bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Analysis of TRPA1, TRPM3, and TRPV1 Channels in Human Dermal Arteries and Their Role in Vascular Modulation
Authors: Rivera-Mancilla Eduardo , Al-Hassany Linda , Marynissen Heleen , Bamps Dorien , Garrelds Ingrid M. , Cornette Jérôme , Danser A. H. Jan , Villalón Carlos M. , de Hoon Jan N. , MaassenVanDenBrink Antoinette
Published: January 2024

Transient receptor potential (TRP) channels are pivotal in modulating vascular functions. In fact, topical application of cinnamaldehyde or capsaicin (TRPA1 and TRPV1 channel agonists, respectively) induces “local” changes in blood flow by releasing vasodilator neuropeptides. We investigated TRP channels’ contributions and the pharmacological mechanisms driving vasodilation in human isolated dermal arteries. Ex vivo studies assessed the vascular function of artery segments and analyzed the effects of different compounds. Concentration–response curves to cinnamaldehyde, pregnenolone sulfate (PregS, TRPM3 agonist), and capsaicin were constructed to evaluate the effect of the antagonists HC030031 (TRPA1); isosakuranetin (TRPM3); and capsazepine (TRPV1). Additionally, the antagonists/inhibitors olcegepant (CGRP receptor); L-NAME (nitric oxide synthase); indomethacin (cyclooxygenase); TRAM-34 plus apamin (K+ channels); and MK-801 (NMDA receptors, only for PregS) were used. Moreover, CGRP release was assessed in the organ bath fluid post-agonist-exposure. In dermal arteries, cinnamaldehyde- and capsaicin-induced relaxation remained unchanged after the aforementioned antagonists, while PregS-induced relaxation was significantly inhibited by isosakuranetin, L-NAME and MK-801. Furthermore, there was a significant increase in CGRP levels post-agonist-exposure. In our experimental model, TRPA1 and TRPV1 channels seem not to be involved in cinnamaldehyde- or capsaicin-induced relaxation, respectively, whereas TRPM3 channels contribute to PregS-induced relaxation, possibly via CGRP-independent mechanisms.
Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics
Authors: Cha Yoonjeong , Kagalwala Mohamedi N. , Ross Jermaine
Published: January 2024

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer’s disease and Parkinson’s disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.
Evaluating the Effectiveness of Videos for Teaching Pharmaceutical Calculations to Pharmacy Students
Authors: Ghazal Heba , Haq Marrium , Crilly Philip , Harrap Nicola , Kayyali Reem
Published: January 2024

Performing pharmaceutical calculations accurately is a fundamental aspect of the pharmacy profession, ensuring treatment efficacy and patient safety. Incorporating videos in teaching can enhance visualisation and reinforce learning. The current study utilised videos to teach calculations and assessed how these affected students’ performance. Initially, pharmacy students were surveyed at one UK University to identify calculation topics they found most challenging, and then two prototype videos were created based on these topics. Feedback was gathered through a follow-up survey on these prototypes, leading to the development of five additional videos. To evaluate the impact of these videos, students were given quizzes before and after watching them. The data were analysed in Microsoft Excel and included paired t-tests to compare mean scores, with significance set at p < 0.05. The survey was completed by 98/130 (75% response rate), with 58% expressing average or low confidence in calculations. A majority (78%) believed that videos would aid their comprehension of calculation concepts. In the subsequent phase, most respondents (92%, 80/87) agreed that the prototype videos improved their understanding of the two topics, but this increase was not statistically significant. However, quiz performance evaluation revealed a significant increase in average scores. This study affirms that videos can boost students’ performance in calculations by allowing them to visualise the relevant practical scenarios.
Optical Asymmetric Cryptosystem Based on Dynamic Foveated Imaging and Bidimensional Empirical Mode Decomposition
Authors: Wu Yunan , Chang Jun , Ji Zhongye , Huang Yi , Wang Junya , Zhao Shangnan
Published: January 2024

