datasets, namely, CCU-DE, USTB and EarVN1.0, are used to evaluate the proposed method. Through the experimental comparison of seven methods including YOLOv5s-MG-CBAM-F, YOLOv5s-MG-SE-F, YOLOv5s-MG-CA-F, YOLOv5s-MG-ECA-F, YOLOv5s, YOLOv7 and YOLOv5s-MG on the EarVN1.0 human ear dataset, it is found that the human ear recognition rate of YOLOv5s-MG-CBAM-F method is the highest. The mAP@0.5 value of the proposed YOLOv5s-MG-CBAM-F method on the EarVN1.0 ear dataset is 91.9%, which is 6.4% higher than that of the YOLOv5s-MG method and 3.7% higher than that of the YOLOv5s method. The params, GFLOPS, model size and the inference time per image of YOLOv5s-MG-CBAM-F method on the EarVN1.0 human ear dataset are 5.2 M, 8.3 G, 10.9 MB and 16.4 ms, respectively, which are higher than the same parameters of the YOLOv5s-MG method, but less than the same parameters of YOLOv5s method. The quantitative results show that the proposed method can improve the ear recognition rate while satisfying the real-time performance and it is especially suitable for applications where high ear recognition rates are required.
A Robust Statistical Methodology for Measuring Enterprise Agility
Authors: Moraga-Díaz Roberto , Leiva-Araos Andrés , García José
Published: July 2023

In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services, where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics.
Improvement of Luminescence and Photocatalytic Performance of ZnO:Eu3+ Nanocrystals Activated by Na+ Ions
Authors: Bouslama Wiem , Nasser Ramzi , Gelloz Bernard , Trabelsi Amira Ben Gouider , Alkallas Fatemah Homoud , Song Ji-Ming , Srasra Ezzeddine , Elhouichet Habib
Published: July 2023

Undoped and codoped (Eu/Na) ZnO nanocrystals (NCs) were successfully manufactured through an economical sol-gel method. X-ray diffraction (XRD) analysis demonstrated pure hexagonal wurtzite structure without secondary phases for all the samples. The size of the NCs was found to decrease with codoping by Eu3+/Na+ which is related to the existence of strain and stress in the lattice. The dominance of the E2(high) mode in Raman spectra indicates the good crystallinity of the samples. The study from the X-ray photoelectron spectroscopy (XPS) shows the successful insertion of both Eu3+ and Na+ ions into the ZnO lattice and the generation of the zinc and oxygen vacancies (Vo) defects. The band gap energy was reduced and the Urbach energy increased with Na+ content, proving the distortion of the lattice. From the photoluminescence (PL) study, the activation of the Eu3+ ions by Na+ ones was evidenced. Longer PL lifetimes were obtained from Eu3+ ions when they were sensitized by Na+, which may be beneficial to several applications. A process of excitation transfer from both the ZnO host and Na+ sensitizers to the Eu3+ ions was evidenced and discussed. As an application, we tested the performances of the prepared NCs as photocatalysts for Rhodamine B photodegradation under sunlight irradiation. The ZnO NCs codoped with 1% Eu/4% Na displayed the best photodegradation rate with a good stability and a high kinetic rate constant k of 0.021 min−1. The photocatalytic mechanism is discussed in terms of longer radiative recombination from Eu3+ and the generated oxygen vacancies.
Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
Authors: Banaszak Zbigniew , Radzki Grzegorz , Nielsen Izabela , Frederiksen Rasmus , Bocewicz Grzegorz
Published: July 2023

This paper presents a declarative model of maintenance logistics for offshore wind farms. Its implementation in decision-making tools supporting wind turbine maintenance enables online prototyping of alternative scenarios and variants of wind turbine servicing, including weather-related operation vessel movement and routing of unmanned aerial vehicle (UAV) fleets carrying out maintenance on these wind turbines during monitoring or component-delivery missions. The possibility of implementing the model was verified via two case studies focusing, separately, on the issues of routing and scheduling of a UAV fleet used for the inspection of wind turbines and the distribution of ordered spare parts. The open structure of the model allows for its easy generalization, expanding the range of supported functions, including vessel fleet routing in an offshore wind farm, staff and competence planning of service teams, and supply chain management, enabling the planning of tool sets distributed to serviced wind turbines. Computer experiments conducted for various weather conditions confirm the competitiveness of the proposed approach.
Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector
Authors: Shang Zhendong , Wei Qinzhang , Li Zhaoying
Published: July 2023

