bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones
Authors: Du Changmin , Jiang Shouzheng , Chen Chuqiang , Guo Qianyue , He Qingyan , Zhan Cun
Published: February 2024

The accurate prediction of cropland evapotranspiration (ET) is of utmost importance for effective irrigation and optimal water resource management. To evaluate the feasibility and accuracy of ET estimation in various climatic conditions using machine learning models, three-, six-, and nine-factor combinations (V3, V6, and V9) were examined based on the data obtained from global cropland eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. Four machine learning models, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and backpropagation neural network (BP), were used for this purpose. The input factors included daily mean air temperature (Ta), net radiation (Rn), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pressure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four machine learning models exhibited significant simulation accuracy across various climate zones, reflected by their global performance indicator (GPI) values ranging from −3.504 to 0.670 for RF, −3.522 to 1.616 for SVM, −3.704 to 0.972 for XGB, and −3.654 to 1.831 for BP. The choice of suitable models and the different input factors varied across different climatic regions. Specifically, in the temperate–continental zone (TCCZ), subtropical–Mediterranean zone (SMCZ), and temperate zone (TCZ), the models of BPC-V9, SVMS-V6, and SVMT-V6 demonstrated the highest simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d−1, average MAE values of 0.177, 0.263, and 0.248 mm d−1, average R2 values of 0.949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respectively. In climate zones with a lower average LAI (TCCZ), there was a strong correlation between LAI and ET, making LAI more crucial for ET predictions. Conversely, in climate zones with a higher average LAI (TCZ, SMCZ), the significance of the LAI for ET prediction was reduced. This study recognizes the impact of climate zones on ET simulations and highlights the necessity for region-specific considerations when selecting machine learning models and input factor combinations.
Variability in Symbiont Chlorophyll of Hawaiian Corals from Field and Airborne Spectroscopy
Authors: Asner Gregory P. , Drury Crawford , Vaughn Nicholas R. , Hancock Joshua R. , Martin Roberta E.
Published: February 2024

Corals are habitat-forming organisms on tropical and sub-tropical reefs, often displaying diverse phenotypic behaviors that challenge field-based monitoring and assessment efforts. Symbiont chlorophyll (Chl) is a long-recognized indicator of intra- and inter-specific variation in coral’s response to environmental variability and stress, but the quantitative Chl assessment of corals at the reef scale continues to prove challenging. We integrated field, airborne, and laboratory techniques to test and apply the use of reflectance spectroscopy for in situ and reef-scale estimation of Chl a and Chl c2 concentrations in a shallow reef environment of Kāne‘ohe Bay, O‘ahu. High-fidelity spectral signatures (420–660 nm) derived from field and airborne spectroscopy quantified Chl a and Chl c2 concentrations with demonstrable precision and accuracy. Airborne imaging spectroscopy revealed a 10-fold range of Chl concentrations across the reef ecosystem. We discovered a differential pattern of Chl a and Chl c2 use in symbiont algae in coexisting corals indicative of a physiological response to decreasing light levels with increasing water depth. The depth-dependent ratio of Chl c2:a indicated the presence of two distinct light-driven habitats spanning just 5 m of water depth range. Our findings provide a pathway for further study of coral pigment responses to environmental conditions using field and high-resolution airborne imaging spectroscopy.
Optimal Integration of Optical and SAR Data for Improving Alfalfa Yield and Quality Traits Prediction: New Insights into Satellite-Based Forage Crop Monitoring
Authors: Chen Jiang , Yu Tong , Cherney Jerome H. , Zhang Zhou
Published: February 2024

Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R2 = 0.846 and RMSE = 0.0354 kg/m2; CP: R2 = 0.636 and RMSE = 1.57%; ADF: R2 = 0.559 and RMSE = 1.926%; NDF: R2 = 0.58 and RMSE = 2.097%; NDFD: R2 = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites.
From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture
Authors: Govi David , Pappalardo Salvatore Eugenio , De Marchi Massimo , Meggio Franco
Published: February 2024

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.
Multi-View Scene Classification Based on Feature Integration and Evidence Decision Fusion
Authors: Zhou Weixun , Shi Yongxin , Huang Xiao
Published: February 2024

Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a misalignment between the visual content and semantic labels, thus complicating the classification process. In addition, as the number of image viewpoints increases, the quality problem for remote sensing images further limits the effectiveness of multi-view image classification. Traditional scene classification methods predominantly employ SoftMax deep learning techniques, which lack the capability to assess the quality of remote sensing images or to provide explicit explanations for the network’s predictive outcomes. To address these issues, this paper introduces a novel end-to-end multi-view decision fusion network specifically designed for remote sensing scene classification. The network integrates information from multi-view remote sensing images under the guidance of image credibility and uncertainty, and when the multi-view image fusion process encounters conflicts, it greatly alleviates the conflicts and provides more reasonable and credible predictions for the multi-view scene classification results. Initially, multi-scale features are extracted from the multi-view images using convolutional neural networks (CNNs). Following this, an asymptotic adaptive feature fusion module (AAFFM) is constructed to gradually integrate these multi-scale features. An adaptive spatial fusion method is then applied to assign different spatial weights to the multi-scale feature maps, thereby significantly enhancing the model’s feature discrimination capability. Finally, an evidence decision fusion module (EDFM), utilizing evidence theory and the Dirichlet distribution, is developed. This module quantitatively assesses the uncertainty in the multi-perspective image classification process. Through the fusing of multi-perspective remote sensing image information in this module, a rational explanation for the prediction results is provided. The efficacy of the proposed method was validated through experiments conducted on the AiRound and CV-BrCT datasets. The results show that our method not only improves single-view scene classification results but also advances multi-view remote sensing scene classification results by accurately characterizing the scene and mitigating the conflicting nature of the fusion process.
Downscaling Land Surface Temperature Derived from Microwave Observations with the Super-Resolution Reconstruction Method: A Case Study in the CONUS
Authors: Li Yu , Sun Donglian , Zhan Xiwu , Houser Paul , Yang Chaowei , Qu John J.
Published: February 2024

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm.
Computer Vision-Based Position Estimation for an Autonomous Underwater Vehicle
Authors: Zalewski Jacek , Hożyń Stanisław
Published: February 2024

Autonomous Underwater Vehicles (AUVs) are currently one of the most intensively developing branches of marine technology. Their widespread use and versatility allow them to perform tasks that, until recently, required human resources. One problem in AUVs is inadequate navigation, which results in inaccurate positioning. Weaknesses in electronic equipment lead to errors in determining a vehicle’s position during underwater missions, requiring periodic reduction of accumulated errors through the use of radio navigation systems (e.g., GNSS). However, these signals may be unavailable or deliberately distorted. Therefore, in this paper, we propose a new computer vision-based method for estimating the position of an AUV. Our method uses computer vision and deep learning techniques to generate the surroundings of the vehicle during temporary surfacing at the point where it is currently located. The next step is to compare this with the shoreline representation on the map, which is generated for a set of points that are in a specific vicinity of a point determined by dead reckoning. This method is primarily intended for low-cost vehicles without advanced navigation systems. Our results suggest that the proposed solution reduces the error in vehicle positioning to 30–60 m and can be used in incomplete shoreline representations. Further research will focus on the use of the proposed method in fully autonomous navigation systems.
A Point Cloud Registration Framework with Color Information Integration
Authors: Han Tianyu , Zhang Ruijie , Kan Jiangming , Dong Ruifang , Zhao Xixuan , Yao Shun
Published: February 2024

Point cloud registration serves as a critical tool for constructing 3D environmental maps. Both geometric and color information are instrumental in differentiating diverse point features. Specifically, when points appear similar based solely on geometric features, rendering them challenging to distinguish, the color information embedded in the point cloud carries significantly important features. In this study, the colored point cloud is utilized in the FCGCF algorithm, a refined version of the FCGF algorithm, incorporating color information. Moreover, we introduce the PointDSCC method, which amalgamates color consistency from the PointDSC method for outlier removal, thus enhancing registration performance when synergized with other pipeline stages. Comprehensive experiments across diverse datasets reveal that the integration of color information into the registration pipeline markedly surpasses the majority of existing methodologies and demonstrates robust generalizability.
Flexible Chitosan-Based Capacitive Humidity Sensors for Respiratory Monitoring
Authors: Pan Miaoxin , Zhou Jumei , Weng Shichen , Wu Xingjian
Published: February 2024

