bluuebunny/crossref_metadata_2025 huggingface dataset:
The Mid-Term Effect of Preservative-Free Artificial Tears Containing Hyaluronic Acid on Dry Eye Incidence after Cataract Surgery: A Retrospective Cohort Study
Authors: Lee Chia-Yi , Yang Shun-Fa , Chen Hung-Chi , Lian Ie-Bin , Huang Chin-Te , Huang Jing-Yang , Chang Chao-Kai
Published: August 2024

The aim of this study is to survey the effectiveness of preservative-free artificial tears containing hyaluronic acid (HA) on post-cataract surgery dry eye disease (DED) prevention. A retrospective cohort study was performed, and patients that received cataract surgeries were divided into either an HA group or non-HA group depending on the artificial tear they used. A total of 37 and 74 eyes were enrolled into the HA and non-HA groups, respectively, after the selection. The primary outcomes are postoperative superficial keratitis and multiple (>3) DED symptoms. The generalized linear model was utilized to calculate the adjusted odds ratio (aOR) and 95% confidence interval (CI) of primary outcomes between the two groups. There were 10 and 2 episodes of superficial keratitis in the non-HA group and HA group, respectively, and the HA group demonstrated a significantly lower incidence of superficial keratitis (p < 0.001). Moreover, 13 and 5 patients developed multiple DED symptoms in the non-HA and HA groups, and the HA group illustrated fewer multiple DED symptoms (p = 0.024). The lower preoperative tear break-up time (TBUT) was correlated with superficial keratitis in the HA group (p = 0.043), while old age, low preoperative TBUT and ocular surface staining were associated with superficial keratitis in the non-HA group (all p < 0.05). Lower preoperative TBUT was correlated with multiple DED symptoms in the HA group (p = 0.020), while female sex, low preoperative TBUT and any DED symptoms were associated with multiple DED symptoms in the non-HA group (all p < 0.05). In conclusion, the usage of preservative-free artificial tears containing HA is associated with lower postoperative DED events.
Prevention of Initial Periodontitis Is an Investment in the Future
Authors: Heikkinen Anna Maria , Raivisto Teija , Räisänen Ismo Tapani , Sorsa Timo
Published: August 2024

Background: Periodontal tissue damage is mainly caused by the active form of collagenolytic matrix metalloproteinase-8, aMMP-8, the concentration of which in the mouth rinse can be measured with a mouth rinse chairside-test. The mouth rinse chair side test can be used to identify adolescents with a risk of periodontitis. Methods: The data were collected at the Kotka Health Centre (2004–2005, N = 501 and 2014–2015, N = 47) and at the Hämeenlinna Health Centre (2017–2018, N = 125) consisting of adolescents aged 14–17. They underwent a complete periodontal examination, and some were subjected to the aMMP-8-test. Results: We identified bacterial plaques in combination with increased bleeding on probing (BOP), elevated aMMP-8 concentration, smoking and male sex as the main risk factors for initial periodontitis. Approximately 10% of adolescents had subclinical periodontitis, they were not periodontally healthy, but also not sick. They may not develop periodontitis, but they are at the risk. The aMMP-8 test positivity had a stronger association with initial periodontitis than BOP. Conclusions: In addition to identifying risk factors, these adolescents need individual prevention and, if necessary, early treatment. For the periodontal health biomarker aMMP-8, test negativity ([-], ≤20 ng/mL) should be sought.
Urine Protein to Creatinine Ratio for the Assessment of Bevacizumab-Associated Proteinuria in Patients with Gynecologic Cancers: A Diagnostic and Quality Improvement Study
Authors: Huang Kuan-Ju , Chang Wen-Chun , Chen Chi-Hau , Lin Wei-Chen , Pan William Wei-Lin , Hsieh Hao-I. , Hsieh Yu-Hsiung , Wei Lin-Hung , Sheu Bor-Ching
Published: August 2024

