bluuebunny/crossref_metadata_2025 huggingface dataset:
Therapeutic ultrasound: an innovative approach for targeting neurological disorders affecting the basal ganglia
Authors: Singh Anurag , Reynolds John N. J.
Published: October 2024

The basal ganglia are involved in motor control and action selection, and their impairment manifests in movement disorders such as Parkinson’s disease (PD) and dystonia, among others. The complex neuronal circuitry of the basal ganglia is located deep inside the brain and presents significant treatment challenges. Conventional treatment strategies, such as invasive surgeries and medications, may have limited effectiveness and may result in considerable side effects. Non-invasive ultrasound (US) treatment approaches are becoming increasingly recognized for their therapeutic potential for reversibly permeabilizing the blood–brain barrier (BBB), targeting therapeutic delivery deep into the brain, and neuromodulation. Studies conducted on animals and early clinical trials using ultrasound as a therapeutic modality have demonstrated promising outcomes for controlling symptom severity while preserving neural tissue. These results could improve the quality of life for patients living with basal ganglia impairments. This review article explores the therapeutic frontiers of ultrasound technology, describing the brain mechanisms that are triggered and engaged by ultrasound. We demonstrate that this cutting-edge method could transform the way neurological disorders associated with the basal ganglia are managed, opening the door to less invasive and more effective treatments.
Optimal performance of silicon nanowire solar cells under low sunlight concentration and their integration as bottom cells in III–V multijunction systems
Authors: Yu Jeco-Espaldon Bernice Mae , Jevasuwan Wipakorn , Okada Yoshitaka , Fukata Naoki
Published: October 2024

Nanostructured silicon solar cells are designed to minimize costs through reduced material usage while enhancing power conversion efficiency via superior light trapping and shorter charge separation distances compared to traditional planar cells. This study identifies the optimal conditions for nanoimprinted silicon nanowire (SiNW) solar cells to achieve maximum efficiency under low sunlight concentration and evaluates their performance as bottom cells in III–V multijunction solar cell systems. The findings indicate that the SiNW solar cell reaches its peak performance at a concentration factor of 7.5 suns and a temperature of 40°C or lower. Specifically, the absolute conversion efficiency under these conditions is 1.05% higher than that under unconcentrated light. Compared to a planar silicon solar cell under identical conditions, the SiNW solar cell exhibits a 3.75% increase in conversion efficiency. Additionally, the SiNW single-junction solar cell, when integrated in series with a commercial lattice-matched InGaP/GaAs dual-junction solar cell, was tested under unconcentrated sunlight, specifically at one-sun, global air mass 1.5 condition, to assess its viability in one-sun multi-junction solar cell applications. The results suggest that a III–V upper subcell with a smaller active area than that of the SiNW subcell is optimal for maximizing current production, which is favorable to the cost reduction of the device. This hybrid configuration is particularly advantageous for terrestrial applications, such as electric vehicles, which demand lightweight, high-performance multijunction solar cell devices. Although the weight reduction of the characterized SiNW solar cell with a full silicon substrate compared to its planar solar cell counterpart is 1.8%, recommendations to increase this reduction to as much as 64.5% are discussed to conclude this paper.
Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis
Authors: Tang Qingqing , Yang Xinyue , Sun Mengmeng , He Min , Sa Ren , Zhang Kaiqiang , Zhu Bing , Li Tie
Published: October 2024

Background

The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body’s recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke.

Methods

A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction.

Results

This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy.

Conclusion

Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.
Association between insulin resistance indices and kidney stones: results from the 2015–2018 National Health and Nutrition Examination Survey
Authors: Shen Yue , Zhu Zhu , Bi Xiao , Shen Yuqi , Shen Aiwen , Deng Bo , He Yining , Wang Wenji , Ding Feng
Published: October 2024

Objective

To explore the association between representative insulin resistance (IR) indices and the risk of kidney stone disease in an American adult population. The representative IR indices referred to metabolic score for IR (METS-IR), triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, triglyceride glucose-body mass index (TyG-BMI), visceral adiposity index (VAI), and homeostatic model assessment of IR (HOMA-IR).