As one of the most important human health indicators, respiratory status is an important basis for the diagnosis of many diseases. However, the high cost of respiratory monitoring makes its use uncommon. This study introduces a low-cost, wearable, flexible humidity sensor for respiratory monitoring. Solution-processed chitosan (CS) placed on a polyethylene terephthalate substrate was used as the sensing layer. An Arduino circuit board was used to read humidity-sensitive voltage changes. The CS-based sensor demonstrated capacitive humidity sensitivity, whereby the capacitance instantly increased from 10−2 to 30 nF when the environmental humidity changed from 43% to 97%. The capacitance logarithm sensitivity and response voltage change was 35.9 pF/%RH and 0.8 V in the RH range from 56% to 97%. And the voltage variation between inhalation and exhalation was ~0.5 V during normal breathing. A rapid response time of ~0.7 s and a recovery time of ~2 s were achieved during respiration testing. Breathing modes (i.e., normal breathing, rest breathing, deep breathing, and fast breathing) and tonal changes during speech could be clearly distinguished. Therefore, such sensors provide a means for economical and convenient wearable respiratory monitoring, and they have the potential to be used for daily health examinations and professional medical diagnoses.
A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks
Authors: Ullah Arif , Khan Fawad Salam , Mohy-ud-din Zia , Hassany Noman , Gul Jahan Zeb , Khan Maryam , Kim Woo Young , Park Youn Cheol , Rehman Muhammad Muqeet
Published: February 2024

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.
Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi
Authors: Xie Jiahao , Li Zhenfen , Feng Chao , Lin Jingzhi , Meng Xianjia
Published: February 2024

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.
Discussion on the Discreteness of the Attenuation Parameters of the Peak Particle Velocity Induced by Blasting
Authors: Yang Zhaowei , Hu Yingguo , Liu Meishan , Li Peng , Yao Erlei , Ma Chenyang
Published: February 2024

The research on the attenuation law of blasting vibration has become the foundation and precondition of the effective control of blasting vibration damage. Aiming at the characteristics of low frequency, low velocity, and strong amplitude of the R wave, an improved wave component separation method based on R wave suppression is proposed. Combined with the measured vibration signals of a field test, the attenuation parameters of different types of waves in the propagation process of blasting seismic waves are studied. The analysis results show that, in the process of blasting seismic wave propagation, the attenuation parameters of different types of waves are significantly different. With an increase in propagation distance, the proportion of the different types of waves will also change. The study of attenuation law with only coupled particle peak vibration velocity often showed high discreteness. The fitting correlation coefficient and prediction accuracy of peak vibration velocity without distinguishing wave modes are lower than those induced by the P wave or R wave alone, which should be attributed to the conversion of dominant wave modes in blasting vibration at different distances.
Coactivation of the Pelvic Floor and Gluteus Medius Muscles While Walking and Running in Female Runners
Authors: Porrón-Irigaray Avelaine , Rodríguez-López Elena Sonsoles , Acevedo-Gómez María Barbaño , Ojedo-Martín Cristina , Benito-de-Pedro María
Published: February 2024

(1) Background: Pelvic-floor-muscle (PFM) activation acts synergistically with multiple muscles while performing functional actions in humans. The purpose of this study was to characterize the activity of the PFMs and gluteus medius (GM) while walking and running in physically active nulliparous females. (2) Methods: The peak and average amplitude of maximal voluntary contractions (MVCs) during 60 s of walking (5 and 7 km/h) and running (9 and 11 km/h) were measured with electromyography of the GM and PFMs in 10 healthy female runners. (3) Results: The activation of both muscles increased (p < 0.001) while walking and running. The MVC of the GM was reached when walking and tripled when running, while the PFMs were activated at half their MVC when running. The global ratio of the GM (75.3%) was predominant over that of the PFMs (24.6%) while static and walking. The ratio reached 9/1 (GM/PFM) while running. (4) Conclusion: The GM and PFMs were active while walking and running. The GM’s MVC tripled at high speeds, while the PFMs reached only half of their maximum contraction.
An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves
Authors: Hernandez-Gonzalez Nieves G. , Montiel-Caminos Juan , Sosa Javier , Montiel-Nelson Juan A.
Published: February 2024

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted.
Digital Twin of a Water Supply System Using the Asset Administration Shell
Authors: Cavalieri Salvatore , Gambadoro Salvatore
Published: February 2024

