bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Rhodamine B Fluorescent Probe Derived from Carboxymethyl Chitosan for the Selective Detection of Fe3+
Authors: Yang Mei , Tang Zixi , Yu Chunwei , Zhang Jun
Published: November 2024

In this study, we synthesized a fluorescent material by modifying the C-2 amino group of carboxymethyl chitosan with a rhodamine B derivative, which was proposed and demonstrated using 1H NMR and FT-IR measurements. A series of experiments including selectivity, sensitivity, reversibility, pH, and water content were conducted to investigate the fluorometric and colorimetric properties of the grafted polymer. Utilizing a Fe3+-induced ring-opening mechanism of the rhodamine B spirolactam, we found that the grafted polymer exhibited a highly selective fluorescence response to Fe3+, with enhanced fluorescence at 583 nm compared to other tested metal ions and anions, accompanied by the characteristic absorption peak of rhodamine B that appeared at 561 nm with a noticeable color change from colorless to pink, facilitating visual observation. Additionally, the modified probe, composed of carboxymethyl chitosan, was easily regenerated through treatment with EDTA.
Demonstrating the Efficacy of Core-Shell Silica Catalyst in Depolymerizing Polycarbonate
Authors: Losito Onofrio , Pisani Pasquale , De Cataldo Alessia , Annese Cosimo , Clausi Marina , Comparelli Roberto , Pinto Daniela , D’Accolti Lucia
Published: November 2024

Polycarbonate (PC) is a highly versatile plastic material that is extensively utilized across various industries due to its superior properties, including high impact strength and heat resistance. However, its durability presents significant challenges for recycling and waste management. Polycarbonate is a thermoplastic polymer representative of the class of condensation reaction polymers obtained from the reaction of bisphenol A (BPA) and a carbonyl source, such as phosgene or alkyl and aryl carbonate. The recycling processes for PC waste include mechanical recycling, blending with other materials, pyrolysis, and chemical recycling. The latter is based on the cleavage of carbonate units to their corresponding monomers or derivatives through alcoholysis and/or hydrolysis and ammonolysis, normally under basic conditions and without catalysts. This study investigates the efficacy of the use of several heterogeneous catalysts based on silica gel as a robust support, including Sc(III)silicate (thortveitite), which has been previously reported for the preparation of polyesters, core-shell Si-ILs, and core-shell Si-ILs-ZnO, which has never been used before in the depolymerization of polycarbonate, proposing a sustainable and efficient method for recycling this valuable polymer. We chose to explore core-shell catalysts because these catalysts are robust and recyclable, and have been used in very harsh industrial processes. The core-shell silica catalysts used in this study were characterized by XRD; SEM_EDX, FT-IR, and ICP-OES analysis. In our experimental protocol, polycarbonate samples were exposed to the catalyst under controlled conditions (60–150 °C, for 12–24 h) using both oxygen and nitrogen nucleophiles. The depolymerization process was systematically monitored using advanced analytical techniques (GC/MS and GPC chromatography). The experimental results indicated that core-shell silica catalyst exhibits high efficacy, with up to 75% yield for the ammonolysis reaction, producing monomers of high purity. These monomers can be reused for the synthesis of new polycarbonate materials, contributing to a more sustainable approach to polycarbonate recycling.
Optimization Research on the Impact of Charging Load and Energy Efficiency of Pure Electric Vehicles
Authors: Xin Huajian , Li Zhejun , Jiang Feng , Mo Qinglie , Hu Jie , Zhou Junming
Published: November 2024

In this paper, the negative impact of the charging load generated by the disorderly charging scheme of large-scale pure electric vehicles on the operation performance of the power grid system and the problem of reducing its charging energy efficiency are studied and analyzed. First, based on Matlab 2022a simulation software and the Monte Carlo random sampling method, the probability density model of the factors affecting the charging load is constructed, and the total charging load of different quantities is simulated. Second, the IEEE33-node distribution network model is introduced to simulate the influence of charging load on the grid under different permeability schemes. Finally, the multi-objective genetic algorithm is used to optimize the charging cost and battery life. Taking the 20% permeability scheme as an example, the research results show that, compared with the disorderly charging scheme, the multi-objective optimization scheme reduces the peaking valley difference rate by 24.34%, the charging load power generation cost by 29.5%, and the charging cost by 23.9%. The power grid profit increased by 45.8%, and the research conclusion has practical significance for the energy efficiency optimization of pure electric vehicles.
The Design and Experimental Research on a High-Frequency Rotary Directional Valve
Authors: Hua Shunming , Liu Siqiang , Qiu Zhuo , Wang Xiaojun , Zhang Xuechang , Zhang Huijuan
Published: November 2024