Methods

We investigated adult participants who joined the 2015–2018 National Health and Nutrition Examination Survey (NHANES) and reported kidney stone histories. Weighted proportions, multivariable regression analysis, and restricted cubic splines were used to evaluate the associations between IR indices and kidney stones after their adjustment for gender, age, race, education, smoking status, alcohol drinking frequency, hypertension and diabetes status, physical activity level, water intake, and levels of calcium, cholesterol, and uric acid.

Results

A total of 19,225 participants were included. The weighted prevalence of kidney stone was 11.1%. A multivariable logistic regression model showed a dose–response relationship between the METS-IR and kidney stone [odds ratio (OR) = 1.02, 95% confidence interval (CI) (1.01, 1.04), p < 0.01]. A similar relationship was observed between the TyG-BMI and kidney stone after full adjustment [OR = 1.0, 95% CI (1.0, 1.01), p < 0.001]. Sex-stratified analyses revealed that the association between METS-IR and nephrolithiasis [OR = 1.03, 95% CI (1.01, 1.05), p < 0.01], and the association between TyG-BMI and nephrolithiasis [OR = 1.01, 95% CI (1.0, 1.01), p <0.001] was significant among the male participants in the fully adjusted model. Moreover, a significant association was found between the METS-IR levels and nephrolithiasis [OR = 1.03, 95% CI (1.01, 1.06), p < 0.01], and between the TyG-BMI levels and nephrolithiasis [OR = 1.01, 95% CI (1.0, 1.01), p < 0.05] among the diabetic participants after full adjustment. Furthermore, a potential nonlinear association was found between other IR indices (i.e., TG/HDL-C, VAI, and HOMA-IR) and the risk of kidney stone disease.

Conclusion

Higher METS-IR and TyG-BMI levels were associated with a higher risk of nephrolithiasis. Future investigations are required to identify the role of IR in the progress of kidney stone formation and to propose prevention measures and health guidelines.
Nutraceuticals in osteoporosis prevention
Authors: Roseti Livia , Borciani Giorgia , Grassi Francesco , Desando Giovanna , Gambari Laura , Grigolo Brunella
Published: October 2024

Nutraceuticals are gaining popularity as they can contribute to bone health by delaying the onset or slowing down the progression of pathological bone loss. Osteoporosis’s bone loss is a concern for older adults and a crucial aspect of aging. Maintaining healthy bones is the key to living a full and active life. Our review explores the current knowledge on the role of nutraceuticals in preventing osteoporosis by focusing on three main aspects. First, we provide an overview of osteoporosis. Second, we discuss the latest findings on natural nutraceuticals and their efficacy in reducing bone loss, emphasizing clinical trials. Third, we conduct a structured analysis to evaluate nutraceuticals’ pros and cons and identify translational gaps. In conclusion, we must address several challenges to consolidate our knowledge, better support clinicians in their prescriptions, and provide people with more reliable nutritional recommendations to help them lead healthier lives.
Educational assessment without numbers
Authors: Scharaschkin Alex
Published: October 2024

