bluuebunny/crossref_metadata_2025 huggingface dataset:
MDFA-Net: Multi-Scale Differential Feature Self-Attention Network for Building Change Detection in Remote Sensing Images
Authors: Li Yuanling , Zou Shengyuan , Zhao Tianzhong , Su Xiaohui
Published: September 2024

Building change detection (BCD) from remote sensing images is an essential field for urban studies. In this well-developed field, Convolutional Neural Networks (CNNs) and Transformer have been leveraged to empower BCD models in handling multi-scale information. However, it is still challenging to accurately detect subtle changes using current models, which has been the main bottleneck to improving detection accuracy. In this paper, a multi-scale differential feature self-attention network (MDFA-Net) is proposed to effectively integrate CNN and Transformer by balancing the global receptive field from the self-attention mechanism and the local receptive field from convolutions. In MDFA-Net, two innovative modules were designed. Particularly, a hierarchical multi-scale dilated convolution (HMDConv) module was proposed to extract local features with hybrid dilation convolutions, which can ameliorate the effect of CNN’s local bias. In addition, a differential feature self-attention (DFA) module was developed to implement the self-attention mechanism at multi-scale difference feature maps to overcome the problem that local details may be lost in the global receptive field in Transformer. The proposed MDFA-Net achieves state-of-the-art accuracy performance in comparison with related works, e.g., USSFC-Net, in three open datasets: WHU-CD, CDD-CD, and LEVIR-CD. Based on the experimental results, MDFA-Net significantly exceeds other models in F1 score, IoU, and overall accuracy; the F1 score is 93.81%, 95.52%, and 91.21% in WHU-CD, CDD-CD, and LEVIR-CD datasets, respectively. Furthermore, MDFA-Net achieved first or second place in precision and recall in the test in all three datasets, which indicates its better balance in precision and recall than other models. We also found that subtle changes, i.e., small-sized building changes and irregular boundary changes, are better detected thanks to the introduction of HMDConv and DFA. To this end, with its better ability to leverage multi-scale differential information than traditional methods, MDFA-Net provides a novel and effective avenue to integrate CNN and Transformer in BCD. Further studies could focus on improving the model’s insensitivity to hyper-parameters and the model’s generalizability in practical applications.
Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
Authors: Du Andrew , Doan Anh-Dzung , Law Yee Wei , Chin Tat-Jun
Published: September 2024

The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the presence of clouds in the multispectral data captured on Earth observation (EO) missions, whereby only clear sky data are downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of onboard multispectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations.
Self-Supervised Learning across the Spectrum
Authors: Shenoy Jayanth , Zhang Xingjian Davis , Tao Bill , Mehrotra Shlok , Yang Rem , Zhao Han , Vasisht Deepak
Published: September 2024

Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4’s novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4’s ability as an effective feature extractor for downstream semantic segmentation.
High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar
Authors: Golubović Dragan , Erić Miljko , Vukmirović Nenad , Orlić Vladimir
Published: September 2024

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.
Cumulative and Lagged Effects: Seasonal Characteristics of Drought Effects on East Asian Grasslands
Authors: Huang Weiwei , Henderson Mark , Liu Binhui , Su Yuanhang , Zhou Wanying , Ma Rong , Chen Mingyang , Zhang Zhi
Published: September 2024

