bluuebunny/crossref_metadata_2025 huggingface dataset:
Model-independent cosmographic constraints from DESI 2024
Authors: Luongo Orlando , Muccino Marco
Published: August 2024

Context. We explore model-independent constraints on the Universe kinematics up to the snap and jerk hierarchical terms, considering the latest baryon acoustic oscillation (BAO) release provided by the DESI collaboration.

Aims. We intend to place novel and more stringent constraints on the cosmographic series, incorporating three combinations of data catalogs: the first made by BAO and observational cosmic chronometers, the second made by BAO and type Ia supernovae, and the last including all the cited data sets.

Methods. Considering the latest BAO data provided by the DESI collaboration and tackling the rd parameter to span within the range [144,152] Mpc, with a fixed step of δrd = 2 Mpc, we employed Monte Carlo Markov chain analyses based on the Metropolis algorithm to fix novel bounds on the cosmographic series, fixing the deceleration, q0 , the jerk, j0 , and the snap, s0, parameters, up to the 2σ level. A comparison between the results of the Planck satellite with those obtained by the DESI collaboration is also reported.

Results. Our findings showcase a significant departure in terms of j0 even at the 1σ confidence level, albeit compatible with the ACDM paradigm in regard to q0 and s0 at the 2σ level. Analogously, the h0 tension appears alleviated in the second hierarchy when including snap.

Conclusions. Our method excludes models that significantly depart from the standard cosmological model. Particularly, direct comparisons with the ACDM and wCDM models and the Chevallier-Polarski-Linder parameterisation are explored, which definitively favour the wCDM scenario over other approaches, contradicting the findings of the original DESI collaboration.
Evidence-based social science: why, what, and future implications
Authors: Yang Wendeng
Published: August 2024

Mankind has always relied on “practical wisdom” in the sense of Aristotle to change people and society. However, practical wisdom is difficult to accumulate and pass on from generation to generation. Therefore, although “natural engineering” that is consistent with “natural technology” can be used effectively to transform the natural world, using “practical wisdom” to change people and society remains difficult. Solving this dilemma requires an effective means to separate “social science”, “social technology”, and “social engineering” to accumulate and inherit “knowledge of changing people and society” similar to other types of scientific knowledge. Today, the prevailing evidence-based social science is the opposition and unity of laws of nature and free will, truth-seeking and goodness-pursuing, individual wisdom and collective wisdom, autonomy and supervision, the universal and the special in practice. The emergence of this concept allows for the possibility to enhance the effectiveness of social science practice.
Study on calculation model of deflection of prefabricated hole bulkhead under the action of internal explosion
Authors: Chen Qing-Hua , Tao Yan-Guang , Liang Zhen-Gang
Published: August 2024

In order to study the plastic deformation law of ship plate frames under the coupling action of internal explosion load and fragment, first, the calculation formula of plastic deformation deflection of the non-porous plate is derived through the law of conservation of energy. According to the numerical calculation results, the influence of the opening position and opening area of the target plate on the deflection gain of the target plate is further explored. The mathematical expression form of the opening area and opening position on the deflection gain of the prefabricated target plate is fitted, and then, the deflection calculation model of the prefabricated target plate is established. It can theoretically predict the deflection of the bulkhead under cabin explosion and has a certain application reference value for ship cabin anti-explosion design and internal explosion damage assessment.
Influence of hybrid nanofiller on elastic behavior and stiffness of basalt/E-glass MWCNTs/SiO2 hybrid nanocomposites
Authors: Boobalan V. , Sathish T. , Giri Jayant , Rahaman Mostafizur
Published: August 2024

Sustainable material development techniques help in finding and employing sustainable materials without compromising quality. Composite materials are crucial in structural design, automotive manufacture, and aeronautical engineering. Advanced materials that use reinforcement and filler ingredients must be developed strategically to improve strength and performance. Hence, this study develops hybrid nanocomposites with hybrid nanofillers (MWCNTs and SiO2) and hybrid fibers (basalt and E-glass) and optimizes the competition to maximize elastic behavior and stiffness. Using a hand layup approach, composite samples were made by altering mass fractions of two filler materials (0%, 1%, 1.5%, and 2% by weight) and epoxy matrices (40%, 38%, 37%, and 36%). Shore D hardness and dynamic mechanical analysis (DMA) were used to evaluate the composites. The storage modulus, loss modulus, and damping coefficients are examined using DMA. Specifically, the largest storage modulus is 4.86 × 1010 Pa at 61 °C, while the peak loss modulus is 1.01 × 1010 Pa at 80 °C. The highest damping coefficient is 0.25. Note that 1.5% MWCNTs and SiO2 filler materials independently contribute to the excellent storage and loss modulus. However, an outstanding damping coefficient was achieved without filler materials. The highest achieved shore D hardness value is 88. Filler materials are used to achieve this high hardness.
A lightweight concrete composite material with improved EMI shielding by using a chiral particle array
Authors: Hamidi Ayoub , Cheldavi Ahmad , Habibnejad Korayem Asghar
Published: August 2024