Proteinuria is a common adverse event arising from treatment with bevacizumab, requiring diagnostic testing via 24-h urine collection. However, this method is cumbersome. We assessed urine screenings in gynecologic cancer patients from February 2021 to May 2022. Along with a simple urine dipstick (UD), the urine microalbumin, total protein, and creatinine were measured and calculated as the urine albumin to creatinine ratio (UACR) and the urine protein to creatinine ratio (UPCR), which were further adjusted through the Modification of Diet in Renal Disease and Chronic Kidney Disease Epidemiology Collaboration equations to be estimated and correlated with 24-h urine total protein content. The incremental cost-effectiveness ratio was used for cost analysis. There were 129 urine samples from 36 patients. The sensitivity and specificity for the UACR were 0.56 and 0.97, and for the UPCR, 0.71 and 0.88, respectively. The 24-h TP correlated strongly with the UACR (r = 0.75; p < 0.001) and UPCR (r = 0.79; p < 0.001) and fair for the simple UD (r = 0.35; p < 0.001). The UPCR saves one unnecessary 24-h urine test for less than a dollar compared to a simple UD. The results indicate that using the UPCR could enhance diagnostic accuracy, lower costs, and reduce unnecessary 24-h urine sampling.
Excessive Daytime Sleepiness in Hypertensive Patients: The Role of Major Depressive Disorder
Authors: Younes Alexandre , Point Camille , Wacquier Benjamin , Lanquart Jean-Pol , Hein Matthieu
Published: August 2024

There is a special relationship between major depressive disorder and excessive daytime sleepiness. However, given the negative impact of excessive daytime sleepiness on life quality and cardiovascular outcome in hypertensive patients, the objective of this study was to investigate the potential role played by major depressive disorder in the occurrence of this complaint for this particular subpopulation. Data from 1404 hypertensive patients recruited from the Sleep Unit’s polysomnographic recordings database were analyzed. A score >10 on the Epworth Sleepiness Scale was used to define excessive daytime sleepiness in this study. Logistic regression analyses were performed to investigate the risk of excessive daytime sleepiness associated with major depressive disorder in hypertensive patients. Excessive daytime sleepiness was frequent (40.0%) in our sample of hypertensive patients. After adjustments for major confounding factors, multivariate logistic regression analyses demonstrated that unlike remitted major depressive disorder, only current major depressive disorder was associated with a higher risk of excessive daytime sleepiness in hypertensive patients. Given this potential implication of current major depressive disorder in the occurrence of excessive daytime sleepiness for hypertensive patients, it is therefore essential to achieve the complete remission of this psychiatric disorder to avoid negative consequences associated with this complaint in this particular subpopulation.
Post-Thrombectomy Subarachnoid Hemorrhage: Incidence, Predictors, Clinical Relevance, and Effect Modulators
Authors: Chen Huanwen , Colasurdo Marco , Khunte Mihir , Malhotra Ajay , Gandhi Dheeraj
Published: August 2024

Background: Subarachnoid hemorrhage (SAH) following endovascular thrombectomy (EVT) is a poorly understood phenomenon, and whether it is associated with clinical detriment is unclear. Methods: This was an explorative analysis of a national database of real-world hospitalizations in the United States. Patients who underwent EVT were included. Patients were divided into SAH and non-SAH groups, and hospitalization outcomes were compared using multivariable logistic regression models. Regression models were also used to identify significant predictors for post-EVT SAH, and significant modulators of SAH’s association with hospitalization outcomes were also assessed. Results: A total of 99,219 EVT patients were identified; 6174 (6.2%) had SAH. Overall, SAH was independently associated with increased odds of in-hospital mortality (21.5% vs. 10.6%, adjusted OR 2.53 [95%CI 2.23–2.87], p < 0.001) and lower odds of routine discharge to home with self-care (18.2% vs. 28.0%, aOR 0.58 [95%CI 0.52–0.65], p < 0.001). Distal/medium vessel occlusion (DMVO), coagulopathy, angioplasty or stenting, concurrent intraparenchymal hemorrhage (IPH), and female sex were associated with higher odds of SAH. DMVO was associated with particularly heightened risk of death (31.8% vs. 7.9%, aOR 6.99 [95%CI 2.99 to 16.3], p < 0.001), which was an effect size significantly larger than other sites of vascular occlusion (interaction p > 0.05). Conclusion: SAH is an uncommon but likely clinically detrimental post-EVT complication. DMVO, coagulopathy, angioplasty or stenting, concurrent IPH, and female sex were independently associated with higher odds of post-EVT SAH. SAH associated with DMVO-EVT may be particularly harmful.
Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification
Authors: Prabhakar Sunil Kumar , Rajaguru Harikumar , Won Dong-Ok
Published: August 2024

