bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Grinding and Correction of Face Gears Based on an Internal Gear Grinding Machine
Authors: Zhengyang Han , Chuang Jiang , Xiaozhong Deng , Congcong Zhang , Longlong Geng , Yong Feng 
Published: May 2024

Abstract

This paper presents a method of calculating and correcting of grinding face gears on an internal gear grinding machine. The generating principle of face gears is studied, and the feasibility of grinding motion on an internal gear grinding machine is analyzed. Then, the motions that need to be followed for grinding are analyzed based on the internal gear machine tool structure. Four main error sources causing tooth surface deviation in the grinding movements are proposed. The mathematical modeling of the grinding of face gears containing proposed error sources on an internal gear grinding machine is accurately established. The influence of the error sources on the topological deviations of the tooth surface is explored. A sensitivity matrix is established for the influence of various error factors on the tooth surface deviations. The correction values of each error factor are obtained in the case of existing tooth surface deviations. A theoretical correction result with zero-free tooth surface deviation is obtained via a least-square calculation. Finally, a virtual machining experiment on an internal gear grinding machine is conducted, which proves the accuracy of the proposed method for characterizing grinding and realizing corrections.
Dynamics analysis and simulation experiments of twist spring drive upper limb rehabilitation robot
Authors: Wang Hai , lu liangyu , Yang Chunlai , Li Shuai , Li He Nian , Gui Jin Song , fu Rong Hua 
Published: May 2024

Abstract

An upper limb rehabilitation robot based on twist spring drive is proposed in this paper, which can realized shoulder and elbow rehabilitation training of patients with upper limb disabilities. The mathematical model of the twist spring drive is established, and the dynamic equation of the robot is built. The mechanical structure of the robot is meticulously designed using SolidWorks. The performance of the robot is numerical simulated in MATLAB/Simulink, and validated in semi-physical experimental platform based on dSPACE1202. The simulation and experimental results show that the proposed robot can realized rehabilitation training of upper limb, which provide an effective rehabilitation training method for people with upper limb disabilities.
Analyzing Vehicle Path Optimization using an Improved Genetic Algorithm in the Presence of Stochastic perturbation Matter
Authors: shengdong Mu , Boyu Liu , Jijian Gu , Chaolung Lien , Xiaojie Chen 
Published: May 2024

Abstract

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm(GA) based on a Gaussian matrix mutation(GMM) operator, which maintains the diversity of the population while speeding up the algorithm’s convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50%~60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%.
Exploring the Motivations and Barriers Affecting Community Health Workers’ Performance in the Noida-Greater Noida Region
Authors: Bibhakar Pratyush , Sinha Manasi , Baba Waseem 
Published: May 2024

Abstract

Community healthcare workers(CHWs) play acrucial role in providing vital healthcare services, particularly in the field of reproductiveand sexual health, as well as other healthcare issues such as maternal care, neonatal health, and child nutrition (RMNCHN) in India. By actively involving the community as healthcare recipients, CHWs contribute to increased acceptance and trust among individuals seeking healthcare services. Therefore, it is crucial to assess the performance motivations and the underlying factors that influence the effectiveness and efficiency of Accredited Social Health Activists (ASHA), Anganwadi Workers (AAW), and Auxiliary Nurse Midwives (ANM).
Objective:The objective of this study is to conduct a comprehensive assessment of the performance motivations and barriers that influence the effectiveness of community health workers, specifically focusing on the ASHAs, ANMs, and AAWs operating in the urban slum areas of Noida-Greater Noida, Uttar Pradesh, India.
Methodology: Thisresearch adopts a cross-sectional design, incorporating a qualitative approach featuring surveys and focus group discussions. The collected data were analysed through NVIVO to extract insights from qualitative data, shedding light on the underlying performance motivations and their determinants.
Conclusions: This study establishes that CHWs in India are overworked and underpaid while highlighting their contributions and relevance to the underserved marginalised masses of India. A high level of performance motivation was found among these frontline workers despite limited resources. However, the sources of motivation seemed to be rooted in concepts such as morality, religion, spirituality, identity, and autonomy. Organisational support, lack of recognition and appreciation from superiors, associated monetary benefits and infrastructure were found to be barriers and demotivators.
Impact of surgical and non-surgical weight loss on echocardiographic and strain parameters in Asian patients
Authors: Poh Kian Keong , Panday Vinay Bahadur , Shabbir Asim , Ngiam Jinghao Nicholas , Sia Ching-Hui , Chan Siew-Pang , Tan Sik Yin , Kong William KF , Richards Mark Arthur , Thomas James D 
Published: May 2024