Psychometrics conceptualizes a person's proficiency (or ability, or competence), in a cognitive or educational domain, as a latent numerical quantity. Yet both conceptual and empirical studies have shown that the assumption of quantitative structure for such phenomena is unlikely to be tenable. A reason why most applications of psychometrics nevertheless continue to treat them as if they were numerical quantities may be that quantification is thought to be necessary to enable measurement. This is indeed true if one regards the task of measurement as the location of a measurand at a point on the real number line (the viewpoint adopted by, for example, the representational theory of measurement, the realist theory of measurement as the discovery of ratios, and Rasch measurement theory). But this is not the only philosophically respectable way of defining the notion of measurement. This paper suggests that van Fraassen's more expansive view of measurement as, in general, location in a logical space (which could be the real continuum, as in metrological applications in the physical sciences, but could be a different mathematical structure), provides a more appropriate conceptual framework for psychometrics. Taking educational measurement as a case study, it explores what that could look like in practice, drawing on fuzzy logic and mathematical order theory. It suggests that applying this approach to the assessment of intersubjectively constructed phenomena, such as a learner's proficiency in an inherently fuzzily-defined subject area, entails recognizing the theory-dependent nature of valid representations of such phenomena, which need not be conceived of structurally as values of quantities. Finally, some connections are made between this “qualitative mathematical” theorization of educational assessment, and the application of techniques from machine learning and artificial intelligence in this area.
Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria
Authors: Hunt Alexandra , Law Heather , Carney Rebekah , Mulholland Rachel , Flores Allan , Tudur Smith Catrin , Varese Filippo , Parker Sophie , Yung Alison R. , Bonnett Laura J.
Published: October 2024

Objectives

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field.

Design

Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST).

Setting

Studies in any setting were included.

Primary and secondary outcome measures

The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias.

Results

Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias.

Conclusions

Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries.

Systematic review registration

https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.
The influence of perceived formative assessment on the learning autonomy of medical students: the chain mediating role of psychological empowerment and positive academic emotions
Authors: Wang Jiali , Zhou Guorun , Guo Juntang , Sun Xiaodong , Sun Lin
Published: October 2024

Introduction

Medical students’ autonomous learning is a cornerstone of their educational journey. Formative assessment is defined as a process to enhance learning and improve academic performance, and the key to the effectiveness of formative assessment is the students’ perceptions of it. The present study investigates the impacts of perceived formative assessment on the learning autonomy of medical students, explores the chain mediating role of psychological empowerment and positive academic emotions, and offers strategies for facilitating medical students’ autonomous learning.

Methods

A cross-sectional investigation was conducted using a convenience sampling method involving 713 medical students (mean age 19.72 ± 1.18 years; 207 male and 506 female students; the proportion of participants is 93.69%) in Shandong Second Medical University. Perceived formative assessment was performed using a self-designed questionnaire of Perceived Formative Evaluation for Medical Students, learning autonomy using the Autonomy in Learning Rating Scale for College Students, positive academic emotions using the General Academic Emotion Questionnaire for College Students, and psychological empowerment of medical students using the Chinese version of Psychological Empowerment Scale (PES). The data were analyzed using descriptive statistics, Pearson’s correlation, multiple regression, and mediation analysis using the SPSS26.0 program and PROCESS3.1.

Results

Perceived formative assessment significantly predicted learning autonomy (β = 0.06, p < 0.05). It also positively predicted psychological empowerment (β = 0.20, p < 0.001), and psychological empowerment positively predicted the learning autonomy of medical students (β = 0.36, p < 0.001). Psychological empowerment also positively predicted positive academic emotions (β = 0.64, p < 0.001), and positive academic emotions positively predicted learning autonomy (β = 0.44, p < 0.001). The direct effect value of perceived formative assessment on learning autonomy was 0.18, while the total indirect effect was 0.43. The mediation effect of psychological empowerment on the relationship between perceived formative assessment and learning autonomy was 0.22, and the chain mediation effect of psychological empowerment and positive academic emotions was 0.18, accounting for 30%, 70%, 36%, and 30% of the total effect, respectively.