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors’ knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform.
An Efficient Privacy Protection Mechanism for Blockchain-Based Federated Learning System in UAV-MEC Networks
Authors: Zhu Chaoyang , Zhu Xiao , Qin Tuanfa
Published: February 2024

The widespread use of UAVs in smart cities for tasks like traffic monitoring and environmental data collection creates significant privacy and security concerns due to the transmission of sensitive data. Traditional UAV-MEC systems with centralized data processing expose this data to risks like breaches and manipulation, potentially hindering the adoption of these valuable technologies. To address this critical challenge, we propose UBFL, a novel privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy (DP), UBFL employs an adaptive nonlinear encryption function to safeguard the privacy of UAV model updates while maintaining data integrity and accuracy. This innovative approach enables rapid convergence, allowing the base station to efficiently identify and filter out severely compromised UAVs attempting to inject malicious data. Additionally, UBFL incorporates the Random Cut Forest (RCF) anomaly detection algorithm to actively identify and mitigate poisoning data attacks. Extensive comparative experiments on benchmark datasets CIFAR10 and Mnist demonstrably showcase UBFL’s effectiveness. Compared to DP-based methods, UBFL achieves accuracy (99.98%), precision (99.93%), recall (99.92%), and F-Score (99.92%) in privacy preservation while maintaining superior accuracy. Notably, under data pollution scenarios with varying attack sample rates (10%, 20%, and 30%), UBFL exhibits exceptional resilience, highlighting its robust capabilities in securing UAV gradients within MEC environments.
Underwater Turbid Media Stokes-Based Polarimetric Recovery
Authors: Wang Zhenfei , Hu Meixin , Zhang Ketao
Published: February 2024

Underwater optical imaging for information acquisition has always been an innovative and crucial research direction. Unlike imaging in the air medium, the underwater optical environment is more intricate. From an optical perspective, natural factors such as turbulence and suspended particles in the water cause issues like light scattering and attenuation, leading to color distortion, loss of details, decreased contrast, and overall blurriness. These challenges significantly impact the acquisition of underwater image information, rendering subsequent algorithms reliant on such data unable to function properly. Therefore, this paper proposes a method for underwater image restoration using Stokes linearly polarized light, specifically tailored to the challenges of underwater complex optical imaging environments. This method effectively utilizes linear polarization information and designs a system that uses the information of the first few frames to calculate the enhanced images of the later frames. By doing so, it achieves real-time underwater Stokes linear polarized imaging while minimizing human interference during the imaging process. Furthermore, the paper provides a comprehensive analysis of the deficiencies observed during the testing of the method and proposes improvement perspectives, along with offering insights into potential future research directions.
FR-PatchCore: An Industrial Anomaly Detection Method for Improving Generalization
Authors: Jiang Zhiqian , Zhang Yu , Wang Yong , Li Jinlong , Gao Xiaorong
Published: February 2024

In recent years, a multitude of self-supervised anomaly detection algorithms have been proposed. Among them, PatchCore has emerged as one of the state-of-the-art methods on the widely used MVTec AD benchmark due to its efficient detection capabilities and cost-saving advantages in terms of labeled data. However, we have identified that the PatchCore similarity principal approach faces significant limitations in accurately locating anomalies when there are positional relationships between similar samples, such as rotation, flipping, or misaligned pixels. In real-world industrial scenarios, it is common for samples of the same class to be found in different positions. To address this challenge comprehensively, we introduce Feature-Level Registration PatchCore (FR-PatchCore), which serves as an extension of the PatchCore method. FR-PatchCore constructs a feature matrix that is extracted into the memory bank and continually updated using the optimal negative cosine similarity loss. Extensive evaluations conducted on the MVTec AD benchmark demonstrate that FR-PatchCore achieves an impressive image-level anomaly detection AUROC score of up to 98.81%. Additionally, we propose a novel method for computing the mask threshold that enables the model to scientifically determine the optimal threshold and accurately partition anomalous masks. Our results highlight not only the high generalizability but also substantial potential for industrial anomaly detection offered by FR-PatchCore.
A Review on 3D Scanners Studies for Producing Customized Orthoses
Authors: Silva Rui , Silva Bruna , Fernandes Cristiana , Morouço Pedro , Alves Nuno , Veloso António
Published: February 2024

