bluuebunny/crossref_metadata_2025_split huggingface dataset:
Uniaxial tensile mechanical properties of NEPE propellant under wide temperature and wide strain rate
Authors: Liu Fan , Qiang Hongfu , Wang Xueren , Wang Zhejun , Lu Chuanjun 
Published: November 2024

Abstract
In order to gain insight into the mechanical properties of the NEPE propellant under a range of temperature and strain rate conditions, the uniaxial tensile mechanical property test of NEPE propellant with a wide temperature range of −40°C-70°C and a wide strain rate range of 0.0012s−1-1s−1 has been carried out based on the in-situ testing device of solid propellant microfabrication under complex working conditions and the supporting high and low temperature integrated machine. Additionally, the morphology of the solid particles of the propellant was observed and analyzed during stretching using the microscope equipment provided by the stretching machine. The results show that it has been demonstrated that both temperature and strain rate exert a more pronounced influence on the mechanical properties of propellants; at low temperatures, it has been demonstrated that the stress-strain curve of the propellant exhibits a more pronounced inflection point as the strain rate increases; the maximum tensile strength of the propellant demonstrates a gradual increase with a reduction in temperature and an increase in strain rate. Furthermore, a linear double logarithmic relationship is observed with the strain rate. In contrast, the maximum elongation exhibits no discernible pattern of change with temperature and strain rate; when observed during stretching, it was found that the main forms of propellant damage destruction at low temperatures were dehumidification between the particles and the matrix and particle rupture.
Torque distribution of distributed drive electric vehicle based on efficiency map of drive system
Authors: Ren Weichen , Li Yanfeng 
Published: November 2024

Abstract
In order to optimize the system efficiency of distributed drive electric vehicles, based on the MAP of the efficiency of the drive motor system, the objective function of the optimal efficiency is established, and the problem of the no-load loss of the drive motor is considered. A torque distribution control strategy method considering the no-load loss of the motor is proposed. Then, AVL CRUISE and Simulink are used to establish a joint simulation platform, and a comparative simulation test analysis is carried out under ECE conditions. The simulation results show that the torque distribution strategy considering the loss makes the distributed drive motor work in a relatively high efficiency range. Compared with the torque distribution strategy without considering the loss, the power consumption is reduced by 3.4%, which effectively improves the vehicle economy. The research results can provide a reference for system efficiency optimization of distributed drive electric vehicles.
A star identification algorithm based on 1D Conv Neural Network
Authors: Zhong Yang , Wang Hao 
Published: November 2024

Abstract
A novel star identification algorithm based on 1D-CNN for celestial navigation is introduced. This approach constructs a distinctive star pattern using radial and angular separations and cyclic angles. Several tests have been conducted on simulated star images, demonstrating the proposed algorithm’s superior performance over conventional algorithms and high resilience to three error types, including spurious stars, positional deviations, and magnitude uncertainties.
Light source deployment study based on downhole visible light communication system
Authors: Haodong Wang , Lincong Zhang 
Published: November 2024

Abstract
With the wide application of visible light communication system, downhole visible light communication system is the best choice for unmanned underground mining in the future. However, there is a lack of research on the deployment of the light source part of the underground visible light communication system. For this reason, this paper proposes a deployment scheme for the light source part of the underground visible light communication system based on the actual operation of underground mining work. Firstly, this paper analyzes the underground visible light communication system model and channel transmission model in detail, and gives the transmission principle model; secondly, it models and analyzes the Light Emitting Diode(LED) at the transmitting end and the Photo-Diode(PD) at the receiving end, and based on the principle model, proposes the channel transmission model in the case of line-of-sight (LOS) and non-line-of-sight (NLOS), and puts forward a scheme to ensure the transmission reliability based on these models. Finally, this paper performs lighting simulation for the proposed scheme. The results show that the light source deployment scheme can meet the reliability requirements of downhole visible light communication systems. The research in this paper provides an important reference for the future deployment of light sources for downhole visible light communication systems.
Effect of electron scattering of microchannel plates on the resolution and halo size of UV image intensifier
Authors: Jing Maoheng , Wang Zhiming , Li Chun , Zhong Meishan , Zhu Shideng , Ma Jian , Zhang Weiyun , Wei Jie , Liu Shengrong 
Published: November 2024