Conclusion

Perceived formative assessment directly enhances medical students’ learning autonomy. This relationship is partially mediated by psychological empowerment and positive academic emotions. The results suggest that formative evaluation boosts psychological empowerment, which fosters positive academic emotions and further promotes learning autonomy.
How do health content creators perform well? An integration research of short video and livestream behaviors
Authors: Liu Jing , Ye Qing , Wu Hong , Ma Rongyang , Guo Shanshan , Long Han
Published: October 2024

Introduction

Short-video platforms have demonstrated vast potential for health education. To meet diverse user requirements, many short-video platforms have integrated livestreaming functionalities. This integration presents challenges for health content creators in formulating effective performance strategies, including decisions about which format to use (short video or livestream) and what type of content to produce. This study utilizes panel data from a prominent short-video platform in China to empirically investigate the impact of different forms and content characteristics on the performance of health content creators.

Methods

We conducted an empirical analysis using panel data obtained from a leading short-video platform in China. Our analysis focused on understanding how the behaviors associated with short videos and livestreaming impact the performance of health content creators. We examined form-level differences, analyzing the distinct roles of short video and livestreaming behaviors. Additionally, we explored content-level characteristics, investigating the effects of content coverage, health knowledge content, and advertising content on both short-term and long-term performance. The moderation effects of the creator’s occupation and certification type were also analyzed.

Results

Our form-level analysis revealed that health creators’ behaviors in short videos and livestreaming play distinct roles in their performance. Livestreaming behaviors resulted in short-term economic returns, while short video behaviors had a more significant effect on follow-ups, which are often viewed as long-term, more sustainable performance indicators. Content-level analysis showed that content coverage and health knowledge content enhance long-term performance but do not increase short-term performance. Conversely, advertising content was found to be essential for securing short-term financial income. The study also identified that the creator’s occupation and certification type moderate the impact of content on performance.

Conclusion

This study integrates two media forms (short video and livestream), providing direct insights into the performance of health content creators in the realm of health education. Health content creators need to strategically balance their use of short videos and livestreaming to optimize both short-term and long-term performance outcomes. Specifically, increasing content coverage and health knowledge can enhance long-term engagement, while incorporating advertising content is crucial for immediate financial gains.
Generative AI with WGAN-GP for boosting seizure detection accuracy
Authors: Abou-Abbas Lina , Henni Khadidja , Jemal Imene , Mezghani Neila
Published: October 2024

Background

Imbalanced datasets pose challenges for developing accurate seizure detection systems based on electroencephalogram (EEG) data. Generative AI techniques may help augment minority class data to facilitate automatic epileptic seizure detection.

New method

This study investigates the impact of various data augmentation (DA) approaches, including Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), Vanilla GAN, Conditional GAN (CGAN), and Cramer GAN, on classification performance with Random Forest models. The best-performing GAN variant, WGAN-GP, was then integrated with a bidirectional Long Short-Term Memory (LSTM) architecture and compared against traditional and synthetic oversampling methods.

Results

The evaluation of different GAN variants for data augmentation with Random Forest classifiers identified WGAN-GP as the most effective approach. The integration of WGAN-GP with bidirectional LSTM yielded substantial performance improvements, outperforming traditional oversampling methods and achieving an accuracy of 91.73% on the augmented data, compared to 86% accuracy on real data without augmentation.

Comparison with existing methods

The proposed generative AI approach combining WGAN-GP and recurrent neural network models outperforms comparative synthetic oversampling methods on metrics relevant for reliable seizure detection from imbalanced EEG datasets.

Conclusions

Incorporating the WGAN-GP generative AI technique for data augmentation and integrating it with bidirectional LSTM elevates seizure detection accuracy for imbalanced EEG datasets, surpassing the performance of traditional oversampling and class weight adjustment methods. This approach shows promise for improving epilepsy monitoring and management through enhanced automated detection system effectiveness.
Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
Authors: Huynh Harry Hoa , Min Cheol-Hong
Published: October 2024

Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time.
Research on Coordinated Control of Vehicle Inertial Suspension Using the Dynamic Surface Control Theory
Authors: Zhao Yanhui , Du Fu , Wang Hujiang , Wang Xuelin , Yang Xiaofeng , Shi Dongyin , Bui Vancuong , Zhang Tianyi
Published: October 2024

