bluuebunny/crossref_metadata_2025 huggingface dataset:
An Assessment of the Factors Impacting Project Success in the Engineering Sector
Authors: Karimulla Uzayr , Gupta Kapil
Published: August 2024

The engineering projects sector has presented numerous challenges in achieving success. The need arises to understand the reasons for these challenges and determine the best way to address the issues and achieve success using the best approach. This study aims to gain a good understanding of the factors impacting project success in the engineering sector. The literature review revealed variables that influence project success. These variables are company culture, effective management, employee engagement, and effective planning. The study presents a statistical analysis of the data. This analysis shows that each independent variable studied has a distinct impact on the dependent variable, project success. The South African engineering projects industry has shown a relationship between identified factors and project success. This study recommends laying a solid foundation to apply and improve the identified factors that have an impact on project success in the industry.
A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
Authors: Li Hongming , Ji Yuqing , Wang Ziwei
Published: August 2024

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.
Simulation and Optimisation of Utility-Scale PV–Wind Systems with Pumped Hydro Storage
Authors: Dufo-López Rodolfo , Lujano-Rojas Juan M.
Published: August 2024

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real-time pricing (RTP) schemes. In this paper, we present a new method to optimise the size of and manage utility-scale wind–PV systems using PHS with energy arbitrage under RTP. PHS is used to supply load consumption and/or energy arbitrage. Further, both load-supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. PHS is not worth considering with the present cost of components. In load-supply systems in Zaragoza (Spain), we found that PHS would be worth considering if its cost was lower than 850 EUR/kW (considering all PHS components except reservoirs) +20 EUR/m3 for reservoirs (equivalent to 105 EUR/kWh with a 70 m head), whereas in Gran Canaria Island (with a considerably higher irradiation and wind speed), the required PHS cost is considerably lower (~350 EUR/kW + 10 EUR/m3). For power-generating systems, PHS required costs ranging from 400–700 EUR/kW + 15–20 EUR/m3 for obtaining the optimal PV–wind–PHS system with economic results similar to those of the optimal power-generating system without PHS. Thus, the renewable–PHS system with energy arbitrage under RTP could be profitable for many locations globally given the wide range of the PHS cost; however, each case is different and must be evaluated individually. The presented model can be used for optimising the renewable–PHS system in any location with any costs and RTP schemes.
A Method for Extracting Dynamic Vortex Acoustic Signal Characteristics in Island and Reef Channels Based on Time-Reversal Mirrors
Authors: Yu Min , Liu Hang , Zhou Wei , Fan Dingfan
Published: August 2024

Ships navigating in channels with vortex fields face increased risks. However, these vortex fields can be monitored using acoustic methods. The key is to extract the phase characteristics of sound signals passing through the vortices. Using time-reversal mirrors, this paper studied the extraction method of characteristics both numerically and experimentally, aiming to verify the effectiveness of the numerical simulation method. Starting from this point, the impact of different movement forms and scale changes in vortex fields on the acoustic signal extraction method was further investigated. The results indicate that with the iterations of time reversal (N < 6), the method is effective for uniformly moving vortex fields, when the vortex center moving speed Vw < 2.2 × 10−3 m/s and the radius diffusion speed Vr < 2.5 × 10−3 m/s. On the other hand, for oscillating vortex fields, it is effective when the oscillation amplitude LD < 0.15 m and the radius diffusion speed Vr < 2.4 × 10−3 m/s; meanwhile, the dynamic characteristics of the vortex field can be ignored by the phase extraction method based on time-reversal mirrors.
Analysis of Penalty Kick Performance in the Spanish Football League: A Longitudinal Study
Authors: Prieto-Lage Iván , Argibay-González Juan Carlos , Bezerra Pedro , Cidre-Fuentes Pablo , Reguera-López-de-la-Osa Xoana , Gutiérrez-Santiago Alfonso
Published: August 2024