Abstract
Aiming at the problem that the current theoretical model for calculating the resolution of UV(ultraviolet) low-light level image intensifier is not perfect enough to give the optimization conditions directly, the effects of parameters such as the thickness of anti-ion feedback film, MCP(microchannel plate) bevel angle, the depth of the rear electrode and the length-diameter ratio of the channel plate on the photoelectron transport characteristics of UV low-light level image intensifier are analyzed through systematic theoretical research. Experimental results show that optimizing these parameters can significantly improve the performance of the low-light image intensifier. Specifically, when the thickness of the anti-ion feedback film is 4nm, the bevel angle is 8°, the length-diameter ratio is 50, and the back-end electrode is deepened to 10μm, the theoretical image quality of the UV low-light image intensifier reaches a better level. This study has theoretical guidance significance for the development of high-resolution ultraviolet image intensifiers in the field of optoelectronic imaging, and also plays a key role in improving angular resolution in the application of sun blind ultraviolet warning.
Acpro: fusion of embedded ambient light sensor and image sensor for visible light positioning
Authors: Liu Xiangyu , Qi Guangxin , Zhang Junqi , Guo Lei 
Published: November 2024

Abstract
With the increasing demand for indoor positioning, visible light positioning (VLP) systems have become an optics technologies research hotspot due to their high accuracy and avoidance of electromagnetic interference. However, current photodiode (PD)-based and image sensor (IS)-based VLP systems still suffer from high costs at the receiver, and the problem of the trade-off between field of view (FoV) and positioning accuracy. Therefore, we propose a smartphone-based low-cost, and high-precision VLP system, Acpro. The system combines the smartphone’s embedded ambient light sensor and IS as the receiver for the positioning. First, for the low sampling rate of the ambient light sensor, we use the frequency aliasing characteristic to identify the LED’s beacon through the dynamic undersampling principle. Then, to increase the FoV and positioning accuracy, we use the standard distance information corrected by the IS to improve the noise distance calculated by the ambient light sensor. Further, the corrected distance information is used for positioning. Extensive results show that our system can achieve sub-meter positioning accuracy.
Artificial Intelligence-Based Scanner to Locate Flow Anomalies in Water Transmission and Distribution Networks
Authors: Davis Nicholas Arthur , Ardi Sankayya Shetty Divakara , Baskaran Geetha , Hamed Al Adwani Ahmed , Ivy Pascual Lizette 
Published: November 2024

Abstract
The Water Transmission and Distribution sector in Oman is confronted with various challenges, such as flow loss during transportation, negative pressure accumulation, and conflicts in billing consumption in the Water Transmission and Distribution Network(WTDN). To address these and resolve the challenges, a research team, consisting of the authors of this paper, has proposed a wireless flow scanner specifically planned for this purpose. Currently, the proposed cutting-edge wireless AI Water Flow Scanner is undergoing development and finally, be tested in a 540 m total length pilot piping network constructed as 9 parallel pipes with three pipe diameters. The AI Water Flow Scanner will help in locating and quantifying the flow transportation losses, automated pressure balancing, and automated mass flow balancing in the pilot piping network. The proposed AI Water Flow Scanner will receive flow properties like flow pressure, velocity, and discharge through wireless pressure and flow sensors installed in, thirty-six strategic points within the pilot piping network. A deep Learning Algorithm will be developed exclusively to analyze the flow data across the pilot piping network. The analyzed results then be simulated to provide the pressure and velocity distribution across the pilot piping network that will be interpreted to accurately locate the flow transportation losses, imbalance in pressure, and flow across the pilot piping network. Once fully developed and tested, the AI Water Flow Scanner will be expected to offer an efficient and accurate method of locating leaks. In this paper, a network of three parallel pipelines with three different diameters is designed in Pipe Flow Expert, an open-access flow simulation software, analyzed at different flow conditions to understand the flow behavior through the network. The outcome of this simulation is found well aligned with the real-time flaws. A fully developed AI Flow Scanner would help greatly in resolving the current issues in the Water Transmission and Distribution Network of Oman.
IoT-Enabled Street Dustbin Monitoring System Based on Machine Learning
Authors: Dhandapani Ragavesh 
Published: November 2024

Abstract
In this article, a novel method is proposed by using the power of the Internet of Things (IoT) and Machine Learning (ML) to monitor and manage street dustbins effectively. The proposed innovative solution aimed at optimizing waste management processes and improper disposing of the waste in street dustbins. The system involves the installation of sensors in dustbins, which collect real-time data about the fill level of the bins. The capability of the proposed model allows for dynamic adjustment of waste collection schedules, reducing unnecessary trips and thus saving time, energy, and resources. In addition to that, the model is also trained in various waste disposal scenario such as improper disposal, massive garbage dumps, and e-waste. The proposed model is able to predict and report the trained conditions with the 100% accuracy. The system is implemented using Raspberry Pi 4, a single-board computer with the necessary hardware interface. A suitable web interface has been developed to oversee the status of dustbins and provide optimum travel route for collection of waste from the street dustbins. The proposed system not only enhances operational efficiency but also contributes to environmental sustainability.
Preparation and Characterization of Nano Cellulose Particles from Date Palm
Authors: Ali Moosa Al Balushi Noura , Devi Geetha 
Published: November 2024