In this paper, we propose an asymmetric cryptosystem based on dynamic foveated imaging and bidimensional empirical mode decomposition (BEMD). Firstly, a novel dynamic foveated imaging algorithm is developed to transform a plaintext image to a globally ambiguous and locally clear image. Then, the image is passed through a phase-truncated Fourier transform system to generate a white noise image. The resulting image is encoded using BEMD to produce an encrypted image. The proposed cryptosystem offers two distinct decryption methods, allowing the receiver to obtain a decrypted image from a specific frame or a combination of frames, depending on the unique keys. This encryption scheme significantly expands the key space and strengthens the system’s anti-iterative attack capability. Numerical simulation results demonstrate the effectiveness, security and robustness of the proposed cryptosystem.
Secretory Tissues and Volatile Components of Disc Florets in Several Wild Helianthus L. Species
Authors: Jocković Jelena , Rajčević Nemanja , Zorić Lana , Jocković Milan , Radanović Aleksandra , Cvejić Sandra , Jocić Siniša , Vujisić Ljubodrag , Miladinović Dragana , Miklič Vladimir , Luković Jadranka
Published: January 2024

Although flower pollinator interactions are known to be mediated by floral traits, not enough attention has been paid to the research of secretory tissues and volatile components of sunflower disc florets as potentially important parameters in breeding programs. (1) To our knowledge, this is the first integrated study aimed at better understanding the attractiveness of sunflower capitula to insects. In the study, we have made a very detailed comparative analysis of secretory tissues and the characterization of the volatile components (VOCs) of disc florets in 10 wild perennial Helianthus species. (2) For anatomical analyses, cross-sections were obtained from the nectary zone of disc florets using a cryotechnique procedure. Micromorphological observation and morphological and anatomical analysis of disc florets were performed using light and scanning electron microscopy. For VOCs, we applied headspace, GC-FID, and GC/MS analyses. (3) The obtained results indicate that there is a difference between the analyzed traits among studied species. H. eggertii, H. hirsutus, H. mollis, H. resinosus, and H. tuberosus had high disc diameter values, a high cross-section area and disc floret corolla length, as well as the largest cross-section area and thickness of the disc florets nectary. In the analyzed VOCs, 30 different compounds were detected. The highest yield and quantity of α-Pinene was observed in H. mollis. (4) Inflorescence features, such as receptacle diameter, corolla and secretory tissue properties, and floret VOCs production and characterization, provided valuable information that can be used as guidelines in sunflower breeding programs to maximize pollinator attractiveness and increase seed yield.
Genetic and Phenotypic Characterization of Soybean Landraces Collected from the Zhejiang Province in China
Authors: Yu Xiaomin , Fu Xujun , Yang Qinghua , Jin Hangxia , Zhu Longming , Yuan Fengjie
Published: January 2024

The soybean is an important feed, industrial raw material, and food crop in the world due to its rich components. There is a long history of soybean cultivation with different types and rich resources in the Zhejiang province of China. It is important to understand genetic diversity as well as phenotypic variation for soybean breeding. The objective of this study was to analyze both genetic and phenotypic characteristics of the 78 soybean landraces collected, and to explore a potential advantage of germplasm resources for further application. These 78 autumn-type soybean landraces have been propagated, identified, and evaluated in both 2021 and 2022. There were agronomic, quality, and genetic variations according to the comprehensive analyses. There was a good consistency between seed size and seed coat color. There were significant differences of seed protein, fat, and sugar contents based upon the seed coat color. These soybean landraces were genotyped using 42 simple sequence repeat markers and then clustered into two groups. The two groups had a consistency with the seed coat color. This study gave us a combined understanding of both the phenotypic variation and the genetic diversity of the soybean landraces. Therefore, the reasonable crossing between different soybean types is highly recommended.
The Phenotype of Mesenchymal Stromal Cell and Articular Chondrocyte Cocultures on Highly Porous Bilayer Poly-L-Lactic Acid Scaffolds Produced by Thermally Induced Phase Separation and Supplemented with Hydroxyapatite
Authors: Ferraro Wally , Civilleri Aurelio , Gögele Clemens , Carbone Camilla , Vitrano Ilenia , Carfi Pavia Francesco , Brucato Valerio , La Carrubba Vincenzo , Werner Christian , Schäfer-Eckart Kerstin , Schulze-Tanzil Gundula
Published: January 2024