With the acceleration of global warming, droughts are expected to both intensify and become more frequent. More so than forests, the productivity of grasslands is largely controlled by soil moisture and is highly susceptible to drought. Drought can impact grasslands though the effects may lag and accumulate over time. Because prior research has mainly focused on the annual or growing season scale, it remains unclear whether there are seasonal differences in the cumulative and lagged effects (CALEs) of drought. This study uses Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI) data to explore the seasonal characteristics of the CALEs of drought on grassland growth in East Asia from 2001 to 2020. The main results include the following: (1) More than 40% of grasslands are significantly affected by the CALEs of drought for all three seasons (spring, summer, and autumn). (2) Grasslands are more sensitive to the CALEs of drought in summer. The spatial variability of the cumulative time scale is the greatest in spring, whereas the spatial variability of the lagged time scale is the greatest in summer. The lag time scale gradually shortens as moisture decreases in summer and autumn but shows an inverted U-shape in spring. As drought conditions intensify, the cumulative time scale gradually increases in spring and autumn, while gradually decreasing in summer. (3) The dominant drought effects vary among different seasons: the lagged effect (LE) predominates in spring and autumn, whereas in summer it is the cumulative effect (CE) that dominates. The LE exceeds the CE in 54.89% of the study area during the growing season. We emphasize that annual- or growing season-scale studies of drought CE and LE may obscure seasonal response characteristics. Given the seasonal nature of droughts and the seasonally varying sensitivities of grassland growth to these droughts, the impacts on vegetation fluctuate significantly across different seasons. The results help us more accurately predict grassland ecosystem changes under the background of global warming and the increasing probability of severe drought, providing important reference values for future grassland ecological protection and planning.
A High-Resolution and Robust Microwave Correlation Imaging Method Based on URRF Using MC-AAMPE Algorithm
Authors: Xue Min , Xing Mengdao , Gao Yuexin , Wu Zhixin , Tang Wangshuo , Wang Yidi
Published: September 2024

This manuscript presents a novel framework for high-resolution and robust microwave correlation imaging. In order to generate a more diverse random radiation field distribution, the unified random radiation field (URRF) model is proposed. The URRF model can accurately characterize the joint random modulation in the signals’ phase, amplitude, and frequency. Furthermore, we build a parametric imaging model based on URRF which clearly describes the relationship between the image to be reconstructed and the signals by the URRF model. By using this imaging model, the reconstruction of an image is converted into solving a multi-parameter optimization problem with multiple constraints. To solve this optimization problem with high efficiency and accuracy, the model-constrained adaptive alternating multiple parameter estimation (MC-AAMPE) algorithm is proposed. This algorithm decomposes the high-dimensional multi-parameter optimization problem into several sub-optimization problems. The renewing solutions to these sub-optimization problems make the multi-parameter optimization converge to the image of the target and the parameters of clutter and noise, which are all unknown before the solution. In comparison with the existing methods, the proposed scheme generates images with higher resolution and is more robust under noise conditions. Extensive simulation experiments confirmed the effectiveness and robustness of the proposed method.
A Multi-Stage Progressive Pansharpening Network Based on Detail Injection with Redundancy Reduction
Authors: Wen Xincan , Ma Hongbing , Li Liangliang
Published: September 2024

In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92–18.7% compared to the latest methods.
Laser Scanner-Based Hyperboloid Cooling Tower Geometry Inspection: Thickness and Deformation Mapping
Authors: Makuch Maria , Gawronek Pelagia , Mitka Bartosz
Published: September 2024

Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use.
Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network
Authors: Pinto Rafael Albuquerque , De Oliveira Hygo Sousa , Souto Eduardo , Giusti Rafael , Veras Rodrigo
Published: September 2024

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.
Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments
Authors: Wang Yuchen , Miao Yanqin , Fu Gang , Lu Peng , Yang Yikun , Gu Wen , Fang Zijie , Niu Lei
Published: September 2024

Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called “conflict-blocking”, where agents’ conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.
Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
Authors: Zhou Baocheng , Zhang Xinwen , Wan Xiaozheng , Liu Tongmu , Liu Yuqiang , Huang Hua , Chen Jing
Published: September 2024

Real-time data transmission and reliable operation are essential for a tsunami monitoring system to provide effective data. In this study, a novel real-time tsunami monitoring system is designed based on a submersible mooring system. This system is equipped with a data acquisition and tsunami wave identification algorithm, which can collect the measured data of the pressure sensor and detect a tsunami wave in real time. It adopts the combination design of underwater inductive coupling transmission and a redundant BeiDou communication device on the water surface to ensure the reliability of real-time data transmission. Compared with traditional tsunami monitoring buoys, it has the advantages of reliable communication, good concealment, high security, and convenient deployment, recovery, and maintenance. The results of laboratory and sea tests show that the system has high reliability of data transmission, stable overall operation of the system, and good application prospects in the field of real-time tsunami monitoring and early warning.
LLMDiff: Diffusion Model Using Frozen LLM Transformers for Precipitation Nowcasting
Authors: She Lei , Zhang Chenghong , Man Xin , Shao Jie
Published: September 2024

