bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancements in Battery Monitoring: Harnessing Fiber Grating Sensors for Enhanced Performance and Reliability
Authors: Yu Kaimin , Chen Wen , Deng Dingrong , Wu Qihui , Hao Jianzhong
Published: March 2024

Batteries play a crucial role as energy storage devices across various industries. However, achieving high performance often comes at the cost of safety. Continuous monitoring is essential to ensure the safety and reliability of batteries. This paper investigates the advancements in battery monitoring technology, focusing on fiber Bragg gratings (FBGs). By examining the factors contributing to battery degradation and the principles of FBGs, this study discusses key aspects of FBG sensing, including mounting locations, monitoring targets, and their correlation with optical signals. While current FBG battery sensing can achieve high measurement accuracies for temperature (0.1 °C), strain (0.1 με), pressure (0.14 bar), and refractive index (6 × 10−5 RIU), with corresponding sensitivities of 40 pm/°C, 2.2 pm/με, −0.3 pm/bar, and −18 nm/RIU, respectively, accurately assessing battery health in real time remains a challenge. Traditional methods struggle to provide real-time and precise evaluations by analyzing the microstructure of battery materials or physical phenomena during chemical reactions. Therefore, by summarizing the current state of FBG battery sensing research, it is evident that monitoring battery material properties (e.g., refractive index and gas properties) through FBGs offers a promising solution for real-time and accurate battery health assessment. This paper also delves into the obstacles of battery monitoring, such as standardizing the FBG encapsulation process, decoupling multiple parameters, and controlling costs. Ultimately, the paper highlights the potential of FBG monitoring technology in driving advancements in battery development.
User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning
Authors: Urwan Sebastian , Cwalina Krzysztof K.
Published: March 2024

In this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for the binary classification of user orientation, neural networks achieved accuracy that was more than 9% higher than that for the well-known threshold method. In addition, the classification of user position angles relative to the reference node was analyzed. It was proven that, using the proposed deep learning approach and the channel impulse response, it was possible to estimate the angle of the user’s position in relation to the antenna geometry. Absolute user orientation angle errors of about 4–7° for convolutional neural networks and of about 14–15° for multilayer perceptrons were achieved in approximately 85% of the cases in both tested scenarios.
High-Density Polyethylene Custom Focusing Lenses for High-Resolution Transient Terahertz Biomedical Imaging Sensors
Authors: Chakraborty Debamitra , Boni Robert , Mills Bradley N. , Cheng Jing , Komissarov Ivan , Gerber Scott A. , Sobolewski Roman
Published: March 2024

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing lenses using a commercially available lens-design software that resulted in about 200 × 200-μm2 focal spot size corresponding to the 1-THz frequency. The lenses are made of high-density polyethylene (HDPE) obtained using a lathe fabrication and are integrated into a THz-TDS system that includes low-temperature GaAs photoconductive antennae as both a THz emitter and detector. The system is used to generate high-resolution, two-dimensional (2D) images of formalin-fixed, paraffin-embedded murine pancreas tissue blocks. The performance of these focusing lenses is compared to the older system based on a pair of short-focal-length, hemispherical polytetrafluoroethylene (TeflonTM) lenses and is characterized using THz-domain measurements, resulting in 2D maps of the tissue refractive index and absorption coefficient as imaging markers. For a quantitative evaluation of the lens effect on the image resolution, we formulated a lateral resolution parameter, R2080, defined as the distance required for a 20–80% transition of the imaging marker from the bare paraffin region to the tissue region in the same image frame. The R2080 parameter clearly demonstrates the advantage of the HDPE lenses over TeflonTM lenses. The lens-design approach presented here can be successfully implemented in other THz-TDS setups with known THz emitter and detector specifications.
A Large-Scale Three-Dimensional Apparatus to Study Failure Mechanisms of Rockfalls in Underground Engineering Contexts
Authors: Xin Gongfeng , Yang Guangyu , Li Fan , Liu Hongliang
Published: March 2024