(1) Background: the penalty kick is an action in football that decides many matches. The aim of this research was to establish the success rate of penalty kicks in Spain between the seasons 2012/2013 and 2019/2020 considering different key performance variables. (2) Methods: A total of 941 penalty kicks were analysed from eight seasons of the first division of the Spanish men’s football league (La Liga). An observational methodology was used and, in order to obtain the results, different analytical techniques were employed: descriptive and chi square with a significance level of p < 0.05. (3) Results: The effectiveness of penalty kicks was 76.7%. Most of the penalties were taken by the home team (60.9%), with a tie on the scoreboard (44.2%) and within the last 30 min of the match (41.9%). The highest success rate was observed for balls kicked to the upper side of the goal (100%), even though the highest number of penalties were taken low, to one of the two sides of the goal (57.0%). Up to 5.6% of the penalties were not kicked inside the goal. (4) Conclusions: Effectiveness does not depend on home–visitor status, player laterality, score or remaining match time, studied independently. A loss of effectiveness is observed when the match scores of both teams are close and in critical moments of the match.
Automatic Estimation of Tropical Cyclone Centers from Wide-Swath Synthetic-Aperture Radar Images of Miniaturized Satellites
Authors: Wang Yan , Fu Haihua , Hu Lizhen , Geng Xupu , Shang Shaoping , He Zhigang , Xie Yanshuang , Wei Guomei
Published: August 2024

Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in TC monitoring. This paper employs an algorithm for automatic TC center location, involving three stages: coarse estimation from a whole SAR image; precise estimation from a sub-SAR image; and final identification of the center using the lowest Normalized Radar Cross-Section (NRCS) value within a smaller sub-SAR image. Using three wide-swath miniaturized SAR images of TC Noru (2022), and TCs Doksuri and Koinu (2023), the algorithm’s accuracy was validated by comparing estimated TC center positions with visually located data. For TC Noru, the distances for the three stages were 21.42 km, 14.39 km, and 8.19 km; for TC Doksuri—14.36 km, 20.48 km, and 17.10 km; and for TC Koinu—47.82 km, 31.59 km, and 5.42 km. The results demonstrate the potential of miniaturized SAR in TC monitoring.
Assessment of Feldkamp-Davis-Kress Reconstruction Parameters in Overall Image Quality in Cone Beam Computed Tomography
Authors: Kim Hajin , Choi Jun-Seon , Lee Youngjin
Published: August 2024

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images.
Multimodal Sentiment Classifier Framework for Different Scene Contexts
Authors: Silva Nelson , Cardoso Pedro J. S. , Rodrigues João M. F.
Published: August 2024

Sentiment analysis (SA) is an effective method for determining public opinion. Social media posts have been the subject of much research, due to the platforms’ enormous and diversified user bases that regularly share thoughts on nearly any subject. However, on posts composed by a text–image pair, the written description may or may not convey the same sentiment as the image. The present study uses machine learning models for the automatic sentiment evaluation of pairs of text and image(s). The sentiments derived from the image and text are evaluated independently and merged (or not) to form the overall sentiment, returning the sentiment of the post and the discrepancy between the sentiments represented by the text–image pair. The image sentiment classification is divided into four categories—“indoor” (IND), “man-made outdoors” (OMM), “non-man-made outdoors” (ONMM), and “indoor/outdoor with persons in the background” (IOwPB)—and then ensembled into an image sentiment classification model (ISC), that can be compared with a holistic image sentiment classifier (HISC), showing that the ISC achieves better results than the HISC. For the Flickr sub-data set, the sentiment classification of images achieved an accuracy of 68.50% for IND, 83.20% for OMM, 84.50% for ONMM, 84.80% for IOwPB, and 76.45% for ISC, compared to 65.97% for the HISC. For the text sentiment classification, in a sub-data set of B-T4SA, an accuracy of 92.10% was achieved. Finally, the text–image combination, in the authors’ private data set, achieved an accuracy of 78.84%.
Impact Features Extracting Method for a Reciprocating Compressor Based on the ABC-SGMD Model
Authors: Li Jiaxun , Bie Fengfeng , Li Qianqian , Zhou Zhaolong , Miao Xinting , Zhang Siyi
Published: August 2024