A directional valve is a core component of the electro-hydraulic shakers in fatigue testing machines, controlling the cylinder or motor that drives the piston for reciprocating linear or rotary motion. In this article, a high-speed rotating directional valve with a symmetrical flow channel layout is designed to optimize the force on the valve core of the directional valve. A comparative analysis is conducted on the flow capacity of valve ports with different shapes. A steady-state hydrodynamic mathematical model is established for the valve core, the theoretical analysis results of which are verified through a Computational Fluid Dynamics (CFD) simulation of the fluid domain inside the directional valve. A prototype of the rotatory directional valve is designed and manufactured, and an experimental platform is built to measure the hydraulic force acting on the valve core to verify the performance of the valve. The displacement curves at different commutation frequencies are also obtained. The experimental results show that the symmetrical flow channel layout can significantly optimize the hydraulic force during the movement of the valve core. Under a pressure of 1 MPa, the hydraulic cylinder driven by the prototype can achieve a sinusoidal curve output with a maximum frequency of 60 Hz and an amplitude of 2.5 mm. The innovation of this design lies in the creation of a directional valve with a symmetric flow channel layout. The feasibility of the design is verified through modeling, simulation, and experimentation, and it significantly optimizes the hydraulic forces acting on the spool. It provides us with the possibility to further improve the switching frequency of hydraulic valves and has important value in engineering applications.
The Evolution of Long-Range Hunting with Stone-Tipped Weapons During the Afrotropic Middle Stone Age: A Testable Framework Based on Tip Cross-Sectional Area
Authors: Sahle Yonatan , Lombard Marlize
Published: November 2024

In the Afrotropic biogeographic realm, with its diverse and high-density mammal population, early humans may have been hunting with stone-tipped weapons since ~500,000 years ago. Being able to hunt effectively from a distance has several important adaptive advantages. Yet, until now, African long-range javelin hunting remained unexplored as intermediate between short/medium-range, hand-delivered and long-range, mechanically projected weapons. Insights gained from a new Afrotropic comparative dataset with 950 weapon tips of known use—including several javelin types—provide a contextually appropriate middle-range tool for assessing the probable effective hunting ranges of Middle Stone Age points. We use a novel application of the ballistically relevant tip cross-sectional area (TCSA) statistic to define contact, short-, medium-, long- and maximum-range hunting and discuss the adaptive advantages for each. The approach is applied to suggest developments and variations in the best-fit hunting ranges of 5597 stone points from 62 Middle Stone Age Afrotropic assemblages. By aligning our results with the Marine Isotope Stage (MIS) record we hypothesize that effective long-range (~20–30 m) hunting with stone-tipped weapons was probably not practiced by ≥MIS 8, and that experimentation with long-range javelins—similar to those used by contemporary Ethiopian hunters—over these distances may have started during MIS 6, becoming part of the everyday Afrotropic hunting arsenal by the end of MIS 5.
The Ground-Penetrating Radar Image Matching Method Based on Central Dense Structure Context Features
Authors: Xu Jie , Lai Qifeng , Wei Dongyan , Ji Xinchun , Shen Ge , Yuan Hong
Published: November 2024

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively.
Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale
Authors: Vazirani Harsh , Wu Xiaofeng , Srivastava Anurag , Dhar Debajyoti , Pathak Divyansh
Published: November 2024

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R2 of 90.16, which was a significant improvement over the base XGBoost model’s R2 of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R2 of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions.
Embedding Tree-Based Intrusion Detection System in Smart Thermostats for Enhanced IoT Security
Authors: Javed Abbas , Awais Muhammad Naeem , Qureshi Ayyaz-ul-Haq , Jawad Muhammad , Arshad Jehangir , Larijani Hadi
Published: November 2024