Abstract

Objective: Surgical weight loss (SWL) improves myocardial mechanics as measured by speckle-tracking imaging. However non-surgical versus SWL and the subsequent impact on myocardial function in overweight Asian subjects has not been evaluated.
Methods: 66 patients underwent a 16-week lifestyle intervention (LSI) programme consisting of dietary interventions and exercise prescription. Echocardiography with speckle tracking was performed at baseline and post-intervention. This group was compared against a group of 12 subjects who had undergone bariatric surgery and a control group of 10 lean Asian subjects. A generalised structural equation model (gSEM) was constructed to ascertain the effect of modality of weight loss on strain parameters, adjusting for BMI.
Results: Participants attained significant weight loss after LSI (28.2±2.66 kg/m2 vs 25.8±2.84 kg/m2, p=0.001). This was associated with a non-significant trend towards improvement in strain parameters. SWL participants had significant improvement in the left ventricular global longitudinal strain (-20.52±3.34 vs -16.68±4.15, p<0.01) and left atrium reservoir strain (44.32±14.23 vs 34.3±19.31, p=0.02). Lean subjects had significantly higher strain parameters than overweight subjects. The gSEM model demonstrated surgical modality of weight loss as an independent predictor of improvement in strain parameters.
Conclusion: Significant improvement in echocardiographic parameters were documented in patients who underwent bariatric surgery.
Synthesis and application of SBA-15 adsorbent for the removal of organic and inorganic substances
Authors: YADOUN BOUCHRA , BENHAMOU Abdellah , HENNOUS Mohammed , BENYOUB Nassima , DEBAB abdelkader 
Published: May 2024

Abstract

This study investigates the adsorption of pollutants with different chemical structures; organic Naphtol Green B (NGB) and inorganic (Copper) on a nanocomposite with a hexagonal structure of the SBA-15 type. This research is divided into two main parts: the first carries out the synthesis of SBA-15 and its derivatives phases functionalized by 3-aminopropyl-triethoxysilane (APTES) and calcined at 823K. The second part presents the results of the removal of Naphtol Green B dye and copper. Parameters such as initial pollutant concentrations, adsorbent mass, solution pH and system temperature were studied. High-resolution X-ray diffractogram (XRD) showed three distinct peaks characteristic of highly ordered mesoporous material. Nitrogen adsorption-desorption isotherm of SBA-15 at 77K° is type IV typical of mesoporous materials. In addition, zeta potential, thermogravimetric analysis and Fourier transform infrared spectroscopy (FT-IR) were also used in the characterization. Pseudo-first order and pseudo-second order models was used to describe the adsorption kinetic results. The modeling of the experimental results of the pollutants followed the Sips isotherm and pseudo-second order models. The primary objective of this study is the potential of the synthesized nanomaterials in the removal of effluents regardless of their different chemical structure.
Reversal of High-Fat Diet-Induced Cognitive Impairment and Oxidative Stress in the Brain through Zingiber officinale Supplementation
Authors: Luciano Thais Fernandes , Souza Claudio Teodoro , Oilveira Jade , Muller Alexandre Pastoris 
Published: May 2024

Abstract

Obesity presents a significant health concern, correlating with various adverse health outcomes. Diet-induced obesity (DIO) has been associated with impair on cognitive function. Pharmacological treatments for obesity are limited and may pose serious adverse effects. Ginger possess anti-inflammatory and antioxidant effects in addition to metabolic effects. The study aimed to assess the effects of ginger supplementation on cognitive function, anxiety levels, neurotrophin levels, as well as inflammatory and oxidative status in the cortex following DIO in mice. Swiss male mice, 2 months old, were fed with DIO or standard chow for 4 months and after were subdivided into (n=10/group): i) CNT (CNT + vehicle); ii) CNT supplemented with ZO (CNT + ZO); iii) obese mice (DIO + vehicle) ; iv) obese mice supplemented with ZO (DIO + ZO) (n=10). Zingiber officinale (ZO) 400 mg/kg/day were supplemented for 35 days by oral gavage. DIO + vehicle group shown impaired on recognition memory task. CNT + ZO group showed a higher number of crossings in the open field. There were no difference between group in plus maze task. DIO + vehicle had increased the DCFH and carbonylation levels in cortex. The DIO + vehicle showed a reduction in catalase activity. The cerebral cortex did not show any difference regarding to inflammatory and neurotrophins markers. In conclusion, our findings indicate that supplementation with ZO reverses cognitive impairment in DIO mice and enhances antioxidant status in the cerebral cortex.
Technology Acceptance and Employability in TVET Graduates
Authors: CHARLES AZAMETI YAO , Amoah-Sei Emmanuel , Razak Bugase Nabase , Dissou Pros. Yarhands Arthur 
Published: May 2024

