bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in IL-6, IL-12, IL-5, IL-10 and TGF-β1 Concentration in Patients with Treatment-Resistant Schizophrenia (TRS) Following Electroconvulsive Therapy (ECT)—A Pilot Study
Authors: Szota Anna Maria , Radajewska Izabela , Ćwiklińska-Jurkowska Małgorzata , Lis Kinga , Grudzka Przemysław , Dróżdż Wiktor
Published: November 2024

Background/Objectives: Treatment-resistant schizophrenia (TRS) may be considered as a neuro-immune disorder. Electroconvulsive therapy (ECT) remains an important therapeutic option for patients with TRS, however, its impact on cytokine profile is barely investigated. Therefore, this study attempts to establish associations between serum cytokines IL-6, IL-12, IL-5, IL-10 and TGF-β1 changes (pre- and post-ECT) and the effectiveness of ECT in TRS patients. The second aim is to search for correlations between serum concentrations of the above specified cytokines and psychometric assessments of clinical schizophrenia symptoms. Methods: The cytokine concentrations were measured in eight TRS patients on psychopharmacological treatment prior to and following ECT and in 13 control subjects. Psychopathology assessment was based on the Positive and Negative Syndrome Scale (PANSS). Results: Prior to ECT, IL-10 concentration was significantly higher in TRS patients, while IL-5 was decreased in comparison to the controls. A significant concentration decrease in the pro-inflammatory cytokines IL-6 (p = 0.012), IL-12 (p = 0.049) and anti-inflammatory IL-10 (p = 0.012) post-ECT vs. pre-ECT was observed, whereas concentrations of IL-5 and TGF-β1 did not significantly change. Also, a significant decrease in schizophrenia symptoms measured by the PANSS post-ECT was found. Furthermore, the pattern of correlations between PANSS scores and cytokine concentrations was different when comparing levels pre- and post-ECT. Additionally, correlations between changes in PANSS scores and cytokine concentrations were found. Conclusions: These results may indicate the probable impact of electroconvulsive therapy on the balance between pro- and anti-inflammatory cytokines, which may correspond to a neurobiological therapeutic effect of ECT in TRS patients.
Advanced Parameters of Myocardial Strain and Cardiac Biomarkers Indicate Subclinical Systolic Myocardial Dysfunction in Patients with Systemic Lupus Erythematous
Authors: Kadoglou Nikolaos P. E. , Dimopoulou Alexandriani , Korakianitis Ioannis , Parperis Konstantinos
Published: November 2024

Background: Systemic lupus erythematosus (SLE) is characterized by inflammation and cardiovascular complications. Our study aimed to investigate subclinical and early indicators of systolic myocardial dysfunction in SLE patients using advanced echocardiographic methods and biomarkers. Methods: In this cross-sectional study, we enrolled 102 SLE patients without known cardiac impairment and 51 healthy controls. Demographics, disease characteristics, laboratory results, disease activity (SLEDAI), and organ damage (SDI) indices were recorded. Left ventricular global longitudinal strain (GLS) and myocardial work indices were assessed by utilizing speckle tracking echocardiography. In addition, high-sensitivity C-reactive protein (hsCRP), high-sensitivity troponin (hsTn), and N-terminal-pro B-type natriuretic peptide (NT-proBNP) levels were measured in blood samples. Results: In comparison with controls, SLE patients had significantly higher GLS (−19.94 ± 2.71% vs. −21.15 ± 1.55%, p < 0.001) and global wasted work (GWW) (94 ± 71 mmHg% vs. 71 ± 49 mmHg%, p = 0.025). Notably, NT-proBNP and hsTn were threefold and twofold higher in the SLE group compared with the control group, respectively (p < 0.001). Within the SLE cohort, in patients with at least moderate disease activity (SLEDAI ≥ 4), both biomarkers were significantly more elevated than those with low disease activity (SLEDAI < 4). Notably, hsTn levels remained within the normal range. Conclusions: Advanced echocardiographic parameters combined with specific biomarkers have a promising role in detecting systolic dysfunction in SLE patients, potentially enabling timely interventions to mitigate cardiovascular risk
Severe Malaria in Angola: The Clinical Profile and Disease Outcome Among Adults from a Low-Endemic Area
Authors: Morais Inês , Rodrigues Soraia , Mas Aida , Escalon Serguei , Borrego Adalzira , Nogueira Fatima , Antunes Maria Lina
Published: November 2024