This paper proposes a structure for concrete composite materials for electromagnetic interference shielding applications. It comprises an array of helical-shaped conductive particles as chiral additives. Unlike the previous studies using large quantities of conductive additives, this study provides a lightweight concrete composite due to the utilization of a small number of additives with a high level of shielding effectiveness in a wide frequency range. The heart of the proposed structure lies in leveraging the evanescent wave propagation in a circular waveguide, resulting in considerable shielding effectiveness. Under particular conditions, the helical particles can imitate a below-cutoff cylindrical waveguide and its dominant mode surface current on the helical wire. This phenomenon significantly attenuates the transmitted power from the array of helical particles in its resonance frequency range. Besides presenting the composition that exploits the magnetoelectric properties of the particles, this paper compares it with a traditional concrete composite, including randomly distributed sinusoidal steel rods. This second approach is examined using both experimental measurement and full-wave simulation methods. The results of this study indicate that the appropriate geometry of the conductive additives, in this case, chiral particles, and their arrangement in a regular array rather than a random distribution can enhance the efficiency of the conductive additives. This idea paves the way for more robust, efficient, and lightweight concrete composite materials, thanks to the recent advances in modern civil engineering manufacturing methods.
Dual receiver series topology for bipolar segmented DWPT system
Authors: Zou Yaqiang , Xu Tian , Li Zhong , Qiang Hao
Published: August 2024

For a segmented dynamic wireless power transfer (DWPT) system, when electric vehicles (EVs) move over two adjacent transmitting coils, the received power fluctuates greatly and has some loss. Arming at this problem, this paper proposes a dual receiver segmented DWPT topology to reduce the received power fluctuation of the receiving coil passing through the rail junction during the moving process of the EV. Through magnetic field analysis in this paper, tuning the current phase difference between two adjacent transmitting coils to 180° can enhance the magnetic field. In this paper, a dual receiver series topology (DRST) is designed to pick up power, which consists of four fully controlled components and four diodes. According to EVs’ real-time position, the segment DWPT system has three working modes and six working states under the control of DRST. Finally, experiments are carried out. Compared to the single-receiving-coil system, DRST is effective in improving the average output power from 2.007 to 5.657 W and significantly reducing the power fluctuation.
Perforating gold can make it stronger
Authors: Grant Andrew
Published: August 2024

Often considered a nuisance, voids in a metal may improve certain mechanical properties if they are small and uniformly distributed.
Sizes of tropical glaciers reach historic lows
Authors: Lopatka Alex
Published: August 2024

New measurements of radioactive isotopes provide evidence for the retreat of Andean glaciers since the end of the last ice age.
Effects of rumen-protected fat on performance, carcass and meat characteristics, and feeding behaviour of Nellore bulls finished in intensive grazing systems
Authors: Silva Edjane Pereira da , Zervoudakis Joanis Tilemahos , Sousa Osvaldo Alex de , Fonseca Mozart Alves , Pereira Danilo Vinicius da Silva , Dutra Franciely de Oliveira , Monge Emanoelly Auxiliadora Paes , Silva Vitória de Almeida e , Mata Deborá Kemelly Xavier da , de Paula Nelcino Francisco
Published: August 2024

Context Beef cattle production in the tropics is based on the grazing system. Concentrate supplementation has a significant influence on carcass gain and meat quality. Additionally, the inclusion of some lipid feedstuffs, such as rumen-protected fat (RPF), can increase the net energy for weight gain, and be an alternative to increase the productive performance and carcass traits of grazing beef cattle. Aims This study evaluated the effects of RPF on performance, carcass traits and feeding behaviour of Nellore bulls in an intensive grazing system during the rainy season. Methods Twenty-four Nellore bulls (initial bodyweight of 454.0 ± 46.1 kg) were used in a randomised completed block design, based on initial bodyweight. The animals were distributed in 12 paddocks (two bulls per paddock) in a continuous grazing system and randomly assigned to one of two treatments: control (n = 6), a concentrate without added protected fat provided ad libitum, and RPF (n = 6), a concentrate with the addition of 3% protected fat (Nutricorp, Araras, São Paulo, Brazil) provided ad libitum. Statistical analysis was performed using the MIXED procedure of SAS 9.4 with paddock as the experimental unit (12 paddocks). The principal model included fixed effect of treatment and block as a random effect. For forage characteristics, the experimental period was a considered fixed effect. The study lasted 105 days, and was divided into four periods. At the end of each period, forage collections were performed, bulls were weighed and feeding behaviour was recorded. After 105 days, the bulls were slaughtered in a commercial slaughterhouse, and three 2.54-cm Longissimus dorsi steaks of each animal were collected to evaluate the colour, the chemical and physical composition, and the sensory analysis of the meat. Keys results The inclusion of RPF in the concentrate did not affect (P > 0.05) the final bodyweight, average daily gain, concentrate intake and feed efficiency, but there was a tendency (P < 0.07) for higher carcass weight for the bulls that received RPF. Bulls supplemented with RPF had a greater (P < 0.05) average carcass gain. For meat quality, an effect (P < 0.01) was observed for hue (25.32 and 27.91 for control and RPF, respectively). Conclusions The addition of 3% RPF in the concentrate ration has the potential to improve carcass traits and meat quality of beef cattle in an intensive pasture finishing system. Implications The inclusion of RPF will increase dietary energy density of grazing beef cattle with a tendency to increase carcass gain. This can be an opportunity to increase the production per animal and income of farmers.
Spatial and temporal distribution and environmental determinants of freeze-thaw erosion intensity in Qiangtang grasslands, China
Authors: Zhang Gaobo , Liu Xiaohuang , Wang Jinjie , Liu Jiufen , Zhao Xiaofeng , Li Hongyu , Wang Ran , Luo Xinping , Xing Liyuan , Wang Chao , Zhao Honghui
Published: August 2024

