bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Anticancer Properties of Argemone mexicana L. and Berberine: A Comparative Study
Authors: Elizondo-Luevano Joel H. , Quintanilla-Licea Ramiro , Monroy-García Imelda N. , Kačániová Miroslava , Castillo-Velázquez Uziel , Bazaldúa-Rodríguez Aldo F. , Garza-Vega Lourdes M. , Torres-Hernández Ángel D. , Chávez-Montes Abelardo
Published: May 2024

Argemone mexicana L. has been used in traditional Mexican medicine. Among its bioactive constituents, berberine (BER) has garnered attention for its cytotoxic properties against different tumor cell lines. This study investigates the in vitro toxicity against HEP-G2 (human hepatocellular carcinoma) and murine lymphoma (L5178Y-R) cells using the MTT assay of the methanol extract (AmexM), sub-partitions of A. mexicana, and BER. Selectivity indices (SIs) were determined by comparing their cytotoxic effects on VERO (monkey kidney epithelial) and PBMC (human peripheral blood mononuclear) non-tumoral cells. Additionally, the anti-hemolytic effect of these treatments was assessed using the AAPH method. The treatment with the most promising activity against tumor cells and anti-hemolytic efficacy underwent further evaluation for toxicity in Artemia salina and antioxidant activities using DPPH, ABTS, and FRAP assays. BER demonstrated an IC50 = 56.86 µg/mL in HEP-G2 cells and IC50 < 5.0 µg/mL in L5178Y-R cells, with SI values of 15.97 and >5.40 in VERO and PBMC cells, respectively. No significant hemolytic effects were observed, although AmexM and BER exhibited the highest anti-hemolytic activity. BER also demonstrated superior antioxidant efficacy, with lower toxicity in A. salina nauplii compared to the control. Additionally, BER significantly attenuated nitric oxide production. This study highlights the antiproliferative effects of A. mexicana, particularly BER, against HEP-G2 and L5178Y-R tumor cell lines, along with its selectivity towards normal cells. Furthermore, its anti-hemolytic and antioxidant potentials were demonstrated, suggesting that BER is a promising candidate for potent chemotherapeutic agents.
Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN
Authors: Ye Rong , Shao Guoqi , Yang Ziyi , Sun Yuchen , Gao Quan , Li Tong
Published: May 2024

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.
Advanced Diagnostics of Electrons Escaping from Laser-Produced Plasma
Authors: Krása Josef , Krupka Michal , Agarwal Shubham , Nassisi Vincenzo , Singh Sushil
Published: May 2024

This article provides an up-to-date overview of the problems associated with the detection of hot electrons escaping from laser-produced plasma and corresponding return current flowing from the ground to the target, which neutralises the positive charge occurring on the target due to the escaped electrons. In addition, the target holder system acts as an antenna emitting an electromagnetic pulse (EMP), which is powered by the return target. If the amount of positive charge generated on the target is equal to the amount of charge carried away from the plasma by the escaping electrons, the measurement of the return current makes it possible to determine this charge, and thus also the number of escaped electrons. Methods of return current detection in the mA–10 kA range is presented, and the corresponding charge is compared to the charge determined using calibrated magnetic electron energy analysers. The influence of grounded and insulated targets on the number of escaped electrons and EMP intensity is discussed. In addition to EMP detection, mapping of the electrical potential near the target is mentioned.
Effects of Curing Defects in Adhesive Layers on Carbon Fiber–Quartz Fiber Bonded Joint Performance
Authors: Yang Xiaobo , Zhang Miaomiao , Zhan Lihua , Ma Bolin , Wu Xintong , Liu Cong , Xiang He
Published: May 2024

Due to their mechanical load-bearing and functional wave transmission, adhesively bonded joints of carbon fiber–quartz fiber composites have been widely used in the new generation of stealth aviation equipment. However, the curing defects, caused by deviations between the process environment and the setting parameters, directly affect the service performance of the joint during the curing cycle. Therefore, the thermophysical parameter evolution of adhesive films was analyzed via dynamic DSC (differential scanning calorimeter), isothermal DSC and TGA (thermal gravimetric analyzer) tests. The various prefabricating defects within the adhesive layer were used to systematically simulate the impacts of void defects on the tensile properties, and orthogonal tests were designed to clarify the effects of the curing process parameters on the joints’ bonding performance. The results demonstrate that the J-116 B adhesive film starts to cure at a temperature of 160 °C and gradually forms a three-dimensional mesh-bearing structure. Furthermore, a bonding interface between the J-116 B adhesive film and the components to be connected is generated. When the curing temperature exceeds 200 °C, both the adhesive film and the resin matrix thermally degrade the molecular structure. The adhesive strength weakens with an increasing defect area ratio and number, remaining more sensitive to triangle, edge and penetration defects. By affecting the molecular structure of the adhesive film, the curing temperature has a significant impact on the bonding properties; when the curing degree is ensured, the curing pressure directly impacts the adhesive’s performance by influencing the morphology, number and distribution of voids. Conversely, the heating rate and heat preservation time have minimal effects on the bonding performance.
Experimental Study on the Gelling Properties of Nano-Silica Sol and Its Spontaneous Imbibition Grouting Mudstone
Authors: Zhao Yiming , Xiang Zhe , Zhang Nong , Dai Jingchen
Published: May 2024