Bilayer scaffolds could provide a suitable topology for osteochondral defect repair mimicking cartilage and subchondral bone architecture. Hence, they could facilitate the chondro- and osteogenic lineage commitment of multipotent mesenchymal stromal cells (MSCs) with hydroxyapatite, the major inorganic component of bone, stimulating osteogenesis. Highly porous poly-L-lactic acid (PLLA) scaffolds with two layers of different pore sizes (100 and 250 µm) and hydroxyapatite (HA) supplementation were established by thermally induced phase separation (TIPS) to study growth and osteogenesis of human (h) MSCs. The topology of the scaffold prepared via TIPS was characterized using scanning electron microscopy (SEM), a microCT scan, pycnometry and gravimetric analysis. HMSCs and porcine articular chondrocytes (pACs) were seeded on the PLLA scaffolds without/with 5% HA for 1 and 7 days, and the cell attachment, survival, morphology, proliferation and gene expression of cartilage- and bone-related markers as well as sulfated glycosaminoglycan (sGAG) synthesis were monitored. All scaffold variants were cytocompatible, and hMSCs survived for the whole culture period. Cross-sections revealed living cells that also colonized inner scaffold areas, producing an extracellular matrix (ECM) containing sGAGs. The gene expression of cartilage and bone markers could be detected. HA represents a cytocompatible supplement in PLLA composite scaffolds intended for osteochondral defects.
The Influence of the Application Layer of Pouring Semi-Flexible Pavement Material on Low-Temperature Stress
Authors: Li Guoxun , Wang Deyong , Zhang Huaizhi , Xu Biao , Yang Fan , Zhang Zhen
Published: January 2024

Pouring semi-flexible pavement material (PSFM) is widely used as a wearing layer material or below pavement due to its excellent resistance to deformation at high temperatures and under heavy loads. However, in cold regions, the material exhibits severe cracking issues. The primary objective of this study is to enhance the resistance of pouring semi-flexible pavements (SFPs) to low-temperature cracking in cold regions by strategically designing pavement structures that incorporate PSFM. To achieve this goal, we conducted indoor tests to determine the relaxation modulus and temperature shrinkage coefficient of PSFM and simulated a pavement structure using COMSOL finite element simulation. The impacts of different application layers and layer thicknesses on low-temperature stresses were investigated based on these findings. The research findings indicate that when PSFM is used as the wearing layer material, the low-temperature stress is 4.7% lower than that of typical materials used in the pavement-wearing layer. When used as the binder layer material, the low-temperature stress on the wearing layer material increases by 3.5%. As the thickness of the wearing layer increases, the low-temperature stress within the pavement structure decreases, but the low-temperature stress on the pavement surface increases. Therefore, it is recommended to use PSFM as the binder layer material and appropriately increase the thickness of the wearing layer to enhance the pavement’s resistance to low-temperature cracking.
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
Authors: Melo Afrânio , Câmara Maurício Melo , Pinto José Carlos
Published: January 2024

This paper presents a comprehensive review of the historical development, the current state of the art, and prospects of data-driven approaches for industrial process monitoring. The subject covers a vast and diverse range of works, which are compiled and critically evaluated based on the different perspectives they provide. Data-driven modeling techniques are surveyed and categorized into two main groups: multivariate statistics and machine learning. Representative models, namely principal component analysis, partial least squares and artificial neural networks, are detailed in a didactic manner. Topics not typically covered by other reviews, such as process data exploration and treatment, software and benchmarks availability, and real-world industrial implementations, are thoroughly analyzed. Finally, future research perspectives are discussed, covering aspects related to system performance, the significance and usefulness of the approaches, and the development environment. This work aims to be a reference for practitioners and researchers navigating the extensive literature on data-driven industrial process monitoring.
The Crucial Impact of Microbial Growth and Bioenergy Conversion on Treating Livestock Manure and Antibiotics Using Chlorella sorokiniana
Authors: Kim Hee-Jun , Jeong Sangjun , Lee YeonA , Lee Jae-Cheol , Kim Hyun-Woo
Published: January 2024

