bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial Distribution and Variation in Debris Cover and Flow Velocities of Glaciers during 1989–2022 in Tomur Peak Region, Tianshan Mountains
Authors: Zhou Weiyong , Xu Min , Han Haidong
Published: July 2024

In this study, we utilized a feature optimization method combining texture and topographical factors with the random forest (RF) approach to identify changes in the extent of the debris cover around the Tianshan Tomur Peak between 1989 and 2022. Based on Sentinel-1 image data, we extracted glacier flow velocities using an offset tracking method and conducted a long-term analysis of flow velocities in combination with existing datasets. The debris identification results for 2022 showed that the debris-covered area in the study region was 409.2 km2, constituting 22.8% of the total glacier area. Over 34 years, the area of debris cover expanded by 69.4 km2, reflecting a growth rate of 20.0%. Analysis revealed that glaciers in the Tomur Peak area have been decelerating at an overall rate of −4.0% per decade, with the complexity of the glacier bed environment and the instability of the glacier’s internal structure contributing to significant seasonal and interannual variability in the movement speeds of individual glaciers.
The Ecological Economics of Light Pollution: Impacts on Ecosystem Service Value
Authors: Anderson Sharolyn J. , Kubiszewski Ida , Sutton Paul C.
Published: July 2024

Light pollution has detrimental impacts on wildlife, human health, and ecosystem functions and services. This paper explores the impact of light pollution on the value of ecosystem services. We use the Simplified All-Sky Light Pollution Ratio (sALR) as a proxy for the negative impact of light pollution and the Copernicus PROBA-V Global Landcover Database as our proxy of ecosystem service value based on previously published ecosystem service values associated with a variety of landcovers. We use the sALR value to ‘degrade’ the value of ecosystem services. This results in a 40% reduction in ecosystem service value in those areas of the world with maximum levels of light pollution. Using this methodology, the estimate of the annual loss of ecosystem service value due to light pollution is USD 3.4 trillion. This represents roughly 3% of the total global value of ecosystem services and 3% of the global GDP, estimated at roughly USD 100 trillion in 2022. A summary of how these losses are distributed amongst the world’s countries and landcovers is also presented.
Analysis of Urbanization-Induced Land Subsidence in the City of Recife (Brazil) Using Persistent Scatterer SAR Interferometry
Authors: Souza Wendson de Oliveira , de Moura Reis Luis Gustavo , da Silva Pereira Cabral Jaime Joaquim , Ruiz-Armenteros Antonio Miguel , Quental Coutinho Roberto , da Penha Pacheco Admilson , Ramos Aragão Junior Wilson
Published: July 2024

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region’s vulnerability to soil subsidence.
Concurrent Validity Evidence for Pressure-Sensing Walkways Measuring Spatiotemporal Features of Gait: A Systematic Review and Meta-Analysis
Authors: Sanders Ozell , Wang Bin , Kontson Kimberly
Published: July 2024

Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of any systematic review of studies conducted to assess the concurrent validity of pressure-sensing walkway technologies. The results of such an analysis could establish the body of evidence needed to confidently use these systems as reference or gold-standard systems when validating novel tools or measures. A comprehensive search of electronic databases including MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed. The initial search yielded 7670 papers. After removing duplicates and applying study inclusion/exclusion criteria, 11 papers were included in the systematic review with 10 included in a meta-analysis. There were 25 spatial and temporal gait parameters extracted from the included studies. The results showed there was not a significant bias for nearly all spatiotemporal gait parameters when the walkway system was compared to the reference systems. The findings from this analysis should provide confidence in using the walkway systems as reference systems in future studies to support the evaluation and validation of novel technologies deriving gait parameters.
Grating Structures for Silver-Based Surface Plasmon Resonance Sensors with Adjustable Excitation Angle
Authors: Sarapukdee Pongsak , Schulz Dirk , Palzer Stefan
Published: July 2024