Background/Objectives: Severe malaria poses a significant public health concern in Angola, particularly among adults. This study assessed the clinical manifestations and outcomes of severe Plasmodium falciparum malaria in adult patients admitted to Hospital Central Dr. António Agostinho Neto of Lubango (HCL), Angola. Methods: The study retrospectively reviewed medical records of patients over 14 years old admitted with severe malaria during the first quarter of 2021 and 2022, coinciding with the peak transmission season. The World Health Organization (WHO) criteria were used to clarify the disease severity. The cohort included 640 patients—167 in 2021 and 473 in 2022—distributed across the following departments: the Intensive Care Unit (ICU; n = 81), Medicine (MED; n = 458) and Infectiology (INF; n = 101). Results: The median age was 26 years and 59.4% were males. Renal impairment was the most frequent severe manifestation, affecting 37.4% of cases. The mortality rate across the study period was 7%, showing a notable decrease from 10.2% in 2021 to 5.9% in 2022. The higher mortality rate in 2021 may reflect the impact of the COVID-19 pandemic, which limited hospital access and delayed care, resulting in more critical cases being admitted at a later stage. In 2022, with reduced COVID-19 pressures, earlier access to treatment may have improved outcomes, contributing to the lower mortality rate. Conclusions: This study emphasizes the need to assess the clinical burden of severe malaria in low-endemic regions, where shifting patterns may signal emerging threats such as antimalarial drug resistance. Further research is essential to optimize control strategies and strengthen surveillance systems, reducing morbidity and mortality.
Simultaneous Localization and Mapping Methods for Snake-like Robots Based on Gait Adjustment
Authors: Tang Chaoquan , Zhang Zhipeng , Sun Meng , Li Menggang , Tang Hongwei , Bai Deen
Published: November 2024

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot’s motion on the sensors. Thirdly, a visual–inertial tightly coupled SLAM method for the snake robot’s serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots.
Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm
Authors: Zhang Bihui , Li Zhuqi , Li Bingjie , Zhan Jingbo , Deng Songtao , Fang Yi
Published: November 2024

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method’s robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference.
Emotional Risk Factors, Rumination, and Self-Criticism in Relation to Suicidal Ideation Among Chinese Depressive Outpatients
Authors: Lo Barbara Chuen Yee , Cheng Sophie Kai Lam
Published: November 2024

Previous studies indicated that individuals with major depressive disorder (MDD) are at high risk of suicide, making the identification of risk factors in suicidal depressive outpatients essential for developing effective prevention and treatment strategies. This study aims to (1) identify emotional risk factors and (2) examine the relationship between cognitive markers—including rumination and self-criticism—and suicidal ideation among depressive outpatients. A total of 165 Chinese clinical outpatients with depression were recruited from local hospitals in Hong Kong. In our sample, 68% of patients with moderate depression and 87.5% of patients with severe depression reported experiencing suicidal thoughts. The results from the logistic regression revealed a significant interaction between rumination and self-critical thoughts in relation to suicidal ideation. Specifically, the ruminative patients with self-critical thoughts were more likely to have suicidal thoughts than those without self-critical thoughts. These findings align with the cognitive model of suicide, suggesting that maladaptive information processing and negative cognitive content are associated with an increased risk of suicide. The current findings have clinical implications for the future development of more specific and accurate mental health assessment, preventive programs, and psychotherapies for depressive outpatients.
Improving Building Floor Acoustics with Innovative Inorganic Sound Insulation Coating
Authors: Li Jie , Meng Qinglin , Lu Haowei , Xu Genyu
Published: November 2024