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, as well as excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic environment and difficulty in keeping the injury clean during treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all of these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which is a time-consuming and error-prone method. From another point of view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but which induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation, have emerged. This article describes the use of various types of scanners capable of digitizing the human body to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology into clinical environment.
Efficient Removal of Tetracycline by Metal–Organic Framework ZIF-67 and Its Mechanism
Authors: Zheng Shengyang , Xu Yaping , Yao Xu , Wang Chenzhe , Liu Ping , Zhao Haitao , Lu Jianbing , Ju Jing
Published: February 2024

The widespread use of tetracycline (TC) poses potential hazards to ecosystems and human health. In this study, ZIF-67 was successfully synthesized using a room-temperature static synthesis method and applied to the efficient removal of TC from water. It was shown that the maximum adsorption of TC by ZIF-67 could reach 1583.128 mg·g−1 at pH = 5.0, an initial TC concentration of 450 mg·g−1, an adsorption time of 720 min, and a temperature of 308K.The pseudo-second-order kinetic model and Langmuir’s isothermal adsorption model could describe the adsorption process better, which proved that the adsorption of ZIF-67 on TC was mainly monolayer adsorption dominated by chemisorption. Mechanistic studies showed that the adsorption process of ZIF-67 on TC was mainly through electrostatic interactions, pore adsorption, π–π interactions, and framework coordination of ZIF-67 surface cations with TC.
Isolation of Macrocyclic Trichothecene Mycotoxins from the Lethal Toxic Mushroom Podostroma cornu-damae and Their Cytotoxic Activities
Authors: Lee Bum Soo , Lee Yun Young , Lee Seoung Rak , Jang Yoon Seo , Ryoo Rhim , Park Wooram , Kim Se-Na , Lee Soah , Park Chun Gwon , Kim Ki Hyun
Published: February 2024

Podostroma cornu-damae, one of the lethal toxic mushrooms, is known to contain macrocyclic trichothecene mycotoxins exhibiting potent cytotoxic effects, attracting attention as an important research subject for scientists interested in natural product chemistry and toxicity research. To investigate the mycotoxins from the toxic mushroom P. cornu-damae and evaluate their cytotoxic activities, the fungus was large-cultured on solid plates and successively extracted to acquire a crude methanol (MeOH) extract. After performing successive separation and purification processes, a total of eight macrocyclic trichothecenes were isolated from the MeOH extract of plate cultures of P. cornu-damae using the liquid chromatography/mass spectrometry (LC/MS)-guided isolation technique. Extensive interpretation of nuclear magnetic resonance (NMR) spectroscopic and high-resolution (HR)-electrospray ionization (ESI)-MS data allowed for the structural identification of all isolated macrocyclic trichothecenes, including satratoxin I (1), satratoxin H (2), roridin E (3), miophytocen D (4), roridin L-2 (5), trichoverritone (6), 12′-episatratoxin H (7), and roridin F (8). We conducted a cytotoxicity evaluation of compounds 1–8 against 4T1 breast cancer cells and fibroblast cell lines (L929 cells) using the Counting Kit-8 (CCK-8) cell viability assay to validate their cytotoxic potential. Our results indicated that compounds 1–6 lack anti-cancer effects on 4T1 cells and have minimal impact on the viability of the fibroblast cell line, L929 cells. In contrast, compounds 7 and 8 exhibited no cytotoxicity in normal cells (L929) and demonstrated specific cytotoxicity in breast cancer cell lines. Notably, the cytotoxic effects of compounds 7 and 8 in 4T1 cells were significantly stronger than those observed with free doxorubicin. These findings suggest that compounds 7 and 8 may possess targeted anti-cancer effects, specifically against breast cancer cells, emphasizing their efficient and selective toxicity towards breast cancer cells.
Peer Verbal Encouragement Is More Effective than Coach Encouragement in Enhancing CrossFit-Specific 1-RM Strength, Functional Endurance, and Psychophysiological Assessment Performance
Authors: Romdhani Amir , Sahli Faten , Trabelsi Omar , Rebhi Mahmoud , Ghouili Hatem , Sahli Hajer , Salem Atef , Trabelsi Khaled , Jahrami Haitham , Ammar Achraf , Zghibi Makram
Published: February 2024