The residual antibiotics in livestock excreta (LE) have been regarded as a potential threat to the ecosystem and human society. Some photoautotrophic microalgae, however, were found to metabolize them during active biomass photosynthesis. This study investigates how the strength of the antibiotics impacts the overall biodiesel yield and composition of the harvested microalgal biomass grown from LE. The microalgal growth results demonstrate that increasing the concentration of residual antibiotics suppresses the microalgal growth rate from 0.87 d−1 to 0.34 d−1. This 61% lower biomass production rate supports the proposition that the kinetic impact of antibiotics may slow lipid synthesis. Moreover, the analytical results of fatty acid methyl ester (FAME) demonstrate that amoxicillin substantially reduces the C16:0 content by over 96%. This study evidences that the functional group similarity of amoxicillin may competitively inhibit the esterification reaction by consuming methanol. This explanation further highlights that residual antibiotics interfere with microalgal lipid synthesis and its transesterification. Moreover, it was confirmed that the presence of residual antibiotics may not affect the major nutrient removal (total nitrogen: 74.5~78.0%, total phosphorus: 95.6~96.8%). This indicates that residual antibiotics inhibit the metabolism associated with carbon rather than those associated with nitrogen and phosphorus, which is connected to the decrease in the biodiesel yield. Overall, these results reveal that the frequent abuse of antibiotics in livestock may harm the eco-friendly conversion of waste-into-bioenergy strategy.
Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning
Authors: Faridi Ibtihaj Khurram , Tsotsas Evangelos , Kharaghani Abdolreza
Published: January 2024

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation.
Xylanase Production by Cellulomonas phragmiteti Using Lignocellulosic Waste Materials
Authors: Buda Kata , Fekete Tünde , Ontañon Ornella M. , Campos Eleonora , Fehér Csaba
Published: January 2024

Lignocellulosic biomass holds promise as a renewable feedstock for various applications, but its efficient conversion requires cost-effective degradation strategies. The main objective of this study was to investigate the effect of the growth conditions of Cellulomonas phragmiteti in the production of (hemi)cellulosic supernatants. To meet this aim, different lignocellulosic residues were used as carbon sources for growth using defined mineral or nutritive culture media. Cell-free culture supernatants with xylanolytic activity were produced in all the conditions evaluated, but the highest xylanase activity (15.3 U/mL) was achieved in Luria–Bertani (LB) medium containing 1% waste paper. Under these conditions, almost negligible β-glucosidase, cellobiohydrolase, β-xylosidase, and α-arabinofuranosidase activity was detected. The xylanolytic supernatant showed tolerance to salt and displayed maximal catalytic efficiency at pH 6 and 45 °C, along with good activity in the ranges of 45–55 °C and pH 5–8. As it showed good stability at 45 °C, the supernatant was employed for the hydrolysis of birchwood xylan (50 g/L) under optimal conditions, releasing 10.7 g/L xylose in 72 h. Thus, C. phragmiteti was found to produce a xylanolytic enzymatic supernatant efficiently by utilizing the cheap and abundant lignocellulosic residue of waste paper, and the produced supernatant has promising attributes for industrial applications.
A Numerical Simulation of the Seismic Performance and Residual Stress of Welded Joints in Building Steel Structures Based on the Finite Element Method
Authors: Peng Jun , Li Xiangyu
Published: January 2024

With the development of society and urbanization, higher requirements have been put forward for the safety and seismic resistance of building structures. The fatigue strength and seismic performance of welded joints have received close attention, especially as a crucial part of building steel structure. This study used the finite element simulation method to analyze the stress-strain of welded joints in building steel structures, and explore the influence of residual stress on their seismic performance. A stress-strain calculation model for welded joints in building steel structures was studied and constructed, and the accuracy of the model was verified through numerical calculation methods. The results showed that the residual stress peaks of the horizontal and vertical directions of the V-groove welded joint structure were 475 MPa and 325 MPa, respectively, and the longitudinal residual stress peaks were 525 MPa and 425 MPa, respectively. The seismic performance of four different steel structural plates was Q960>Q690>Q460>Q345. In summary, the numerical simulation of residual stress in the seismic performance of welded joints in building steel structures, when based on the finite element method, makes a contribution of clear value to the field of seismic performance of welded joints in building steel structures.
Atmospheric HDO Abundance Measurements in the Tibetan Plateau Based on Laser Heterodyne Radiometer
Authors: Lu Xingji , Huang Yinbo , Wu Pengfei , Huang Jun , Luo Tao , Liu Qiang , Cao Zhensong
Published: January 2024