Floor impact sound insulation is essential for improving living environments and has become a mandatory requirement for green buildings in Southern China. This study introduces an innovative inorganic sound insulation coating technology for enhancing building floor acoustic performance. Through comprehensive laboratory experiments and field tests, we evaluated inorganic coatings of 3 mm and 5 mm thickness, comparing their performance against traditional methods, including organic coatings and soundproof mortar. Standardized impact sound pressure level measurements, conducted in accordance with the China GB/T 50121 standard, demonstrated significant acoustic improvements. Laboratory testing revealed impact sound reductions of 6–7 dB and 9–10 dB for the 3 mm and 5 mm inorganic coatings, respectively, while field applications of the 3 mm coating achieved an average reduction of 14.3 dB. The inorganic coating exhibited superior performance characteristics compared to both organic coatings and soundproof mortar in terms of sound insulation efficiency, fire resistance, and application feasibility, demonstrating particularly effective attenuation in the mid- to high-frequency range. This investigation presents an innovative, cost-effective, and environmentally sustainable solution for improving floor sound insulation in green buildings.
A Comprehensive Evaluation Method for Assessing the Seismic Resilience of Building Sites Based on the TOPSIS Model and the Entropy Method
Authors: Wang Yuting , Peng Da , Bo Tao , Zhao Xinlong , Bo Jingshan
Published: November 2024

The seismic design philosophy based on resilience represents the latest development in structural seismic design theory and is currently the most advanced concept in the field of seismic design research. A building site serves as the foundation and environment for structures, and evaluating its seismic resilience is a crucial aspect of designing and assessing buildings’ seismic resilience. To meet the needs of evaluating building seismic resilience, the concept of seismic resilience for building sites is introduced in this paper. This evaluation is approached from three dimensions: seismic action, site seismic capacity, and site recovery capability. An indicator system for evaluating the seismic resilience of building sites is constructed using 23 key indicators that reflect the site’s seismic resilience. A seismic resilience evaluation model for building sites is established based on the TOPSIS model and the entropy method. The feasibility and rationality of the proposed evaluation method are demonstrated through application examples. The research findings in this paper provide a valuable reference for advancing the evaluation of both building seismic resilience and site seismic resilience.
Optimization Design of Cotton-Straw-Fiber-Modified Asphalt Mixture Performance Based on Response Surface Methodology
Authors: Hu Guihua , Chen Xiaowei , Cao Zhonglu , Yang Lvzhen
Published: November 2024

This research explored the application of cotton straw fiber in asphalt mixtures, aiming to optimize the asphalt mixtures’ performance. Firstly, 17 experiments were designed using Response Surface Methodology (RSM). Subsequently, the Box–Behnken Design (BBD) was used to examine how the asphalt content, fiber length, and cotton straw fiber content interacted to affect the modified asphalt mixes’ pavement performance. Based on the experimental findings, performance prediction models were created to direct optimization. The optimized design was then validated through pavement performance tests and bending fatigue tests. The findings revealed that cotton straw fiber content, length, and asphalt content significantly influence the performance of modified asphalt mixtures. The inclusion of cotton straw fibers enhanced various properties of the mixtures. When the fiber content was set at 0.3%, fiber length at 6 mm, and asphalt content at 5.3%, the response indicators, including Marshall stability, dynamic stability, flexural strength, and freeze–thaw strength ratio, were measured at 12.246 kN, 2452.396 times/mm, 12.30 MPa, and 92.76%, respectively. These results indicate that the cotton-straw-fiber-modified asphalt mixture achieved optimal performance while meeting regulatory requirements. Additionally, fatigue tests showed that the cotton-straw-fiber-modified asphalt mixture exhibited superior fatigue resistance compared with the SBS-modified asphalt mixture. The maximum error between the RSM predictions and the experimental measurements was within 10%, demonstrating the accuracy of the predictive models in estimating the impact of different factors on asphalt mixture performance. The application of RSM in designing and optimizing cotton-straw-fiber-modified asphalt mixtures proved to be highly effective, offering valuable insights for utilizing cotton straw fibers in road construction.
Study on the Stability Evolution Mechanism of a Red Mud Dam During Construction and Safety Under Earthquake During Operation
Authors: Long Sitong , Ma Shaokun , An Pengtao
Published: November 2024