This study compares the effects of coach verbal encouragement (CVE) and peer verbal encouragement (PVE) on CrossFit-specific one-repetition maximum (1-RM) strength, functional endurance, and psychophysiological assessments. A total of 36 sports science students (18 males, 18 females; mean age: 21.3 ± 0.5 years) participated in a randomized, counterbalanced crossover study in which 1-RM strength and endurance assessment sessions were undertaken under PVE, CVE, and no verbal encouragement (NVE) on separate days. Here, 1-RM strength was assessed through squat, deadlift, and bench press exercises, while endurance was evaluated using 8 min time trials (8MTT). Following the physical assessments, psychophysiological evaluations were conducted using the Borg Rating of Perceived Exertion (RPE) and the Feeling Scale (FS). The findings revealed that, after PVE, all the 1-RM strength test, 8MTT, RPE, and FS values exhibited significant increases compared to those of CVE (p [<0.001–<0.01], r = −0.84 [large]) and NVE (p [<0.001–<0.05], r [−0.43–0.52] [small]). Exceptions were noted in 1-RM-deadlift (p > 0.05, r = −0.43 [small]) and 1-RM-bench-press (p > 0.05, r = −0.43 [small]), where CVE demonstrated higher scores (1-RM-squat, 8MTT, RPE, and FS) (p [<0.001–<0.05], r = −0.64 [large]) in comparison to NVE. In conclusion, the study established that PVE is more impactful than CVE in enhancing CrossFit-specific 1-RM strength, functional endurance, and psychophysiological assessment performance. These findings suggest that coaches/teachers should consider involving their athletes in the reinforcement process for evaluated peers. This collaborative approach may not only optimize performance outcomes but also foster a supportive and motivational training environment.
Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology
Authors: Shambira Ngwarai , Makaka Golden , Mukumba Patrick
Published: February 2024

Wind energy, renowned for cost-effectiveness and eco-friendliness, addresses global energy needs amid fossil fuel scarcity and environmental concerns. In low-wind speed regions, optimising wind turbine performance becomes vital and achievable by augmenting wind velocity at the turbine rotor using augmentation systems such as concentrators and diffusers. This study focuses on developing a velocity augmentation model that correctly predicts the throat velocity in an empty concentrator-diffuser-augmented wind turbine (CDaugWT) design and determines optimal geometrical parameters. Utilising response surface methodology (RSM) in Design Expert 13 and computational fluid dynamics (CFD) in ANSYS Fluent, 86 runs were analysed, optimising parameters such as diffuser and concentrator angles and lengths, throat length, and flange height. The ANOVA analysis confirmed the model’s significance (p < 0.05). Notably, the interaction between the concentrator’s length and the diffuser’s length had the highest impact on the throat velocity. The model showed a strong correlation (R2 = 0.9581) and adequate precision (ratio value of 49.655). A low coefficient of variation (C.V.% = 0.1149) highlighted the model’s reliability. The findings revealed a 1.953-fold increase in inlet wind speed at the throat position. Optimal geometrical parameters for the CDaugWT included a diffuser angle of 10°, concentrator angle of 20°, concentrator length of 375 mm (0.62Rth), diffuser length of 975 mm (1.61Rth), throat length of 70 mm (0.12Rth), and flange height of 100 mm (0.17Rth) where Rth is the throat radius. A desirability value of 0.9, close to 1, showed a successful optimisation. CFD simulations and RSM reduced calculation cost and time when determining optimal geometrical parameters for the CDaugWT design.
Biomonitoring: Developing a Beehive Air Volatiles Profile as an Indicator of Environmental Contamination Using a Sustainable In-Field Technique
Authors: Ilić Daria , Brkić Boris , Sekulić Maja Turk
Published: February 2024

The wellbeing of the honey bee colonies and the health of humans are connected in numerous ways. Therefore, ensuring the wellbeing of bees is a crucial component of fostering sustainability and ecological harmony. The colony collapse disorder (CCD) phenomenon was first reported in 2006 when the majority of bee colonies in Europe died out, due to an increase in infections, contamination of hives with agrochemical pesticides, and persistent organic pollutants (POPs). Only 6 years after the emergence of CCD, more than 6.5 million premature deaths were reported, as a consequence of persistent human exposure to air pollution. The insect species such as the honey bee Apis mellifera L. and the air matrix inside the beehive can be used as tools in biomonitoring, instead of traditional monitoring methods. This may have advantages in terms of cost-effective bioindicators of the environmental health status, showing the ability to record spatial and temporal pollutant variations. In this study, we present the sustainable in-field usage of the portable membrane inlet mass spectrometry (MIMS) instrument for an instant and effective determination of the level of environmental pollution by analytical identification of hive atmosphere volatile organic compound (VOC) contaminants, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), monocyclic aromatic hydrocarbons (BTEX) compounds, and pesticides. The samples were taken from hives located in urbanized and rural regions, highlighting variations in contamination. The MIMS results were benchmarked against a conventional laboratory sampling technique, such as GC-MS.
Analysis of Systemic Risk on the Financial Performance during the COVID-19 Pandemic: The Case of the Colombian Banking Industry
Authors: Rojas Rincón Joan Sebastián , Mejía Martínez Andrés Mauricio , Riveros Tarazona Andrés Ricardo , Acosta-Prado Julio César
Published: February 2024