IoT devices with limited resources, and in the absence of gateways, become vulnerable to various attacks, such as denial of service (DoS) and man-in-the-middle (MITM) attacks. Intrusion detection systems (IDS) are designed to detect and respond to these threats in IoT environments. While machine learning-based IDS have typically been deployed at the edge (gateways) or in the cloud, in the absence of gateways, the IDS must be embedded within the sensor nodes themselves. Available datasets mainly contain features extracted from network traffic at the edge (e.g., Raspberry Pi/computer) or cloud servers. We developed a unique dataset, named as Intrusion Detection in the Smart Homes (IDSH) dataset, which is based on features retrievable from microcontroller-based IoT devices. In this work, a Tree-based IDS is embedded into a smart thermostat for real-time intrusion detection. The results demonstrated that the IDS achieved an accuracy of 98.71% for binary classification with an inference time of 276 microseconds, and an accuracy of 97.51% for multi-classification with an inference time of 273 microseconds. Real-time testing showed that the smart thermostat is capable of detecting DoS and MITM attacks without relying on a gateway or cloud.
Fault Diagnosis Method for Converter Stations Based on Fault Area Identification and Evidence Information Fusion
Authors: Wang Shuzheng , Wang Xiaoqi , Ren Xuchao , Wang Ye , Xu Sudi , Ge Yaming , He Jiahao
Published: November 2024

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station.
Unsupervised Canine Emotion Recognition Using Momentum Contrast
Authors: Bhave Aarya , Hafner Alina , Bhave Anushka , Gloor Peter A.
Published: November 2024

We describe a system for identifying dog emotions based on dogs’ facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp as ‘Exploring’, ‘Sadness’, ‘Playing’, ‘Rage’, ‘Fear’, ‘Affectionate’ and ‘Lust’. We modified the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieved an accuracy of 43.2% and a baseline of 14%. We also trained this model on a second publicly available dataset that resulted in an accuracy of 48.46% but had a baseline of 25%. We compared our unsupervised approach with a supervised model based on a ResNet50 architecture. This model, when tested on our dataset with the seven Panksepp labels, resulted in an accuracy of 74.32%
Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications
Authors: Xing Zhifang , Qin Yunhui , Du Changhao , Wang Wenzhang , Zhang Zhongshan
Published: November 2024

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively.
High-Precision Two-Dimensional Angular Sensor Based on Talbot Effect
Authors: Song Liuxing , Wang Xiaoyong , He Jinping , Tian Guoliang , Zhao Kailun
Published: November 2024

The precision of two-dimensional angular sensing is crucial for applications such as navigation, robotics, and optical alignment. Conventional methods often struggle to balance precision, dynamic range, and affordability. We introduce a novel method leveraging the Talbot effect, enhanced by 3D printing technology, to fabricate a grating prototype for high-precision angular measurements. The method detects amplitude grating displacement at the self-imaging position and employs a frequency filtering algorithm for improved accuracy. Rigorous validation through simulations and physical experiments demonstrates that our method achieves a detection resolution of 0.4 arcseconds and a dynamic range exceeding 1400 arcseconds. This research presents a cost-effective, high-precision angular detection solution with potential for broad application in precision mechanical assembly, optical alignment, and other relevant domains.
Demand Response Strategy Considering Industrial Loads and Energy Storage with High Proportion of Wind-Power Integration
Authors: Tian Chongyi , Li Julin , Wang Chunyu , Lin Longlong , Yan Yi
Published: November 2024

To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost.
Research Progress on Applying Intelligent Sensors in Sports Science
Authors: Zhao Jingjing , Yang Yulong , Bo Leng , Qi Jiantao , Zhu Yongqiang
Published: November 2024

Smart sensors represent a significant advancement in modern sports science, and their effective use enhances the ability to monitor and analyze athlete performance in real time. The integration of these sensors has enhanced the accuracy of data collection related to physical activity, biomechanics, and physiological responses, thus providing valuable insights for performance optimization, injury prevention, and rehabilitation. This paper provides an overview of the research progress in the application of smart sensors in the field of sports science; highlights the current advances, challenges, and future directions in the deployment of smart sensor technologies; and anticipates their transformative impact on sports science and athlete development.
Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning
Authors: Qin Min , Xie Zhenbo , Xie Jing , Yu Xiaolin , Ma Zhongyuan , Wang Jinrui
Published: November 2024