Rockfalls are an important factor affecting underground engineering safety. However, there has been limited progress in understanding and predicting these disasters in the past few years. Therefore, a large-scale three-dimensional experimental simulation apparatus to study failure mechanisms of rockfalls occurring during underground engineering was developed. This apparatus, measuring 4 m × 4 m × 3.3 m in size, can achieve vertical and horizontal symmetric loading. It not only simulates the structure and stress environment of a rock mass but also simulates the stepwise excavation processes involved in underground engineering. A complete simulation experiment of rockfalls in an underground engineering context was performed using this apparatus. Dynamic evolution characteristics of block displacement, temperature, natural vibration frequency, and acoustic emissions occurring during rockfalls were studied during the simulation. These data indicate there are several indicators that could be used to predict rockfalls in underground engineering contexts, leading to better prevention and control.
Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes
Authors: Zhao Ling , Zhang Zheng , Zhu Jinlin , Wang Hongchao , Xie Zhenping
Published: March 2024

For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label sparsity when modeling the soft sensors has always posed challenges across various processes. In this paper, a fledgling technique, called co-training, is studied for leveraging only a small ratio of labeled data, to hone and formulate a more advantageous framework in soft sensor modeling. Dissimilar to the conventional routine where only two players are employed, we investigate the efficient number of players in batch processes, making a multiple-player learning scheme to assuage the sparsity issue. Meanwhile, a sliding window spanning across both time and batch direction is used to aggregate the samples for prediction, and account for the unique 2D correlations among the general batch process data. Altogether, the forged framework can outperform the other prevalent methods, especially when the ratio of unlabeled data is climbing up, and two case studies are showcased to demonstrate its effectiveness.
Phase Error Reduction for a Structured-Light 3D System Based on a Texture-Modulated Reprojection Method
Authors: Shi Chenbo , Qin Zheng , Hu Xiaowei , Zhu Changsheng , Mo Yuanzheng , Li Zelong , Yan Shaojia , Yu Yue , Zang Xiangteng , Zhang Chun
Published: March 2024

Fringe projection profilometry (FPP), with benefits such as high precision and a large depth of field, is a popular 3D optical measurement method widely used in precision reconstruction scenarios. However, the pixel brightness at reflective edges does not satisfy the conditions of the ideal pixel-wise phase-shifting model due to the influence of scene texture and system defocus, resulting in severe phase errors. To address this problem, we theoretically analyze the non-pixel-wise phase propagation model for texture edges and propose a reprojection strategy based on scene texture modulation. The strategy first obtains the reprojection weight mask by projecting typical FPP patterns and calculating the scene texture reflection ratio, then reprojects stripe patterns modulated by the weight mask to eliminate texture edge effects, and finally fuses coarse and refined phase maps to generate an accurate phase map. We validated the proposed method on various texture scenes, including a smooth plane, depth surface, and curved surface. Experimental results show that the root mean square error (RMSE) of the phase at the texture edge decreased by 53.32%, proving the effectiveness of the reprojection strategy in eliminating depth errors at texture edges.
Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models
Authors: Altwaijry Najwa , Al-Turaiki Isra , Alotaibi Reem , Alakeel Fatimah
Published: March 2024

Phishing is one of the most dangerous attacks targeting individuals, organizations, and nations. Although many traditional methods for email phishing detection exist, there is a need to improve accuracy and reduce false-positive rates. Our work investigates one-dimensional CNN-based models (1D-CNNPD) to detect phishing emails in order to address these challenges. Additionally, further improvement is achieved with the augmentation of the base 1D-CNNPD model with recurrent layers, namely, LSTM, Bi-LSTM, GRU, and Bi-GRU, and experimented with the four resulting models. Two benchmark datasets were used to evaluate the performance of our models: Phishing Corpus and Spam Assassin. Our results indicate that, in general, the augmentations improve the performance of the 1D-CNNPD base model. Specifically, the 1D-CNNPD with Bi-GRU yields the best results. Overall, the performance of our models is comparable to the state of the art of CNN-based phishing email detection. The Advanced 1D-CNNPD with Leaky ReLU and Bi-GRU achieved 100% precision, 99.68% accuracy, an F1 score of 99.66%, and a recall of 99.32%. We observe that increasing model depth typically leads to an initial performance improvement, succeeded by a decline. In conclusion, this study highlights the effectiveness of augmented 1D-CNNPD models in detecting phishing emails with improved accuracy. The reported performance measure values indicate the potential of these models in advancing the implementation of cybersecurity solutions to combat email phishing attacks.
A Vehicle-Edge-Cloud Framework for Computational Analysis of a Fine-Tuned Deep Learning Model
Authors: Khan M. Jalal , Khan Manzoor Ahmed , Turaev Sherzod , Malik Sumbal , El-Sayed Hesham , Ullah Farman
Published: March 2024