Precipitation nowcasting, which involves the short-term, high-resolution prediction of rainfall, plays a crucial role in various real-world applications. In recent years, researchers have increasingly utilized deep learning-based methods in precipitation nowcasting. The exponential growth of spatiotemporal observation data has heightened interest in recent advancements such as denoising diffusion models, which offer appealing prospects due to their inherent probabilistic nature that aligns well with the complexities of weather forecasting. Successful application of diffusion models in rainfall prediction tasks requires relevant conditions and effective utilization to direct the forecasting process of the diffusion model. In this paper, we propose a probabilistic spatiotemporal model for precipitation nowcasting, named LLMDiff. The architecture of LLMDiff includes two networks: a conditional encoder–decoder network and a denoising network. The conditional network provides conditional information to guide the denoising network for high-quality predictions related to real-world earth systems. Additionally, we utilize a frozen transformer block from pre-trained large language models (LLMs) in the denoising network as a universal visual encoder layer, which enables the accurate estimation of motion trend by considering long-term temporal context information and capturing temporal dependencies within the frame sequence. Our experimental results demonstrate that LLMDiff outperforms state-of-the-art models on the SEVIR dataset.
Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications
Authors: Liu Songtao , Zhu Yaonan , Aoyama Tadayoshi , Nakaya Masayuki , Hasegawa Yasuhisa
Published: September 2024

Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO’s detection performance.
Multimodal Image-Based Indoor Localization with Machine Learning—A Systematic Review
Authors: Łukasik Szymon , Szott Szymon , Leszczuk Mikołaj
Published: September 2024

Outdoor positioning has become a ubiquitous technology, leading to the proliferation of many location-based services such as automotive navigation and asset tracking. Meanwhile, indoor positioning is an emerging technology with many potential applications. Researchers are continuously working towards improving its accuracy, and one general approach to achieve this goal includes using machine learning to combine input data from multiple available sources, such as camera imagery. For this active research area, we conduct a systematic literature review and identify around 40 relevant research papers. We analyze contributions describing indoor positioning methods based on multimodal data, which involves combinations of images with motion sensors, radio interfaces, and LiDARs. The conducted survey allows us to draw conclusions regarding the open research areas and outline the potential future evolution of multimodal indoor positioning.
Optimization of the Pixel Design for Large Gamma Cameras Based on Silicon Photomultipliers
Authors: Wunderlich Carolin , Paoletti Riccardo , Guberman Daniel
Published: September 2024

Most single-photon emission computed tomography (SPECT) scanners employ a gamma camera with a large scintillator crystal and 50–100 large photomultiplier tubes (PMTs). In the past, we proposed that the weight, size and cost of a scanner could be reduced by replacing the PMTs with large-area silicon photomultiplier (SiPM) pixels in which commercial SiPMs are summed to reduce the number of readout channels. We studied the feasibility of that solution with a small homemade camera, but the question on how it could be implemented in a large camera remained open. In this work, we try to answer this question by performing Geant4 simulations of a full-body SPECT camera. We studied how the pixel size, shape and noise could affect its energy and spatial resolution. Our results suggest that it would be possible to obtain an intrinsic spatial resolution of a few mm FWHM and an energy resolution at 140 keV close to 10%, even if using pixels more than 20 times larger than standard commercial SiPMs of 6 × 6 mm2. We have also found that if SiPMs are distributed following a honeycomb structure, the spatial resolution is significantly better than if using square pixels distributed in a square grid.
Dining Bowl Modeling and Optimization for Single-Image-Based Dietary Assessment
Authors: Li Boyang , Sun Mingui , Mao Zhi-Hong , Jia Wenyan
Published: September 2024