Abstract

In the quickly changing digital landscape, integrating technology into Technical and Vocational Education and Training (TVET) curricula (Yasak & Alias, 2015) has become increasingly important (Saud et al., 2011). The acceptance and incorporation of technology into TVET programs has emerged as a crucial factor of educational quality and workforce preparedness in a time of fast technological innovation (Legg-Jack & Ndebele, 2023). This study examines the crucial connection between employability, acquiring digital skills, and embracing technology in the context of technical and vocational education and training (TVET). The ability of TVET graduates to embrace and utilize digital technology substantially influence their prospects in the job market in a world marked by rapids technical breakthroughs (Joseph, 2020). This study intends to evaluate how graduates' employability and success in finding jobs are impacted by the degree of technology acceptance (Ismail & Hassan, 2019) and the digital skills acquired during TVET programs. A mixed-methods research approach was used to collect empirical data, including surveys, interviews, and focus groups with recent TVET graduates, employers, and industry experts. Regression analysis and other quantitative approaches were combined with qualitative methods during the data analysis process to provides a thorough knowledge of the intricate relationships being studied.
Research on the Construction of Ecological Security Pattern and Optimization Strategy of Fuyang City Region Based on MSPA and Circuit Theory
Authors: Yang Ting , Xiao Tieqiao , Dong Dong , Luo Yi , Zhang Shaojie 
Published: May 2024

Abstract

Constructing an ecological security pattern is of great significance for protecting and restoring the biodiversity of urban ecological space, this study takes Fuyang city as an example, selects elevation, slope, land cover, vegetation cover, geohazard susceptibility, distance from the water system, distance from the road, and distance as the resistance surface factors, and constructs a comprehensive resistance surface based on the circuit theory utilizing the Linkage Mapper tool. ecological corridor of Fuyang city, identify ecological nodes, and then optimize the ecological security pattern of Fuyang city. The results show that (1) the core area analyzed by MSPA is 87.26km2, accounting for 56.00% of the prospective elements. (2) 28 ecological source areas were screened out based on the patch landscape connectivity analysis, with an area of 79.98km2. (3) There are 66 ecological corridors, and the corridor network formed from southeast to northwest is of good ecological quality, with little obstructed connection. (4) There are 15 ecological pinch points in Fuyang City, except for Linquan County, where only 1 pinch point occurs in Taihe County, Yingquan District, Yinzhou District, and Funan County, 2 pinch points in Yindong District and Jieshou City, and 7 pinch points in Yingshang County. (5) There are 93 obstacle areas in Fuyang City, with patch areas above 0.2 km2, and the total area of all obstacle points reaches 2,394.92 km2 mainly distributed along the Quanhe and Yinghe Rivers in Fuyang City, including Jieshou City, Taihe County, Yingquan District, Yingzhou District, Yindong District, Yingshang County, and Funan County. Fuyang City is located in the traditional agricultural area of northern Anhui Province, with typical characteristics of farming plains and relatively homogeneous surface composition. The use of circuit theory to simulate the ecological security pattern of Fuyang City is more targeted, and the active maintenance and protection of ecological pinch points and the gradual removal of ecological barriers can promote the comprehensive construction of the ecological security pattern of Fuyang City.
Enhancement of microstructural, mechanical and tribological properties of hybrid Al- CNTs through electroless deposition of innovative h-BNs contents coated Ag and Ni for purpose of ball bearing
Authors: Barakat Waheed S. , Samir Ahmed , Elkady Omayma A. , Abu-Okail Mohamed , Mohamed Abdelkarim Y. A. , EL-Nikhaily A. , Abu-Oqail A. M.I. 
Published: May 2024

Abstract

Ball bearings face numerous challenges under harsh operating conditions of elevated pressure between the balls and other contacting parts of the bearing like drop in tribological properties. To address these challenges, a new attempt was successfully made for the first time in this paper through an experimental investigation by the impact of incorporating hexagonal boron nitride (h-BN) into Aluminum-Carbon nanotubes (Al-0.6 wt. % CNTs) nanocomposites by an innovative coated with silver (Ag) and nickel (Ni) using a novel electroless chemical deposition technique, to improve the wettability and scattering between matrix and reinforcement. Various h-BN ratios (2, 4, 6, 8 and 10 wt. %) are incorporated and consolidated through high-energy ball milling and hot compaction techniques. The produced samples were tested and analyzed physically, mechanically, tribologically, and microstructurally. X-ray diffraction (XRD) and scanning electron microscope (SEM) analyses were used to explore the new morphologies and structures. The study delves into density, hardness, and wear resistance. The optimal h-BN content is determined to be 8 wt.%, enhancing wettability and dispersion within the Al-CNTs matrix. Thus, the properties of hardness, compressive strength, wear rate, and COF at 8 wt.% of h-BN content were enhanced by 105%, 60%, 74.5%, and 78.5%, respectively, compared to pure Al. This is due to the uniform scattering of h-BN nanoparticles across the entire surface, despite a significant decrease in relative density. In conclusion, the combination of high-energy ball milling, electroless deposition, and hot compaction techniques proves to be effective in producing Al-CNTs/h-BN nanocomposites coated with Ag and Ni nanoparticles.
Lippia thymoides essential oils activity against Candida species from HIV+ individual oral lesions
Authors: de Azevedo Fernanda Oliveira , de Oliveira Ana Paula Rios Santana , de Farias Antonio Pedro Fróes , Santos Rebeca Pereira Bulhosa , de Oliveira Yuri Andrade , Falcão Michelle Miranda Lopes , Gomes-Filho Isaac Suzart , Lucchese Angélica Maria , Kusterer Liliane Lins , Sarmento Viviane Almeida , Rocha-Filho José Tadeu Raynal , de Sá Maria da Conceição Aquino , Trindade Soraya Castro 
Published: May 2024