The low-permeability argillaceous rock mass is an unfavorable geological body commonly found in the construction process of underground engineering conditions such as roadways and tunnels. Due to the compact structure and low permeability of the rock mass, grouting with conventional materials cannot effectively seal the micro-cracks of the rock mass. Based on the low efficiency of high-pressure grouting of nano-silica sol, this paper preliminarily explores the regularities and mechanism of grouting and pore sealing of low-permeability rock mass under the action of silica sol imbibition from the aspects of gelling properties of silica sol, core pore structure, imbibition law, and pore sealing characteristics. The results show the following: (1) The increase in particle size during the gel process reduced the injectability and wettability of the silica sol. The imbibition properties of silica sol were time-varying, and the deterioration inflection points of injectability and wettability appeared at 10 h and 9 h, respectively. (2) Catalyst, temperature, gel process, and rock mass permeability will affect the law of core imbibition, and the injectability and capillary force of the grouting material and rock mass will jointly affect the imbibition process of silica sol. (3) Silica sol imbibition changed the pore size distribution of the core, the pore volume above 50 nm decreased, and the pore volume below 50 nm increased. Silica sol has multiple effects such as filling, adsorption, and percolation in the imbibition process of the micro-pores of rock mass, and the adsorption and percolation of silica are related to the nano micro-pores.
A Li-Ion Battery State of Charge Estimation Strategy Based on the Suboptimal Multiple Fading Factor Extended Kalman Filter Algorithm
Authors: Wu Weibin , Zeng Jinbin , Jian Qifei , Tang Luxin , Hou Junwei , Han Chongyang , Song Qian , Luo Yuanqiang
Published: May 2024

The state of charge (SOC) is an important indicator for evaluating a battery management system (BMS), which is crucial for the reliability, performance, and life management of a battery. In this paper, the characteristics of a Li-ion battery are deeply studied to explore the charge/discharge curve under different environments. Meanwhile, a second-order RC equivalent circuit model is constructed. The function identification of the EMF and SOC is performed based on the least squares method. The model estimation error is verified by simulation to be less than 0.05 V. Based on the Suboptimal Multiple Fading Factor Extended Kalman Filter (SMFEKF) algorithm, the SOC under constant current and UDDS conditions are estimated. Matlab/simulink simulations illustrate that the estimated accuracy of the proposed algorithm is improved by 79.36% compared with the EKF algorithm. Finally, the validity of the algorithm is verified jointly with the BMS. The results show that the estimation error is within 4% in both constant current condition as well as UDDS conditions, and it can still be predicted quickly and accurately under the uncertainty in the initial value of the SOC.
Chemical Modification of Birch Bark (Betula L.) for the Improved Bioprocessing of Cadmium(II), Chromium(VI), and Manganese(II) from Aqueous Solutions
Authors: Chwastowski Jarosław , Staroń Paweł
Published: May 2024

This study aimed to assess the sorption capacity of a natural sorbent, specifically birch bark (BB), and its modification using chemical reagents, including nitric and hydrochloric acid, sodium hydroxide, and chloride. The objective of the chemical modification was to enhance the sorption capacity of the heavy metals cadmium(II), chromium(VI), and manganese(II). The most effective modification for adsorbing cadmium and manganese from aqueous solutions was achieved by treating the sorbent with a 0.1 M sodium hydroxide solution (BBNa). Conversely, in the case of chromium, each modification adversely affected its adsorption by the sorbent. Concentrations of the solutions were analyzed using atomic absorption spectrometry at appropriate time intervals. The adsorption process was described using Langmuir, Freundlich, and Temkin isotherms. The Freundlich isotherm provided the best fit for cadmium and chromium (R2 = 0.988 and 0.986, respectively), while the Langmuir isotherm was most suitable for manganese (R2 = 0.996). The sorption capacity varied for each metal ion: Cd (II)—33.13 mg/g, Cr (VI)—35.98 mg/g, and Mn (II)—24 mg/g for the highest concentration tested. This study employed pseudo–first-rate order, pseudo–second-rate order model kinetics, and the Weber–Morris model to examine the adsorption kinetics. The pseudo–second-rate order kinetics demonstrated the best fit (R2 > 0.94) for each heavy metal, which underlines the process’s chemical nature.
Short-Term Climatic Oscillations in the Central Region of the East-European Plain at the Beginning of the Holocene Based on Palynological Studies of Lacustrine Deposits
Authors: Borisova Olga , Naryshkina Natalia , Panin Andrey
Published: May 2024