Silver-based grating structures offer means for implementing low-cost, efficient grating couplers for use in surface plasmon resonance (SPR) sensors. One-dimensional grating structures with a fixed periodicity are confined to operate effectively within a single planar orientation. However, two-dimensional grating structures as well as grating structures with variable periodicity allow for the plasmon excitation angle to be seamlessly adjusted. This study demonstrates silver-based grating designs that allow for the plasmon excitation angle to be adjusted via rotation or beam position. The flexible angle adjustment opens up the possibility of developing SPR sensor designs with an expanded dynamic range and increased flexibility in sensing applications. The results demonstrate that efficient coupling into two diffraction orders is possible, which ultimately leads to an excitation angle range from 16° to 40° by rotating a single structure. The findings suggest a promising direction for the development of versatile and adaptable SPR sensing platforms with enhanced performance characteristics.
Transformable Gaussian Reward Function for Socially Aware Navigation Using Deep Reinforcement Learning
Authors: Kim Jinyeob , Kang Sumin , Yang Sungwoo , Kim Beomjoon , Yura Jargalbaatar , Kim Donghan
Published: July 2024

Robot navigation has transitioned from avoiding static obstacles to adopting socially aware navigation strategies for coexisting with humans. Consequently, socially aware navigation in dynamic, human-centric environments has gained prominence in the field of robotics. One of the methods for socially aware navigation, the reinforcement learning technique, has fostered its advancement. However, defining appropriate reward functions, particularly in congested environments, holds a significant challenge. These reward functions, crucial for guiding robot actions, necessitate intricate human-crafted design due to their complex nature and inability to be set automatically. The multitude of manually designed reward functions contains issues such as hyperparameter redundancy, imbalance, and inadequate representation of unique object characteristics. To address these challenges, we introduce a transformable Gaussian reward function (TGRF). The TGRF possesses two main features. First, it reduces the burden of tuning by utilizing a small number of hyperparameters that function independently. Second, it enables the application of various reward functions through its transformability. Consequently, it exhibits high performance and accelerated learning rates within the deep reinforcement learning (DRL) framework. We also validated the performance of TGRF through simulations and experiments.
Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery
Authors: Zhang Chuanqi , Weng Xiangrui , Cao Yunfeng , Ding Meng
Published: July 2024

In recent years, there has been extensive research and application of unsupervised monocular depth estimation methods for intelligent vehicles. However, a major limitation of most existing approaches is their inability to predict absolute depth values in physical units, as they generally suffer from the scale problem. Furthermore, most research efforts have focused on ground vehicles, neglecting the potential application of these methods to unmanned aerial vehicles (UAVs). To address these gaps, this paper proposes a novel absolute depth estimation method specifically designed for flight scenes using a monocular vision sensor, in which a geometry-based scale recovery algorithm serves as a post-processing stage of relative depth estimation results with scale consistency. By exploiting the feature correspondence between successive images and using the pose data provided by equipped navigation sensors, the scale factor between relative and absolute scales is calculated according to a multi-view geometry model, and then absolute depth maps are generated by pixel-wise multiplication of relative depth maps with the scale factor. As a result, the unsupervised monocular depth estimation technology is extended from relative depth estimation in semi-structured scenes to absolute depth estimation in unstructured scenes. Experiments on the publicly available Mid-Air dataset and customized data demonstrate the effectiveness of our method in different cases and settings, as well as its robustness to navigation sensor noise. The proposed method only requires UAVs to be equipped with monocular camera and common navigation sensors, and the obtained absolute depth information can be directly used for downstream tasks, which is significant for this kind of vehicle that has rarely been explored in previous depth estimation studies.
Numerical Simulation and Field Monitoring of Blasting Vibration for Tunnel In-Situ Expansion by a Non-Cut Blast Scheme
Authors: Guan Zhenchang , Xie Lifu , Chen Dong , Shi Jingkang
Published: July 2024