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera’s view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references. Although these references do not need a manual placement procedure, there is a unique challenge when a dining bowl is used as a reference. Unlike a dining plate, whose shallow shape does not usually block the view of the food, a dining bowl does obscure the food view, and its shape may not be fully observable from the single-view food image. As a result, significant errors may occur in food volume estimation due to the unknown shape of the bowl. To address this challenge, we present a novel method to premeasure both the size and shape of the empty bowl before it is used in a dietary assessment study. In our method, an image is taken with a labeled paper ruler adhered to the interior surface of the bowl, a mathematical model is developed to describe its shape and size, and then an optimization method is used to determine the bowl parameters based on the locations of observed ruler makers from the bowl image. Experimental studies were performed using both simulated and actual bowls to assess the reliability and accuracy of our bowl measurement method.
Investigation of Graphene Single Layer on P-Type and N-Type Silicon Heterojunction Photodetectors
Authors: Bonavolontà Carmela , Vettoliere Antonio , Pannico Marianna , Crisci Teresa , Ruggiero Berardo , Silvestrini Paolo , Valentino Massimo
Published: September 2024

Photodetectors are of great interest in several technological applications thanks to their capability to convert an optical signal into an electrical one through light–matter interactions. In particular, broadband photodetectors based on graphene/silicon heterojunctions could be useful in multiple applications due to their compelling performances. Here, we present a 2D photodiode heterojunction based on a graphene single layer deposited on p-type and n-type Silicon substrates. We report on the electro-optical properties of the device that have been measured in dark and light conditions in a spectral range from 400 nm to 800 nm. The comparison of the device’s performance in terms of responsivity and rectification ratio is presented. Raman spectroscopy provides information on the graphene single layer’s quality and oxidation. The results showcase the importance of the doping of the silicon substrate to realize an efficient heterojunction that improves the photoresponse, reducing the dark current.
Digital Twin Approach for Operation and Maintenance of Transportation System—Systematic Review
Authors: Werbińska-Wojciechowska Sylwia , Giel Robert , Winiarska Klaudia
Published: September 2024

There is a growing need to implement modern technologies, such as digital twinning, to improve the efficiency of transport fleet maintenance processes and maintain company operational capacity at the required level. A comprehensive review of the existing literature is conducted to address this, offering an up-to-date analysis of relevant content in this field. The methodology employed is a systematic literature review using the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2012 and 2024, resulting in 201 highly relevant papers. These papers were categorized into seven groups: (a) air transportation, (b) railway transportation, (c) land transportation (road), (d) in-house logistics, (e) water and intermodal transportation, (f) supply chain operation, and (g) other applications. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, revealing the evolution of DT applications over the past decade and identifying key areas such as predictive maintenance, condition monitoring, and decision-making processes. This study highlights the varied levels of adoption across different transport sectors and underscores promising areas for future development, particularly in underrepresented domains like supply chains and water transport. Additionally, this paper identifies significant research gaps, including integration challenges, real-time data processing, and standardization needs. Future research directions are proposed, focusing on enhancing predictive diagnostics, automating maintenance processes, and optimizing inventory management. This study also outlines a framework for DT in transportation systems, detailing key components and functionalities essential for effective maintenance management. The findings provide a roadmap for future innovations and improvements in DT applications within the transportation industry. This study ends with conclusions and future research directions.
A Wide-Bandwidth Inexpensive Current Sensor Based on the Signal Fusion of Tunneling Magnetoresistance and a Current Transformer
Authors: Wang Kun , Li Bin , Wang Lixin , Wang Jiafu , Li Chuansheng , Ding Zhiwen , Shao Haiming
Published: September 2024

In technology and industrial production, many applications require wide-bandwidth current measurements. In this paper, a signal fusion scheme for a current sensor comprising tunneling magnetoresistance and a current transformer is proposed, achieving a flat frequency response in the DC to MHz range. The measurement principles in different cases of the scheme are introduced, and the total transfer function of the entire scheme is derived by analyzing each section separately. Furthermore, the feasibility and selected parameters of the scheme are verified through a systematic simulation utilizing the MATLAB software. Based on the proposed scheme, a group of principal prototypes are built to experimentally evaluate the bandwidth, amplitude and phase flatness, accuracy, sensitivity, and impulse response. The relative amplitude variation in the passband of the fusion sensor is less than 4%, and the estimated bandwidth of the fusion sensor is close to 17 MHz. The accuracy is better than 0.6%, even when measuring the current at 1 MHz, and the relative standard deviation is 5% when measuring the impulse signal. The sensors developed using this scheme, with a low financial cost, have advantages in many wide-bandwidth current measuring scenarios.
Natural Antioxidants from Acmella oleracea Extract as Dermatocosmetic Actives
Authors: Maxim Claudia , Blaga Alexandra Cristina , Cimpoeșu Ramona , Zinicovscaia Inga , Peshkova Alexandra , Danu Maricel , Barna Ana Simona , Suteu Daniela
Published: September 2024