In the typical vibration signal of a reciprocating air compressor, multi-source nonlinear characteristics are exhibited and are often drowned out in background noise, which leads to a lack of robustness in traditional feature analysis methods and difficulty in effective extraction. To address this issue, an algorithm based on ABC-SGMD is proposed in this paper. The Symplectic Geometry Mode Decomposition (SGMD), which is optimized with the Artificial Bee Colony algorithm (ABC), is utilized to decompose the signal, and a multi-feature fusion model is constructed for fault feature extraction. The extracted features are then input into the Self-Adaptive Evolutionary Extreme Learning Machine (SaDE-ELM), and a fault diagnosis model based on ABC-SGMD and SaDE-ELM is established. Ultimately, the signals of reciprocating air compressors and experimental data are used to demonstrate the applicability of the method. The results manifest that this framework has superiority in handling nonlinear and non-stationary signals.
A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
Authors: Li Menghao , Liu Miao , Zhang Weiwei , Guo Wenfeng , Chen Enqing , Zhang Cheng
Published: August 2024

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.
Classification of Rock Mass Quality in Underground Rock Engineering with Incomplete Data Using XGBoost Model and Zebra Optimization Algorithm
Authors: Yang Bo , Liu Yongping , Liu Zida , Zhu Quanqi , Li Diyuan
Published: August 2024

Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model’s robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications.
Research on the Image-Motion Compensation Technology of the Aerial Camera Based on the Multi-Dimensional Motion of the Secondary Mirror
Authors: Zhang Hongwei , Qu Rui , Chen Weining , Guo Huinan
Published: August 2024

Targeting the dynamic image-motion problem of aerial cameras in the process of swing imaging, the image-motion compensation technology of aerial cameras based on the multi-dimensional motion of the secondary mirror was adopted. The secondary mirror was used as the image-motion compensation element, and the comprehensive image-motion compensation of the aerial camera was realized through the multi-dimensional motion of the secondary mirror. However, in the process of compensating for the image motion, the secondary mirror would be eccentric and inclined, which would cause the secondary mirror to be off-axis and affect the image quality. Therefore, a misalignment optical system model was established to study the relationship between the deviation vector and the misalignment of the secondary mirror, and the influence of the secondary mirror’s motion on the distribution of the aberration was analyzed. In order to verify the image-motion compensation ability of the multi-dimensional motion of the secondary mirror, an experimental platform was built to conduct a laboratory imaging experiment and flight experiment on the aerial camera. The experimental results showed that the dynamic resolution of the aerial camera using the image-motion compensation technology could reach 74 lp/mm, and the image-motion compensation accuracy was better than 0.5 pixels, which met the design expectation. In conclusion, the image-motion compensation technology is expected to be applied to various high-precision optical imaging as well as optical detection systems.
Analytical Estimation of Hydrogen Storage Capacity in Depleted Gas Reservoirs: A Comprehensive Material Balance Approach
Authors: Albadan Deema , Delshad Mojdeh , Fernandes Bruno Ramon Batista , Eltahan Esmail , Sepehrnoori Kamy
Published: August 2024

The efficient use of depleted gas reservoirs for hydrogen storage is a promising solution for transitioning to carbon-neutral energy sources. This study proposes an analytical framework for estimating hydrogen storage capacity using a comprehensive material balance approach in depleted gas reservoirs. The methodology integrates basic reservoir engineering principles with thermodynamic considerations to accurately estimate hydrogen storage capacity in both volumetric drive and water drive gas reservoirs through an iterative approach based on mass conservation and the real gas law. This framework is implemented in a Python program, using the CoolProp library for phase behavior modeling with the Soave–Redlich–Kwong (SRK) equation of state. The methodology is validated with numerical simulations of a tank model representing the two reservoir drive mechanisms discussed. Also, a case study of a synthetic complex reservoir demonstrates the applicability of the proposed approach to real-world scenarios. The findings suggest that precise modeling of fluid behavior is crucial for reliable capacity estimations. The proposed analytical framework achieves an impressive accuracy, with deviations of less than 1% compared to estimates obtained through numerical simulations. Insights derived from this study can significantly contribute to the assessment of strategic decisions for utilizing depleted gas reservoirs for hydrogen storage.
Developing a Robust Training Dataset for AI-Driven Architectural Spatial Layout Generation
Authors: Park Hyejin , Gu Hyeongmo , Hong Soonmin , Choo Seungyeon
Published: August 2024