The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm.
Learning Spatial–Spectral-Dimensional-Transformation-Based Features for Hyperspectral Image Classification
Authors: Wu Jun , Sun Xinyi , Qu Lei , Tian Xilan , Yang Guangyu
Published: July 2023

Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled information for non-central pixels of image patches. Therefore, it is challenging to achieve remarkable classification performance via the traditional convolutional neural networks (CNN) in the absence of label information. Moreover, due to the limitation of convolutional kernel sizes and convolution operations, the spectral information of HSI cannot be fully utilized with a traditional CNN framework. In this paper, we implement pixel-based classification by a special data division strategy and propose a novel spatial–spectral dimensional transformation (SSDT) to obtain spectral features containing more spectral information. Then, we construct a fully convolutional network (FCN) with two branches based on 3D-FCN and 2D-FCN to achieve broader spatial and spectral information interaction. Finally, the fused features are utilized to realize accurate pixel-based classification. We verify our proposed method on three classic publicly available datasets; the overall classification accuracy and average accuracy reach 82.27%/87.85%, 83.81%/81.55%, and 85.97%/83.89%. Compared with the latest proposed method SS3FCN in the no-information-leakage scenario, the overall classification accuracy of our proposed method is improved by 1.72%, 4.95% and 0.2%, and the average accuracy is improved by 0.95%, 3.92% and 2.67% on the three databases, respectively. Experimental results demonstrate the effectiveness of the proposed SSDT and the proposed CNN framework.
Experimental Study on the Dynamic Response of an Immersed Tunnel Element Moored inside the Harbor
Authors: Ouyang Qunan , Qi Chunfeng , Sun Shipeng , Yan Bing , Pan Wenbo , Huang Yuxin , Ma Jiajie
Published: July 2023

Floating, pouring, prefabrication, and temporary storage of the immersed tunnel element require mooring inside a harbor or a dock. However, the dynamics of a moored tunnel element in such semi-open waters are rarely studied, and some mechanisms are not well understood yet. Clarifying the dynamic response characteristics of a tunnel element moored inside a harbor is useful for the evaluation and design of the mooring system. In this paper, by carrying out physical model tests, the law of wave development in the harbor was investigated, and the motion response of the tunnel element and cable force of the mooring system were also measured. Dynamic response characteristics under different environmental factors, the influence of tunnel element load conditions, and the mutual interference of multiple elements moored simultaneously were analyzed. The results show that dynamic response characteristics under wind are significantly different from those under waves. Under no-load or half-load conditions, which correspond to greater freeboard-to-draft ratios, the dynamic responses of the tunnel element are more unfavorable than those under heavy-load or full-load conditions. The scheme of simultaneous mooring of multiple elements inside a harbor is feasible if there is appropriate spacing between them.
Dissolution of Magnesia in Silicate Melts and Diffusivity Determination from CLSM Studies
Authors: Burhanuddin  , Harmuth Harald
Published: July 2023

Magnesia is one of the vital and extensively used refractory components. In this study, the dissolution of magnesia is investigated at 1450, 1500, and 1550 °C in three silicate slags in the CaO–Al2O3–SiO2–MgO system using high-temperature confocal laser scanning microscopy to determine its effective binary diffusivity. The pore-free fragments of single-crystal fused magnesia particles were used, and the effects of experimental parameters and slag properties on the dissolution of magnesia were assessed. The ranking of dissolution times in the three slags at the three temperatures did not agree with the trend expected from the CaO/SiO2 ratio of each slag. Instead, several quotients serving as reference numbers were tested. The effective binary diffusivities were calculated considering all the impacting phenomena and parameters. The diffusivities of magnesia at 1500 °C in the slags with CaO/SiO2 weight ratios of 0.65, 0.93, and 1.17 are 2.67 × 10−10, 1.81 × 10−10, and 3.20 × 10−10 m2/s, respectively. The diffusivity of magnesia in one of the three slags was compared with rotating finger test results, which showed good agreement. The plausibility of diffusivity was checked using an Arrhenius plot.
Research on Deformation Failure Mechanism and Stability of Bedding Cataclastic Rock Slope Containing Multi-Muddy Interlayers
Authors: Qiu Peicheng , Long Yunkai , Zhang Jiaming
Published: July 2023