There have been ever more in-situ tunnel extension projects due to the growing demand for transportation. The traditional blast scheme requires a large quantity of explosive and the vibration effect is hard to control. In order to reduce explosive consumption and the vibration effect, an optimized non-cut blast scheme was proposed and applied to the in-situ expansion of the Gushan Tunnel. Refined numerical simulation was adopted to compare the traditional and optimized blast schemes. The vibration attenuation within the interlaid rock mass and the vibration effect on the adjacent tunnel were studied and compared. The simulation results were validated by the field monitoring of the vibration effect on the adjacent tunnel. Both the simulation and the monitoring results showed that the vibration velocity on the adjacent tunnel’s back side was much smaller than its counterpart on the blast side, i.e., the presence of cavity reduced the blasting vibration effect significantly. The optimized non-cut blast scheme, which effectively utilized the existing free surface, could reduce the explosive consumption and vibration effect significantly, and might be preferred for in-situ tunnel expansion projects.
Switchable Terahertz Metasurfaces for Spin-Selective Absorption and Anomalous Reflection Based on Vanadium Dioxide
Authors: Mao Jinxian , Yang Fengyuan , Wang Qian , Chen Yuzi , Wang Nan
Published: July 2024

Conventional chiral metasurfaces are constrained by predetermined functionalities and have limited versatility. To address these constraints, we propose a novel chirality-switchable terahertz (THz) metasurface with integrated heating control circuits tailored for spin-selective anomalous reflection, leveraging the phase-change material vanadium dioxide (VO2). The reversible and abrupt insulator-to-metal phase transition feature of VO2 is exploited to facilitate a chiral meta-atom with spin-selectivity capabilities. By employing the Pancharatnam–Berry phase principle, complete 2π reflection phase coverage is achieved by adjusting the orientation of the chiral structure. At the resonant frequency of 0.137 THz, the designed metasurface achieves selective absorption of a circularly polarized wave corresponding to the state of the VO2 patches. Concurrently, it reflects the circularly polarized wave of the opposite chirality anomalously at an angle of 28.4° while maintaining its handedness. This chirality-switchable THz metasurface exhibits promising potential across various applications, including wireless communication data capacity enlargement, polarization modulation, and chirality detection.
Monitoring and Analysis of Prestress Loss in Prestressed Box Girder Bridges Strengthened with External Prestressing
Authors: Guo Haoran , Yang Jing , Tang Renjie , Yang Caiqian , Xu Fu
Published: July 2024

To investigate the effects of long-term prestress loss on concrete box girders strengthened with external prestressing, a large-span box girder, in service for over 20 years and strengthened with external prestressing, was monitored for four months. Prestress loss in the longitudinal, vertical, and transverse directions of the box girder was calculated according to Chinese code requirements. Magnetic flux rope force transducers were used to monitor the prestress loss in the external prestressing cables. Fiber Bragg Grating (FBG) sensors were used to monitor deflection changes at the mid-span of the bridge. Finally, the effect of prestress loss in the longitudinal, vertical, and transverse tendons on mid-span deflection was investigated through simulations using ABAQUS software. The results show that instantaneous prestress loss accounts for most of the total loss compared to long-term loss, and that longitudinal prestress loss has the most significant effect on mid-span deflection. The impact of longitudinal prestress loss on deflection before and after strengthening was also compared. The downward deflection and up-ward arch caused by longitudinal tendon prestress loss were reduced after strengthening, con-firming the effectiveness of the external prestressing method.
Online Flow Measurement of Liquid Metal Solutions Based on Impact Force Sequences: Modeling Analysis, Simulation, and Validation of Experimental Results
Authors: Li Qiguang , Zheng Xiru , He Yu , Xu Fangmin , Zhuang Yulin , Zeng Bingji , Duan Bofang
Published: July 2024

Aiming at the existing high-temperature liquid metal flow online accurate measurement by the metal melt characteristics, installation space, and high-temperature environment adaptability limitations, this paper innovatively puts forward a soft measurement method based on the impact force generated in the fluid flow process as an observational variable series. Fluid mechanics theory and simulation software are used to analyze and verify the feasibility of the impact force as an observable variable to measure the flow rate, followed by the construction of the CNN-LSTM-CNN-Double (CLCD) flow measurement model of impact force and flow rate based on the parameters of the learning rate and the number of training times, and finally the construction of a test platform for the flow measurement, and the validity of the method is verified through actual operation.
Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations
Authors: Zhan Qihua , Ding Yuxin , Lei Tian , Yin Xiaohong , Wei Leyu , Liu Yunpeng , Luo Qin
Published: July 2024