Recent advancements in AI research, particularly in spatial layout generation, highlight its capacity to enhance human creativity by swiftly providing architects with numerous alternatives during the pre-design phase. The complexity of architectural design data, characterized by multifaceted elements and varying representations, presents significant challenges in creating uniform and robust datasets. This study addresses this challenge by developing a robust training dataset specifically tailored for AI-driven spatial layout generation in architecture. An algorithm capable of extracting spatial relationship diagrams from raster-based floor plan images and converting them into vector-based data was introduced. Through extensive web crawling, a dataset comprising 10,000 data rows, categorized into 21 classes and three spatial relationship categories, was collected. When tested with the You-Only-Look-Once (YOLO) model, the detection rate was 99%, the mean average precision was 85%, and the MIoU was 74.2%. The development of this robust training dataset holds significant potential to advance knowledge-based artificial intelligence design automation studies, paving the way for further innovation in architectural design.
PE-SLAM: A Modified Simultaneous Localization and Mapping System Based on Particle Swarm Optimization and Epipolar Constraints
Authors: Li Cuiming , Shang Zhengyu , Wang Jinxin , Niu Wancai , Yang Ke
Published: August 2024

Due to various typical unstructured factors in the environment of photovoltaic power stations, such as high feature similarity, weak textures, and simple structures, the motion model of the ORB-SLAM2 algorithm performs poorly, leading to a decline in tracking accuracy. To address this issue, we propose PE-SLAM, which improves the ORB-SLAM2 algorithm’s motion model by incorporating the particle swarm optimization algorithm combined with epipolar constraint to eliminate mismatches. First, a new mutation strategy is proposed to introduce perturbations to the pbest (personal best value) during the late convergence stage of the PSO algorithm, thereby preventing the PSO algorithm from falling into local optima. Then, the improved PSO algorithm is used to solve the fundamental matrix between two images based on the feature matching relationships obtained from the motion model. Finally, the epipolar constraint is applied using the computed fundamental matrix to eliminate incorrect matches produced by the motion model, thereby enhancing the tracking accuracy and robustness of the ORB-SLAM2 algorithm in unstructured photovoltaic power station scenarios. In feature matching experiments, compared to the ORB algorithm and the ORB+HAMMING algorithm, the ORB+PE-match algorithm achieved an average accuracy improvement of 19.5%, 14.0%, and 6.0% in unstructured environments, respectively, with better recall rates. In the trajectory experiments of the TUM dataset, PE-SLAM reduced the average absolute trajectory error compared to ORB-SLAM2 by 29.1% and the average relative pose error by 27.0%. In the photovoltaic power station scene mapping experiment, the dense point cloud map constructed has less overlap and is complete, reflecting that PE-SLAM has basically overcome the unstructured factors of the photovoltaic power station scene and is suitable for applications in this scene.
Discrete Fracture Network (DFN) as an Effective Tool to Study the Scale Effects of Rock Quality Designation Measurements
Authors: Wang Rongzhen , Elmo Davide
Published: August 2024

Rock quality designation (RQD) is a parameter that describes rock mass quality in terms of percentage recovery of core pieces greater than 10 cm. The RQD represents a basic element of several classification systems. This paper studies scale effects for RQD measurements using synthetic rock masses generated using discrete fracture network (DFN) models. RQD measurements are performed for rock masses with varying fracture intensities and by changing the orientation of the simulated boreholes to account for orientation bias. The objective is to demonstrate the existence of a representative elementary length (REL, 1D analogue of a 3D representative elementary volume, or REV) above which RQD measurements would represent an average indicator of rock mass quality. For the synthetic rock masses, RQD measurements were calculated using the relationship proposed by Priest and Hudson and compared to the simulated RQD measurements along the boreholes. DFN models generated for a room-and-pillar mine using mapped field data were then used as an initial validation, and the conclusion of the study was further validated using the RQD calculation results directly obtained from the depth data collected at an iron cap deposit. The relationship between rock mass scale and assumed threshold length used to calculate RQD is also studied.
Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities
Authors: Rupanetti Dulana , Kaabouch Naima
Published: August 2024

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations.
Effect of Tomato Juice and Different Drying Methods on Selected Properties of Courgette
Authors: Kręcisz Magdalena , Stępień Bogdan , Pikor Karol
Published: August 2024