Instability in red mud dam bodies is not uncommon. In order to study the stability evolution mechanism during the process of red mud landfill and the deformation characteristics under earthquake action when the landfill site is closed, the deformation law and potential sliding surface motion characteristics of the landfill site were explored based on the finite difference method, revealing the influence of peak ground acceleration (PGA) on red mud deformation. The results showed that: (1) As the height of the red mud landfill increases, the shear force of the red mud landfill gradually increases. Meanwhile, the maximum shear force always occurs near the initial dam, indicating that under the action of gravity, the possibility of shear slip occurring near the initial dam is the highest. (2) The distribution pattern of the plastic zone in the red mud pile during the filling process is relatively complex, and continuous monitoring of the filling process should be carried out to ensure the safety of the filling project. (3) With the increase in earthquake acceleration, the shear force of red mud piles gradually increases. Meanwhile, as the acceleration increases, the maximum shear stress always occurs at the bottom of the initial dam body. Under the action of power, special attention should be paid to the stability of the pile near the initial dam.
Bending Test of Rectangular High-Strength Steel Fiber-Reinforced Concrete-Filled Steel Tubular Beams with Stiffeners
Authors: Liu Shiming , Ji Zhaoyang , Li Shangyu , Li Xiaoke , Liu Yongjian , Zhao Shunbo
Published: November 2024

To better understand the bending performance of rectangular high-strength steel fiber-reinforced concrete (HSFRC)-filled steel tubular (HSFRCFST) beams with internal stiffeners, ten beams were subjected to a four-point bending test. The primary considerations were the strength grade of the HSFRC, the steel fiber content, the internal stiffener type, and the circular hole spacing of the perfobond stiffener. The moment–curvature and flexural load–deflection curves were calculated. The mode of failure and the distribution of cracks of the infill HSFRC were observed. The presence of steel fibers greatly improved the bending stiffness and moment capacity of HSFRCFST beams, with the optimal effect happening at a steel fiber content of 1.2% by volume, according to the experimental findings. The type of stiffener influenced the failure modes of the exterior rectangular steel tube, which were unaffected by the compressive strength of the infill HSFRC. On the tension surface of HSFRCFST beams, the crack spacing of the infill HSFRC was virtually identical to the circular hole spacing of perfobond stiffeners. When the circular hole spacing was between two and three times the diameter, the perfobond stiffener worked best with the infill HSFRC. The test beams’ ductility index was greater than 1.16, indicating good ductility. The test beams’ rotational capacities ranged from 6.26 to 13.20, which were greater than 3.0 and met the requirements of the specification. The experimental results demonstrate that a reasonable design of the steel fiber content and the spacing between circular holes of perfobond stiffeners can significantly improve the bending resistance of rectangular HSFRCFST beams. This provides relevant parameter design suggestions for improving the ductility and bearing capacity of steel fiber-reinforced concrete beams in practical construction. Finally, a design formula for the moment capacity of rectangular HSFRCFST beams with stiffeners is presented, which corresponds well with the experimental findings.
The Effect of Moderate Temperature Rise on Emitted Chemicals from Modern Building Materials
Authors: Chepaitis Patrick S. , Zhang Qian , Kalafut David , Waddey Taryn , Wilson Mark J. , Black Marilyn
Published: November 2024