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%.
VerFormer: Vertebrae-Aware Transformer for Automatic Spine Segmentation from CT Images
Authors: Li Xinchen , Hong Yuan , Xu Yang , Hu Mu
Published: August 2024

The accurate and efficient segmentation of the spine is important in the diagnosis and treatment of spine malfunctions and fractures. However, it is still challenging because of large inter-vertebra variations in shape and cross-image localization of the spine. In previous methods, convolutional neural networks (CNNs) have been widely applied as a vision backbone to tackle this task. However, these methods are challenged in utilizing the global contextual information across the whole image for accurate spine segmentation because of the inherent locality of the convolution operation. Compared with CNNs, the Vision Transformer (ViT) has been proposed as another vision backbone with a high capacity to capture global contextual information. However, when the ViT is employed for spine segmentation, it treats all input tokens equally, including vertebrae-related tokens and non-vertebrae-related tokens. Additionally, it lacks the capability to locate regions of interest, thus lowering the accuracy of spine segmentation. To address this limitation, we propose a novel Vertebrae-aware Vision Transformer (VerFormer) for automatic spine segmentation from CT images. Our VerFormer is designed by incorporating a novel Vertebrae-aware Global (VG) block into the ViT backbone. In the VG block, the vertebrae-related global contextual information is extracted by a Vertebrae-aware Global Query (VGQ) module. Then, this information is incorporated into query tokens to highlight vertebrae-related tokens in the multi-head self-attention module. Thus, this VG block can leverage global contextual information to effectively and efficiently locate spines across the whole input, thus improving the segmentation accuracy of VerFormer. Driven by this design, the VerFormer demonstrates a solid capacity to capture more discriminative dependencies and vertebrae-related context in automatic spine segmentation. The experimental results on two spine CT segmentation tasks demonstrate the effectiveness of our VG block and the superiority of our VerFormer in spine segmentation. Compared with other popular CNN- or ViT-based segmentation models, our VerFormer shows superior segmentation accuracy and generalization.
Influence of Bridging Stent Graft Implantation into the Renal Artery during Complex Endovascular Aortic Procedures on the Renal Resistance Index
Authors: Reitnauer Daniela , Stoklasa Kerstin , Dueppers Philip , Reutersberg Benedikt , Zimmermann Alexander , Stadlbauer Thomas H. W.
Published: August 2024

Comparative sonographic examination of the renal resistance index (RRI) can provide evidence of renal artery stenosis. The extent to which the RRI is changed after stent graft implantation is not known. The aim of this study was to investigate the influence of stent graft implantation into non-diseased renal arteries during endovascular treatment of pararenal aortic aneurysms on the RRI. Sonographic examinations of the kidneys were conducted using a GE ultrasound system. The evaluation was performed according to the European Society for Vascular Surgery (ESVS) 2D standard criteria. RRI values were determined in consecutive patients on the day before and after stent graft implantation and compared for each kidney. A total of 32 consecutive patients (73.9 ± 8.2 years, 5 females, 27 males) were treated with a fenestrated or branched aortic stent graft including bridging stent graft implantations into both renal arteries and received pre- and postinterventional examinations. Sonomorphologically, the examined kidneys were inconspicuous. The arborisation of the renal perfusion was preserved pre- and post-implantation. The RRI did not differ (0.66 ± 0.06 versus 0.67 ± 0.07; p = ns). Successful stent graft implantation into non-stenosed renal arteries did not lead to a relevant change in RRI. Therefore, the RRI is a suitable tool for assessing renal perfusion after fenestrated or branched endovascular aortic therapy.
Reconnecting Anisometropic Amblyopic Eyes to the Cortex: VEP-Based Auditory Biofeedback
Authors: Nemeș-Drăgan Iulia-Andrada , Țîpcu Alexandru , Hapca Mădălina Claudia , Pașcalău Raluca , Nicoară Simona-Delia
Published: August 2024