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.
A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition
Authors: Feng Jianchao , Si Yujuan , Zhang Yu , Sun Meiqi , Yang Wenke
Published: July 2024

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.
Blockchain-Based Caching Architecture for DApp Data Security and Delivery
Authors: Kim Daun , Park Sejin
Published: July 2024

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model’s effectiveness in providing secure and high-performance DApp solutions.
Analysis of the Generalization Ability of Defogging Algorithms on RICE Remote Sensing Images
Authors: Miao Guisheng , Zhang Zhongpeng , Wang Zhanbei
Published: July 2024

This paper explores the generalization ability of defogging algorithms on RICE (A Remote Sensing Image Dataset for Cloud Removal) remotely sensed images. RICE is a dataset of remotely sensed images used for removing clouds, allowing the researcher to better evaluate the performance of defogging algorithms for cloud removal from remotely sensed images. In this paper, four classical defogging algorithms, including AOD-Net, FFA-Net, dark channel prior, and DehazeFormer, are selected and applied to the task of de-cloud in RICE remote sensing images. The performance of these algorithms on the RICE dataset is analyzed by comparing the experimental results, and their differences, advantages, and disadvantages in dealing with de-clouded remote sensing images are explored. The experimental results show that the four defogging algorithms are capable of performing well on uniform thin cloud images, but there is a color distortion and the performance is weak when it comes to inhomogeneous clouds as well as thick clouds. So, the generalization ability of the algorithms is weak when the defogging algorithms are applied to the problem of cloud removal. Finally, this paper proposes improvement ideas for the de-cloud problem of RICE remote sensing images and looks forward to possible future research directions.
Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications
Authors: Im Eunji , Choi Minji , Cho Kyunghoon
Published: July 2024

This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the controller to emulate human expert behavior. Rules are represented as Signal Temporal Logic (STL) formulas. A robustness margin, quantifying the degree of rule satisfaction, is learned from expert demonstrations using a Conditional Variational Autoencoder (CVAE). This learned margin is then applied in the MPC process to guide the prioritization or exclusion of rules. In a track driving simulation, our method demonstrates the ability to generate behavior resembling that of human experts and effectively manage rule-based dilemmas.
Breathing Monitoring in Soccer: Part I—Validity of Commercial Wearable Sensors
Authors: Innocenti Lorenzo , Romano Chiara , Greco Giuseppe , Nuccio Stefano , Bellini Alessio , Mari Federico , Silvestri Sergio , Schena Emiliano , Sacchetti Massimo , Massaroni Carlo , Nicolò Andrea
Published: July 2024

Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer.
Do Photopletysmographic Parameters of Arterial Stiffness Differ Depending on the Presence of Arterial Hypertension and/or Atherosclerosis?
Authors: Szołtysek-Bołdys Izabela , Zielińska-Danch Wioleta , Łoboda Danuta , Gołba Krzysztof S. , Sarecka-Hujar Beata
Published: July 2024