Context Analysing freeze-thaw erosion is of great significance to ecological environment protection and land resource utilisation in high altitude areas. Aims We used seven indicators (temperature, precipitation, vegetation cover, elevation, slope, slope orientation, and sand content) to calculate the freeze-thaw erosion intensity index for different seasons from 2000 to 2019. Methods We used a graded weighted evaluation model and a geographical detector method to analyse spatiotemporal pattern and driving factors of freeze-thaw erosion intensity in Qiangtang grasslands. Key results (1) From 2000 to 2019, the total area of freeze-thaw erosion was higher in the non-growing season than in the growing season. The area of moderate and above-average freeze-thaw erosion increased over time in the non-growing season but decreased in the growing season. The spatial distribution of freeze-thaw erosion was mainly determined by the annual range of precipitation and temperature, which reflect the intensity and frequency of freezing and thawing cycles. (2) Vegetation cover was an indirect factor that influenced the soil moisture and stability. The slope was another important factor that affected the spatial distribution of freeze-thaw erosion in different regions. Conclusions The results show that in 2000–2019 the area of freeze-thaw erosion showed a downward trend. The erosion degree in the non-growing season is on the rise. Implications Our study provides new insights into the dynamics and mechanisms of freeze-thaw erosion in Qiangtang grasslands and contributes to the understanding and management of water and climate change impacts on this region.
Unexpected increase of the deuterium to hydrogen ratio in the Venus mesosphere
Authors: Mahieux Arnaud , Viscardy Sébastien , Yelle Roger Vincent , Karyu Hiroki , Chamberlain Sarah , Robert Séverine , Piccialli Arianna , Trompet Loïc , Erwin Justin Tyler , Ubukata Soma , Nakagawa Hiromu , Koyama Shungo , Maggiolo Romain , Pereira Nuno , Cessateur Gaël , Willame Yannick , Vandaele Ann Carine
Published: August 2024

This study analyzes H
2
O and HDO vertical profiles in the Venus mesosphere using Venus Express/Solar Occultation in the InfraRed data. The findings show increasing H
2
O and HDO volume mixing ratios with altitude, with the D/H ratio rising significantly from 0.025 at ~70 km to 0.24 at ~108 km. This indicates an increase from 162 to 1,519 times the Earth’s ratio within 40 km. The study explores two hypotheses for these results: isotopic fractionation from photolysis of H
2
O over HDO or from phase change processes. The latter, involving condensation and evaporation of sulfuric acid aerosols, as suggested by previous authors [X. Zhang
et al.
,
Nat. Geosci.
3, 834–837 (2010)], aligns more closely with the rapid changes observed. Vertical transport computations for H
2
O, HDO, and aerosols show water vapor downwelling and aerosols upwelling. We propose a mechanism where aerosols form in the lower mesosphere due to temperatures below the water condensation threshold, leading to deuterium-enriched aerosols. These aerosols ascend, evaporate at higher temperatures, and release more HDO than H
2
O, which are then transported downward. Moreover, this cycle may explain the SO
2
increase in the upper mesosphere observed above 80 km. The study highlights two crucial implications. First, altitude variation is critical to determining the Venus deuterium and hydrogen reservoirs. Second, the altitude-dependent increase of the D/H ratio affects H and D escape rates. The photolysis of H
2
O and HDO at higher altitudes releases more D, influencing long-term D/H evolution. These findings suggest that evolutionary models should incorporate altitude-dependent processes for accurate D/H fractionation predictions.
Optimization of electrical control equipment interface layout with cognitive load based on pso algorithm
Authors: Zhang Ting , Liu Ruizhi , Sun Liang
Published: August 2024