The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt micro looseness monitoring method using deep learning was proposed. Specifically, the addition of batch normalization methods enables the established Batch Normalized Stacked Autoencoders (BNSAEs) model to converge quickly and effectively, making the model easy to build and effective. Additionally, using characterization functions preprocess the original response signal not only simplifies the data structure but also ensures the integrity of features, which is beneficial for network training and reduces time costs. Finally, the effectiveness of the proposed method was verified by taking the bolted connection structures of two key components of aircraft engines, namely bolt connection structures and flange connection structures, as examples.
Automatic Image Registration Provides Superior Accuracy Compared with Surface Matching in Cranial Navigation
Authors: Frisk Henrik , Jensdottir Margret , Coronado Luisa , Conrad Markus , Hager Susanne , Arvidsson Lisa , Bartek Jiri , Burström Gustav , El-Hajj Victor Gabriel , Edström Erik , Elmi-Terander Adrian , Persson Oscar
Published: November 2024

Objective: The precision of neuronavigation systems relies on the correct registration of the patient’s position in space and aligning it with radiological 3D imaging data. Registration is usually performed by the acquisition of anatomical landmarks or surface matching based on facial features. Another possibility is automatic image registration using intraoperative imaging. This could provide better accuracy, especially in rotated or prone positions where the other methods may be difficult to perform. The aim of this study was to validate automatic image registration (AIR) using intraoperative cone-beam computed tomography (CBCT) for cranial neurosurgical procedures and compare the registration accuracy to the traditional surface matching (SM) registration method based on preoperative MRI. The preservation of navigation accuracy throughout the surgery was also investigated. Methods: Adult patients undergoing intracranial tumor surgery were enrolled after consent. A standard SM registration was performed, and reference points were acquired. An AIR was then performed, and the same reference points were acquired again. Accuracy was calculated based on the referenced and acquired coordinates of the points for each registration method. The reference points were acquired before and after draping and at the end of the procedure to assess the persistency of accuracy. Results: In total, 22 patients were included. The mean accuracy was 6.6 ± 3.1 mm for SM registration and 1.0 ± 0.3 mm for AIR. The AIR was superior to the SM registration (p < 0.0001), with a mean improvement in accuracy of 5.58 mm (3.71–7.44 mm 99% CI). The mean accuracy for the AIR registration pre-drape was 1.0 ± 0.3 mm. The corresponding accuracies post-drape and post-resection were 2.9 ± 4.6 mm and 4.1 ± 4.9 mm, respectively. Although a loss of accuracy was identified between the preoperative and end-of-procedure measurements, there was no statistically significant decline during surgery. Conclusions: AIR for cranial neuronavigation consistently delivered greater accuracy than SM and should be considered the new gold standard for patient registration in cranial neuronavigation. If intraoperative imaging is a limited resource, AIR should be prioritized in rotated or prone position procedures, where the benefits are the greatest.
Influence of Bladder Filling on Parameters of Body Composition by Bioimpedance Electrical Analysis: Observational Study
Authors: Ferri-Morales Asunción , Ando-Lafuente Sara , Lirio-Romero Cristina , Marzetti Emanuele , Bravo-Esteban Elisabeth
Published: November 2024

Bioelectrical impedance analysis (BIA) is a widely used method for estimating body composition, and its accuracy may be influenced by various factors, including bladder filling. This study aims to investigate the impact of bladder filling on the accuracy of BIA measurements. An experimental crossover study was conducted with sedentary young adults. The influence of bladder filling on total body water (TBW), fat mass (FM), fat-free mass (FFM), and basal metabolic rate (BMR) was assessed. Participant in underwear followed an overnight fast. They were instructed to abstain from vigorous physical activity and alcohol for at least 24 h prior to the session. The results obtained from single-frequency and multi-frequency BIA devices were compared. The findings suggest that bladder filling does not affect measured impedance; however, changes in weight following bladder voiding influenced derived BIA results. Specifically, TBW, FM, and BMR values significantly reduced after voiding (p < 0.05). Furthermore, the study found poor agreement between single-frequency and multi-frequency BIA devices, indicating that they are not interchangeable. Bladder filling does affect BIA measurements, not clinically meaningful. Further research is needed to explore the implications of these findings for clinical practice and research protocols.
Research on Improved GPC of Pantograph Considering Actuator Time Delay and External Disturbance
Authors: Wang Ying , Wang Yixuan , Chen Xiaoqiang , Wang Yuting , Ma Aiping
Published: November 2024