Based on the excavated slope of a waste incineration power plant in Yuxi, Yunnan Province, China, the finite element strength reduction method was used to investigate the variation rules of the safety factor, displacement, and deformation of the bedding cataclastic rock slope containing multi-muddy interlayers under the different conditions of number of muddy interlayers n, inclination angle θ, cohesion c, and angle of internal friction φ. Moreover, the deformation failure mechanism and stability of bedding cataclastic rock slope containing multi-muddy interlayers were revealed. The results showed that, as n increased, the maximum horizontal displacement of the slope increased, the factor of safety decreased, and the key influence on the stability of the slope was the muddy interlayers through the toe of the slope. As θ increased, the horizontal displacement of the slope first increased, then decreased, and then increased again, the safety factor first decreased, then increased, and then decreased again, and the stability of the upright laminar slope was slightly larger than that of the horizontal laminar slope. When the slope angle β was 45°, with the increase in θ, the failure mechanism of the slope manifested as a compression-shear failure, traction-sliding failure, traction-sliding-bending-shear failure, flexural deformation, and bending-buckling-collapse failure in sequence. As c increased, the slope evolved from traction-sliding failure to traction-sliding-bending-shear failure; the stability of the slope increased as c and φ increased.
Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
Authors: Liu Jiehui , Qiao Hongchao , Yang Lijie , Guo Jinxi
Published: July 2023

During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve the problem of the difficulty of detecting foreign objects at high-speed movement in an underground conveyor belt. The Kmeans++ clustering method was used to preprocess the data set to obtain the anchor box suitable for the foreign object size. The GhostNet lightweight module replaced the backbone network, reducing the model’s parameters. The CBAM attention module was introduced to enhance the ability of feature extraction facing the complex environment under the mine. The depth separable convolution was used to simplify the model structure and reduce the number of parameters and calculations. The detection accuracy of the improved method on the foreign body data set reached 99.32%, and the detection rate reached 54.7 FPS, which was 6.83% and 42.1% higher than the original YOLOv4 model, respectively. The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection. In comparison experiments with similar methods, our proposed method also demonstrated good performance, verifying its effectiveness.
Controlling the Spin Hall Effect in the Sharp Focus of an Axial Superposition of Two Optical Vortices with Left- and Right-Handed Circular Polarization
Authors: Kotlyar Victor V. , Nalimov Anton G. , Kovalev Alexey A.
Published: July 2023

We consider sharp focusing of an axial superposition of two optical vortices with identical topological charges, but different amplitudes and circular polarizations of different signs. The ratio of the amplitudes of the two beams is a parameter. When this parameter changes, the polarization state of the superposition changes from linear polarization to right-hand circular polarization. Based on the Richards–Wolf theory, exact expressions are obtained for the longitudinal components of the spin angular momentum (SAM) density and orbital angular momentum (OAM) density at the focus of the considered superposition. It follows from these expressions that the sum of the total longitudinal components of the SAM and OAM is conserved upon focusing, and also that, due to the spin-orbit conversion, the total longitudinal component of the SAM decreases during focusing, while the total longitudinal component of the OAM increases by the same amount. By changing the ratio of the amplitudes of the constituent beams from 1 to 0, one can change the value of the spin-orbit conversion from zero (for linear polarization) to a maximum (for circular polarization). Also, by changing this parameter, one can control the spin Hall effect at the focus, which takes place at the focus of the considered beam. This study can be applied for controlling the rotation velocity of microparticles trapped in the focus.
Impact Analysis and Optimization of Key Material Parameters of Embedded Water-Stop in Tunnels
Authors: Wang Lin , Guan Chao , Wu Yimin , Feng Chengkui
Published: July 2023

Leakage diseases frequently occur in mountain tunnels, and the lining joints are weak waterproof links. In order to solve the problems such as insufficient waterproof technology for existing tunnel joints, this paper relies on the scientific and technological project of the Hubei Provincial Department of Transportation to optimize the key parameters of the tunnel water-stop material and uses numerical simulation methods to analyze the impact of the hardness of the water-stop and the bond strength between the water-stop and concrete on its deformation and waterproof ability. Through the adhesion test, the optimization method of the bond strength between the water-stop and concrete is explored. The results show that: (1) the deformation stress of the water-stop will increase with the increase in hardness and reducing the hardness of the water-stop can improve its stress deformation state. Considering that low hardness increases the risk of damage and reduces the quality, it is recommended that the hardness be 55–60 (HA). (2) The adhesion between the water-stop and the concrete lining is the key to waterproofing the water-stop. Increasing the adhesion between the water-stop and the concrete is an effective optimization method. However, to prevent excessive deformation stress, it is necessary to optimize the geometric structure of the water-stop simultaneously. (3) Placing an epoxy–quartz sand coating on the surface of the water-stop can increase the adhesive force between the water-stop and the concrete. When the thickness of the epoxy layer is 2 mm, and the particle size of quartz sand is 26–40 mesh, the effect is best.
Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
Authors: Aldayel Mashael , Kharrat Amira , Al-Nafjan Abeer
Published: July 2023