Addressing the complexity of current impregnation machine electrical control interfaces, this study proposes optimization methods for their layout design. It incorporates factors affecting cognitive load during interface interaction, formulating three principles: highlighting essential elements, operation sequence, and element relevance. These principles guide the construction of a mathematical model for the optimization of electrical equipment control interface layouts. The Particle Swarm Optimization algorithm is applied to solve the model’s objective function, producing optimized layouts, which are then validated through eye-tracking experiments. The results demonstrate the method’s effectiveness in optimizing control interface layouts, aligning with operators’ intuitive logic.
The effect of the structure of a water distribution pan in an evaporation drum on a flake ice machine
Authors: Zhou Qiang
Published: August 2024

Through experimental research, the performance of the flake ice machine under different structures of the water distribution pan was comparatively analyzed to enhance performance and optimize the design of the water distribution pan in the evaporation drum. An experimental bench for testing the performance of the flake ice machine was constructed, and the optimal scraping cycle under varying water distribution pan structures was determined. The results revealed a 33% improvement in the ice-making area within the water distribution pan structure, along with a 17.5% reduction in power consumption per unit of ice production, achieved through the optimal operational conditions of the flake ice machine using R404A as the refrigerant. Furthermore, the dry area was enhanced by 40%, leading to an 8% reduction in power consumption per unit of ice production. Notably, for this flake ice machine, the optimal ice-making drying ratio was found to be 1.6.
Fuzzy-pid control design of biped robot based on motion capture technology
Authors: Fu Rongchang , Yu Jinan , Yang Xiaoyu
Published: August 2024

To improve the precision of the biped robot’s walking control system, a fuzzy PID controller with a variable theory domain was added to the design of the biped robot’s lower limb control system to optimize the tracking process of joint angular displacement and improve the motion effect. Firstly, the motion capture experiment is carried out to obtain the angular displacement data of the joint. The theoretical angle was calculated according to the biped robot’s lower limb diagram model and kinematics equation, and the expected trajectory was obtained by simple gait planning and using MATLAB to verify the validity of the angle parameters. Then the fuzzy PID controller of the lower limb joint was established, and the variable theory domain fuzzy controller was established by introducing the expansion factor. Finally, the Simulink test bench is built to simulate the two controllers, and the expected data and simulation data are compared. The results show that the error range of the variable theory domain fuzzy controller is only ±2°, and the variable theory domain adaptive fuzzy controller can accurately track the desired trajectory, reduce the joint angular displacement error of the biped robot, ensure the precision and stability of the biped robot motion, and basically meet the requirements of practical production.
A comprehensive investigation into the vibration fatigue characteristics of safety hangers for metro vehicles
Authors: Zeng Qihang , Zhang Fubing , Tan Guanglin , Chen Fuxing , Chen Qing
Published: August 2024

To meticulously investigate the reasons behind the cracking of the safety crane in a subway vehicle, we conducted a comprehensive analysis of its modes and conducted mode tests accordingly. These tests revealed the natural frequencies of the crane to be approximately 340 Hz and 990 Hz. Additionally, to gain insights into the acceleration and dynamic stress levels experienced by the safety crane during operation, we conducted a line tracking test on the vehicle. Analysis of the test data revealed that the acceleration and dynamic stress of the crane exhibited significant energy levels around 340 Hz, indicating that the first-order mode was excited, resulting in resonance. A comparative evaluation of the fatigue data of dynamic stress, both before and after rail grinding, revealed that the fatigue damage caused by rail wave grinding significantly shortened the service life of the safety crane.
Design and research of fuzzy PID-based liquid pump performance testing platform
Authors: Hou Yuntao , Wu Zequan , Cai Xiaohua , Li Zhancheng
Published: August 2024

The pump is a crucial component of agricultural sprayer machinery, directly impacting its operational quality. This paper designs a performance testing platform for pumps. Utilizing a brushless DC motor as the power source for the pump, a closed-loop mathematical model for the brushless DC motor is established. Simulation experiments and indoor experimental tests are conducted. Simulation results indicate that the motor control system exhibits fast response, no overshooting, and excellent dynamic and static performance. Experimental tests on the platform show that the error range of motor speed is less than 0.3%, achieving excellent control performance. The constructed pump performance testing system achieves precise measurement of pump flow rates at different speeds, demonstrating outstanding performance.
A self-cleaning glue feeding system based on slit coating
Authors: Zhang Hong , Wang Guanglei , Luo Wei , Xu Jie , Lin Jiansha , Li Xiaoquan , He Daiquan
Published: August 2024

This paper introduces a self-cleaning glue feeding system. Based on the slit coating process, a mid-line adhesive glue feeding system suitable for KDF3E filter rod forming machine has been developed through innovative design of nozzle structure, improvement of coating process flow, optimization of process parameters, and other methods and measures. After the machine test, we can solve the current glue feeding problem and improve the overall efficiency and quality of mid-line glue.
An improved dynamical variational autoencoder framework for predicting aero-engine remaining useful life
Authors: Gong Yude , Dang Wei , Li Peng
Published: August 2024