The Tibet Plateau is known as the “third pole” of the world, and its environmental change profoundly impacts East Asia and even the global climate. HDO is the stable isotope of water vapor, which acts as an ideal tracer for studying the water cycle, and which is commonly used for atmospheric circulation and climatic studies. To monitor the water vapor isotopic abundance in the Tibetan Plateau, a portable laser heterodyne radiometer was operated in Golmud in August 2019. The radiometer utilizes a narrow-linewidth 3.66 μm distributed feedback interband cascade laser as the local oscillator, the heterodyne module is been optimized and the radiometer performs with high resolution and stability in obtaining spectral data. Furthermore, the absorption spectra of atmospheric HDO and H2O are obtained, and the retrieval method for water vapor isotopic abundance is discussed. The optimal estimation method is adopted to retrieve the density of HDO and H2O. The average column density of H2O was 1.22 g/cm2, and the HDO/H2O ratio in Golmud was 178 ± 15 × 10−6 during the observation. For a better understanding of the retrieval, the retrieval errors are analyzed and compared. The results indicate that the smoothing error is significantly higher than the measurement error in this work. The backward trajectory analysis of atmospheric transport is used to investigate the relationship between water vapor density and atmospheric motion. The results indicate that the variation of H2O column density and HDO/H2O ratio have a relationship with atmospheric movements.
Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM
Authors: Tao Sinuo , Song Lisheng , Zhao Gengle , Zhao Long
Published: January 2024

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.
Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications
Authors: Toming Kaire , Liu Hui , Soomets Tuuli , Uuemaa Evelyn , Nõges Tiina , Kutser Tiit
Published: January 2024

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.
Estimation of the Bio-Parameters of Winter Wheat by Combining Feature Selection with Machine Learning Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Images
Authors: Zhang Changsai , Yi Yuan , Wang Lijuan , Zhang Xuewei , Chen Shuo , Su Zaixing , Zhang Shuxia , Xue Yong
Published: January 2024

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.
Real-Time Walk Error Compensation Method Using Echo Signal Magnitude Measurement in ToF Laser Scanners
Authors: Sędek Bartosz , Zygmunt Marek , Jakubaszek Marcin , Drozd Tadeusz , Wojtanowski Jacek
Published: January 2024

The rapid advancement of mobile laser scanner technology used for terrain mapping, among other things, imposes increasing requirements for scanning frequency and distance measurement accuracy. To meet these requirements, rangefinder modules are expected to operate with high echo signal dynamics and to allow accurate distance measurement even based on single-laser-pulse echo detection. Such performance can be potentially achieved using pulsed time-of-flight (ToF) laser rangefinders (LRF). In conventional ToF modules, however, the STOP signal (for time counter interruption) is generated using a straightforward fixed-threshold comparator method. Unfortunately, it corresponds to the so-called walk error, i.e., the dependence of the measured time of flight on the magnitude of the echo signal. In most ranging applications, however, the LRF detection channel can be exposed to an extremely large span of received echo power levels, which depend on the distance measured, type of target surface, atmospheric transmission, etc. Thus, the walk error is an inseparable element of the conventional ToF technique and creates a fundamental limit for its precision. This article presents a novel method of walk error compensation in real time. By using our authorial electronic circuit for measuring the magnitude of the echo signal, it is possible to effectively compensate for the walk error even when the echo signal brings the detection channel amplifiers into saturation. In addition, the paper presents a laboratory method for calibrating the walk error compensation curve.
An In-Depth Study of Vibration Sensors for Condition Monitoring
Authors: Hassan Ietezaz Ul , Panduru Krishna , Walsh Joseph
Published: January 2024

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.
Design of Virtual Hands for Natural Interaction in the Metaverse
Authors: Cerdá-Boluda Joaquín , Mora Marta C. , Lloret Nuria , Scarani Stefano , Sastre Jorge
Published: January 2024