Individual choices and preferences are important factors that impact decision making. Artificial intelligence can predict decisions by objectively detecting individual choices and preferences using natural language processing, computer vision, and machine learning. Brain–computer interfaces can measure emotional reactions and identify brain activity changes linked to positive or negative emotions, enabling more accurate prediction models. This research aims to build an individual choice prediction system using electroencephalography (EEG) signals from the Shanghai Jiao Tong University emotion and EEG dataset (SEED). Using EEG, we built different deep learning models, such as a convolutional neural network, long short-term memory (LSTM), and a hybrid model to predict choices driven by emotional stimuli. We also compared their performance with different classical classifiers, such as k-nearest neighbors, support vector machines, and logistic regression. We also utilized ensemble classifiers such as random forest, adaptive boosting, and extreme gradient boosting. We evaluated our proposed models and compared them with previous studies on SEED. Our proposed LSTM model achieved good results, with an accuracy of 96%.
Influence of Overlying Strata Movement on the Stability of Coal in Fully Mechanized Top-Coal Caving Mining
Authors: Ren He , Song Yimin , Dai Lianpeng , Zou Junpeng , Zhu Chun
Published: July 2023

Overlying strata movement has an important effect on the stability of coal along goaf. In order to study the impact of overlying strata movement on the stability of coal during fully mechanized top-coal caving mining, a simulation experiment was conducted on the Liuxiang Coal Mine using digital image correlation (DIC) and stress electric measurement methods. The characteristics of overlying strata movement, stress concentration and deformation evolution of coal, and interlayer sliding between coal and rock are analyzed. The following conclusions could be drawn. The movement of overlying strata can be classified into two stages: the first stage is characterized by vertical displacement, while the second stage is characterized by horizontal displacement. In the first stage, the vertical movement of overlying strata causes the vertical stress concentration of the coal. In the second stage, the horizontal movement causes frictional sliding between coal and rock, which produces a shearing effect on the coal. Under these two effects, the coal along goaf gradually reaches its strength limit, and local deformation occurs, and the micro-cracks are further concentrated and fused in this area. When macro-cracks are formed, occur and expand unstable, the elastic energy is released suddenly, which will lead to dynamic disasters such as rock bursts.
Current Options and Future Perspectives on Bone Graft and Biomaterials Substitutes for Bone Repair, from Clinical Needs to Advanced Biomaterials Research
Authors: Georgeanu Vlad Al. , Gingu Oana , Antoniac Iulian V. , Manolea Horia O.
Published: July 2023

The ideal biomaterials substitute for bone repair should possess the following characteristics: provide osteogenic, osteoinductive and osteoconductive properties; stimulate the neo-angiogenesis process; absence of antigenic, teratogenic or carcinogenic reactions; avoid the systemic toxicity complications; assure satisfactory support and stability from mechanical properties point of view; hydrophilic nature of the surface properties and good interface with human bone; good handling in clinical condition and ability to be easy sterilized; and able to be supplied in sufficient quantities with reduced costs. Despite years of effort, the perfect bone reconstruction material has not yet been developed; further effort is required to make this objective feasible. The aim of this article is to provide a contemporary and comprehensive overview of the grafting materials that can be applied for the treatment of bone defects by the clinicians from orthopedics surgery, neurosurgery and dentistry, discussing their properties, advantages and disadvantages, and illuminating present and future perspectives in the field of bone graft and biomaterials substitutes for bone repair, from clinical needs to advanced biomaterials research.
Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
Authors: Esmail Maged Abdullah
Published: July 2023