Accurate remaining useful life (RUL) prediction is vital for ensuring equipment safety, optimizing maintenance schedules, and reducing operational costs. Deep learning networks have emerged as effective means for RUL prediction. However, these methods encounter challenges of multiple sensors and data noise when handling large and complex industrial equipment, which impede the prediction model’s ability to generalize. To address these issues, this paper proposes an improved Dynamical Variational Autoencoders (DVAE) Framework combining principal component analysis (PCA) and long short-term memory (LSTM) networks. The effectiveness of our DVAE RUL prediction framework is validated through experiments conducted on the NASA C-MAPSS dataset. Results show that the approach significantly improves the prediction accuracy.
CFD-based prediction of flow noise in offshore pipeline systems
Authors: Sun Kailang , Yin Wenhui
Published: August 2024

The ship’s water injection system exhibits significant vibration and noise issues, necessitating the prompt implementation of targeted control measures. In this study, the simulation technique of computational fluid dynamics (CFD) combined with the finite element method and automatic matching layer method was applied to emulate the flow field environment of the outlet pipeline of a side valve in a seawater cooling system. Pulsating pressure data on the pipeline and valve’s wall were extracted from the flow field calculation and utilized as dipole noise sources and structural vibration excitations, enabling the prediction and separation of flow noise and flow-induced vibration noise in the water injection system. Leveraging the data from the flow field simulation, an analysis was conducted on the radiated noise at the side valve’s outlet, comparing the impact of different underwater depths and outlet pipe diameters on the underwater flow noise. The findings suggest that measures such as increasing the side valve’s diameter and lowering the discharge port’s underwater position can effectively reduce system flow noise, offering valuable insights for the design of noise reduction in seawater cooling systems for silent surface ships and submarines.
Research on risk data analysis of hazardous chemical vessels based on stream computing
Authors: Lv Taizhi , Wu Enze , Tang Peiyi
Published: August 2024

To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.
Adaptive neural network for dynamic prediction of machine tool energy consumption
Authors: Li Chengchao , Zhang Qiongzhi , He Shuangyu , Cao Huajun
Published: August 2024

As the “mother machine” of manufacturing, machine tools are extensively utilized, but suffer from high energy consumption and low energy efficiency. Energy consumption prediction for machine tools holds significant importance for energy planning in the manufacturing industry. Some scholars have achieved the prediction of machine tool energy consumption using data-driven modeling methods. However, these studies are based on single processing conditions, requiring a substantial amount of data support to achieve accurate predictions under specific conditions. In practical processing, machine tools have multiple working conditions, making it impractical to obtain prediction models for each condition. To this end, this paper proposes an adaptive neural network algorithm for energy consumption prediction under different working conditions. When machine tool conditions change, the existing prediction model can be adjusted by acquiring a limited quantity of current condition data to obtain a prediction model for the current condition. It reduced data collection time and lowered model reconstruction costs. A milling experiment was conducted to validate the practicality and feasibility of the proposed algorithm. The unadapted neural networks and other algorithmic models exhibit error rates of over 15% on the training set, but the adaptive neural networks maintain an error rate of essentially below 8%.
Research on fault diagnosis algorithm of automobile noise based on deep learning
Authors: Hao Yaodong , Chen Daliang
Published: August 2024

This study is devoted to improving the accuracy and efficiency of automobile noise fault diagnosis by using deep learning technology. By constructing a convolutional neural network (CNN) model, we deeply analyze and learn the automobile noise data, aiming at realizing the automatic identification of different types of faults. The experimental results show that the deep learning model shows significant improvement in several performance indicators compared with the traditional methods. Different parameter configurations are used to train the deep learning model. The model structure includes a convolution layer, a maximum pooling layer, and a full connection layer. By extracting the hierarchical features of the data, the model can better adapt to the complex characteristics of noise data. Compared with traditional methods, the CNN model has achieved obvious advantages in accuracy, recall, and F1 score. The research results not only verify the effectiveness of deep learning in automobile noise fault diagnosis but also show the robustness and adaptability of the model under more complex parameter configurations. The conclusion of this study provides empirical support for the application of deep learning technology in the field of automobile noise fault diagnosis.
Exploring the vibration attenuation capabilities of fish surface mucus films: a novel biomimetic model for underwater vehicle drag reduction
Authors: Zeng Qihang , Duan Zhengyong , Chen Qing
Published: August 2024