Active control of pantograph is an effective method to improve the current received quality in electrified railway systems. To alleviate the negative impact of time delay in pantograph actuator, a Controlled Auto-Regressive Integrated Moving Average (CARIMA) model was designed for pantograph active control. In conjunction with the Euler-Bernoulli catenary model, an improved generalized predictive control (IGPC) algorithm was proposed, and its stability was analyzed. Then, the control performance was verified and discussed through testing. Subsequently, the effects of external disturbances and time delay on control performance were discussed. The results indicate that the proposed controller with a larger control gain, exhibits better performance in reducing fluctuation in contact force between pantograph and catenary, despite being affected by external disturbance and actuator time delay, it still shows significant control performance.
Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network
Authors: Ullah Rahat , Ullah Sibghat , Ren Jianxin , Mao Yaya , Qi Zhipeng , Hussain Jamil , Wang Feng , Khan Faheem , Imtiaz Waqas Ahmed
Published: November 2024

High-capacity communication networks are built to provide high throughput and low latency to accommodate the growing demand for bandwidth. However, the provision of these features is subject to a robust underlying network, which can provide high capacity with maximum reliability in terms of the system’s connection availability. This work optimizes an existing 2D spectral–spatial optical code division multiple access (OCDMA) passive optical network (PON) to maximize connection availability while maintaining desirable communication capacity and capital expenditure. Optimization is performed by employing ring topology at the feeder level, which is used to provide a redundant path in case of connection failures. Furthermore, high transmission capacity is ensured by utilizing a pseudo-3D double-weight zero cross-correlation (DW-ZCC) code. The analysis is performed with Optisystem simulations to observe the performance of the system in terms of bit error rate (BER), received power, and eye openings. It is observed that the introduction of ring topology at the feeder level of the PON does not impact the overall transmission capacity of the system. The system can still support maximum transmission capacity at receiver sensitivities of up to −19 dB. Reliability analysis also shows that the optimized ring-based architecture can provide desirable connection availability compared to the existing system.
Improving the Rapidity of Magnitude Estimation for Earthquake Early Warning Systems for Railways
Authors: Noda Shunta , Iwata Naoyasu , Korenaga Masahiro
Published: November 2024

To improve the performance of earthquake early warning (EEW) systems, we propose an approach that utilizes the time-dependence of P-wave displacements to estimate the earthquake magnitude (M) based on the relationship between M and the displacement. The traditional seismological understanding posits that this relationship achieves statistical significance when the displacement reaches its final peak value, resulting in the adoption of time-constant coefficients. However, considering the potential for earlier establishment of the relationship’s significance than conventionally assumed, we analyze waveforms observed in Japan and determine the intercept in the relationship as a function of time from the P-wave onset. We demonstrate that our approach reduces the underestimation of M in the initial P-wave stages compared to the conventional technique. Consequently, we find a significant rise in the number of earlier warnings in the Japanese railway EEW system. Due to the inherent trade-off between the immediacy and accuracy of alarm outputs, the proposed method unavoidably leads to an increase in the frequency of alerts. Nonetheless, if deemed acceptable by system users, our approach can contribute to EEW performance improvement.
Fast Multi-Distance Time-Domain NIRS and DCS System for Clinical Applications
Authors: Nabacino Marco , Amendola Caterina , Contini Davide , Re Rebecca , Spinelli Lorenzo , Torricelli Alessandro
Published: November 2024