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model’s accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production.
Patient’s Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots
Authors: Guo Bingjing , Li Zhenzhu , Huang Mingxiang , Li Xiangpan , Han Jianhai
Published: March 2024

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore damaged central nervous system function in patients. Patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb’s movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient’s expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient’s ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human–robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient’s movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient’s healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient’s pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.
GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things
Authors: Chen Ruidong , Dai Tianci , Zhang Yanfeng , Zhu Yukun , Liu Xin , Zhao Erfan
Published: March 2024

The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks. Most of researchers have had some success in IoT botnet detection using artificial intelligence (AI). However, they have not considered the impact of dynamic network data streams on the models in real-world environments. Over time, existing detection models struggle to cope with evolving botnets. To address this challenge, we propose an incremental learning approach based on Gradient Boosting Decision Trees (GBDT), called GBDT-IL, for detecting botnet traffic in IoT environments. It improves the robustness of the framework by adapting to dynamic IoT data using incremental learning. Additionally, it incorporates an enhanced Fisher Score feature selection algorithm, which enables the model to achieve a high accuracy even with a smaller set of optimal features, thereby reducing the system resources required for model training. To evaluate the effectiveness of our approach, we conducted experiments on the BoT-IoT, N-BaIoT, MedBIoT, and MQTTSet datasets. We compared our method with similar feature selection algorithms and existing concept drift detection algorithms. The experimental results demonstrated that our method achieved an average accuracy of 99.81% using only 25 features, outperforming similar feature selection algorithms. Furthermore, our method achieved an average accuracy of 96.88% in the presence of different types of drifting data, which is 2.98% higher than the best available concept drift detection algorithms, while maintaining a low average false positive rate of 3.02%.
Measurement Method for Contact Wire Wear Based on Stereovision
Authors: Zhou Wei , Qin Zhe , Du Xinyu , Xue Xiantang , Wang Haiying , Li Hailang
Published: March 2024

The contact wire wear is an important parameter to ensure the safety operation of electric railways. The contact wire may break if the wear is serious, which leads to transportation interruptions. This study proposes an optical measurement method of contact wire wear, using stereovision technology. The matching method of stereovision based on line-scan cameras is proposed. A lookup-table method is developed to exactly determine the image resolution caused by the contact wire being in different spatial positions. The wear width of the contact wire is extracted from catenaries’ images, and the residual thickness of the contact wire is calculated. The method was verified by field tests. The round-robin tests of the residual thickness at the same location present excellent measurement repetitiveness. The maximum difference value between dynamic test results and ground measurement results is 0.13 mm. This research represents a potential way to implement condition-based maintenance for contact wire wear in the future in order to improve the maintenance efficiency and ensure the safety of catenary infrastructure.
Visualizing a Cold Stress-Specific Pulse Wave in Traditional Pulse Diagnosis (‘Tight Pulse’) Correlated with Vascular Changes in the Radial Artery Induced by a Cold Pressor Trial
Authors: Song Jichung , Choi Jae Young , Lee Byung-Wook , Eom Dongmyung , Song Chang-Hyun
Published: March 2024