Objective: This study aimed to evaluate the effectiveness of a visual rehabilitation method for anisometropic amblyopia that uses visual evoked potential (VEP) parameters and sound biofeedback to increase objectiveness. Design: an observational, case-controlled trial. Setting: Ophthalmology Clinic, Emergency County Hospital, Cluj-Napoca, Romania. Participants: Sixteen subjects with amblyopic anisometropia, aged 15–57, and sixteen controls, aged 24–33, were included. Interventions: Subjects were divided into two groups. The control group, composed of normal-vision subjects, and the amblyopic group received 10 training sessions. The rehabilitation program lasted 10 min, took place twice a week, and ran over five consecutive weeks. During each session, the subjects were asked to fixate on a target on the computer screen and were instructed to keep the fixation and maintain the sound of the biofeedback at high frequency. Main Measures: The study assessed the main visual parameters at baseline, after 10 sessions, and 1, 3, 6, and 12 months after treatment. Performance was evaluated by measuring visual acuity, contrast sensitivity, and reading velocity (words/minute). Results: In the experimental group, mean BCVA improved with two rows, which means an improvement in the LogMARLogMAR scale with an average of nine letters. These values tended to be maintained over time. Both groups showed better reading velocities after training, but this parameter has undergone large variability during follow-ups. Contrast sensitivity was also improved and stable. Conclusions: Visual rehabilitation with the Retimax Vision Trainer can improve visual performance in patients with amblyopia after the critical period, an improvement that is maintained in most cases for at least one year after treatment.
Asthma and Hyperglycemia: Exploring the Interconnected Pathways
Authors: Narendra Dharani K. , Khurana Sandhya
Published: August 2024

The interplay between asthma and glucose metabolism disorders, such as hyperglycemia, has gained increasing attention due to the potential exacerbation of asthma symptoms and severity. This review explores the complex relationship between hyperglycemia and asthma, emphasizing the pathophysiological links, the impact of glucose metabolism disorders on asthma, and the effects of asthma medications on glucose levels. Hyperglycemia, often induced by asthma treatments like corticosteroids, has been associated with an increased risk of asthma exacerbations. This review delves into the pathophysiology underlying this association, highlighting the role of insulin resistance, metabolic syndrome, and obesity in both the development and management of asthma. Metabolic syndrome, characterized by abdominal obesity and hyperglycemia, independently increases the risk of worsening respiratory symptoms and asthma. Furthermore, this review examines the influence of various antidiabetic medications on asthma outcomes. Biguanides, like metformin, have shown promise in improving asthma outcomes in patients with type 2 diabetes mellitus and asthma. However, other medications have mixed results regarding their impact on asthma control and lung function. Considering these findings, this review advocates for further research into the role of metabolic pathways in asthma management. It calls for comparative studies and the inclusion of asthma-related outcomes in clinical trials of antidiabetic drugs to better understand their potential benefits for individuals with obesity and concurrent asthma.
Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study
Authors: Badahman Fatima , Alsobhi Mashael , Alzahrani Almaha , Chevidikunnan Mohamed Faisal , Neamatallah Ziyad , Alqarni Abdullah , Alabasi Umar , Abduljabbar Ahmed , Basuodan Reem , Khan Fayaz
Published: August 2024

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: Using new software called Therapha, this study aimed to assess the accuracy level of artificial intelligence as a Clinical Decision Support System (CDSS) compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in three stages. Firstly, a case series was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Finally, to determine the accuracy of the newly developed software, a cross-sectional study was undertaken involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusions: The study’s findings revealed that CDSS using Therapha has a reasonable level of efficacy, and this can be utilized clinically to acquire a faster and more accurate screening of patients with lumbar disc herniation.
Utility of Extraction-Free SARS-CoV-2 Detection by RT–qPCR for COVID-19 Testing in a Resource-Limited Setting
Authors: Yalley Akua K. , Ahiatrogah Selasie , Moro Iddrisu I. , Gmagna Peter , Yankson Isaac K. , Kafintu-Kwashie Anna A. , Nii-Trebi Nicholas I.
Published: August 2024

The COVID-19 epidemic had a profound impact on global health and the economy and Ghana was no exception to its far-reaching consequences. Regarding detection of the causative agent—the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), reverse-transcription–qPCR (RT–qPCR) is widely recognized as a very sensitive and reliable diagnostic technique used globally. There are, however, high operational costs in acquiring test kits, equipment, and accessories for RT–qPCR testing, which pose significant challenges in resource-limited settings. Hence, this proof-of-concept study set out to develop a more affordable COVID-19 protocol for use in low or lower-middle-income settings, such as Ghana, that would bypass the traditional extraction process using inexpensive reagents and evaluate the possibility of processing samples collected using wooden shaft swabs. Several less expensive media were used for the extraction-free process. Results demonstrated that direct RT–qPCR assay after 5 min heat inactivation of virus at 95 °C in 0.1× PBS or molecular grade water resulted in viral detection with quantification cycle (Cq) values that are comparable to results obtained following the extraction process. Also, wooden shaft swabs could be used for sampling if incubation times are kept to less than 6 h. The study demonstrates that extraction-free protocols are one way to minimize the cost of COVID-19 testing by RT–qPCR.
Military Image Captioning for Low-Altitude UAV or UGV Perspectives
Authors: Pan Lizhi , Song Chengtian , Gan Xiaozheng , Xu Keyu , Xie Yue
Published: August 2024

Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs.
A Minimal Solution Estimating the Position of Cameras with Unknown Focal Length with IMU Assistance
Authors: Yan Kang , Yu Zhenbao , Song Chengfang , Zhang Hongping , Chen Dezhong
Published: August 2024

Drones are typically built with integrated cameras and inertial measurement units (IMUs). It is crucial to achieve drone attitude control through relative pose estimation using cameras. IMU drift can be ignored over short periods. Based on this premise, in this paper, four methods are proposed for estimating relative pose and focal length across various application scenarios: for scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be computed from four-point correspondences; for planar motion scenarios where the camera’s focal length varies between adjacent moments and is unknown, the relative pose and focal length can be determined from three-point correspondences; for instances of planar motion where the camera’s focal length is equal between adjacent moments and is unknown, the relative pose and focal length can be calculated from two-point correspondences; finally, for scenarios where multiple cameras are employed for image acquisition but only one is calibrated, a method proposed for estimating the pose and focal length of uncalibrated cameras can be used. The numerical stability and performance of these methods are compared and analyzed under various noise conditions using simulated datasets. We also assessed the performance of these methods on real datasets captured by a drone in various scenes. The experimental results demonstrate that the method proposed in this paper achieves superior accuracy and stability to classical methods.
Performance Estimation of Fixed-Wing UAV Propulsion Systems
Authors: Etewa Mohamed , Hassan Ahmed F. , Safwat Ehab , Abozied Mohammed A. H. , El-Khatib Mohamed M. , Ramirez-Serrano Alejandro
Published: August 2024

The evaluation of propulsion systems used in UAVs is of paramount importance to enhance the flight endurance, increase the flight control performance, and minimize the power consumption. This evaluation, however, is typically performed experimentally after the preliminary hardware design of the UAV is completed, which tends to be expensive and time-consuming. In this paper, a comprehensive theoretical UAV propulsion system assessment is proposed to assess both static and dynamic performance characteristics via an integrated simulation model. The approach encompasses the electromechanical dynamics of both the motor and its controller. The proposed analytical model estimates the propeller and motor combination performance with the overarching goal of enhancing the overall efficiency of the aircraft propulsion system before expensive costs are incurred. The model embraces an advanced blade element momentum theory underpinned by the development of a novel mechanism to predict the propeller performance under low Reynolds number conditions. The propeller model utilizes XFOIL and various factors, including post-stall effects, 3D correction, Reynolds number fluctuations, and tip loss corrections to predict the corresponding aerodynamic loads. Computational fluid dynamics are used to corroborate the dynamic formulations followed by extensive experimental tests to validate the proposed estimation methodology.
Characterization of Strategic Deconflicting Service Impact on Very Low-Level Airspace Capacity
Authors: Liu Zhiqiang , Munoz-Gamarra Jose Luis , Ramos Gonzalez Juan José
Published: August 2024

European airspace is poised for significant transformation as it prepares to accommodate a new class of unmanned traffic that will reshape the transport of people and goods. Unmanned aerial vehicle traffic will introduce a new level of services, but it remains unclear how safety and operators’ time flexibility in flight planning will impact capacity. This study focuses on the impact of strategic deconflicting services on the capacity of the very low-level airspace, a critical area in the future management of unmanned aerial vehicle traffic. The results validate the assumptions regarding the roles of airspace managers and drone operators through simulation studies; highlight the limitations of the first come, first served policy; and propose a batch policy as a potential optimization strategy for future airspace capacity management. The forecasting model developed using regression techniques provides a general method for predicting airspace capacity under specific conditions, contributing to the safe and efficient integration of unmanned aerial vehicles into European airspace.
Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification
Authors: Yan Yongzhao , Cao Huazhen , Zhang Boyang , Ni Wenjun , Wang Bo , Ma Xiaoping
Published: August 2024