Abstract
The increased awareness on environmental protection and rising demand for more eco-friendly and renewable materials has resulted in the minimization of fossil fuel consumption. Date palm plantations are an important agricultural crop in the sultanate of Oman and considered as the staple food for millions of people. More than seven million date palms are available in the northern governorates of Oman. Only 50% of the dates produced are utilized for human consumption and the remaining 50% are thrown into the environment, posing threat to human and animal. This improper disposal of discarded dates leads to pollution, habitat degradation, disease transmission, soil contamination, and water pollution, threatening the environment and human/animal health. This research aims to extract nanocellulose particles from three different parts of the date palm tree (i.e coir, front, and leaves) samples using both mechanical and Soxhlet extraction methods at varying processing conditions. The acid hydrolysis of the biomass using H2SO4 (20% v/v) was carried out at 120 °C for 30 min followed by filtration, neutralization, centrifugation and drying to form the particles. The nanocellulose particles were characterized using Scanning Electron Microscopy (SEM) X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Energy Dispersive X-Ray Spectroscopy (EDX), and UV – Visible Spectroscopic analysis. Among the three types of palm residues (coir, front, and leaves), nanocellulose particles derived from coir exhibited better yield (1.5 g), whereas front and leaves has 1.2 g and 0.9 g, yield respectively. The EDX analysis demonstrated 98% purity of nanocellulose indicates the successful extraction. The FTIR spectroscopic analysis demonstrated the existence of hydroxyl groups in nanocellulose at wave numbers corresponding to 3355.69 cm−1, 2916.26 cm−1, and 2914.22 cm−1 respectively for coir, front, and leaf respectively. The XRD analysis demonstrated the crystalline structure of the nanocellulose particles.
Maximizing Date Palm Yield: Automatic Extension Sprinklers for Enhanced Aerial Pollination
Authors: Rehna V J , Naik Sreenivas , Siddique Abid , Al-Shammakhi Nasser , Al-Muqrashi Nahid , Al-Shukri Shafi 
Published: November 2024

Abstract
Date palms require pollen transfer from male to female palms in order to yield a fruitful harvest. The traditional method of accomplishing this artificial cross-pollination involved climbing each palm tree and physically putting male flower spikelets on female inflorescences. This process required a great deal of labor and was time-consuming. Due to developments in farming practices during the modern age, farmers have experimented with mechanical pollination systems. Recently, to pollinate date palms very quickly, safely, affordably, and with a minimum of risk of injury, agricultural drones have been used. In this research, experiments on drone dispersal pollination are conducted on three Omani date palm cultivars—Al Naghal, Al Khanezi, and Al Kasab. The applicability of this method is evaluated by comparing its fruit yield properties to those of the conventional method, which is commonly employed, and the less popular machine/hand pollination methods. The main limitations of drone dispersal pollination are, its inability to disperse exact amount of pollen to the target location of female inflorescences, especially in shorter palms; wastage of pollens and environmental pollution. The study recommends the use of a novel design of extendable pollen sprinkler for target-specific pollination in order to increase pollination efficiency and decrease DPP waste. The designed system comprises of a robotic arm extension with a remotely controlled sprayer mounted on a computer vision android device; that can shoot themselves out to the female flowers and disperse the required amount of pollens in the specific location. This unique spraying mechanism can overcome the drawbacks of aerial pollination and increase its effectiveness.
Design of wastewater treatment unit using End-of-Life (EoL) Reverse Osmosis membrane
Authors: Alauifi Almoatasem , Geetha Devi M. 
Published: November 2024