In the process of driving, the steering, braking, and driving conditions and different road conditions affect the vibration characteristics of the vehicle in the vertical, roll, and pitch directions. These factors greatly impact the riding comfort of the vehicle. Among them, the uneven distribution of vertical load between the left and right or the front and rear suspension is one of the important factors affecting the performance indicators of the vehicle’s roll angle acceleration and pitch angle acceleration. In order to improve the ride comfort of the vehicle in vertical, roll, and pitch motion, the inerter is introduced in this paper to form a new type of suspension structure with the “spring-damping” base element, inertial suspension. It breaks away from the traditional “spring-damping” base element of the inherent suspension structure. In this paper, the mechatronic inerter is taken as the actual controlled object, and the inertial suspension structure is considered as the controlled model based on the dynamic surface control theory and the pseudo-inverse matrix principle. Thus, the coordinated control of the inertial suspension can be achieved. Under random road input, compared with passive suspension, the ride comfort performance indicators of the vehicle with inertial suspension based on dynamic surface control are significantly improved. Finally, a Hardware-in-the-Loop (HiL) test of the controller based on dynamic surface control is carried out to verify that the performance of the vehicle inertial suspension using the dynamic surface control algorithm had improved in terms of vehicle ride comfort. The error between the experimental results and the simulation results is about 8%, which verifies the real-time performance and effectiveness of the dynamic surface controller in the real controller.
Comparative Analysis of Active Bonded Piezoelectric Repair Systems for Damaged Structures under Mechanical and Thermo-Mechanical Loads
Authors: Abdulla Mohammed , Hrairi Meftah , Aabid Abdul , Abdullah Nur Azam , Baig Muneer
Published: October 2024

Active repair systems employing piezoelectric (PZT) patches have emerged as promising solutions for mitigating crack propagation and enhancing structural integrity in various engineering applications. However, the existing literature predominantly focuses on the application of PZT patches for repairing structures under mechanical loading. In this study, a finite element analysis (FEA) is employed to investigate the repair of a centre-cracked aluminium plate under both mechanical and thermo-mechanical loading conditions. This study explores the influence of key parameters, including temperature, PZT patch thickness, type of PZT material, adhesive material, and adhesive thickness, on the structural integrity and crack propagation behaviour. The results reveal significant differences in stress distribution and crack propagation tendencies under varying loading conditions and parameter settings. These findings emphasize the necessity of considering thermo-mechanical loading conditions and parameter variations when designing effective active repair systems. In conclusion, this study provides valuable insights into optimizing PZT patch-based repair strategies for improved structural integrity and crack mitigation in aerospace and other engineering applications under diverse loading scenarios.
Bionic Walking Control of a Biped Robot Based on CPG Using an Improved Particle Swarm Algorithm
Authors: Wu Yao , Tang Biao , Qiao Shuo , Pang Xiaobing
Published: October 2024

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.
Turbopump Parametric Modelling and Reliability Assessment for Reusable Rocket Engine Applications
Authors: Gulczyński Mateusz T. , Hahn Robson H. S. , Deeken Jan C. , Oschwald Michael
Published: October 2024

The development of modern reusable launchers, such as the Themis project with its LOX/LCH4 Prometheus engine, CALLISTO—a reusable VTVL-launcher first-stage demonstrator with a LOX/LH2 RSR2 engine, and SpaceX’s Falcon 9 with its Merlin 1D engine, underscores the need for advanced control algorithms to ensure reliable engine operation. The multi-restart capability of these engines imposes additional requirements for throttling, necessitating an extended controller-validity domain to safely achieve low thrust levels across various operating regimes. This capability also increases the risk of component failure, especially as engine parameters evolve with mission profiles. To address this, our study evaluates the dynamic reliability of reusable rocket engines (RREs) and their subcomponents under different failure modes using multi-physics system-level modelling and simulation, with a particular focus on turbopump components. Transient condition modelling and performance analysis, conducted using EcosimPro-ESPSS software (version 6.4.34), revealed that turbopump components maintain high reliability under nominal conditions, with turbine blades demonstrating significant fatigue life even under varying thermal and mechanical loads. Additionally, the proposed predictive model estimates the remaining useful life of critical components, offering valuable insights for improving the longevity and reliability of turbopumps in reusable rocket engines. This study employs deterministic, thermally dependent structural simulations, with key control objectives including end-state tracking of combustion chamber pressure and mixture ratios and the verification of operational constraints, exemplified by the LUMEN demonstrator engine and the LE-5B-2 engine class.
A Dual-Dimension Convolutional-Attention Module for Remaining Useful Life Prediction of Aeroengines
Authors: Zhu Yixin , Liu Zhidan
Published: October 2024