The purpose of this study was to determine the impact of vacuum impregnation on selected physical properties of courgettes, the drying process, and kinetics of the drying process. Vacuum impregnation was used as a pretreatment in the conducted research. The drying process was carried out using three techniques (convection drying, freeze drying, and vacuum drying). In the presented work, selected properties of courgettes, i.e., water activity, dry weight, density, VGI, shrinkage, and color were investigated, and the best model describing the kinetics of the drying process was selected. As a result of the study, it was found that the pretreated courgette was characterized by increased dry matter (0.44% to 4.08%) and density content (15.52% to 33.78%) and reduced or increasing water activity (−5.08 to 38.62%) depending on the drying method. The process also resulted in reduced drying shrinkage (−2.13% to −6.97%). Tomato juice was used as an impregnating solution, resulting in an increase in red intensity (8.44) and a decrease in the L* color index (80.16 to 58.00 for the fresh courgette). Dries with the most favorable properties were obtained using the freeze-drying method. The best model of the drying process kinetics was the logistic model.
Influence of Mouse Grip Type on Flicking and Tracking Tasks Performance
Authors: Sanchis-Sanchis Roberto , Encarnación-Martínez Alberto , Catalá-Vilaplana Ignacio , Priego-Quesada Jose Ignacio , Aparicio Inmaculada , Pérez-Soriano Pedro
Published: August 2024

The First-Person Shooter is a very popular genre in electronic sports (esports), where there are different aiming techniques such as Aim Flicking and Aim Tracking. Although the mouse grip is suggested as one of the most important factors in gaming performance, there is a lack of evidence on this topic. Therefore, the purpose of the present study was to determine the performances of three types of mouse grips (palm grip, claw grip, and fingertip grip) in two different tasks (flicking and tracking tasks) by analyzing kinematic and scoring variables. Twenty-two participants performed the tasks on a computer with the same monitor and mouse, each using their preferred grip: palm grip, claw grip, and fingertip grip. The aim400kg was used to perform the tasks, and a camera system (Optitrack’s Flex 3) was used to capture the mouse movement. The results from the flicking task indicated significant differences in the minimum cursor trajectory, with higher values observed in the claw grip compared to both the palm and fingertip grips. However, no significant differences were observed in the tracking task in terms of velocity, acceleration, or hand movement. Moreover, only high correlations were observed in the flicking task between Score and Reaction Time (r = −0.911) and between Score and Total Distance (r = 0.724). In conclusion, the mouse grip does not affect the Aim Flicking or Aim Tracking task performance. This study has revealed interesting results related to performance, including correlations between the Score, Reaction Time, and Total Distance in flicking tasks.
Association of Serum Extracellular Vesicle miRNAs with Cognitive Functioning and Quality of Life in Parkinson’s Disease
Authors: Vaitkienė Paulina , Pranckevičienė Aistė , Radžiūnas Andrius , Mišeikaitė Augustina , Miniotaitė Giedrė , Belickienė Violeta , Laucius Ovidijus , Deltuva Vytenis
Published: August 2024

The identification of mechanisms associated with Parkinson disease (PD) development in cognitive functioning would be of great usefulness to clarify PD pathogenesis and to develop preventive and therapeutic strategies. In this study, blood serum extracellular vesicle (EV) levels of the candidate microRNAs (small noncoding RNAs that play a role in gene expression regulation):,miR-7, miR-21, miR-153, miR-155, miR-200a and miR-214, have been investigated for association with PD in a group of 93 patients with cognitive parameters, PD symptoms, affected quality of life and some clinical characteristics. MiRNA was extracted from patients’ blood serum EVs, transcribed into cDNA and their expression was evaluated using RT-PCR. The miR-153 and miR-200a showed the most plausible correlations with cognitive functioning parameters such as general intellectual functioning, psychomotor speed, mental flexibility, and nonverbal executive functions. Moreover, lower levels of miR-153 were associated with attention span, working memory and psychomotor speed with learning. Increased levels of miR-200a, miR-7, miR-214, and miR-155 were also linked with neurological functioning, such as bradykinesia, tremor, balance and others. Despite the fact that due to small sample size, our results should be considered as preliminary, our study suggests that miRNA expression in EVs could be associated with symptom severity, cognitive impairment and quality of life in PD.
P-tau217 as a Reliable Blood-Based Marker of Alzheimer’s Disease
Authors: Lai Roy , Li Brenden , Bishnoi Ram
Published: August 2024