Radial pulse diagnosis is the most common method to examine the human health state in Traditional East Asian Medicine (TEAM). A cold stress-related suboptimal health state (subhealth) is often undetectable during routine medical examinations, however, it can be detected through the palpation of specific pulse waves, particularly a ‘tight pulse’, in TEAM. Therefore, this study examined a correlation between ‘tight pulse’ and vascular changes in the radial artery (RA) induced by a cold pressor trial (CPT). Twenty healthy subjects underwent sequentially control trial and CPT with room-temperature and ice-cold water, respectively, on the right forearm. The radial pulse and vascular changes were then examined on the left arm. The radial pulse scores for frequencies of ‘tight pulse’ with strong arterial tension increased after the CPT compared with the control trial. The pulse scores were reversely correlated with the RA thickness and volumes in ultrasonography, but not with changes in the systolic/diastolic blood pressure. The RA thickness-based vascular surface and three-dimensional images visualized a ‘tight pulse’ showing the vasoconstriction and bumpy-/rope-shaped vascular changes in the radial pulse diagnostic region after the CPT. These findings provide valuable insights into the potential integration of clinical radial pulse diagnosis with ultrasonography for cold-related subhealth.
A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets
Authors: Edrisi Sahab , Enayati Javad , Rahimnejad Abolfazl , Gadsden Stephen Andrew
Published: March 2024

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.
Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch
Authors: Bouse Leana , King Scott A. , Chu Tianxing
Published: March 2024

Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.
Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds
Authors: Liu Yan , Meng Xiaolin , Hu Liangliang , Bao Yan , Hancock Craig
Published: March 2024

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.
A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms
Authors: Rebelo Paulo M. , Lima José , Soares Salviano Pinto , Moura Oliveira Paulo , Sobreira Héber , Costa Pedro
Published: March 2024

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs’ trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution.
Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram
Authors: Slapničar Gašper , Su Jie , Wang Wenjin
Published: March 2024

Electrocardiogram (ECG) reconstruction from contact photoplethysmogram (PPG) would be transformative for cardiac monitoring. We investigated the fundamental and practical feasibility of such reconstruction by first replicating pioneering work in the field, with the aim of assessing the methods and evaluation metrics used. We then expanded existing research by investigating different cycle segmentation methods and different evaluation scenarios to robustly verify both fundamental feasibility, as well as practical potential. We found that reconstruction using the discrete cosine transform (DCT) and a linear ridge regression model shows good results when PPG and ECG cycles are semantically aligned—the ECG R peak and PPG systolic peak are aligned—before training the model. Such reconstruction can be useful from a morphological perspective, but loses important physiological information (precise R peak location) due to cycle alignment. We also found better performance when personalization was used in training, while a general model in a leave-one-subject-out evaluation performed poorly, showing that a general mapping between PPG and ECG is difficult to derive. While such reconstruction is valuable, as the ECG contains more fine-grained information about the cardiac activity as well as offers a different modality (electrical signal) compared to the PPG (optical signal), our findings show that the usefulness of such reconstruction depends on the application, with a trade-off between morphological quality of QRS complexes and precise temporal placement of the R peak. Finally, we highlight future directions that may resolve existing problems and allow for reliable and robust cross-modal physiological monitoring using just PPG.
SEG-SLAM: Dynamic Indoor RGB-D Visual SLAM Integrating Geometric and YOLOv5-Based Semantic Information
Authors: Cong Peichao , Li Jiaxing , Liu Junjie , Xiao Yixuan , Zhang Xin
Published: March 2024

Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms.
Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
Authors: Malumbres Victor , Saldana Jose , Berné Gonzalo , Modrego Julio
Published: March 2024