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.
A Stochastic Drone-Scheduling Problem with Uncertain Energy Consumption
Authors: He Yandong , Zheng Zhong , Li Huilin , Deng Jie
Published: August 2024

In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case study results indicate the superiority of our algorithm over genetic algorithms. Additionally, a comparison between deterministic and stochastic models suggests that considering the uncertainty in energy consumption can significantly enhance the average returns of unmanned aerial vehicle scheduling systems.
Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms
Authors: Fallah Mortezanejad Seyedeh Azadeh , Mohammad-Djafari Ali
Published: August 2024

In any Bayesian computations, the first step is to derive the joint distribution of all the unknown variables given the observed data. Then, we have to do the computations. There are four general methods for performing computations: Joint MAP optimization; Posterior expectation computations that require integration methods; Sampling-based methods, such as MCMC, slice sampling, nested sampling, etc., for generating samples and numerically computing expectations; and finally, Variational Bayesian Approximation (VBA). In this last method, which is the focus of this paper, the objective is to search for an approximation for the joint posterior with a simpler one that allows for analytical computations. The main tool in VBA is to use the Kullback–Leibler Divergence (KLD) as a criterion to obtain that approximation. Even if, theoretically, this can be conducted formally, for practical reasons, we consider the case where the joint distribution is in the exponential family, and so is its approximation. In this case, the KLD becomes a function of the usual parameters or the natural parameters of the exponential family, where the problem becomes parametric optimization. Thus, we compare four optimization algorithms: general alternate functional optimization; parametric gradient-based with the normal and natural parameters; and the natural gradient algorithm. We then study their relative performances on three examples to demonstrate the implementation of each algorithm and their efficiency performance.
Prognostic Properties of Instantaneous Amplitudes Maxima of Earth Surface Tremor
Authors: Lyubushin Alexey , Rodionov Eugeny
Published: August 2024

A method is proposed for analyzing the tremor of the earth’s surface, measured by GPS, in order to highlight prognostic effects. The method is applied to the analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of the principal components from the clusters, with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes, averaged over the number of clusters and the times of earthquakes in the vicinity of Japan with minimum magnitude thresholds of 5.5 for the time interval 2012–2023. It is shown that the sequence of the largest local maxima of instantaneous amplitudes significantly more often precedes the moments of time of earthquakes (roughly speaking, has an “influence”) than the reverse “influence” of earthquakes on the maxima of amplitudes.
F-Deepwalk: A Community Detection Model for Transport Networks
Authors: Guo Jiaao , Liang Qinghuai , Zhao Jiaqi
Published: August 2024

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified.
Assessing the Effects of Exchange Rate Volatility on Zambia’s Economic Growth: Evidence from ARDL and NARDL Models
Authors: Mwiya Tabo , Simaundu Briven Muchanga , Nyau Maria , Phiri Joseph
Published: August 2024

This study investigated the interplay between exchange rate volatility, inflation rates, and real interest rates on Zambia’s economic growth from 1992 to 2022, utilizing annualized time series data. The study was necessitated by the limited published literature and relatively varying findings on the variables’ relationships in resource-dependent countries, such as Zambia. Diagnostic tests, including stationarity and co-integration analyses, were employed to determine integration orders and potential long-run relationships. The linear and nonlinear autoregressive distributed lag models were employed to assess short- and long-run dynamics of the variables on economic growth. The results established a positive short-run relationship between inflation rates and Gross Domestic Product (GDP) growth in the linear autoregressive distributive lag model, while an inverse relationship was observed in the nonlinear autoregressive distributive lag model, suggesting that negative shocks in inflation rates had a highly significant positive impact on economic growth. Furthermore, interest rates exhibited a positive relationship with economic growth, further suggesting that positive shocks had a greater significant direct effect on economic growth in comparison to negative shocks in the short and long run, respectively. Finally, exchange rates in both models exhibited an inverse relationship with economic growth irrespective of positive or negative shocks in the long run, highlighting the adverse effect of exchange rate volatility on economic growth prospects in developing countries, such as Zambia. The speed of adjustment to convergence following any disruptions was determined to be 75.18% (ARDL) and 89.19% (NARDL), highlighting relatively fast speeds of adjustments from any short-run disruptions. Notably, some of the policy recommendations included regular assessments of exchange rate volatility influences on import prices, domestic inflation, and production costs in key sectors. Additionally, the implementation of currency hedging options and forwards as well as bulking of foreign exchange reserves will ensure the stability of exchange rates against other major currencies in various economic conditions.
Emerging Economies: An Investigation of National Culture and Sustainable Development
Authors: Cahyadi Afriyadi , Raneo Agung Putra , Marwa Taufiq , Adam Mohamad , Szabó Katalin
Published: August 2024