Compounds from plant extracts make dermatocosmetic products more effective as they avoid the adaptation and resistance of the organism and achieve a synergistic effect of the molecular properties of interest. Acmella oleracea extract is considered to have great potential in preventing oxidative damage and improving the appearance of the skin. The purpose of this article is to support the product formulated by preliminary studies of two types of O/W emulsions with 3% and 5% concentrations of Acmella oleracea extract. Physico-chemical methods were performed to evaluate the stability, microbiological control, rheological behavior and diffusion through the membrane. Good homogeneity, structural strength and flexibility, adequate skin diffusion, and high physico-chemical and microbiological stability were confirmed. The conclusions lead to the idea that these results require further in vivo studies as well as studies of toxicity and cytotoxicity to obtain the necessary data to place this product on the market.
How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
Authors: Didaci Luca , Pani Sara Maria , Frongia Claudio , Fraschini Matteo
Published: September 2024

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).
A Software Tool for ICESat and ICESat-2 Laser Altimetry Data Processing, Analysis, and Visualization: Description, Features, and Usage
Authors: Silva Bruno , Lopes Luiz Guerreiro
Published: September 2024

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth’s surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool’s use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world’s largest complex of shallow-water coastal lagoons.
Reliability, Objectivity, Validity, and Reference Levels of the Austrian Pole Climbing Test (APCT)—A Novel Monitoring Tool for Assessing General Fitness in Children, Adolescents, and Young Adults
Authors: Jarnig Gerald , Kerbl Reinhold , van Poppel Mireille N. M.
Published: September 2024

Climbing is an activity involving many major muscle groups and, therefore, it is suitable for assessing general physical fitness. The Austrian pole climbing test (APCT) was developed as a reliable and valid test for the assessment of general fitness levels in children, adolescents, and young adults. In this cross-sectional pilot study, 677 participants (aged 6 to 19 years) were assessed via the APCT. Subsequently, test quality criteria (reliability and objectivity) were assessed, and the test’s validity was evaluated through comparisons with other established fitness tests (hand grip strength, standing long jump, and pull- and push-ups). Additionally, age- and gender-specific reference values were generated. The reliability (ICC2.1 = 0.97, 95%CI 0.95 to 0.98) and objectivity (ICC2.1 = 0.99, 95%CI 0.99 to 0.99) of the APCT were found to be excellent. The APCT results correlated strongly with the hand strength per kilogram of body weight (right: r = 0.58; left: r = 0.53), number of pull-ups (with upper grip: r = 0.74; with lower grip: r = 0.69) and standing long jump (r = 0.61); a moderate correlation with the push-up test was observed (r = 0.44). The APCT is reliable, objective, and suitable for children, adolescents, and young adults with an affinity for sports. It offers a novel opportunity to assess fitness without time pressure, considering the anthropometric requirements.
Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach
Authors: Zheng Yuanfan , Chen Liang , Zhao Haipeng
Published: September 2024

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.
Nature-Based Solutions for Conservation and Food Sovereignty in Indigenous Communities of Oaxaca
Authors: Acevedo-Ortiz Marco Aurelio , Lugo-Espinosa Gema , Ortiz-Hernández Yolanda Donají , Pérez-Pacheco Rafael , Ortiz-Hernández Fernando Elí , Martínez-Tomás Sabino Honorio , Tavera-Cortés María Elena
Published: September 2024