Chemical emissions from building materials may significantly impact indoor air quality and potentially human health, since individuals spend most of their time indoors. With rising global temperatures and more frequent heatwaves, building materials’ resilience becomes more crucial for indoor air quality and structural integrity. However, the effects of temperature rise on building material emissions are not systematically studied. This study investigates the effect of a moderate temperature rise on the volatile organic compound (VOC) and aldehyde emissions of eighteen commonly used building materials, such as engineered hardwood, nylon carpet, terrazzo flooring, and acoustic tile, at two elevated yet realistic temperature points. The chemical emissions were collected using a micro-chamber setup and analyzed using thermal desorption/gas chromatography/mass spectrometry and high-performance liquid chromatography. The results showed that 78% of the materials tested demonstrated increased chemical emissions at higher temperatures. Wood-flooring materials showed statistically significant increases in formaldehyde at elevated temperatures, which could be associated with health risks. Eight of the tested materials, particularly those used in large surface area applications, showed significant increases in emissions at increased temperatures, and half of these were labeled as “low-VOC”. These findings may inform the updating of building standards and third-party certification with respect to temperature variation when assessing building material emissions. This research aims to provide a comprehensive understanding of VOC and aldehyde emissions at emerging indoor environmental conditions due to extreme heat climate scenarios.
Facile Synthesis of a Micro–Nano-Structured FeOOH/BiVO4/WO3 Photoanode with Enhanced Photoelectrochemical Performance
Authors: Li Ruixin , Zhan Faqi , Wen Guochang , Wang Bing , Qi Jiahao , Liu Yisi , Feng Chenchen , La Peiqing
Published: November 2024

In the realm of photoelectrocatalytic (PEC) water splitting, the BiVO4/WO3 photoanode exhibits high electron–hole pair separation and transport capacity, rendering it a promising avenue for development. However, the charge transport and reaction kinetics at the heterojunction interface are suboptimal. This study uses the hydrothermal–electrodeposition–dip coating–calcination method to prepare a microcrystalline WO3 photoanode thin film as the substrate material and combines it with nanocrystalline BiVO4 to form a micro–nano-structured heterojunction photoanode to enhance the intrinsic and surface/interface charge transport properties of the photoanode. Under the condition of 1.23 V vs. RHE, the photoelectric current density reaches 1.09 mA cm−2, which is twice that of WO3. Furthermore, by using a simple impregnation–mineralization method to load the amorphous FeOOH catalyst, a noncrystalline–crystalline composite structure is formed to increase the number of active sites on the surface and reduce the overpotential of water oxidation, lowering the onset potential from 0.8 V to 0.6 V (vs. RHE). The photoelectric current density is further increased to 2.04 mA cm−2 (at 1.23 V vs. RHE). The micro–nano-structure and noncrystalline–crystalline composite structure proposed in this study will provide valuable insights for the design and synthesis of high-efficiency photoelectrocatalysts.
Highly Efficient Visible-Light Photocatalysts: Bi2O3@TiO2 Derived from Ti-MOFs for Eriochrome Black T Degradation: A Joint Experimental and Computational Study
Authors: Meng Jing , Ashry Asmaa G. , Abou-Elyazed Ahmed S. , Zhang Zhe , Li Xiaolin , Sharara Tamer Z. , El-Demerdash Safinaz H.
Published: November 2024

Herein, we synthesized Ti-MOF through a solvothermal method and subsequently calcined it to form anatase TiO2. We further developed a Bi2O3@TiO2 mixed oxide using impregnation and calcination processes. These oxides showed significant photocatalytic activity for degrading Eriochrome Black T (EBT) dye under visible light irradiation. We characterized the prepared samples using various techniques, including XRD, XPS, FTIR, BET, SEM, EDX, TEM, and UV-DRS analyses. Our results indicated that TiO2 and 10%Bi2O3@TiO2 achieved 80% and 100% degradation of EBT dye solution (50 ppm) within 30 min in acidic medium with a 50 mg catalyst dose, respectively. The calcination of the Ti-MOF into TiO2 improved its sensitivity to visible light. The Bi2O3@TiO2 composite was also effective in degrading other organic pollutants, such as Congo Red (degradation ~99%), Malachite Green (degradation ~95%), Methylene Blue (degradation ~81%), and Safranine O (degradation ~69%). The impregnation of Bi2O3 increased the surface acidity of TiO2, enhancing its photocatalytic activity by promoting hydroxyl group formation through increased water adsorption. Additionally, 10%Bi2O3@TiO2 demonstrated excellent chemical stability and reusability, maintaining high degradation efficiency over four cycles. Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT) calculations were performed to understand the degradation mechanisms. UV-Vis absorption spectrum simulations suggested that the anionic HEB−2 (O24) or EB−3 forms of the EBT dye are likely to undergo degradation. This study highlights the potential of Bi2O3@TiO2 composites for effective photocatalytic applications in environmental remediation.
Automated Generation of Lung Cytological Images from Image Findings Using Text-to-Image Technology
Authors: Teramoto Atsushi , Kiriyama Yuka , Michiba Ayano , Yazawa Natsuki , Tsukamoto Tetsuya , Imaizumi Kazuyoshi , Fujita Hiroshi
Published: November 2024