The capacity to update firmware is a vital component in the lifecycle of Internet of Things (IoT) devices, even those with restricted hardware resources. This paper explores the best way to wirelessly (Over The Air, OTA) update low-end IoT nodes with difficult access, combining the use of unicast and broadcast communications. The devices under consideration correspond to a recent industrial IoT project that focuses on the installation of intelligent lighting systems within ATEX (potentially explosive atmospheres) zones, connected via LoRa to a gateway. As energy consumption is not limited in this use case, the main figure of merit is the total time required for updating a project. Therefore, the objective is to deliver all the fragments of the firmware to each and all the nodes in a safe way, in the least amount of time. Three different methods, combining unicast and broadcast transmissions in different ways, are explored analytically, with the aim of obtaining the expected update time. The methods are also tested via extensive simulations, modifying different parameters such as the size of the scenario, the number of bytes of each firmware chunk, the number of nodes, and the number of initial broadcast rounds. The simulations show that the update time of a project can be significant, considering the limitations posed by regulations, in terms of the percentage of airtime consumption. However, significant time reductions can be achieved by using the proper method: in some cases, when the number of nodes is high, the update time can be reduced by two orders of magnitude if the correct method is chosen. Moreover, one of the proposed methods is implemented using actual hardware. This real implementation is used to perform firmware update experiments in a lab environment. Overall, the article illustrates the advantage of broadcast approaches in this kind of technology, in which the transmission rate is constant despite the distance between the gateway and the node. However, the advantage of these broadcast methods with respect to the unicast one could be mitigated if the nodes do not run exactly the same firmware version, since the control of the broadcast update would be more difficult and the total update time would increase.
Optimized Centrifugal Partition Chromatography (CPC) Protocol for Isolation of Urease Inhibitors: Magnoflorine and Berberine from Berberis vulgaris Extracts
Authors: Nakonieczna Sylwia , Susniak Katarzyna , Bozhadze Anna , Grabarska Aneta , Głowniak-Lipa Anna , Głowniak Kazimierz , Kukula-Koch Wirginia
Published: March 2024

In recent years, an increasing interest in phytotherapy has been observed. Parallel to the research on the total extracts of plant material, numerous studies on the activity of single molecules derived from plants are being conducted to address their mechanisms of action and determine active doses and eventual interactions. Despite this phenomenon, the isolation of individual compounds is a bottleneck due to its difficulty and cost. This work presents the results of a careful optimization of magnoflorine and berberine (isoquinoline alkaloids) recovery from a commonly distributed shrub, Berberis vulgaris, growing in Poland and Georgia, using CPC. Both compounds are known for their numerous medicinal properties, which makes the isolation methodology an important area of research. Additionally, CPC has the ability to isolate high-quality compounds in large quantities, which makes it an effective and easy-to-commercialize method. For a successful separation, the biphasic solvent system composed of hexane, butanol, ethanol, and water in a ratio (3:12:4:16 v/v/v/v) was used in the ascending mode, together with the flow rate of 8 mL/min and rotation speed of 1600 rpm. The method was selective for both compounds, and it delivered good results for both root and stem extracts from the plant. The qualitative composition of alkaloids in the studied extracts determined by HPLC-ESI-QTOF-MS/MS confirmed the presence of berberine, magnoflorine, jatrorhizine, and palmatine alkaloids from the group of isoquinolines. The isolates, magnoflorine and berberine, were subjected to the Helicobacter pylori growth inhibition assay and urease inhibition test to assess whether, next to the previously proved anticancer properties, these compounds are characterized by H. pylori inhibition. MGN was found to exhibit inhibitory potential against urease (IC50 = 25 mg/L).
Flotation Separation of Cassiterite from Calcite Using Low-Molecular-Weight Citrus Pectin as Depressant
Authors: Yao Chenyang , Li Yachao , Li Peng , Che Xiaokui , Li Guosheng , Zhang Fanfan , Peng Weijun , Wu Bozeng , Fan Guixia
Published: March 2024

This paper presents the development of an environmentally friendly, small molecular depressant citrus pectin for improving the recovery of cassiterite resources. Citrus pectin extracted from citrus peel was utilized as the depressant, and it demonstrated significant potential in separating calcite from cassiterite in micro-flotation tests. The molecular weight of the citrus pectin extracted in this paper decreased from 11,485,412 Da to 32,959 Da compared to commercial pectin, resulting in the depressant efficiency of the reagent. The results of a zeta potential and adsorption test indicated that citrus pectin had less and weaker adsorption on the cassiterite surface and could be replaced with NaOL. The chemical adsorption process of citrus pectin on the surface of calcite was determined through FTIR spectroscopy analysis. XPS analysis results indicated that the interaction between the carboxyl groups of citrus pectin and calcium atoms enables adsorption to occur. The AFM revealed that citrus pectin displayed a uniform and dense pattern of point-like adsorption on the surface of calcite. Micro-flotation experiments showed that cassiterite recovery of 80% can be obtained at a citrus pectin dosage of 10 mg/L. Citrus pectin has the advantages of being low-cost, highly selective, and environmentally suitable, making it a promising alternative to conventional reagents.
Ghost Gears in the Gulf of Gabès: Alarming Situation and Sustainable Solution Perspectives
Authors: Ghaouar Hana , Boussellaa Wiem , Jribi Imed
Published: March 2024