The increasing demand for food and the overexploitation of natural resources rapidly deplete the planet’s ecosystems, highlighting the urgent need for sustainable alternatives. Nature-based solutions effectively promote agricultural sustainability and environmental conservation but require continuous financial and political support to overcome existing barriers. This research examines these solutions’ effectiveness in Santa María Jacatepec, a biocultural region of Indigenous peoples in the Papaloapan Basin, Oaxaca, Mexico. A mixed-methods approach combining quantitative and qualitative analyses uses National Institute of Statistics and Geography data to evaluate socioeconomic and environmental indicators. The results indicate that despite pressures to develop cattle ranching, communities have maintained rain-fed agriculture, especially the milpa system, ensuring food sovereignty and preserving agricultural biodiversity. Additionally, implementation of Voluntarily Conserved Areas has facilitated access to payments for environmental services, incentivizing ecosystem protection. However, financial and structural challenges persist, limiting the expansion of these solutions. Santa María Jacatepec exemplifies how Indigenous communities can apply nature-based solutions to strengthen agricultural sustainability and environmental conservation. Integrating traditional knowledge and strengthening conservation policies can enhance community resilience and ensure sustainable development amid increasing pressure on natural ecosystems.
Finding the “Secret Sauce” for Organizational Sustainability: Towards Successful Completion of IT Implementation Projects
Authors: Harison Elad , Lahav Yael
Published: September 2024

This paper identifies the major aspects that affect the sustainability of organizations in terms of success and failure of IT implementation projects. A significant relationship between the deployment of internal project teams and sustainable results of IT implementation projects compared to professionals employed via outsourcing was found. Moreover, involving professional managers with hands-on experience and knowledge of the organization in decision-making processes positively affects the project outcomes and the sustainability of the organization. Employee workload concerns are the least impactful on project overrun, while other concerns are less sustainable. Finally, a methodology that enhances the likelihood of sustainable results in IT implementation projects by involving professional managers and forming in-house teams is presented.
Enhancing Daylight Comfort with Climate-Responsive Kinetic Shading: A Simulation and Experimental Study of a Horizontal Fin System
Authors: Brzezicki Marcin
Published: September 2024

This study employs both simulation and experimental methodologies to evaluate the effectiveness of bi-sectional horizontal kinetic shading systems (KSS) with horizontal fins in enhancing daylight comfort across various climates. It emphasizes the importance of optimizing daylight levels while minimizing solar heat gain, particularly in the context of increasing energy demands and shifting climatic patterns. The study introduces a custom-designed bi-sectional KSS, simulated in three distinct climates—Wroclaw, Tehran, and Bangkok—using climate-based daylight modeling methods with the Ladybug and Honeybee tools in Rhino v.7 software. Standard daylight metrics, such as Useful Daylight Illuminance (UDI) and Daylight Glare Probability (DGP), were employed alongside custom metrics tailored to capture the unique dynamics of the bi-sectional KSS. The results were statistically analyzed using box plots and histograms, revealing UDI300–3000 medians of 78.51%, 88.96%, and 86.22% for Wroclaw, Tehran, and Bangkok, respectively. These findings demonstrate the KSS’s effectiveness in providing optimal daylight conditions across diverse climatic regions. Annual simulations based on standardized weather data showed that the KSS improved visual comfort by 61.04%, 148.60%, and 88.55%, respectively, compared to a scenario without any shading, and by 31.96%, 54.69%, and 37.05%, respectively, compared to a scenario with open static horizontal fins. The inclusion of KSS switching schedules, often overlooked in similar research, enhances the reproducibility and clarity of the findings. A physical reduced-scale mock-up of the bi-sectional KSS was then tested under real-weather conditions in Wroclaw (latitude 51° N) during June–July 2024. The mock-up consisted of two Chambers ‘1’ and ‘2’ equipped with the bi-sectional KSS prototype, and the other one without shading. Stepper motors managed the fins’ operation via a Python script on a Raspberry Pi 3 minicomputer. The control Chamber ‘1’ provided a baseline for comparing the KSS’s efficiency. Experimental results supported the simulations, demonstrating the KSS’s robustness in reducing high illuminance levels, with illuminance below 3000 lx maintained for 68% of the time during the experiment (conducted from 1 to 4 PM on three analysis days). While UDI and DA calculations were not feasible due to the limited number of sensors, the Eh1 values enabled the evaluation of the time illuminance to remain below the threshold. However, during the June–July 2024 heat waves, illuminance levels briefly exceeded the comfort threshold, reaching 4674 lx. Quantitative and qualitative analyses advocate for the broader application and further development of KSS as a climate-responsive shading system in various architectural contexts.