We have designed and built an improved system for combined Time-Domain Near-Infrared Spectroscopy (TD NIRS) and Diffuse Correlation Spectroscopy (DCS) measurements. The system features two independent channels, enabling TD NIRS and DCS acquisition at short and long source-detector distances to enhance depth sensitivity in layered tissues. Moreover, the device can operate at fast acquisition rates (up to 50 Hz) to monitor hemodynamic oscillations in biological tissues. An OEM (Original Equipment Manufacturer) TD NIRS device enables stable and robust acquisition of photon distribution of time-of-flight. For the DCS signals, the use of a time tagger and a software correlator allows us flexibility in post-processing. A user-friendly GUI controls TD NIRS data acquisition and online data analysis. We present results for the system characterization on calibrated tissue phantoms according to standardized protocols for performance assessment of TD NIRS and DCS devices. In-vivo measurements during rest and during vascular occlusions are also reported to validate the system in real settings.
DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR
Authors: Wei Xiaolong , Yin Ling , Zhang Liangliang , Wu Fei
Published: November 2024

For drone-based detection tasks, accurately identifying small-scale targets like people, bicycles, and pedestrians remains a key challenge. In this paper, we propose DV-DETR, an improved detection model based on the Real-Time Detection Transformer (RT-DETR), specifically optimized for small target detection in high-density scenes. To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. Experimental results on the VisDrone2019 dataset show that DV-DETR achieves an mAP@0.5 of 50.1%, a 1.7% improvement over the baseline model, while increasing detection speed from 75 FPS to 90 FPS, meeting real-time processing requirements. These improvements not only enhance the model’s accuracy and efficiency but also provide practical significance in complex, high-density urban environments, supporting real-world applications in UAV-based surveillance and monitoring tasks.
Microdetection of Nucleocapsid Proteins via Terahertz Chemical Microscope Using Aptamers
Authors: Ding Xue , Murakami Mana , Wang Jin , Inoue Hirofumi , Kiwa Toshihiko
Published: November 2024

In the detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), several methods have been employed, including the detection of viral ribonucleic acid (RNA), nucleocapsid (N) proteins, spike proteins, and antibodies. RNA detection, primarily through polymerase chain reaction tests, targets the viral genetic material, whereas antigen tests detect N and spike proteins to identify active infections. In addition, antibody tests are performed to measure the immune response, indicating previous exposure or vaccination. Here, we used the developed terahertz chemical microscope (TCM) to detect different concentrations of N protein in solution by immobilizing aptamers on a semiconductor substrate (sensing plate) and demonstrated that the terahertz amplitude varies as the concentration of N proteins increases, exhibiting a highly linear relationship with a coefficient of determination (R2 = 0.9881), indicating that a quantitative measurement of N proteins is achieved. By optimizing the reaction conditions, we confirmed that the amplitude of the terahertz wave was independent of the solution volume. Consequently, trace amounts (0.5 μL) of the N protein were successfully detected, and the detection process only took 10 min. Therefore, this study is expected to develop a rapid and sensitive method for the detection and observation of the SARS-CoV-2 virus at a microdetection level. It is anticipated that this research will significantly contribute to reducing the spread of novel infectious diseases in the future.
Discovering the Hidden Work of Commodified Care: The Case of Early Childhood Educators
Authors: Press Frances , Bittman Michael , Harrison Linda Joan , Brown Judith E. , Wong Sandie , Gibson Megan
Published: November 2024

The COVID-19 pandemic has drawn attention to the care economy, including commodified early childhood education and care (ECEC). While there is some literature about the low paid, invisible, and undervalued skills among the predominantly female workforce in the ECEC sector, there is little research into what these educators do in their working day and how this contributes to quality education and care for young children. This article provides a detailed examination of ten defined domains of ECEC work tasks, derived from data generated by educators’ use of ‘intensive hour’ time-diary methodology. The results reveal that the outstanding characteristics of this occupation are multi-tasking and the rapid switching of tasks as educators manage diverse expectations arising from work with groups of very young children, families, other staff, and meeting legislated responsibilities. Drawing on William J. Baumol’s economic theory, we consider the implications for productivity and cost tensions in ECEC.
The Spatial Effect of Digital Economy Enabling Common Prosperity—An Empirical Study of the Yellow River Basin
Authors: Yang Mu , An Qiguang , Zheng Lin
Published: November 2024