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance.
The Influence of Ultrasonic Irradiation of a 316L Weld Pool Produced by DED on the Mechanical Properties of the Produced Component
Authors: Lehnert Dennis , Bödger Christian , Pabel Philipp , Scheidemann Claus , Hemsel Tobias , Gnaase Stefan , Kostka David , Tröster Thomas
Published: November 2024

Additive manufacturing of metallic components often results in the formation of columnar grain structures aligned along the build direction. These elongated grains can introduce anisotropy, negatively impacting the mechanical properties of the components. This study aimed to achieve controlled solidification with a fine-grained microstructure to enhance the mechanical performance of printed parts. Stainless steel 316L was used as the test material. High-intensity ultrasound was applied during the direct energy deposition (DED) process to inhibit the formation of columnar grains. The investigation emphasized the importance of amplitude changes of the ultrasound wave as the system’s geometry continuously evolves with the addition of multiple layers and assessed how these changes influence the grain size and distribution. Initial tests revealed significant amplitude fluctuations during layer deposition, highlighting the impact of layer deposition on process uniformity. The mechanical results demonstrated that the application of ultrasound effectively refined the grain structure, leading to a 15% increase in tensile strength compared to conventionally additively manufactured samples.
Comparison of the Safety and Efficacy of Ustekinumab and Vedolizumab in Patients with Crohn’s Disease: A Systematic Review and Meta-Analysis of Propensity Score Matched Cohort Studies
Authors: Pasta Andrea , Calabrese Francesco , Marabotto Elisa , Furnari Manuele , Demarzo Maria Giulia , Pellegrino Raffaele , Gravina Antonietta Gerarda , Federico Alessandro , Giannini Edoardo Giovanni , Bodini Giorgia
Published: November 2024

Background: Ustekinumab and vedolizumab represent both valid therapeutic options in patients with Crohn’s Disease. Data comparing the safety and efficacy of these drugs are indirect, with conflicting results reported. We aim to conduct a systematic review and metanalysis to assess the safety and effectiveness profile of ustekinumab and vedolizumab in patients with Crohn’s Disease, including only studies that applied propensity scores to reduce confounding bias. Methods: We identified 59 reports that compared ustekinumab and vedolizumab after a propensity score match analysis, of which 16 were assessed for eligibility, and finally, ten retrospective studies were included. The main outcomes considered were clinical steroid-free remission at 14 ± 4, 24 ± 4, and 52 ± 4 weeks, drug discontinuation rate, adverse events, serious infections, and hospitalization during the first year of treatment. Results: A total of 4398 patients were treated with ustekinumab (n = 2774, 63.1%) or vedolizumab (1624, 36.9%). Steroid-free clinical remission was not significantly different between ustekinumab and vedolizumab at 12 ± 4 weeks (OR 1.31, 95%CI 0.88–1.94, p = 0.180), at 24 ± 4 weeks (OR 1.18, 95%CI 0.79–1.75, p = 0.420), and at 52 ± 4 weeks (1.35, 95%CI 0.91–2.01, p = 0.140). In patients receiving ustekinumab, the rate of adverse events (OR 0.54, 95%CI 0.35–0.83, p = 0.005), infection (OR 0.61, 95%CI 0.47–0.80, p < 0.001) and the need of hospitalization at 1-year (OR 0.68, 95%CI 0.58–0.80, p < 0.001) appeared to be lower. Conclusion: Ustekinumab and vedolizumab do not significantly differ in inducing and maintaining clinical steroid-free remission, while ustekinumab was associated with a lower risk of serious infections and hospitalization during the first year of treatment.
Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication
Authors: Duangsuwan Sarun , Jamjareegulgarn Punyawi
Published: November 2024