Background: Hypertension and atherosclerotic cardiovascular diseases (ASCVD) increase cardiovascular risk and worsen patients’ prognoses. One early predictor of increased risk is a change in arterial stiffness. This study aimed to evaluate arterial stiffness parameters using the non-invasive photoplethysmography (PPG) method in Polish patients with arterial hypertension (AH) and/or atherosclerosis (AS). Methods: The study group consisted of 333 patients (Caucasians, both sexes, aged 30–85 years old). Patients were analyzed in four groups depending on AH and AS (Group I: patients without AH or AS, Group II: AH patients, Group III: AS patients, and Group IV: AH/AS patients) and, in addition, according to sex and history of SARS-CoV-2 infection. Arterial stiffness parameters, i.e., reflection index (RI), peak-to-peak time (PPT), and stiffness index (SI) were automatically calculated with PPG based on the analysis of the pulse wave contour. Results: Mean values of RI and SI were higher in men than women (p < 0.001 each). Diastolic blood pressure (DBP) also differed between sexes (p = 0.010). Mean SI values differed between the study groups (p = 0.038) with the highest SI found in AS/AH patients and the lowest—in patients without AH or AS. The mean SI values were significantly lower in women compared to men in both Group I and Group II (p = 0.006 and p < 0.001, respectively). The mean values of RI were also greater in men than in women in Group I and Group II (p < 0.001 for each group). Regarding COVID-19 history, only HR values differed between patients with and without COVID-19 in AH patients (p = 0.012). In AH patients, men had higher values of RI and SI compared to women (p < 0.001 and p < 0.001). On the other hand, AS women with COVID-19 had significantly greater mean values of SI (9.66 m/s ± 1.61) than men with COVID-19 (7.98 m/s ± 1.09) (p = 0.045). Conclusions: The present study confirmed that sex had a significant impact on arterial stiffness parameters. Both AH and AS affected arterial stiffness. Heart rate was greater in hypertensive patients after COVID-19 compared to hypertensive patients without COVID-19.
Short-Term vs. Long-Term: A Critical Review of Indoor Radon Measurement Techniques
Authors: Mphaga Khathutshelo Vincent , Mbonane Thokozani Patrick , Utembe Wells , Rathebe Phoka Caiphus
Published: July 2024

Radon is a known carcinogen, and the accurate assessment of indoor levels is essential for effective mitigation strategies. While long-term testing provides the most reliable data, short-term testing (STT) offers a quicker and more cost-effective alternative. This review evaluated the accuracy of STT in predicting annual radon averages and compared testing strategies in Europe (where long-term measurements are common) and the United States (where STT is prevalent). Twenty (20) studies were systematically identified through searches in scientific databases and the grey literature, focusing on STT accuracy and radon management. This review revealed several factors that influence the accuracy of STT. Most studies recommended a minimum four-day test for initial screening, but accuracy varied with radon levels. For low levels (<75 Bq/m3), a one-week STT achieved high confidence (>95%) in predicting annual averages. However, accuracy decreased for moderate levels (approximately 50% success rate), necessitating confirmation with longer testing periods (3 months). High radon levels made STT unsuitable due to significant fluctuations. Seasonality also played a role, with winter months providing a more representative picture of annual radon averages. STT was found to be a useful method for screening low-risk areas with low radon concentrations. However, its limitations were evident in moderate- and high-level scenarios. While a minimum of four days was recommended, longer testing periods (3 months or more) were crucial for achieving reliable results, particularly in areas with potential for elevated radon exposure. This review suggests the need for further research to explore the possibility of harmonizing radon testing protocols between Europe and the United States.
Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors
Authors: Li Hui , Zhu Pei , Shao Quan
Published: July 2024

Air traffic controllers’ mental workload significantly impacts their operational efficiency and safety. Detecting their mental workload rapidly and accurately is crucial for preventing aviation accidents. This study introduces a mental workload detection model for controllers based on power spectrum features related to gamma waves. The model selects the feature with the highest classification accuracy, β + θ + α + γ, and utilizes the mRMR (Max-Relevance and Min-Redundancy) algorithm for channel selection. Furthermore, the channels that were less affected by ICA processing were identified, and the reliability of this result was demonstrated by artifact analysis brought about by EMG, ECG, etc. Finally, a model for rapid mental workload detection for controllers was developed and the detection rate for the 34 subjects reached 1, and the accuracy for the remaining subjects was as low as 0.986. In conclusion, we validated the usability of the mRMR algorithm in channel selection and proposed a rapid method for detecting mental workload in air traffic controllers using only three EEG channels. By reducing the number of EEG channels and shortening the data processing time, this approach simplifies equipment application and maintains detection accuracy, enhancing practical usability.
Smart Medical Evacuation Support System for the Military
Authors: Krygier Jaroslaw , Lubkowski Piotr , Maslanka Krzysztof , Dobrowolski Andrzej P. , Mrozek Tomasz , Znaniecki Wojciech , Oskwarek Pawel
Published: July 2024