The digital economy enhances economic efficiency and improves economic structure, driving economic growth through transformations in efficiency, momentum, and quality. It has become a new driving force for advancing common prosperity. This study uses SDM, SDID, and SPSTR models to explore the impact of digital economy on common prosperity, which constructs the index system to evaluate the common prosperity from process index and outcome index. According to the panel data of 76 cities in the Yellow River Basin from 2011 to 2021, and the findings are as follows: (1) The digital economy exhibits a development pattern characterized by high activity downstream and lower activity upstream, and the development trend is stable. The development pattern of common prosperity has changed from sporadic distribution to regional agglomeration, and the level of common prosperity in most cities has improved. (2) The digital economy has a significant positive spatial effect on common prosperity. And the findings are robust after introducing the “Big Data” exogenous policy impact, dynamic SDM model, and other methods. Moreover, spatial heterogeneity exists. The promotion effect in the upper and lower reaches is stronger, while the middle reaches are weakly affected by the digital economy. (3) The spatial spillover effect of the digital economy on common prosperity has a boundary, and the positive spillover reaches a maximum value at 600–650 km. (4) Nonlinear analysis confirms that the digital economy provides momentum for common prosperity industrial structure optimization that can effectively stimulate the “endogenous” growth mechanism, strengthen the marginal increasing effect of the digital economy driving common prosperity and enhance the effect of “making a bigger pie”. The digital economy makes effective use of digital resources and technologies, promotes the equalization of public services, exerts a positive impact on the realization of common prosperity, and consolidates the effect of “dividing a better cake”.
The Exposure Status of Environmental Chemicals in South Korea: The Korean National Environmental Health Survey 2018–2020
Authors: Hong Sooyeon , Kim Ok-Jin , Jung Sun Kyoung , Jeon Hye Li , Kim Suejin , Kil Jihyon
Published: November 2024

In South Korea, a Human Biomonitoring (HBM) program, known as the Korean National Environmental Health Survey (KoNEHS), was launched in 2009. This study aims to provide an overview of environmental chemical exposures in South Korea based on data from the KoNEHS cycle 4 (2018–2020). To ensure population representativeness, Koreans aged 3 years and older were recruited from 426 sites across the country. A total of 6381 participants joined in the collection of biospecimens, which were subsequently analyzed for 33 environmental chemicals or their metabolites, including nine that were not included in the previous cycle. The five most common PFASs were detected in more than 99.7% of the participants. The GM of serum PFOS was the highest in adults at 15.1 µg/L (13.9, 16.4) and in adolescents at 7.97 µg/L (7.42, 8.56). In adults, there was a gradual decrease in the detection rate and concentration of some heavy metals and phthalate metabolites. In children and adolescents, the detection rate of BPA in urine decreased, while the rate of its substitutes BPF and BPS increased, and the rate of propyl paraben in urine decreased significantly. The results of the KoNEHS cycle 4 indicate that exposure levels to certain environmental chemicals are still high, highlighting further monitoring and on-going surveys to determine their trends, especially for newly investigated substances, such as PFASs.
Gut Dysbiosis and Its Role in the Anemia of Chronic Kidney Disease
Authors: Coll Elisabet , Cigarran Secundino , Portolés Jose , Cases Aleix
Published: November 2024

The gut dysbiosis present in chronic kidney disease (CKD) has been associated with anemia. Factors such as the accumulation of gut-derived uremic toxins, increased gut barrier permeability-induced inflammation, and a reduced intestinal production of short-chain fatty acids (SCFAs), all associated with changes in the intestinal microbiota composition in CKD, may lead to the development or worsening of anemia in renal patients. Understanding and addressing these mechanisms related to gut dysbiosis in CKD patients can help to delay the development of anemia and improve its control in this population. One approach is to avoid or reduce the use of drugs linked to gut dysbiosis in CKD, such as phosphate binders, oral iron supplementation, antibiotics, and others, unless they are indispensable. Another approach involves introducing dietary changes that promote a healthier microbiota and/or using prebiotics, probiotics, or symbiotics to improve gut dysbiosis in this setting. These measures can increase the presence of SCFA-producing saccharolytic bacteria and reduce proteolytic bacteria, thereby lowering the production of gut-derived uremic toxins and inflammation. By ameliorating CKD-related gut dysbiosis, these strategies can also improve the control of renal anemia and enhance the response to erythropoiesis-stimulating agents (ESAs) in ESA-resistant patients. In this review, we have explored the relationship between gut dysbiosis in CKD and renal anemia and propose feasible solutions, both those already known and potential future treatments.
Current Density Induced by a Cosmic String in de Sitter Spacetime in the Presence of Two Flat Boundaries
Authors: Oliveira dos Santos Wagner , Santana Mota Herondy F. , Bezerra de Mello Eugênio R.
Published: November 2024