The current study highlights the significance of national culture in advocating sustainable development, aligning with the goals outlined in the 2030 Agenda. It draws upon theories from various perspectives, including Classical Marxism, System Justification, Weber and the Ethic of Protestantism, and Postmaterialism. By examining the relationship between national culture and sustainable development, particularly in emerging economies, it addresses a gap in existing discourse. Using data from The Culture Factor Group website and the Global Sustainable Competitiveness Index provided by Solability in 2023, this study investigates how six of Hofstede’s cultural dimensions relate to the sustainable competitiveness of emerging economies. The findings, based on a sample of 21 emerging economies sourced from diverse online platforms, indicate a significant relationship between national culture and sustainable development, particularly in terms of sustainable competitiveness. This study underscores the importance of recognizing culture as a key factor in achieving sustainable development goals, supporting its inclusion as a fundamental aspect in shaping policies and strategies aimed at fostering sustainable development in emerging economies.
Federated or Non-Federated Sports: The Influence on Children, the Youth Population and Family Life
Authors: Álvarez Muñoz José Santiago , Hernández Prados Mª Ángeles , Palazón Segura César
Published: August 2024

Family time must be combined with the personal time of each of its members, with special importance regarding children due to the need for attention and monitoring by parents of their performance. Within the leisure of minors in the home, physical activity and sports are the most in-demand typologies, thus some of them participate in federated clubs, attending a more institutionalized or professional sport, while others develop in an environment based on entertainment. In this way, depending on the demand for these activities, it leads to a series of different consequences in the family environment. The main objective of this study is to establish whether or not children’s attendance at federated sports affects family dynamics. A total of 224 Spanish families with children from 6 to 18 years of age who practice sport activities participated and completed a questionnaire with 36 items about the family’s involvement in children’s sports. The results show that parents with children participating in federated sports have a more positive perception of their children’s sports practices; they provide greater support to their children regarding their performance and see this activity as an excellent opportunity for education in values. However, they do identify it as an obstacle to the development of family leisure practices, resulting in an element that conditions family reconciliation. In this way, there is a need to establish alliances between families and sports professionals, providing family participation bodies in sports clubs and providing a better conciliation time to accommodate family time.
Schoolchildren’s Thinking on the Subject and Teachers of Physical Education According to Gender and Educational Grade
Authors: González-Gutiérrez Iván , López-García Sergio , Barcala-Furelos Martín , Mecías-Calvo Marcos , Navarro-Patón Rubén
Published: August 2024

The objective of this study was to understand the perceptions of Primary (PrE) and Secondary Education (SE) students in Cantabria about the subject of physical education (PE) and their teachers. A descriptive, comparative relational analytical cross-sectional design was used. A total of 1164 students (387 from PrE and 777 from SE) answered an ad hoc questionnaire on their satisfaction with their PE classes (eight items) and teachers (nine items). The results indicate that the PrE students were more satisfied than the SE students regarding the following statements about PE: more hours per week (p < 0.001); classes and subjects that I like the most (p < 0.001); I enjoy and have fun (p < 0.001); interesting and motivating (p < 0.001); and useful for life (p < 0.001) and easy (p = 0.006). The boys’ responses reflected higher values than the girls’ on all the previous items (p < 0.005). Regarding their thinking about their PE teachers, statistically significant differences were found in the PrE students’ responses compared with those of the SE students for the following: explains well and is easily understood (p = 0.006); stimulates and encourages participation (p = 0.050); cares and is interested in the students (p = 0.031); treat boys and girls the same (p < 0.001); and I prefer a woman because she understands me better (p = 0.021). Therefore, the male and primary-stage students showed more positive attitudes towards PE. In general, there was a favorable disposition towards PE and towards teachers, which must be taken into account to achieve SDG 4.
Content and Languages Integration: Pre-Service Teachers’ Culturally Sustaining Social Studies Units for Emergent Bilinguals
Authors: Son Minhye
Published: August 2024