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations. This paper presents the technical details of the decision support system for medical evacuation to support this process. The basis for the functioning of this system is the continuous measurement of vital signs of soldiers via a specialized measurement module with a set of medical sensors. Vital signs values are then transmitted via the communication module to the analysis and inference module, which automatically determines the color of medical triage and the soldier’s chance of survival. This paper presents the results of tests of our system to validate it, which were carried out using test vectors of soldiers’ vital signs, as well as the results of the system’s performance on a group of volunteers who performed typical activities of tactical operations. The results of this study showed the usefulness of the developed system for supporting military medical services in military operations.
Circular SAW Resonators: Influence of Sensitive Element Dimensions on Strength Characteristics and First Experimental Samples
Authors: Shevchenko Sergey Yu. , Mikhailenko Denis A. , Kukaev Alexander S. , Venediktov Vladimir Yu.
Published: July 2024

In preceding research endeavors, the frequency characteristics of a ring resonator on surface acoustic waves made of various materials were studied. Investigations encompassed fixation techniques within the housing, the impact of external variables on these components, and the most efficient configuration of the interdigital transducer within the ring resonator to curtail bandwidth. This current study is dedicated to investigating the correlation between sensitivity and the highest measurable acceleration concerning the dimensions of these sensitive elements. Furthermore, it involves assessing the attributes of produced experimental samples to verify the simulation results. The results obtained represent the possibility of creating a micromechanical accelerometer that can be used in the automotive industry as a g-sensor shock, as well as in industries where the numerical value of high overloads is required.
Noise-Adaptive State Estimators with Change-Point Detection
Authors: Hou Xiaolei , Zhao Shijie , Hu Jinjie , Lan Hua
Published: July 2024

Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters. By combining variational inference with change-point detection in an online Bayesian fashion, two adaptive estimators—a change-point-based adaptive Kalman filter (CPAKF) and a change-point-based adaptive Kalman smoother (CPAKS)—are proposed in a recursive detection and estimation process. In each iteration, the run-length probability of the current maneuver mode is first calculated, and then the joint posterior of the target state and process noise parameter conditioned on the run length is approximated by variational inference. Compared with existing variational noise-adaptive Kalman filters, the proposed methods are robust to initial iterative value settings, improving their capability of tracking sharply maneuvering targets. Meanwhile, the change-point detection divides the non-stationary time sequence into several stationary segments, allowing for an adaptive sliding length in the CPAKS method. The tracking performance of the proposed methods is investigated using both synthetic and real-world datasets of maneuvering targets.
A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity
Authors: Wang Baofeng , Li Qinghai , Liu Jia , Wang Zumin , Yu Qiudong , Liang Rui
Published: July 2024

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP’s contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs’ contribution, including APs’ health states and APs’ positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs’ discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.
A Positioning Alarm System for Explosive Impact Debris Protective Suit Based on an Accelerometer Array
Authors: Hu Jianing , Liu Chaoran , Wang Xucong , Wang Zai , Tong Xin , Li Fangqi , Jin Zhenyu , Wang Xiaoyuan , Che Lufeng , Yu Jing , Yao Defei , Wang Gaofeng , Dong Linxi
Published: July 2024

Protection suits are vital for firefighters’ safety. Traditional protection suits physically protect firemen from burns, but cannot locate the position of bodily injuries caused by impact debris. Herein, we present a wearable impact debris positioning system for firefighter protection suits based on an accelerometer array. Wearable piezoelectric accelerometers are distributed regularly on the suit to detect the vibration on different body parts, which is conducive to determining the position of injured body parts. In addition, the injured parts can be displayed on a dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11 ms, attributed to the smart signal processing method. This work provides a reliable and smart method for locating and assessing the position of bodily injuries caused by impact debris, which is significant because it enables fire commanders to rescue injured firefighters in time.
Research on Leak Detection of Low-Pressure Gas Pipelines in Buildings Based on Improved Variational Mode Decomposition and Robust Kalman Filtering
Authors: Lin Wenfeng , Tian Xinghao
Published: July 2024