The emergence of the Metaverse is raising important questions in the field of human–machine interaction that must be addressed for a successful implementation of the new paradigm. Therefore, the exploration and integration of both technology and human interaction within this new framework are needed. This paper describes an innovative and technically viable proposal for virtual shopping in the fashion field. Virtual hands directly scanned from the real world have been integrated, after a retopology process, in a virtual environment created for the Metaverse, and have been integrated with digital nails. Human interaction with the Metaverse has been carried out through the acquisition of the real posture of the user’s hands using an infrared-based sensor and mapping it in its virtualized version, achieving natural identification. The technique has been successfully tested in an immersive shopping experience with the Meta Quest 2 headset as a pilot project, where a transactions mechanism based on the blockchain technology (non-fungible tokens, NFTs) has allowed for the development of a feasible solution for massive audiences. The consumers’ reactions were extremely positive, with a total of 250 in-person participants and 120 remote accesses to the Metaverse. Very interesting technical guidelines are raised in this project, the resolution of which may be useful for future implementations.
The Development of a Stereo Vision System to Study the Nutation Movement of Climbing Plants
Authors: Ruiz-Melero Diego Rubén , Ponkshe Aditya , Calvo Paco , García-Mateos Ginés
Published: January 2024

Climbing plants, such as common beans (Phaseolus vulgaris L.), exhibit complex motion patterns that have long captivated researchers. In this study, we introduce a stereo vision machine system for the in-depth analysis of the movement of climbing plants, using image processing and computer vision. Our approach involves two synchronized cameras, one lateral to the plant and the other overhead, enabling the simultaneous 2D position tracking of the plant tip. These data are then leveraged to reconstruct the 3D position of the tip. Furthermore, we investigate the impact of external factors, particularly the presence of support structures, on plant movement dynamics. The proposed method is able to extract the position of the tip in 86–98% of cases, achieving an average reprojection error below 4 px, which means an approximate error in the 3D localization of about 0.5 cm. Our method makes it possible to analyze how the plant nutation responds to its environment, offering insights into the interplay between climbing plants and their surroundings.
The Secret Lives of Miniature Batteries
Authors: Toledo Sivan , Mendel Shai
Published: January 2024

This article describes the design, implementation, and use of a new system to investigate the behavior of small batteries that power sensor and wireless systems that consume relatively high power during infrequent short activity periods. The system enables simple, low-cost, long-term (days to weeks) monitoring of batteries under such loads. Data collected by this system revealed a major cause of failures in wildlife tracking tags, an effect called concentration polarization, which causes a transient increase in the internal resistance of the battery. The article describes the goals and the design of the system, failures that it revealed, mechanisms to mitigate the limitations of miniature batteries, as well as a methodology to optimize and validate the design of tags powered by miniature batteries.
A Variational Autoencoder Cascade Generative Adversarial Network for Scalable 3D Object Generation and Reconstruction
Authors: Yu Min-Su , Jung Tae-Won , Yun Dai-Yeol , Hwang Chi-Gon , Park Sea-Young , Kwon Soon-Chul , Jung Kye-Dong
Published: January 2024

Generative Adversarial Networks (GANs) for 3D volume generation and reconstruction, such as shape generation, visualization, automated design, real-time simulation, and research applications, are receiving increased amounts of attention in various fields. However, challenges such as limited training data, high computational costs, and mode collapse issues persist. We propose combining a Variational Autoencoder (VAE) and a GAN to uncover enhanced 3D structures and introduce a stable and scalable progressive growth approach for generating and reconstructing intricate voxel-based 3D shapes. The cascade-structured network involves a generator and discriminator, starting with small voxel sizes and incrementally adding layers, while subsequently supervising the discriminator with ground-truth labels in each newly added layer to model a broader voxel space. Our method enhances the convergence speed and improves the quality of the generated 3D models through stable growth, thereby facilitating an accurate representation of intricate voxel-level details. Through comparative experiments with existing methods, we demonstrate the effectiveness of our approach in evaluating voxel quality, variations, and diversity. The generated models exhibit improved accuracy in 3D evaluation metrics and visual quality, making them valuable across various fields, including virtual reality, the metaverse, and gaming.
Mining the Micro-Trajectory of Two-Wheeled Non-Motorized Vehicles Based on the Improved YOLOx
Authors: Zhou Dan , Zhao Zhenzhong , Yang Ruixin , Huang Shiqian , Wu Zhilong
Published: January 2024