This study represents a first step in investigating the prevalence, causes, and spatial distribution of Abandoned, Lost, or Discarded Fishing Gears (ALDFGs) in the Gulf of Gabès, a critical fishing area in Tunisia. Five main fishing gear types—benthic trawls, encircling nets, longlines, gillnets, and trammel nets were analysed for their loss rates and contributing factors. The study involved extensive surveys among local fishermen and the use of geographic information system (GIS) tools for spatial mapping. The findings reveal varying loss rates among different gear types, with longlines experiencing the highest losses (59%), followed by trammel nets (45%), gillnets (40%), trawls (38%), and encircling nets (36%). Causes of gear loss include entanglement on obstructions, conflicts with other fishing gears, entanglement with marine animals, adverse weather conditions, and intentional dumping at sea. The study highlights the challenges posed by the consistent increase in fishing effort in the region. Spatial distribution mapping indicates two significant concentrations of ALDFGs, correlating with specific fishing activities and depths. The study emphasizes the importance of addressing gear losses as a threat to marine biodiversity and ecosystem health. Recommendations for mitigation include improving boat equipment, raising awareness among fishermen, implementing effective waste management, and exploring economic incentives for ALDFG recovery. This research contributes essential insights for fisheries management, environmental conservation, and sustainable practices in the Gulf of Gabès. Collaborative efforts are crucial for developing and implementing strategies that minimize the impact of ALDFGs on the marine environment while supporting the socio-economic interests of fishermen.
Simulation-Based Learning and Argumentation to Promote Informed Design Decision-Making Processes within a First-Year Engineering Technology Course
Authors: Feijoo-Garcia Miguel A. , Holstrom Mark S. , Magana Alejandra J. , Newell Brittany A.
Published: March 2024

Analyzing the integration of scientific knowledge to guide decision-making processes supporting design challenges in engineering education is critical. However, effectively engaging in informed design decision-making processes is challenging, particularly in the context of online education. Simulation-based learning can bring authentic design practices to online education, but effective guidance and scaffolding must be provided to learners. Therefore, this research investigates the implications of integrating simulation-based learning with an argumentation framework to guide students in incorporating scientific knowledge into their design decisions, particularly in designing energy-efficient housing. This study took place during online learning due to the COVID-19 pandemic and was implemented within a first-year engineering technology undergraduate course. It aimed to analyze students’ decision-making processes when designing a zero-energy home for a Midwestern city using Aladdin, an integrated CAD/CAE platform that can be used to design a structure and simulate its function within a single system. This study investigates how students informed their decision-making processes in design for energy-efficient homes and the recurring trends in students’ designs related to economic decision making and energy science. The overall results show how cost constraints significantly influenced students’ observation and argumentation processes during their design challenge, highlighting the pivotal role of economic considerations in shaping their decision making. Moreover, the findings underscore the importance of holistic approaches in providing insights into teaching strategies for online learning, particularly in navigating the intersection of scientific and economic factors in design challenges.
Approaches, Trends, and Gaps in Community-Based Ecotourism Research: A Bibliometric Analysis of Publications between 2002 and 2022
Authors: Guerrero-Moreno Mayerly Alexandra , Oliveira-Junior José Max Barbosa
Published: March 2024

Community-based ecotourism (CBET) has emerged as an alternative that seeks to integrate environmental, cultural, social, and economic sustainability through community participation, generating increasing interest in research. In this context, we conducted a bibliometric analysis to understand the research patterns, trends, and gaps associated with scientific production on CBET between 2002 and 2022. To achieve this, articles related to CBET and its variants were extracted from Scopus and Web of Science. A total of 1145 publications were selected. Sustainability is the journal with the most articles published on CBET. The countries with the highest scientific production were the USA and China. Most of the studies were conducted in protected areas. The most used data sources are interviews and case studies. The field research focused on evaluating impacts and identifying perceptions, attitudes, or experiences. Despite the recent increase in the number of publications, there are still gaps related to the inclusion of indigenous populations, the use of quantitative methods in the research, the evaluation of the impact of CBET on biodiversity conservation, and the disparity between countries leading academic production and those developing CBET initiatives. These patterns and gaps suggest areas of opportunity for future research, strategies, and policies in the field of CBET.
A Playground and Arts for a Community in Transition: A Circular Model for Built Heritage Regeneration in the Sanità District (Naples, Italy)
Authors: Ciampa Francesca , Fabbricatti Katia , Freda Gianluigi , Pinto Maria Rita
Published: March 2024