This study seeks to analyze the financial performance of the Colombian banking industry during the COVID-19 pandemic. The frame of reference is based on the concept of systemic risk; concerning this, the pandemic is conceived as an external shock, which impacted the dynamics of the banking industry. To conduct this study, a descriptive-correlational scope is proposed, from which an analysis of different accounting items related to the banking business is made and validated by expert judgment. The analysis horizon covers six years, but the focus is placed on March 2020, when COVID-19 was declared a pandemic by the World Health Organization (WHO). For this purpose, a longitudinal design is proposed, which analyzes the time series describing the behavior of some relevant items in the management of the banking business, such as operating revenue, provisions, interest on deposits and drawings, valuation of trading derivatives, and technology-related expenses. In addition, these items’ correlation with banking establishments’ performance is analyzed. The results of the study show that during the pandemic period, there was a significant increase in the level of volatility in the foreign exchange market, which impacted the operating revenue of banking establishments. It is concluded that, although exchange rate volatility affected the results of the banking industry, the main factor related to the financial performance of Colombian banks is their business itself, i.e., revenue from the loan portfolio and the quality of the loan portfolio. Therefore, systemic risk must be addressed regarding its implications on banks’ main profit drivers, such as portfolio revenue, cost of deposits, and provisions. Based on the above, it is recommended that Colombian banking establishments make greater efforts to diversify their sources of income to reduce their exposure to systemic risk situations.
Analyzing Factors That Affect Korean B2B Companies’ Sustainable Performance
Authors: Lee Sungchang , Kim Young Jun
Published: February 2024

This study empirically examines factors that can influence the sustainable corporate performance of Korean business-to-business (B2B) companies with the help of unique survey data. Factors such as technological capability, the chief executive officer (CEO)’s risk-taking propensity, B2B seller skill, and key account management (KAM) are analyzed to clarify their impact on sustainable financial and non-financial performance. In particular, given that environment, society, and governance (ESG) reporting has recently been widely recognized as an important evaluation factor for companies, we look at the mediating effects of ESG management on sustainable business performance. The results show that the CEO’s risk-taking propensity and B2B seller skill significantly impact the company’s sustainable financial performance, while technological capability and the CEO’s risk-taking propensity significantly impact sustainable non-financial performance. The fact that a CEO’s risk-taking propensity affects both sustainable financial and non-financial performance indicates the importance of entrepreneurial competency in the sustainability of the company. Furthermore, the findings reveal that ESG management plays a crucial role in sustainable corporate performance. The mediating role of ESG management allows technological capability, B2B seller skill, and KAM to influence sustainable financial performance significantly. Likewise, all of the explanatory factors contribute to the company’s sustainable non-financial performance through ESG management. The findings are important for both practitioners and scholars because they emphasize the need to establish an optimal ESG management strategy for corporate survival and sustainability. Furthermore, this study underscores that ESG management should be implemented by all organizational members, from CEOs to employees. Future research will include more comprehensive samples and analyze various strategic factors not covered in this study to derive effective ways by which companies can increase their performance and sustainability. We will also explore the factors that contribute to good ESG management practices.
Opportunity Assessment of Virtual Power Plant Implementation for Sustainable Renewable Energy Development in Indonesia Power System Network
Authors: Setiawan Agus , Jufri Fauzan Hanif , Dzulfiqar Fatih , Samual Muhammad Gillfran , Arifin Zainal , Angkasa Fahmi Firdaus , Aryani Dwi Riana , Garniwa Iwa , Sudiarto Budi
Published: February 2024

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.