Remaining useful life (RUL) prediction of aeroengines not only enhances aviation safety and operational efficiency but also significantly lowers operational costs, offering substantial economic and social benefits to the aviation industry. Aiming at RUL prediction, this paper proposes a novel dual-dimension convolutional-attention (DDCA) mechanism. DDCA consists of two branches: one includes channel attention and spatial attention mechanisms, while the other applies these mechanisms to the inverted dimensions. Pooling and feature-wise pooling operations are employed to extract features from different dimensions of the input data. These branches operate in parallel to capture more complex temporal and spatial feature correlations in multivariate time series data. Subsequently, an end-to-end DDCA-TCN network is constructed by integrating DDCA with a temporal convolutional network (TCN) for RUL prediction. The proposed prediction model is evaluated using the C-MAPSS dataset and compared to several state-of-the-art RUL prediction models. The results show that the RMSE and SCORE metrics of DDCA-TCN decreased by at least 12.8% and 4.6%, respectively, compared to other models on the FD002 subset, and by at least 10.6% and 18.4%, respectively, on the FD004 subset, which demonstrates that the DDCA-TCN model exhibits excellent performance in RUL prediction, particularly under multiple operating conditions.
Analyzing the Indirect Effects of Lightning on Unmanned Aerial Vehicle Navigation Receivers
Authors: Ma Zhenyang , He Shaonan , Duan Zhaobin , Liu Jiahao
Published: October 2024

To investigate the interference effects of lightning on unmanned aerial vehicle (UAV) navigation receivers, an analytical approach for the evaluation of indirect lightning effects is suggested. The blocking mechanism of lightning pulses on the receiver is analyzed using the vector method. A behavioral-level simulation model of the receiver’s radio frequency (RF) front-end is constructed, and a lightning pulse injection simulation is performed based on the advanced design system (ADS). The simulation results indicate that lightning pulses induce blocking interference on the RF front-end of the navigation receiver, reducing its sensitivity and dynamic range. This interference is attributed to gain compression and degradation of the noise figure in the low-noise amplifier (LNA). To further validate the simulation results, a pin injection test is carried out to examine the influence of lightning pulses with different peak voltages on receiver sensitivity. The test results indicate a notable reduction in the receiver sensitivity when the lightning pulse peak reaches 250 V. The sensitivity trends across various test panels are consistent, and the damage thresholds are similar. These findings confirm the accuracy of the simulation results.
Optimization of a Boom Height Ultrasonic Detecting Model for the Whole Growth Cycle of Wheat: Affected by the Oscillation of the Three-Section Boom of the Sprayer
Authors: Wu Jianguo , Yang Shuo , Gao Yuanyuan , Pan Xiaoyong , Zou Wei , Wei Yibo , Zhai Changyuan , Chen Liping
Published: October 2024

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height.
Efficient Real-Time Droplet Tracking in Crop-Spraying Systems
Authors: Huynh Truong Nhut , Burgers Travis , Nguyen Kim-Doang
Published: October 2024