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.
Edge Computing-Driven Real-Time Drone Detection Using YOLOv9 and NVIDIA Jetson Nano
Authors: Hakani Raj , Rawat Abhishek
Published: November 2024

Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments.
Research and Design of an Active Light Source System for UAVs Based on Light Intensity Matching Model
Authors: Ming Rui , Wu Tao , Zhou Zhiyan , Luo Haibo , Hassan Shahbaz Gul
Published: November 2024

The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. Therefore, this paper proposes an adaptive active light source system based on light intensity matching to address the issue of visual feature loss caused by environmental light intensity and scale variations in multi-UAV collaborative navigation. The system consists of three components: an environment sensing and control module, a variable active light source module, and a light source power module. This paper first designs the overall framework of the active light source system, detailing the functions of each module and their collaborative working principles. Furthermore, optimization experiments are conducted on the variable active light source module. By comparing the recognition effects of the variable active light source module under different parameters, the best configuration is selected. In addition, to improve the robustness of the active light source system under different lighting conditions, this paper also constructs a light source color matching model based on light intensity matching. By collecting and comparing visible light images of different color light sources under various intensities and constructing the light intensity matching model using the comprehensive peak signal-to-noise ratio parameter, the model is optimized to ensure the best vision tracking performance under different lighting conditions. Finally, to validate the effectiveness of the proposed active light source system, quantitative and qualitative recognition comparison experiments were conducted in eight different scenarios with UAVs equipped with active light sources. The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. Qualitative experiments also demonstrated that the active light source effectively improved the recognition success rate under low lighting conditions. Extensive qualitative and quantitative experiments confirm that the UAV active light source system based on light intensity matching proposed in this paper effectively enhances the effectiveness and robustness of vision-based tracking for multi-UAVs, particularly in complex and variable environments. This research provides an efficient and computationally effective solution for vision-based multi-UAV systems, further enhancing the visual tracking capabilities of multi-UAVs under complex conditions.
Mapping Stratigraphy and Artifact Distribution with Unmanned Aerial Vehicle-Based Three-Dimensional Models—A Case Study from the Post Research Area in Northwestern Texas, USA
Authors: Hurst Stance , Johnson Eileen , Cunningham Doug
Published: November 2024

This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These were processed using Agisoft Metashape to generate 3D models. These models enabled the precise mapping of stratigraphic boundaries, revealing the distinctions between Triassic-age bedrock, Pleistocene-age alluvial deposits, and Holocene-age aeolian sediments. Field surveys from 2022 to 2024 documented over 5000 artifacts with sub-centimeter accuracy, including diagnostic projectile points and ceramics. This research highlights the advantages of UAV-derived 3D models in rapidly and accurately documenting stratigraphy and archaeological data. It demonstrates the value of UAV technology for visualizing landscape-scale processes and artifact contexts, offering a new approach to understanding the interactions between geomorphology and archaeology. The findings contribute to advancing UAV applications in both geomorphological and archaeological research.
T–S Fuzzy Observer-Based Output Feedback Lateral Control of UGVs Using a Disturbance Observer
Authors: Lee Seunghoon , Hwang Sounghwan , Kim Han Sol
Published: November 2024