This groundbreaking research delves into the ability of naturally occurring mucus films on fish surfaces to attenuate vibrations, utilizing nanomechanical systems for a comprehensive analysis. The mucus samples were collected from healthy Asian carp. We grouped the test subjects based on the varying durations of mucus adsorption on the fish scales: 1, 5, 10, and 30 minutes, with the bare scale serving as a benchmark. To evaluate the mucus film’s mechanical characteristics, we employed a nano-indentation tester, while a customized vibration excitation apparatus assessed its vibration attenuation capabilities. Our results indicate that the mucus film adheres to the fish scales via electrostatic forces, forming a smooth inner layer. As the adsorption time increases, the accumulation of additional proteins leads to a thicker outer layer. Remarkably, this outer layer exhibits significant vibration attenuation, assisting Asian carp in mitigating turbulence resistance and enhancing their swimming prowess.
A novel prediction model of total drag coefficient of an MPV vehicle based on LSA optimization
Authors: Yang Li , Jiang Shuyun , Zhang Jun , Liu Chuanbo
Published: August 2024

Environmental degradation and energy scarcity are caused by the ongoing rise in the number of automobiles. The most efficient way to lower air resistance while driving is to boost vehicle energy efficiency and reduce energy consumption. In this case, a specific MPV model serves as the study subject in this work, and we use CFD simulation to estimate the drag coefficient along with the selection of important factors. Furthermore, to increase prediction accuracy, a lightning search algorithm (LSA)-based BP-neural-network prediction model is created and developed. The results show that the average relative error reduction of 2.83% in the drag resistance model optimized by the lightning search method represents a significant improvement in accuracy. This phenomenon can be explained by the LSA’s ability to resolve the BP neural network’s initial weight and threshold uncertainty as well as the issue of local minima being quickly reached. The high prediction accuracy drag prediction model, obtained herein, is useful for creating and producing automobile shapes with low drag coefficients.
A novel motherboard test item yield prediction model based on parallel feature extraction
Authors: Yan Zhangpeng , Zhai Weimin , Li Chao
Published: August 2024

Functional testing of motherboards in Surface Mount Technology (SMT) assembly lines is crucial. Accurate yield prediction for each test item optimizes testing strategies, reduces costs, and ensures test coverage. Manual estimation of test item yields remains common, hindering accurate on-site predictions. Existing research on motherboard yield lacks predictions for individual test items and ignores temporal correlations during placement. This paper introduces a method, a convolutional bidirectional long short-term memory attention network (CBA-Net), which combines a convolutional neural network and a bidirectional long short-term memory network with an attention mechanism for parallel processing. It preprocesses historical test data, leveraging both networks to identify key features and extract temporal correlations. The attention mechanism optimizes yield predictions by assigning weights to information at different time steps. Experimental validation using actual production data demonstrates that the proposed method performs better compared to traditional models.
Improvement of YOLOv5s-Ghost model
Authors: Huang Qiuling , Li Zhizhong
Published: August 2024

Based on the YOLOv5s-Ghost model, the changes in the number of channels and feature scales of the YOLOv5s-Ghost model, as well as the structure and function of GhostConv convolution, were studied. It was found that the YOLOv5s-Ghost model has insufficient information extraction and a YOLOv5s-EGhost model with fewer model parameters and higher detection accuracy was designed. GhostConv convolution has the functions of compressing channels and downsampling. The YOLOv5s-Ghost model has a lower number of channels at low channels, and each channel doubling of the model is accompanied by a downsampling operation. Applying GhostConv convolution to the low channel of the YOLOv5s-Ghost model can cause insufficient information extraction. Therefore, ECRS and EC3Ghost2 modules were designed to be placed in the low channel of the model, and a regular 3×3 convolution was used to replace GhostConv convolution at the third downsampling point, improving the situation of insufficient information extraction in the low channel of the YOLOv5s-Ghost model. Gc3-Ghost and GSPPF modules were designed in the high channels of the model, which can reduce the number of model parameters while maintaining model accuracy. On the self-made defect dataset, the improved YOLOv5s-EGhost model reduces the number of parameters by 58.6% and 21.1% respectively compared to YOLOv5s and YOLOv5s Ghost, improves the R index by 2.0% and 1.8 percentage points respectively, and improves Map_0.5 by 0.7 percentage points and 1.8 percentage points respectively, which can meet the requirements of real-time detection.
PRP protocol optimization method based on Q-learning under the Linux operating system
Authors: Wang Ruini , Hu Xianlang , Kong Xiangying
Published: August 2024

In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. The experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability.
Research on low-speed unmanned vehicle diagnosis system based on fault tree
Authors: Jin Zhigang , Zhang Qingyu , Wang Yaofu
Published: August 2024

To reduce the safety risks of low-speed unmanned vehicles losing control and colliding with pedestrians or vehicles due to malfunctions during autonomous task execution, Fault Tree Analysis (FTA) is used to analyze the safety of low-speed unmanned vehicles. A low-speed unmanned vehicle fault diagnosis system is designed to take corresponding measures in case of vehicle faults and improve vehicle driving safety.
Research on vehicle A-pillar visualization based on A-pillar detection and decision tree model
Authors: Yuan Huan , Sun Jiaqi , Zhang Xiaoya , Zhu Yao
Published: August 2024