This qualitative study examines how pre-service teachers incorporate asset-based, multilingual and equity-focused pedagogies, prioritizing students’ funds of knowledge and advocating for culturally relevant and sustaining approaches. Through an analysis of 92 social studies unit plans developed by pre-service teachers in the final semester of their teacher credential program, the study highlights students’ efforts in integrating locally relevant curriculum, translanguaging, and inclusive pedagogies in their teaching. The study emphasizes the transformative potential of integrating linguistic and cultural resources into content area teaching to cultivate inclusive and equitable learning environments for emergent bilinguals.
Incorporating the Developmental Considerations in a Proposed Forgiveness Education Intervention for Suicidal Adolescents
Authors: Wang Xu Jiahe , Huang Shih-Tseng Tina , Enright Robert D.
Published: August 2024

This paper first explores the risk factors associated with adolescents’ suicidal thoughts, attempts, and behaviors within different developmental domains and in the environment, using the developmental systems theory (DST). The most frequently identified risk factor for suicide, psychological pain, is examined in terms of its nature, expression, and possible cause. This paper criticizes the current empirical research of suicidal adolescents’ interventions on two issues: (1) lack of concern for fundamental issues in lifespan development and (2) inadequate treatment of the emotional needs resulting from the psychological pain in adolescents. Finally, this paper proposes forgiveness education intervention as targeting the emotional needs, particularly psychological pain, for suicidal adolescents.
Successful School Principalship: A Meta-Synthesis of 20 Years of International Case Studies
Authors: Sun Jingping , Day Christopher , Zhang Rong , Zhang Huaiyue , Huang Ting , Lin Junqi
Published: August 2024

This meta-synthesis crosses international borders by analyzing successful principalship across nine countries. With careful, detailed, systematic, and analytic synthesis, it critically explores research-informed understandings of successful principal leadership over the last 20 years. The paper is an interim report of reviews of the 85 articles and 23 chapters produced by the International Successful School Principal Project (ISSPP), the largest, longest-running international research project in this field. In that project, success is defined as the principal’s ability to grow and sustain students’ academic, personal, and social developments and achievements over time. The results add to existing knowledge of how successful school principals achieve success by examining high-quality, published case study evidence from Australia, Cyprus, Israel, New Zealand, Norway, Sweden, Spain, the UK, and the US. Results suggest common sets of core values, qualities, and practices among school principals, regardless of national contexts, conditions, or cultures. Because the research design is multi-perspective and multi-layered, the case studies also show significant, context-informed differences in timing, pace, and extent of application of improvement strategies. Thus, the findings of this synthesis paint a thorough and cohesive picture of who successful principals are and what they do over time at the global level to achieve success, as they navigate values-led school success in different contexts and improvement phases within complex internal and external ecological chrono-, macro-, exo-, meso-, and microsystems.
Mobile Smartphones as Tools for ICT Integration in Geography Teaching
Authors: Gubevu Bongumusa Welcome Selby , Mncube Vusumzi Sthembiso
Published: August 2024

This article seeks to reflect on the opportunities that mobile smartphones (MSPs) present as ICT integration tools in teaching geography. The more extensive study, underpinned by the Professional Development Framework for Digital Learning (PDFDL) in ICT integration, employed a qualitative research approach. Lensed by the Professional Development Framework for Digital Learning (PDFDL), the article used the qualitative approach to garner insights from the participants regarding using MSPs as tools to integrate ICT in geography teaching. Data collection tools included interviews, observations, and document reviews. Researchers sampled (n = 4) schools, interviewed and observed (n = 13) teachers, and interviewed (n = 10) learners and (n = 8) parents in the province of KwaZulu-Natal. Furthermore, they used a purposive sampling technique to access the participants, basing the research on the premise that MSPs promote virtual reality for an array of learners. As the findings revealed, although some participants viewed the use of MSPs as a distractor in the learning space, teachers felt compelled to heed the call to modify their teaching pedagogies, such that they integrated mobile phones fruitfully in their teaching. The findings further revealed that such a paradigm shift would benefit homeschooling and facilitate a dual teaching mode at learning institutions. Curriculum planners are responsible for helping teachers accept that uncertainty is the only certainty about the future, considering the volatility, uncertainty, complexity, and augmentation (VUCA) challenges brought on by the COVID-19 pandemic. Extended lockdown periods accelerated the use of MSPs in teaching, requiring every stakeholder in the educational space to become a life-long learner by using a range of technologies and platforms.