Abstract
Sultanate of Oman is facing severe challenges due to water scarcity, shortage of rainfall, and limited freshwater resources. Over the past few decades, 90% of the natural water resources are consumed by agriculture. Hence, there is an urgent need to find an alternative to obtain fresh water. Water desalination by Reverse Osmosis (RO) is currently practiced in Oman as freshwater source, since it contributes 80% of the total desalination plants installed worldwide. The RO process results in environmental concerns triggered by emission of gases, and discharge of Reverse Osmosis (RO) membranes. The lifespan of membranes depends on the water quality and the operating conditions, and the membrane life is estimated as 5 to 10 years. In order to mitigate the environmental impact for a sustainable desalination output, reuse of discarded end-of-life (EoL) RO membrane is a feasible solution to enhance the water quality. The efficiency of RO membranes is affected by the fouling, scaling, and chemical cleaning for the yearlong operation which lead to reduced salt rejection rate. Hence, this research aimed to reuse the old RO elements by transforming them into Ultrafiltration unit (UF) by removing the active layer and increasing the pore size of membrane. Additionally, modifying the membrane by open configuration and separating all 28 membrane sheets from the permeating tubes are designed as RO-MBR panels. The study shows that, after 5 weeks of operation, the average turbidity rejection rate was 94%, and the average permeated flux per unit was 6.9 LMH. The new configuration shows the permeate flux stability and acceptable BOD, and COD reduction.
Coupled Tanks Level Control Based on Volume Balance Method
Authors: Ruzhekov Georgi 
Published: November 2024

Abstract
The laboratory bench “Coupled tanks” represents a model of real technological plans where the levels and flow rates of liquids must be controlled. Such training benches are very common and there is a very large number of publications related to their control. All publications work with a non-linear model based on the Bernoulli equation for ideal fluids, which is non-linear – the square root of the fluid height is present. This equation or system of equations is linearized and attempts are made to control such systems by means of linear models. This article shows that this approach is not correct and proposes a completely different one based on the volume balance method.
Identifying Factors that Increase False-Positive Rates on Embedded Performance Validity Testing in ADHD Evaluations
Authors: Finley John-Christopher A , Tufty Logan M , Abalos Steven A , Keszycki Rachel , Woloszyn Mary , Shapiro Greg , Cerny Brian M , Ulrich Devin M , Phillips Matthew S , Robinson Anthony D , Soble Jason R 
Published: November 2024

Abstract

Objective
This study investigated why certain embedded performance validity indicators (EVIs) are prone to higher false-positive rates (FPRs) in attention-deficit/hyperactivity disorder (ADHD) evaluations. The first aim was to establish the relationship between FPRs and 15 EVIs derived from six cognitive tests when used independently and together among adults with ADHD who have valid test performance. The second aim was to determine which specific EVIs increase the FPRs in this population.

Method
Participants were 517 adult ADHD referrals with valid neurocognitive test performance as determined by multiple performance validity tests and established empirical criteria. FPRs were defined by the proportion of participants who scored below an empirically established EVI cutoff with ≥0.90 specificity.

Results
EVIs derived from two of the six tests exhibited unacceptably high FPRs (&gt;10%) when used independently, but the total FPR decreased to 8.1% when the EVIs were aggregated. Several EVIs within a sustained attention test were associated with FPRs around 11%. EVIs that did not include demographically adjusted cutoffs, specifically for race, were associated with higher FPRs around 14%. Conversely, FPRs did not significantly differ based on whether EVIs included timed versus untimed, verbal versus nonverbal, or graphomotor versus non-graphomotor components, nor whether they had raw versus standardized cut scores.

Conclusions
Findings suggest that practitioners should consider both the type of test from which an EVI is derived and the aggregate number of EVIs employed to minimize the FPRs in ADHD evaluations. Findings also indicate that more nuanced approaches to validity test selection and development are needed.
Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction
Authors: Phan Han , Brouard Céline , Mourad Raphaël 
Published: November 2024

Abstract
Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on supervised learning, which requires DNA sequences associated with functional data, and whose amount is severely limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is growing exponentially due to ongoing large-scale sequencing projects, but in most cases without functional data. To alleviate the limitations of supervised learning, we propose a novel semi-supervised learning (SSL) based on pseudo-labeling, which allows to exploit unlabeled DNA sequences from numerous genomes during model pre-training. We further improved it incorporating principles from the Noisy Student algorithm to predict the confidence in pseudo-labeled data used for pre-training, which showed improvements for transcription factor with very few binding (very small training data). The approach is very flexible and can be used to train any neural architecture including state-of-the-art models, and shows in most cases strong predictive performance improvements compared to standard supervised learning. Moreover, small models trained by SSL showed similar or better performance than large language model DNABERT2.
Prototype-based contrastive substructure identification for molecular property prediction
Authors: He Gaoqi , Liu Shun , Liu Zhuoran , Wang Changbo , Zhang Kai , Li Honglin 
Published: November 2024

Abstract
Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https://github.com/VRPharmer/POSIT.