This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions derived from a Takagi–Sugeno fuzzy model and a relaxation technique that incorporates additional null terms. The state observer is developed to estimate both the vehicle’s state and external disturbances, such as road curvature. By incorporating the disturbance observer, the proposed approach effectively mitigates performance degradation caused by discrepancies between the system and observer dynamics. The simulation results, conducted in MATLAB and a commercial autonomous driving simulator, demonstrate that the proposed control method substantially enhances state estimation accuracy and improves the robustness of path tracking under varying conditions.
Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload
Authors: Kartal Muhammed Rasit , Ignatyev Dmitry I. , Zolotas Argyrios
Published: November 2024

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance.
Recognition of Maize Tassels Based on Improved YOLOv8 and Unmanned Aerial Vehicles RGB Images
Authors: Wei Jiahao , Wang Ruirui , Wei Shi , Wang Xiaoyan , Xu Shicheng
Published: November 2024

The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as an identifying feature. However, during the tasseling stage, its apical ear blooms and has distinctive features that can be used as an identifying feature. However, the sizes of the maize tassels are small, the background is complex, and the existing network has obvious recognition errors. Therefore, in this paper, unmanned aerial vehicle (UAV) RGB images and an improved YOLOv8 target detection network are used to enhance the recognition accuracy of maize tassels. In the new network, a microscale target detection head is added to increase the ability to perceive small-sized maize tassels; In addition, Spatial Pyramid Pooling—Fast (SPPF) is replaced by the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) in the backbone network part to connect different levels of detailed features and semantic information. Moreover, a dual-attention module synthesized by GAM-CBAM is added to the neck part to reduce the loss of features of maize tassels, thus improving the network’s detection ability. We also labeled the new maize tassels dataset in VOC format as the training and validation of the network model. In the final model testing results, the new network model’s precision reached 93.6% and recall reached 92.5%, which was an improvement of 2.8–12.6 percentage points and 3.6–15.2 percentage points compared to the mAP50 and F1-score values of other models. From the experimental results, it is shown that the improved YOLOv8 network, with high performance and robustness in small-sized maize tassel recognition, can accurately recognize maize tassels in UAV images, which provides technical support for automated counting, accurate cultivation, and large-scale intelligent cultivation of maize seedlings.
Optimising Data Analytics to Enhance Postgraduate Student Academic Achievement: A Systematic Review
Authors: Ncube Mthokozisi Masumbika , Ngulube Patrick
Published: November 2024

This systematic review investigated how Higher Education Institutions (HEIs) optimise data analytics in postgraduate programmes to enhance student achievement. Existing research explores the theoretical benefits of data analytics but lacks practical guidance on strategies to effectively implement and utilise data analytics for student success. As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Five databases were searched. Studies that examined data analytics in HEIs postgraduate programmes and their impact on student learning were included. Studies that were solely theoretical or in non-postgraduate settings were excluded. Twenty-six studies were included. Quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed. The review identified various data analytics approaches including descriptive, predictive, and prescriptive analytics, among others. These approaches can improve foundational skills, create supportive learning environments, and optimise teaching strategies. However, limitations (standardised tests, data integration) and privacy concerns were acknowledged. Recommendations include developing a comprehensive evaluation system, equipping educators with the skills to utilise diverse analytics to enhance student achievement, fostering open communication about data use, and cultivating a data-literate student body. While diverse approaches were explored, the review’s lack of specific contextual details may limit the generalisability of findings. To mitigate this, the review categorised techniques and provided references for further exploration.
An Improved Variable Step SAMP Method Based on Correlation Principle
Authors: Wang Xiaolei , Jiang Yingqi , Ding Guoqiang
Published: November 2024

The fixed step size in the sparse adaptive matching pursuit algorithm can result in limited accuracy and overestimation. To address this, this paper proposes a variable-step sparse adaptive matching pursuit algorithm based on the Spearman correlation coefficient. By measuring the Spearman correlation coefficient between the candidate set and the input signal, and introducing an adaptive step size adjustment method based on the parameter values of the correlation coefficient, the performance of the SAMP algorithm is optimized and its adaptability is enhanced. Extensive experiments demonstrate that the proposed method achieves good reconstruction results for one-dimensional sparse signals and two-dimensional images.