In this paper, we investigate the vacuum bosonic current density induced by a carrying-magnetic-flux cosmic string in a (D+1)-de Sitter spacetime considering the presence of two flat boundaries perpendicular to it. In this setup, the Robin boundary conditions are imposed on the scalar charged quantum field on the boundaries. The particular cases of Dirichlet and Neumann boundary conditions are studied separately. Due to the coupling of the quantum scalar field with the classical gauge field, corresponding to a magnetic flux running along the string’s core, a nonzero vacuum expectation value for the current density operator along the azimuthal direction is induced. The two boundaries divide the space in three regions with different properties of the vacuum states. In this way, our main objective is to calculate the induced currents in these three regions. In order to develop this analysis we calculate, for both regions, the positive frequency Wightman functions. Because the vacuum bosonic current in dS space has been investigated before, in this paper we consider only the contributions induced by the boundaries. We show that for each region the azimuthal current densities are odd functions of the magnetic flux along the string. To probe the correctness of our results, we take the particular cases and analyze some asymptotic limits of the parameters of the model. Also some graphs are presented exhibiting the behavior of the current with relevant physical parameter of the system.
Review of the Role of Urban Green Infrastructure on Climate Resiliency: A Focus on Heat Mitigation Modelling Scenario on the Microclimate and Building Scale
Authors: Salih Kawar , Báthoryné Nagy Ildikó Réka
Published: November 2024

This systematic review explores the role of urban green infrastructure (UGI) in enhancing climate resilience, focusing mainly on heat mitigation modelling and its application at both urban and building scales. The study analyses 207 articles published in the last five years at the screening stage and 50 at the inclusion stage, highlighting the effectiveness of UGIs in reducing ambient temperatures and improving building energy efficiency through shading and evapotranspiration. Advanced simulation tools like Computational Fluid Dynamics (CFD) and Building Performance Simulation (BPS) are increasingly relied upon, though challenges remain in accurately modelling vegetation and urban-climate interactions. The review identifies critical research gaps, particularly in evaluating UGI’s performance under future climate change and seasonal variation scenarios, emphasising the need for refined simulation techniques. Moreover, the evapotranspiration modelling of UGIs needs to be developed on the BPS scale. Addressing these gaps is essential for optimising UGI design to ensure their effectiveness in future urban climates. The review calls for further studies on long-term UGI resilience, especially in rising global temperatures and evolving urban environments.
Impact of Immunosenescence on Vaccine Immune Responses and Countermeasures
Authors: Chen Li , Shao Chengwei , Li Jingxin , Zhu Fengcai
Published: November 2024

The biological progression of aging encompasses complex physiological processes. As individuals grow older, their physiological functions gradually decline, including compromised immune responses, leading to immunosenescence. Immunosenescence significantly elevates disease susceptibility and severity in older populations while concurrently compromising vaccine-induced immune responses. This comprehensive review aims to elucidate the implications of immunosenescence for vaccine-induced immunity and facilitate the development of optimized vaccination strategies for geriatric populations, with specific focus on COVID-19, influenza, pneumococcal, herpes zoster, and respiratory syncytial virus (RSV) vaccines. This review further elucidates the relationship between immunosenescence and vaccine-induced immunity. This review presents a systematic evaluation of intervention strategies designed to enhance vaccine responses in older populations, encompassing adjuvant utilization, antigen doses, vaccination frequency modification, inflammatory response modulation, and lifestyle interventions, including physical activity and nutritional modifications. These strategies are explored for their potential to improve current vaccine efficacy and inform the development of next-generation vaccines for geriatric populations.