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.
Control Based on Nonlinear Estimators of Parametric Uncertainties Applied to an Agricultural Tractor Equipped with a Towed Implement System
Authors: Acosta Lúa Cuauhtémoc , Vera Vaca Claudia Verónica , Hinojosa-Dávalos Joel , Vaca García Claudia Carolina
Published: October 2024

This article presents a nonlinear control strategy designed to address parametric uncertainties in an agricultural tractor system coupled to a towed implement. The controller ensures accurate tracking of lateral and yaw velocities relative to desired reference trajectories, even under the presence of parametric variations and external disturbances. The reference trajectories are derived from an “ideal” tractor model, excluding the effects of the towed implement. A High-Order Sliding Mode (HOSM) estimator is employed to provide an estimation of disturbances, which are subsequently mitigated by the controller to maintain system stability and precision. The effectiveness of the proposed control strategy is validated through Matlab-Simulink simulations, which include a double-step steer maneuver. This maneuver tests the system’s ability to handle abrupt steering changes, providing insight into the controller’s robustness and its capacity to ensure accurate trajectory tracking in demanding conditions.
Improved and Novel Methods for Investigating Organophosphate Esters in Particulate Matter
Authors: Gathof Annie , Bonanno Tess , Rossicone Paige , Clark Adelaide E.
Published: October 2024

A pressurized liquid extraction (PLE) method for the extraction of 31 organophosphate esters (OPEs) and novel organophosphate esters (NOPEs) has been developed. Unlike previously published methods, this method utilizes the high-throughput nature of PLE (as opposed to Soxhlet or sonication methods) without using potentially harmful organic solvents like methylene chloride. Combinations of hexane and acetone and hexane and ethyl acetate at various temperatures were examined. Extracts were concentrated and analyzed via gas chromatography–mass spectrometry. The final optimized method utilized 1:1 v/v hexane/ethyl acetate at 100 °C for three static cycles (5 min each) at 80% flush volume and a 100 s N2 purge. This provided average surrogate corrected target analyte percent recoveries in spike and recovery experiments (n = 6) for OPEs and NOPEs of 106 ± 13%, with average surrogate recoveries of 88.6 ± 7.3%. The developed method was further validated using standard reference materials and was then applied to atmospheric particulate matter samples collected in the city of Providence, RI. The dataset reflected ambient concentrations of 16 OPEs and NOPEs (reported in pg m−3) for the first time in the greater Providence metropolitan area, including one of the first reports of NOPEs in atmospheric particulate matter in the U.S.
Near-Infrared Spectroscopy in Animal Nutrition: Historical Insights, Technical Principles, and Practical Applications
Authors: Maduro Dias Cristiana , Nunes Helder , Borba Alfredo
Published: October 2024

Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR’s growing role in promoting more sustainable and efficient animal production systems.
Birth Traits Associated with Pre-Adulthood Disease Manifestations in Calves
Authors: Yang Jiayu , Yang Zhangping , Zhang Zhipeng
Published: October 2024

The objective of this study was to explore the relationship between calf birth traits and their susceptibility to diseases before reaching adulthood. A total of 5253 birth traits of Chinese Holstein calves were examined, including gestation length (GL), calf weight at birth (CW), and calving ease score (CES), which ranges from 1 (easy) to 5 (very difficult). Furthermore, monthly medical records were scrutinized for pneumonia and diarrhea in these calves. The study assessed five aspects of disease manifestation in calves: age at first onset, frequency of illness, longest duration of treatment, and total duration of treatment. The link between age at onset and disease manifestation prior to adulthood was analyzed using general linear models and regression models. The GL of calves significantly correlated with the risk of pneumonia, with the risk decreasing as the GL increases. A higher CES was associated with a later onset of diarrhea in calves. Furthermore, the CES was significantly negatively correlated with the duration of diarrhea treatment in calves. These results suggest that implementing different preventive measures for calves with different birth traits and modifying treatment protocols for affected calves could enhance the productivity of dairy cows and reduce losses on farms.
Adaptation and Validation of the Pet Bereavement Questionnaire (PBQ) for Chinese Population
Authors: Yiu Winnie W. Y. , Cheung H. N. , Wong Paul W. C.
Published: October 2024