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%.
BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks
Authors: Begum Khadija , Mozumder Md Ariful Islam , Joo Moon-Il , Kim Hee-Cheol
Published: July 2024

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.
Combined Effect of Sertraline and Capecitabine on Breast Cancer Cell Lines In Vitro and In Silico Evidence for Synergistic Interaction
Authors: Gul Serap Ozkaya , Korkut Alaaddin , Aydemir Esra
Published: July 2024

Background: Depression is a common mood disorder that manifests itself simultaneously with chronic diseases. It is especially common in patients diagnosed with cancer, and when neglected, it reduces the success of cancer treatment. The fact that breast cancer is the most common type of cancer in women shows that the treatment of depression in women with cancer is very important. As a result, cancer patients undergoing chemotherapy in oncology units also use antidepressants simultaneously. It is critical to correctly understand the interactions between drugs used in combination. Method: In this study, doses were prepared for MCF7 and MDAMB-231 cell lines by serial dilution from 1000 ng/mL to 1.95 ng/mL. Cell viability was calculated with the WST-1 kit by applying the prepared doses of capecitabine and sertraline alone. In the sertraline/capecitabine combination study, cell viability was examined in MDAMB-231 and MCF-7 cells by applying doses of 300, 100, 50, 25, 10 ng/mL. Combinations that showed selective cytotoxicity after the combination were analyzed with the CompuSyn program and the combination index (CI<1 = synergism) was calculated. Studies on caspase 3-8-9, DNA fragmentation and mTOR were continued using a combination that showed a synergistic effect. Result: It was determined that compared to drug use alone, the sertraline/capecitabine combination decreased cell viability. There is no significant difference in caspase-3,-8,-9 and DNA fragmentation in cancer cells, but there is a reduction in the level of mTOR. This suggests that the death mechanism may be autophagy. Docking studies with autophagy pathway-related proteins further support our results. It is noteworthy that the AKT1-sertraline complex had the best binding affinity among the target proteins (−9.1 kcal/mol).
Gender Differences and Critical Thinking: A Study on the Written Compositions of Primary Education Students
Authors: Albarracín-Vivo Domingo , Encabo-Fernández Eduardo , Jerez-Martínez Isabel , Hernández-Delgado Lourdes
Published: July 2024

The development of critical thinking is essential to the treatment of gender perspective in Primary Education. For this reason, this study focused on determining the critical ability of primary students depending on gender, analysing the possible connections between the participants’ breadth of thought and spelling competence. The methodology was based on a humanistic–interpretative ad hoc approach to categorical organisation. A total of 636 Primary Education students from the Region of Murcia (Spain) participated in a field activity in which they created a written composition based on a pretext that contained an image showing a controversial gender situation; this illustration incited a critical response in the creation of each story. The compositions were analysed individually and qualitatively using the Atlas.Ti software (v.8.4). The most relevant results appear to prove the higher critical ability of the girls who participated in this study, and additionally show connections with the spelling competence of these students. However, the critical thinking and spelling values shown were still far from what would be expected of a developed society.
Life Satisfaction: Insights from the World Values Survey
Authors: Homocianu Daniel
Published: July 2024

This paper explores enduring influences on life satisfaction using empirical analysis of World Values Survey (WVS) data (four versions of the most comprehensive dataset, namely 1.6, 2.0, 3.0 and 4.0). Five significant values emerged—financial satisfaction, happiness, freedom of choice, health, and democracy. Through rigorous selection processes and various statistical techniques, a subset of three determinants resulted, along with consecrated socio-demographic variables such as age, gender, marital status, social class, and settlement size. Advanced methodologies such as feature selection, random and non-random cross-validations, overfitting removal, collinearity and reverse causality checks, and different regressions served to evaluate and validate robust models. Nomograms helped to predict life satisfaction probabilities. The findings contribute to understanding life satisfaction dynamics and offer practical insights for future research and policy.