Abstract

Candidiasis is the most common oral infection in HIV-positive individuals and appears as a result the decline of the immune system, representing a marker of disease progression. The treatment of this condition, however, becomes a challenge since non-albicans species tend to be frequent and more resistant to the most used antifungal agents. Thus, the search for new therapeutic options becomes necessary. The species Lippia thymoides a herbal medicine used is used because it has antimicrobial activity. Thus, this study evaluated the antifungal activity of Lippia thymoidesessential oils, collected in the cities of Feira de Santana and Palmeiras, Bahia, Brazil, in C. albicans and C. glabrata strains isolated from oral lesions of individuals with HIV. Nine fungi of the genus Candida were isolated, identified through biochemical tests. The essential oils were analyzed with respect to the phytochemical aspects by gas chromatography and mass spectrometry. The minimum inhibitory concentration test was used to assess the antifungal activity. The essential oil from species collected in Feira de Santana city had a contents rate of 2.80±0.80%, while the essential oil from species of the municipality of Palmeiras showed a rate of 4.20±1.71%. The essential oil of Feira de Santana was rich in sesquiterpenes, with E-caryophyllene as the main constituent (27.25±1.96%). In oil of Palmeiras, monoterpenes predominated and the major metabolite was 1.8-cineole (30.35±2.17%), indicating that it is a new chemotype. The results revealed that theessential oils of L. thymoides are not cytotoxic and showed antifungal activity against the different species of Candida when in a concentration of 10%, and may be an ally in the treatment of oral candidiasis.
Classifying native versus foreign speech perception from EEG using linguistic speech features
Authors: Puffay Corentin , Vanthornhout Jonas , Gillis Marlies , De Clercq Pieter , Accou Bernd , hamme Hugo Van , Francart Tom 
Published: May 2024

Abstract

When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.
An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds
Authors: Leng Xin , Chen Jiakai , Huang Jianping , Zhang Lei , Li Zongxuan 
Published: May 2024

Abstract

Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, pests and diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. To address this challenge, this paper introduces the YOLO-DBD network model for detecting of harmful tea leaves. The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds, providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the C2f-DCN module, Bi-Level Routing Attention, Dynamic Head, and Focal-CIoU Loss function, enhancing the model's feature extraction, computation allocation, and perception capabilities. Comparative analysis with the YOLOv8s model demonstrates a 6% improvement in mAP and a reduction of 3.3G FLOPs in the YOLO-DBD model.
An Innovative Deep Learning Approach for Simultaneous Load and Price Prediction in Smart Power Grids
Authors: Rahmatinia S-Mozhgan , seno Seyed-Amin Hosseini 
Published: May 2024

Abstract

Predicting time series data in the electricity domain serves as a fundamental basis for implementing Demand Response (DR) techniques in smart grid networks, a topic widely addressed in the literature. In this paper, we propose a flexible deep learning approach based on the encoder-decoder architecture for simultaneous prediction of both load and electricity price. The ability of the model to capture long-term dependencies and extract meaningful patterns from the data is crucial for its effectiveness. To enhance this capability, we incorporate a scaled attention mechanism into the model to attend over the hidden encoder outputs, enabling better capture of relevant information. Additionally, we introduce a preprocessing step using the Fast Fourier Transform (FFT) to extract frequency-domain features from the input data. By applying FFT at the beginning of the proposed model in the encoder section, we aim to better capture the underlying patterns in the data and facilitate the learning process. It is worth noting that while FFT is utilized for preprocessing, the decoder section of the model operates directly on the original data without employing inverse FFT, ensuring data integrity and preventing information leakage. The proposed model is trained and evaluated on [38] data and compared with several deep learning approaches, demonstrating improved prediction accuracy in all cases. This work contributes to the field by presenting a novel approach that effectively leverages both attention mechanisms and Fourier domain preprocessing for enhanced time series prediction in the electricity domain.