Despite the increasing prevalence of pet ownership in Chinese societies, standardized tools to assess grief from pet loss remain lacking. Research predominantly focuses on Western populations, creating a gap in understanding pet bereavement in Chinese cultural settings. This study aimed to adapt and validate the Pet Bereavement Questionnaire (PBQ-C) for a Chinese context to create a culturally appropriate assessment tool. A total of 246 participants with companion animal loss experiences were recruited through the university of the research team. They were invited to complete an online survey including the PBQ-C, the Depression subscale of the Depression Anxiety Stress Scales (DASS-21), and the Inventory of Complicated Grief (ICG). Both Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to examine the psychometric properties of the PBQ-C and the findings supported a three-factor structure—grief, anger, and guilt—aligned with the original PBQ, with three items reassigned to different factors. Despite these adjustments, the PBQ-C demonstrated strong internal consistency, reflecting the reliability of the questionnaire in measuring the same construct across its items; split-half reliability, indicating its ability to produce consistent results when divided into two parts; and concurrent validity, showing that the PBQ-C correlates well with other established measures of grief. The validated PBQ-C provides a culturally sensitive tool for assessing pet bereavement in Chinese society that can promote research and counselling support for this under-researched and under-recognized type of loss of human-animal relationships.
Changes in Articulatory Contact Pressure as a Function of Vocal Loudness
Authors: Searl Jeff , Evitts Paul
Published: October 2024

This study evaluated the impact of vocal loudness on the articulatory contact pressure (ACP) between the tongue and palate during the production of lingua-alveolar consonants. Fourteen adults with typical speech produced phrases with the phonemes /t, d, s/ embedded while ACP was sensed with a miniature pressure transducer attached to a palatal appliance. Stimuli were produced at four loudness levels: habitual, twice as loud (loud), half as loud (soft), and whisper. There was a statistically significant difference in ACP as a function of loudness for all three phonemes (p < 0.001 for each). Post hoc comparisons indicated that ACP during loud speech was significantly greater than habitual for each phoneme. ACP during soft speech was significantly less than habitual for /t/ and /d/, but not /s/. Whispered speech ACP values were significantly lower than soft for /t/ and /d/, but not /s/. The results indicate that changes in vocal loudness cause changes in ACP that are most evident for stop consonants /t, d/, and, to a lesser extent, the fricative /s/. A louder voice was associated with higher ACP. Elevated ACP may have implications for oral aerodynamics that could help explain why loud-focused clinical treatments improve articulation, although this remains to be empirically confirmed.
Discrimination Ability and Concentration Measurement Accuracy of Effective Components in Aroma Essential Oils Using Gas Sensor Arrays with Machine Learning
Authors: Itoh Toshio , Choi Pil Gyu , Masuda Yoshitake , Shin Woosuck , Arai Junichirou , Takeda Nobuaki
Published: October 2024

Aroma essential oils contain ingredients that are beneficial to the human body. A gas sensor array is required to monitor the concentration of these essential oil components to regulate their concentration by air conditioning systems. Therefore, we investigated the discrimination ability and concentration measurement accuracy of 14 effective components, including four aroma essential oils (lavender, melissa, tea tree, and eucalyptus), from a single gas sample and mixtures of two gases using sensor arrays. To obtain our data, we used two sensor arrays comprising commercially available semiconductor sensors and our developed semiconductor sensors. For machine learning, principal component analysis was used to visualize the dataset obtained from the sensor signals, and an artificial neural network was used for a detailed analysis. Our developed sensor array, which included sensors that possessed excellent sensor responses to 14 effective components and combined different semiconductive sensor principles, showed a better discrimination and prediction accuracy than the commercially available sensors investigated in this study.