The demand for network capacity has increased due to the introduction of new digital applications and services, which rely heavily on optical communication networks. While fiber networks serve as the optical networks’ backbone, deploying fiber in certain scenarios is not feasible, making it necessary to use other technologies conjointly. A hybrid all-optical fiber/free space optic (FSO) link is proposed to avoid such a challenge. The all-optical system avoids using electronics that have limited bandwidth. Hence, it supports high-capacity communication. However, the all-optical system comes with challenges arising from fiber and FSO channel impairments. To monitor the amount and type of distortion in the optical channel, machine learning (ML) techniques are exploited. In this work, Gaussian process regression (GPR) is utilized as an ML technique to predict three main channel impairments that arise in the hybrid all-optical fiber/FSO channels, which are turbulence, optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). The model’s performance is evaluated using boxplot graphs, root mean square error (RMSE) metric, and R-squared metric. The results indicate that the model can predict the various impairments with high accuracy, except under strong amplified spontaneous emission (ASE) noise, where the model demonstrated lower accuracy in predicting light turbulence parameters. The proposed approach provides a self-aware and self-adaptive communication system and can optimize network resources in the future.
An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background
Authors: Ma Zelong , Zhao Qinglei , Che Xin , Qi Xinda , Li Wenxian , Wang Shuxin
Published: July 2023

For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details.
An Analytical Model for Understanding Active Directivity Noise Control with Near-Field Error Sensing
Authors: Duan Hongji , Wang Shuping , Tao Jiancheng
Published: July 2023

Recent research shows that a directional quiet zone from near to far field can be created with a near-field microphone array. However, there is no systematic theoretical analysis of this error-sensing strategy. In this paper, an analytical model of the angular spectrum of point sources in the free field is proposed, based on which the near-field error-sensing strategy for active directivity noise control can be quantitively analyzed. It is found that the noise reduction in the far field is determined by the angular spectrum estimation error of both the primary and secondary sources. The impacts of the error microphone array configuration, sound source position, and target direction on the estimation error of angular spectrum are investigated. Finally, experiments were conducted in the anechoic chamber to validate the impact of the location of the secondary source.
Exploring the Effects of Low-Level Laser Therapy on the Cytocompatibility and Osteo/Odontogenic Potential of Gingival-Derived Mesenchymal Stem Cells: Preliminary Report
Authors: Alhazmi Yaser A. , Aljabri Mohammed Y. , Raafat Shereen N. , Gomaa Shaimaa M. , Shamel Mohamed
Published: July 2023

Numerous tissue engineering uses for gingival-derived mesenchymal stem cells (GMSCs) have been demonstrated. Recently, low-level laser therapy (LLLT) has been projected as a factor that can improve MSCs’ regeneration capacity. Therefore, the aim of this research was to examine the impact of LLLT at 1.5 J/cm2 and 3 J/cm2 on the viability and osteo/odontogenic potential of GMSCs. An MTT assay was performed to detect viability. Osteo/odontogenic differentiation was evaluated using Alizarin Red S staining and qRT-PCR for the evaluation of the RUNX2, OC, DMP1, and DSPP genes. A two-way ANOVA with Tukey’s post hoc test was used to determine the statistical significance between groups. The results revealed that LLLT of both energy densities had no cytotoxic effect on GMSC viability. LLLT of 1.5 J/cm2 demonstrated better viability than the higher energy density (3 J/cm2). Furthermore, the osteo/odontogenic differentiation potential was promoted following LLLT radiation, where both groups exhibited mineralized nodule formation, with the low-energy laser having a significantly higher Alizarin Red S stain level. A qRT-PCR analysis revealed higher expression levels of osteogenic and odontogenic markers in the LLLT groups compared to the control group. In conclusion, this study showed the potential application of LLLT as a non-toxic and effective strategy to enhance the regenerative capacity of GMSCs for tissue engineering and clinical treatments in the oral and craniofacial fields.
Optimization of Growth Conditions for Magnetospirillum magnetotacticum and Green Synthesis of Metallic Nanoparticles
Authors: Sancho Rebekah Eleasa , Govindsamy Anushka , Pillay Karen
Published: July 2023

Nanotechnology is especially useful in biotechnological and biomedical applications as nanomaterials have unique physicochemical properties. Current physical and chemical techniques used for the production of nanoparticles have various disadvantages that has led to the evaluation of biological strategies. This study focused on the use of a bacterial species known as Magnetospirillum magnetotacticum for the production of metallic nanoparticles. The cultivation of MTB is known to be tedious and time-consuming using the current standardized magnetic spirillum growth media (MSGM). This study explored the optimization of MSGM for improved growth and nanoparticle yield. It was found that glucose significantly improved and sustained the growth of M. magnetotacticum compared to other sole carbon sources having a sustainable OD of ~1.15. However, use of a higher concentration of sodium nitrate (40 mM) as a nitrogen source was able to significantly improve iron-containing nanoparticle yield by 1.6× with a final yield of 22 mg/50 mL when compared to the yield obtained from the MSGM original media. Growth media with a combination of glucose, sodium nitrate, ammonium sulphate and yeast extract showed the highest exponential growth of Magnetospirillum magnetotacticum compared to all other MSGM modifications with the highest OD being 1.7. Silver and gold nanoparticles were also successfully produced in addition to iron-containing nanoparticles. Overall, no direct correlation between growth and nanoparticle yield was found.
Two Revised Deep Neural Networks and Their Applications in Quantitative Analysis Based on Near-Infrared Spectroscopy
Authors: Huang Hong-Hua , Luo Jian-Fei , Gan Feng , Hopke Philip K.
Published: July 2023

Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were to use 1D convolutional layers to replace the fully connected layers. We also designed and combined average pooling and maximum pooling in our revised networks, respectively. We applied these revised network architectures to three publicly available data sets and the resulting calibration models can generate acceptable results for general quantitative analysis. It also generated rather good results for data sets that concern calibration transfer. It demonstrates that constructing network architectures by properly revising existing successful network architectures may provide additional choices in the exploration of the application of deep neural network in analytical chemistry.
Path Tracking of Agricultural Vehicles Based on 4WIS–4WID Structure and Fuzzy Control
Authors: Xu Lijun , Chen Qinhan , Wang Linlin , Yao Lijian
Published: July 2023

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse.
Neural Mechanisms Related to the Enhanced Auditory Selective Attention Following Neurofeedback Training: Focusing on Cortical Oscillations
Authors: Shim Hwan , Gibbs Leah , Rush Karsyn , Ham Jusung , Kim Subong , Kim Sungyoung , Choi Inyong
Published: July 2023

Selective attention can be a useful tactic for speech-in-noise (SiN) interpretation as it strengthens cortical responses to attended sensory inputs while suppressing others. This cortical process is referred to as attentional modulation. Our earlier study showed that a neurofeedback training paradigm was effective for improving the attentional modulation of cortical auditory evoked responses. However, it was unclear how such neurofeedback training improved attentional modulation. This paper attempts to unveil what neural mechanisms underlie strengthened auditory selective attention during the neurofeedback training paradigm. Our EEG time–frequency analysis found that, when spatial auditory attention was focused, a fronto-parietal brain network was activated. Additionally, the neurofeedback training increased beta oscillation, which may imply top-down processing was used to anticipate the sound to be attended selectively with prior information. When the subjects were attending to the sound from the right, they exhibited more alpha oscillation in the right parietal cortex during the final session compared to the first, indicating improved spatial inhibitory processing to suppress sounds from the left. After the four-week training period, the temporal cortex exhibited improved attentional modulation of beta oscillation. This suggests strengthened neural activity to predict the target. Moreover, there was an improvement in the strength of attentional modulation on cortical evoked responses to sounds. The Placebo Group, who experienced similar attention training with the exception that feedback was based simply on behavioral accuracy, did not experience these training effects. These findings demonstrate how neurofeedback training effectively improves the neural mechanisms underlying auditory selective attention.
Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
Authors: Sportelli Mino , Apolo-Apolo Orly Enrique , Fontanelli Marco , Frasconi Christian , Raffaelli Michele , Peruzzi Andrea , Perez-Ruiz Manuel
Published: July 2023

The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection.
Double-Tongue Worm Shell Structure on Plastic Centrifugal Pump Performance Study
Authors: Rao Zhi , Tang Lingfeng , Zhang Hui
Published: July 2023

Aiming at the problem of high vibration and high wear of centrifugal pump tongue, this paper proposes a double-tongue volute structure. Under the condition of ensuring the reliability of CFD results, the influence of various combinations of tongue and volute base circle on the turbulent kinetic energy of centrifugal pump and the radial force of impeller is explored. The traditional single-tongue volute centrifugal pump is compared with various characteristic indexes, and the unsteady numerical calculation is carried out based on different working conditions. It is concluded that the double-tongue volute structure can improve the pressure fluctuation at the monitoring points near the tongue. The results show that the double-tongue volute structure can improve the static pressure gradient and velocity gradient of the middle section of the centrifugal pump and reduce the maximum turbulent kinetic energy value at the tongue under small flow conditions. When the working condition is 1.0 Q, the radial force of the impeller of the C-type double-tongue volute reaches the minimum value of 3.03 N, which can effectively balance part of the radial force.