When turning or passing through intersections, the vehicle’s A-pillar blind spot may obscure road conditions. Using cameras to capture and display external images on the A-pillar screen can eliminate the A-pillar blind spot. However, this solution still faces a mismatch between the displayed screen image and the surrounding environment image. To obtain more accurate blind spot images, accurate blind spot cropping areas are calculated through vehicle A-pillar detection and image registration technology. By introducing eye position and posture as features and considering the corresponding blind spot cropping areas as target variables, a decision tree model is trained to predict the blind spot cropping areas. This model can rapidly and accurately predict the blind spot areas for display on the A-pillar screen, with an average pixel error of 12.7, a coefficient of determination of 0.85, and an image processing speed within one millisecond. Experiments show that the A-pillar blind spot visualization system based on A-pillar detection and decision tree model prediction effectively eliminates the A-pillar blind spot, enhancing driving safety.
Dynamic facial expression recognition based on attention mechanism
Authors: Bao Nengsheng , Ma Yawei , Wei Xiang , Liang Zuodong
Published: August 2024

In the wild, dynamic facial emotion recognition is a highly challenging task. Traditional approaches often focus on extracting discriminative features or preprocessing data to remove noisy frames. The former overlooks differences between keyframes and noise frames, while the latter can be complex and less robust. To address this issue, we propose a spatiotemporal feature extraction network based on an attention mechanism. In the spatial feature extraction stage, our method incorporates prior knowledge through an attention mechanism, allowing the model to precisely select and focus on key features. Additionally, incorporating prior knowledge reduces unnecessary search space, leading to faster convergence to optimal solutions. To suppress the impact of irrelevant frames, our study introduces a dynamic-aware temporal feature extraction mechanism, enabling the network to accurately capture key frames in video sequences, thereby enhancing model performance. Finally, at the decision stage, we design a fragment-based feature fusion filter to mitigate the influence of irrelevant frames on sequence classification. Our method achieves accuracy rates of 54.18% on DFEW, 42.37% on FERV39k, and 75.55% on CAER tests.
Towards a precise measurement of 157Tb nuclear decay data: Sample purification using resonance ionization mass spectrometry
Authors: Studer Dominik , Schumann Dorothea , Chiera Nadine Mariel , Maugeri Emilio Andrea , Kieck Tom , Kossert Karsten , Wendt Klaus
Published: August 2024

In nuclear physics, 157Tb emerges as a prime candidate
for experiments aimed at elucidating neutrino mass constraints and
at searching for sterile neutrinos. Despite its importance,
157Tb exhibits highly uncertain values for its nuclear decay
properties. A significant challenge in many efforts to measure such
data lies in the simultaneous undesired presence of 158Tb in
the samples, which hinders precise activity determination. Mass
separation emerges as a crucial method for obtaining pure 157Tb
specimens. This work outlines the production of an isotopically-pure
157Tb sample through mass separation and ion implantation,
using the RISIKO facility at the University of Mainz. The initial
material was obtained from proton-irradiated Ta samples through
radiochemical separation at the Paul Scherrer Institute. In total, a
sample containing 8.7(9) · 1012 atoms of 157Tb was
obtained. The efficiency of the mass separation and ion implantation
was 13(2) %. The purified material served as the basis for new
research endeavors at the Physikalisch Technische Bundesanstalt
Braunschweig aiming at the determination of nuclear data for
157Tb with significantly improved precision.
Effect of micelle concentration on degumming of sesame oil (Sesamum indicum L) with ultrafiltration membrane
Authors: Une S , Sutardi  , Hastuti P , Susanto H
Published: August 2024

Sesame oil (Sesamum indicum L.) is obtained from roasted and pressed sesame seeds. The existence of phospholipids and contaminants in sesame oil is unfavorable, as it induces a darkening of the oil’s hue, thereby diminishing its oxidative stability. Failure to eliminate phospholipids compounds from sesame oil through the degumming process leads to a deterioration in quality thus influencing consumer acceptability. This research aimed to investigate the impact of micelle concentrations in hexanes-sesame oil mixture on the degumming process employing ultrafiltration membranes. This investigation involved the preparation of sesame oil through a procedure that entailed roasting at 180°C for 30 minutes, followed by pressing at a pressure of 140 kN for 5 minutes. Micelles of sesame oil roasted with hexane were prepared at concentrations of 20%, 25% and 30%. Moreover, the degumming of sesame oil micelles was accomplished by utilizing PES (Polyether sulfone) and PVDF (Polyvinylidene diflouride) ultrafiltration membranes and then by analyzing the resulting sesame oil’s properties. The results indicated that using a PVDF membrane for sesame oil degumming resulted in a higher permeate flux than a PES membrane. Furthermore, treatment with a 30% micelle concentration resulted in a yield of 70.63%, with phospholipid levels of 13.96 ppm and membrane rejection value of 92.9%.
Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation
Authors: Huynh Bao Ngoc , Groendahl Aurora Rosvoll , Tomic Oliver , Liland Kristian Hovde , Knudtsen Ingerid Skjei , Hoebers Frank , van Elmpt Wouter , Dale Einar , Malinen Eirik , Futsaether Cecilia Marie
Published: August 2024