Amyloid plaques and tau tangles are the hallmark pathologic features of Alzheimer’s disease (AD). Traditionally, these changes are identified in vivo via cerebrospinal fluid (CSF) analysis or positron emission tomography (PET) scans. However, these methods are invasive, expensive, and resource-intensive. To address these limitations, there has been ongoing research over the past decade to identify blood-based markers for AD. Despite the challenges posed by their extremely low concentrations, recent advances in mass spectrometry and immunoassay techniques have made it feasible to detect these blood markers of amyloid and tau deposition. Phosphorylated tau (p-tau) has shown greater promise in reflecting amyloid pathology as evidenced by CSF and PET positivity. Various isoforms of p-tau, distinguished by their differential phosphorylation sites, have been recognized for their ability to identify amyloid-positive individuals. Notable examples include p-tau181, p-tau217, and p-tau235. Among these, p-tau217 has emerged as a superior and reliable marker of amyloid positivity and, thus, AD in terms of accuracy of diagnosis and ability for early prognosis. In this narrative review, we aim to elucidate the utility of p-tau217 as an AD marker, exploring its underlying basis, clinical diagnostic potential, and relevance in clinical care and trials.
High Stability and Low Power Nanometric Bio-Objects Trapping through Dielectric–Plasmonic Hybrid Nanobowtie
Authors: Colapietro Paola , Brunetti Giuseppe , di Toma Annarita , Ferrara Francesco , Chiriacò Maria Serena , Ciminelli Caterina
Published: August 2024

Micro and nano-scale manipulation of living matter is crucial in biomedical applications for diagnostics and pharmaceuticals, facilitating disease study, drug assessment, and biomarker identification. Despite advancements, trapping biological nanoparticles remains challenging. Nanotweezer-based strategies, including dielectric and plasmonic configurations, show promise due to their efficiency and stability, minimizing damage without direct contact. Our study uniquely proposes an inverted hybrid dielectric–plasmonic nanobowtie designed to overcome the primary limitations of existing dielectric–plasmonic systems, such as high costs and manufacturing complexity. This novel configuration offers significant advantages for the stable and long-term trapping of biological objects, including strong energy confinement with reduced thermal effects. The metal’s efficient light reflection capability results in a significant increase in energy field confinement (EC) within the trapping site, achieving an enhancement of over 90% compared to the value obtained with the dielectric nanobowtie. Numerical simulations confirm the successful trapping of 100 nm viruses, demonstrating a trapping stability greater than 10 and a stiffness of 2.203 fN/nm. This configuration ensures optical forces of approximately 2.96 fN with an input power density of 10 mW/μm2 while preserving the temperature, chemical–biological properties, and shape of the biological sample.
Clinical, Cortical, Subcortical, and White Matter Features of Right Temporal Variant FTD
Authors: Kleinerova Jana , McKenna Mary Clare , Finnegan Martha , Tacheva Asya , Garcia-Gallardo Angela , Mohammed Rayan , Tan Ee Ling , Christidi Foteini , Hardiman Orla , Hutchinson Siobhan , Bede Peter
Published: August 2024

The distinct clinical and radiological characteristics of right temporal variant FTD have only been recently recognized. Methods: Eight patients with right temporal variant FTD were prospectively recruited and underwent a standardised neuropsychological assessment, clinical MRI, and quantitative neuroimaging. Results: Our voxelwise grey analyses captured bilateral anterior and mesial temporal grey matter atrophy with a clear right-sided predominance. Bilateral hippocampal involvement was also observed, as well as disease burden in the right insular and opercula regions. White matter integrity alterations were also bilateral in anterior temporal and sub-insular regions with a clear right-hemispheric predominance. Extra-temporal white matter alterations have also been observed in orbitofrontal and parietal regions. Significant bilateral but right-predominant thalamus, putamen, hippocampus, and amygdala atrophy was identified based on subcortical segmentation. The clinical profile of our patients was dominated by progressive indifference, decline in motivation, loss of interest in previously cherished activities, incremental social withdrawal, difficulty recognising people, progressive language deficits, increasingly rigid routines, and repetitive behaviours. Conclusions: Right temporal variant FTD has an insidious onset and may be mistaken for depression at symptom onset. It manifests in a combination of apathy, language, and behavioural features. Quantitative MR imaging captures a characteristic bilateral but right-predominant temporal imaging signature with extra-temporal frontal and parietal involvement.
Exploring the Relationship between Abnormal Communication Efficiency of Cerebral Cortex and Multiple Cognitive Functions in Mild Subcortical Stroke: A Resting-State fMRI Study
Authors: Liu Chang , Jing Jing , Zhu Wanlin , Zuo Lijun
Published: August 2024