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model’s feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.
Multispectral Demosaicing Based on Iterative-Linear-Regression Model for Estimating Pseudo-Panchromatic Image
Authors: Jeong Kyeonghoon , Kim Sanghoon , Kang Moon Gi
Published: January 2024

This paper proposes a method for demosaicing raw images captured by multispectral cameras. The proposed method estimates a pseudo-panchromatic image (PPI) via an iterative-linear-regression model and utilizes the estimated PPI for multispectral demosaicing. The PPI is estimated through horizontal and vertical guided filtering, with the subsampled multispectral-filter-array-(MSFA) image and low-pass-filtered MSFA as the guide image and filtering input, respectively. The number of iterations is automatically determined according to a predetermined criterion. Spectral differences between the estimated PPI and MSFA are calculated for each channel, and each spectral difference is interpolated using directional interpolation. The weights are calculated from the estimated PPI, and each interpolated spectral difference is combined using the weighted sum. The experimental results indicate that the proposed method outperforms the State-of-the-Art methods with regard to spatial and spectral fidelity for both synthetic and real-world images.
Highly Sensitive and Selective Defect WS2 Chemical Sensor for Detecting HCHO Toxic Gases
Authors: Cui Zhen , Wang Hanxiao , Yang Kunqi , Shen Yang , Qin Ke , Yuan Pei , Li Enling
Published: January 2024

The gas sensitivity of the W defect in WS2 (VW/WS2) to five toxic gases—HCHO, CH4, CH3HO, CH3OH, and CH3CH3—has been examined in this article. These five gases were adsorbed on the VW/WS2 surface, and the band, density of state (DOS), charge density difference (CDD), work function (W), current–voltage (I–V) characteristic, and sensitivity of adsorption systems were determined. Interestingly, for HCHO-VW/WS2, the energy level contribution of HCHO is closer to the Fermi level, the charge transfer (B) is the largest (0.104 e), the increase in W is more obvious than other adsorption systems, the slope of the I–V characteristic changes more obviously, and the calculated sensitivity is the highest. To sum up, VW/WS2 is more sensitive to HCHO. In conclusion, VW/WS2 has a great deal of promise for producing HCHO chemical sensors due to its high sensitivity and selectivity for HCHO, which can aid in the precise and efficient detection of toxic gases.
The Design, Modeling and Experimental Investigation of a Micro-G Microoptoelectromechanical Accelerometer with an Optical Tunneling Measuring Transducer
Authors: Barbin Evgenii , Nesterenko Tamara , Koleda Aleksej , Shesterikov Evgeniy , Kulinich Ivan , Kokolov Andrey , Perin Anton
Published: January 2024

This treatise studies a microoptoelectromechanical accelerometer (MOEMA) with an optical measuring transducer built according to the optical tunneling principle (evanescent coupling). The work discusses the design of the accelerometer’s microelectromechanical sensing element (MSE) and states the requirements for the design to achieve a sensitivity threshold of 1 µg m/s2 at a calculated eigenvalue of the MSE. The studies cover the selection of the dimensions, mass, eigenfrequency and corresponding stiffness of the spring suspension, gravity-induced cross-displacements. The authors propose and experimentally test an optical transducer positioning system represented by a capacitive actuator. This approach allows avoiding the restrictions in the fabrication of the transducer conditioned by the extremely high aspect ratio of deep silicon etching (more than 100). The designed MOEMA is tested on three manufactured prototypes. The experiments show that the sensitivity threshold of the accelerometers is 2 µg. For the dynamic range from minus 0.01 g to plus 0.01 g, the average nonlinearity of the accelerometers’ characteristics ranges from 0.7% to 1.62%. For the maximum dynamic range from minus 0.015 g to plus 0.05 g, the nonlinearity ranges from 2.34% to 2.9%, having the maximum deviation at the edges of the regions. The power gain of the three prototypes of accelerometers varies from 12.321 mW/g to 26.472 mW/g. The results provide broad prospects for the application of the proposed solutions in integrated inertial devices.
Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions
Authors: Abu Awwad Yaser , Rana Omer , Perera Charith
Published: January 2024

The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.