Objective. Target volumes for radiotherapy are usually contoured manually, which can be time-consuming and prone to inter- and intra-observer variability. Automatic contouring by convolutional neural networks (CNN) can be fast and consistent but may produce unrealistic contours or miss relevant structures. We evaluate approaches for increasing the quality and assessing the uncertainty of CNN-generated contours of head and neck cancers with PET/CT as input. Approach. Two patient cohorts with head and neck squamous cell carcinoma and baseline 18F-fluorodeoxyglucose positron emission tomography and computed tomography images (FDG-PET/CT) were collected retrospectively from two centers. The union of manual contours of the gross primary tumor and involved nodes was used to train CNN models for generating automatic contours. The impact of image preprocessing, image augmentation, transfer learning and CNN complexity, architecture, and dimension (2D or 3D) on model performance and generalizability across centers was evaluated. A Monte Carlo dropout technique was used to quantify and visualize the uncertainty of the automatic contours. Main results. CNN models provided contours with good overlap with the manually contoured ground truth (median Dice Similarity Coefficient: 0.75–0.77), consistent with reported inter-observer variations and previous auto-contouring studies. Image augmentation and model dimension, rather than model complexity, architecture, or advanced image preprocessing, had the largest impact on model performance and cross-center generalizability. Transfer learning on a limited number of patients from a separate center increased model generalizability without decreasing model performance on the original training cohort. High model uncertainty was associated with false positive and false negative voxels as well as low Dice coefficients. Significance. High quality automatic contours can be obtained using deep learning architectures that are not overly complex. Uncertainty estimation of the predicted contours shows potential for highlighting regions of the contour requiring manual revision or flagging segmentations requiring manual inspection and intervention.
Collective Victims Beyond Reach?
Authors: Nili Shmuel
Published: August 2024

Affluent democracies have been hosting trillions stolen by foreign kleptocrats from some of the world’s poorest people. This chapter assumes an elusive intuition: affluent democracies who have been culpably entangled in foreign kleptocrats’ larceny have weighty (albeit defeasible) moral reasons to disgorge the fruits of this larceny, even if such disgorgement has little to no prospect of improving the lot of the victims of kleptocracy whom they cannot reach. The chapter’s main effort is to trace this disgorgement intuition to a much more general idea: that a liberal democracy as a collective agent has its own moral integrity. Much as with individual persons, this collective integrity provides independent moral reasons for and against certain actions—reasons whose force is not reducible to what the democracy in question owes to others. The juxtaposition of this collective integrity with the specific problem of kleptocratic loot helps us to learn more about both.
Traumatic Legacies and Narrative Claims
Authors: Lawlor Hannie
Published: August 2024

Chapter 1 takes as its starting point Marianne Hirsch’s notion of postmemory and explores the potential for appropriation that Hirsch posits when the second and third generations are compelled to give voice to a trauma that is not their ‘own’. It focuses on two responses to the Holocaust that stage a genealogical search to uncover a suppressed family trauma: Colombe Schneck’s autobiographical account in La Réparation (Reparation) (2012) and Juana Salabert’s novel Velódromo de Invierno (Winter Velodrome) (2001). As it examines the ways in which the postgenerational narrators interrogate and navigate the indirect connection to the harrowing legacy that has passed down to them, the chapter questions the use of appropriation as a marker by which to evaluate the ethics of postmemorial narratives.
Stigmatized Professions and Ambiguous Subjects: Methodological Reflections from Sanitation Workers and Opioid Consumption in Sierra Leone
Authors: Bangura Ibrahim Bakarr , Leblond Nelly , Walker Julian Hugo
Published: August 2024

This paper explores ethical dilemmas in relation to practices of alcohol and drug consumption in the workplace by manual pit emptiers in Freetown, Sierra Leone. Based on observations and interviews with workers, we come to understand the consumption of painkillers and gin as a mechanism to alleviate stigma, rather than an issue of addiction. Indeed, the consumption of psychoactive substances before manual pit emptying appears as a performance to create a symbolic distance between the worker entering half-naked in a tank filled with faecal sludge and the social being, who would never do so in a ‘normal state of mind’. This analysis calls both for a deconstruction of the policies and rules that shape the sanitation sector as shameful and ‘inhuman’ and for proposals to ameliorate those conditions. Furthermore, we explore our positions as researchers on why revealing such practices can make sense in action-oriented research but also must be thought through ethically. Beyond the guidance of institutional ethics boards, the question of short- and long-term engagements with research subjects is central in shaping what ought or ought not to be investigated. We thus contribute to the discussions on how to support better science and practices with and for already stigmatized populations.