Background: The purpose of this study was to explore the specific regions of abnormal cortical communication efficiency in patients with mild subcortical stroke and to investigate the relationship between these communication efficiency abnormalities and multidimensional cognition. Methods: The research involved 35 patients with mild strokes affecting the basal ganglia and 29 healthy controls (HC). Comprehensive neuroimaging and neuropsychological assessments were conducted. Stroke patients were categorized into post-stroke cognitive impairment (PSCI) (MoCA ≤ 22) and non-cognitively impaired stroke patients (NPSCI) (MoCA ≥ 23) based on their cognitive performance. Additionally, 22 patients were reassessed three months later. Results: PSCI patients, compared to HC and NPSCI groups, had significantly higher communication efficiency in specific brain regions. A notable finding was the significant correlation between increased communication efficiency in the medioventral occipital cortex and multidimensional cognitive decline. However, this increased communication efficiency in PSCI patients lessened during the three-month follow-up period. Conclusions: the heightened communication efficiency in the medio-ventral occipital cortex may represent a compensatory mechanism for cognitive impairment in PSCI patients, which undergoes adjustment three months after stroke.
A Latent Profile Analysis of Emotion Regulation in Relation to Distress of Symptoms in Youth with Prodromal Psychotic Symptoms
Authors: Zhang Peiyu , Long Manling , Shi Jingyu
Published: August 2024

(1) Background: Although abnormal emotion regulation is a key characteristic of youth with prodromal psychotic symptoms and is closely related to the degree of distress caused by these symptoms, research on the internal heterogeneity of emotion regulation within this clinically high-risk population has been insufficient. (2) Methods: The current study analyzed data from 394 college students with prodromal psychotic symptoms, using latent profile analysis (LPA) to identify emotion regulation profiles based on differences in expressive suppression and cognitive reappraisal. One-way ANOVA was employed to examine the relationship between the identified latent profiles and distress of symptoms. (3) Results: Three latent profiles were identified: “high cognitive reappraisal group” (n = 117, 29.70%), “moderate cognitive reappraisal group” (n = 233, 59.14%), and “low cognitive reappraisal group” (n = 44, 11.16%). Significant differences in distress of negative symptoms (F = 9.122, p < 0.05) and perceptual abnormalities (F = 3.103, p < 0.05) were observed across the latent profiles but not in unusual thought content and specific perceptual abnormalities (both p > 0.05). The “low cognitive reappraisal group” exhibited the most severe distress of symptoms, followed by the “moderate cognitive reappraisal group”, while the “high cognitive reappraisal group” experienced the least distress. (4) Conclusions: The current study provides evidence for the heterogeneity of emotion regulation among youth with prodromal psychotic symptoms and reveals differences in distress of perceptual abnormalities and negative symptoms between the latent profiles of emotion regulation. These findings support the development of targeted psychological interventions to alleviate the distress of symptoms and improve quality of life.
When Games Influence Words: Gaming Addiction among College Students Increases Verbal Aggression through Risk-Biased Drifting in Decision-Making
Authors: Teng Huina , Zhu Lixin , Zhang Xuanyu , Qiu Boyu
Published: August 2024