Open spaces for collective use in the built heritage of cities are the places to promote and sustain social, cultural, and economic changes. The transformation of the built environment raises awareness of following the policies and tools for the implementation of the EU New Generation Programme, redefining the framework of intervention priorities at the urban scale. Tackling the increasing physical degradation, underutilization, social disparity, and loss of the tangible and intangible culture of these types of spaces, this research identifies artistic production and social cohesion as the enabling factors of maintenance. The regeneration of areas of collective use returns an experimentation of strategic importance for the participatory and shared care of consolidated heritage contexts. The Sanità district in Naples represents a case study of a community-built custom playground, testing a co-design approach, maintaining the consolidated qualities, and awakening the responsibility of the youngest. The outcome is a circular model that focuses on the regeneration of such spaces, transforming an abandoned built environment from waste into a resource.
Comparison of the Profile of Portuguese Companies That Receive Public Support for Innovation: National Support vs. European Support
Authors: Rosário Cátia , Varum Celeste , Botelho Anabela
Published: March 2024

Innovation has emerged as the key driver of economic growth, technological advancement, and societal well-being. Recognizing the significance of fostering innovation, governments and policymakers worldwide have increasingly emphasized the need for public support to bolster the innovation ecosystem. This article explores the crucial importance of public support for innovation and delves into the characteristics of companies that receive such support. Based on the Community Innovation Survey of 2014, 2016, and 2018, information was compiled from 1857 companies. Of these companies, common to these 6 years of information, 755 received national support for innovation and 490 received European support. Based on these data, the main objective was to identify and distinguish the characteristics of Portuguese companies that receive European support and national support for innovation. To achieve this objective, logit models were estimated using Stata software. The results suggest that national support favors companies that belong to sectors with greater technological development, which develop new products and processes. Companies that establish cooperation agreements and that belong to groups of companies are also preferred for national support. In turn, of the companies that received European support, it appears that there is less differentiation in preference for the level of technological development. Companies that invest internally in R&D and that establish cooperation agreements for innovation are more likely to obtain European support, as are companies that have a higher proportion of workers with higher education. Finally, both national and European support favor companies with a greater volume of business from foreign markets.
Comparative Analysis of Sustainable Electrification in Mediterranean Public Transportation
Authors: Miraftabzadeh Seyed Mahdi , Ranjgar Babak , Niccolai Alessandro , Longo Michela
Published: March 2024

The Mediterranean region is a hot spot for climate change, with transportation accounting for a quarter of global CO2 emissions. To meet the 2030 Sustainable Development Goals (SDGs), a sustainable urban transport network is needed to cut carbon emissions and improve air quality. This study aims to investigate the electrification of public transport in both developed and underdeveloped countries by examining the existing public transport network of two modes of transportation (buses and trams) across the Mediterranean region. This study suggests that the electrification of public transportation could result in a significant additional demand for more than 200 GWh of electricity, depending on the size and congestion of the city. It also studies the potential reduction of greenhouse gas (GHG) emissions through the electrification of buses. Results show that electrification significantly impacts decreasing GHG emissions, helping achieve SDG 13. Furthermore, a financial analysis was conducted to determine the feasibility of using different bus fuel technologies. Regarding economic benefits, electric buses are not consistently optimal solutions, and diesel buses can be advantageous. Our finding shows that, at a 5% discount rate, the diesel bus is most favorable for Marseille, and, as discount rates increase, the advantage of electric buses diminishes. However, the high purchase price of electric buses compared to diesel buses is currently a major obstacle in achieving SDG 11, particularly for developing countries.