Increased aggression due to gaming addiction is a widespread and highly publicized problem. The underlying processes by which verbal aggression, a more harmful and persistent subcategory of aggression, is affected by gaming addiction may differ from other types of aggression. In this study, data came from 252 randomly recruited current university students (50.79% male, mean age 19.60 years, SD: 1.44 years, range 17 to 29 years). Participants reported gaming addiction and different types of aggression through questionnaires. In addition, two important explanatory processes, inhibitory control, and risk preference, were measured through behavioral experiments. A Bayesian hierarchical drift-diffusion model was employed to interpret the data from the risk preference task. In contrast to previous work, the study found that inhibitory control did not significantly correlate with either gaming addiction or any form of aggression However, the drift rate, a measure of decision-making inclination under risk, partially mediates the relationship between gaming addiction and verbal aggression (but not other forms of aggression). The findings illuminate risk preference under adverse conditions as a key predictor of verbal aggression, offering avenues for early intervention and suggesting game design modifications to mitigate verbal aggression by adjusting reward mechanisms.
A Study on the Characteristics of Sports Athletes’ YouTube Channels and User Reactions
Authors: Moon Bora , Oh Taeyeon
Published: August 2024

This study examined the content characteristics and user responses of athlete-run sports YouTube channels, providing empirical insights for content production strategies and contributing to the development of athlete-run sports YouTube channels. Content analysis was conducted on 3306 videos posted on 20 popular YouTube channels of South Korean athletes from 1 January 2020 to 31 December 2021. The formal characteristics analyzed included video length, the presence of foreign language subtitles, paid advertisements, and information sources. The content characteristics examined were the types of sports events, main content themes, and whether the content matched the athlete’s sport. Results revealed significant differences in content characteristics and user responses based on whether the athletes were active or retired. This study’s distinctive contribution lies in highlighting the evolving role of athletes as content creators and providing strategic implications for enhancing the competitiveness of athlete-run sports YouTube channels. Future research should consider a broader range of sports YouTubers and a wider variety of YouTube channels to gain comprehensive insights into the sports content ecosystem on this platform.
Emotional Reactions of Professionals to Challenging Behaviors in People with Intellectual and Developmental Disability
Authors: Reyes-Martín Juliana , Simó-Pinatella David , Andrés Ana
Published: August 2024

Professionals working with people with intellectual and developmental disability (IDD) can be exposed to challenging behaviors (CBs), which may result in professionals exhibiting emotional reactions that can impact their practices. This study examined these reactions and analyzed how they are influenced by the individual characteristics of people with IDD (gender, age, and level of IDD) and the variables related to CB (type of behavior and its frequency and behavioral function). A total of 125 professionals assessed 293 people with IDD who exhibited CBs. The professionals were asked to complete the Behavior Problems Inventory—Short Form, the Emotional Reactions to Challenging Behavior Scale, and the Questions About Behavior Function measure. It was revealed that positive emotional reactions predominated over negative ones. Significant results were found regarding the level of disability and the age of people with IDD. Emotional reactions were related to the severity of CBs, especially self-injurious and aggressive/destructive behavior, as well as certain behavioral functions. The severity of CBs and the age of people with IDD emerge as key predictors of the emotional reactions of professionals. In short, these reactions vary according to different variables, highlighting the importance of interventions that address professionals’ emotional needs.
Emotional Regulation, Coping, and Resilience in Informal Caregivers: A Network Analysis Approach
Authors: Panzeri Anna , Bottesi Gioia , Ghisi Marta , Scalavicci Cecilia , Spoto Andrea , Vidotto Giulio
Published: August 2024

Public health emergencies such as the COVID-19 pandemic can further strain the mental health of informal caregivers who provide unpaid assistance to family members or friends who need support due to illness, disability, or aging. However, there is a lack of research exploring the resources and adaptive strategies that promote resilience in informal caregivers. This cross-sectional study used psychometric network analysis to model the interplay between coping strategies, emotion regulation, trait resilience, and anxiety and depression symptoms in 351 Italian informal caregivers. The results showed that coping through a positive attitude, emotional reappraisal, and trait resilience were the most central and interconnected nodes in the network. These adaptive strategies buffered against the negative impact of anxiety and depression symptoms, providing valuable insights into the mechanisms underlying resilience and well-being in informal caregivers. Clinically, it is crucial to assess and foster these resilience-promoting factors (positive attitude coping, cognitive reappraisal, and trait resilience) to help mitigate the mental health challenges faced by informal caregivers, especially in the context of public health crises such as the COVID-19 pandemic.