The Preboreal (11.75–10.70 ka BP) is still the least paleogeographically studied time interval in the central part of the East European Plain. High-resolution multi-proxy studies of lacustrine sediments at the Seltso site located in the Desna River floodplain (Dnieper River basin) were conducted. Radiocarbon dating, loss-on-ignition determination, sedimentological and palynological studies and identification of Non-Pollen Palynomorphs in lacustrine sediments allow us to reconstruct changes in vegetation caused by rapid warming at the Younger Dryas–Holocene boundary, short-term climatic fluctuations within the Preboreal and subsequent resumption of warming. Initial Preboreal warming reached its maximum at about 11.5 ka BP when a relatively dry continental climate existed. Between 11.4 and 11.2 ka BP, a short-term cooling corresponding to the Preboreal Oscillation in Greenland occurred, as indicated by a significant reduction of woody vegetation and expansion of open plant communities. In the Late Preboreal, approximately 11.2–10.7 ka BP, warming resumed, which was accompanied by a decrease in the climate continentality. Comparison with high-resolution lithological and palynological data from eight reliably dated sections of the central East European Plain indicates that in northwestern and central Europe, the impact of the Preboreal Oscillation cooling on the vegetation and the lake ecosystems’ development was probably somewhat stronger.
Refined Analysis of Vegetation Phenology Changes and Driving Forces in High Latitude Altitude Regions of the Northern Hemisphere: Insights from High Temporal Resolution MODIS Products
Authors: Yin Hanmin , Liu Qiang , Liao Xiaohan , Ye Huping , Li Yue , Ma Xiaofei
Published: May 2024

The vegetation patterns in high-latitude and high-altitude regions (HLAR) of the Northern Hemisphere are undergoing significant changes due to the combined effects of global warming and human activities, leading to increased uncertainties in vegetation phenological assessment. However, previous studies on vegetation phenological changes often relied on long-term time series of remote sensing products for evaluation and lacked comprehensive analysis of driving factors. In this study, we utilized high temporal resolution seamless MODIS products (MODIS-NDVISDC and MODIS-EVI2SDC) to assess the vegetation phenological changes in High-Latitude-Altitude Regions (HLAR) of the Northern Hemisphere. We quantified the differences in vegetation phenology among different land-use types and determined the main driving factors behind vegetation phenological changes. The results showed that the length of the growing season (LOS) derived from MODIS-NDVISDC was 8.9 days longer than that derived from MODIS-EVI2SDC, with an earlier start of the growing season (SOS) by 1.5 days and a later end of the growing season (EOS) by 7.4 days. Among different vegetation types, deciduous needleleaf forests exhibited the fastest LOS extension (p < 0.01), while croplands showed the fastest LOS reduction (p < 0.05). Regarding land-use transitions, the conversion of built-up land to forest and grassland had the longest LOS. In expanding agricultural areas, the LOS of land converted from built-up land to cropland was significantly higher than that of other land conversions. We analyzed human activities and found that as the human footprint gradient increased, the LOS showed a decreasing trend. Among the climate-related factors, the dominant response of phenology to temperature was the strongest in the vegetation greening period. During the vegetation browning period, the temperature control was weakened, and the control of radiation and precipitation was enhanced, accounting for 20–30% of the area, respectively. Finally, we supplement and prove that the highest contributions to vegetation greening in the Northern Hemisphere occurred during the SOS period (May–June) and the EOS period (October). Our study provides a theoretical basis for vegetation phenological assessment under global change. It also offers new insights for land resource management and planning in high-latitude and high-altitude regions.
Evaluation of Ocean Color Algorithms to Retrieve Chlorophyll-a Concentration in the Mexican Pacific Ocean off the Baja California Peninsula, Mexico
Authors: Alvarado-Graef Patricia , Martín-Atienza Beatriz , Sosa-Ávalos Ramón , Durazo Reginaldo , Hernández-Walls Rafael
Published: May 2024

Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area.
Global Wavenumber Spectra of Sea Surface Salinity in the Mesoscale Range Using Satellite Observations
Authors: Yi Daling Li , Wang Peng
Published: May 2024

Sea surface salinity (SSS) variability at mesoscales has become an important research topic in recent decades, thanks to satellite missions enabling observations of SSS with global capacity and mesoscale resolution. Here, we analyze the near-global data of the Aquarius/SAC-D along-track SSS, focusing on the slopes of SSS variance spectra in the mesoscale range from 180 to 430 km. In the vast extratropics, the spectral slope is close to −2, indicating a dynamical regime for the inverse cascade of depth-integrated energy identified by the surface quasi-geostrophic theory. However, the spectral slopes in regions near the mouths of the largest rivers are steeper than −2, reaching −3, possibly due to the large river freshwater flux. In addition, data from high-resolution thermosalinograph are used to validate satellite measurements and show good consistency in terms of SSS variance spectral slopes.
Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa
Authors: Datti Aminu Dalhatu , Zeng Gang , Tarnavsky Elena , Cornforth Rosalind , Pappenberger Florian , Abdullahi Bello Ahmad , Onyejuruwa Anselem
Published: May 2024

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.
Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning
Authors: Du Qiang , Wang Zhiguo , Huang Pingping , Zhai Yongguang , Yang Xiangli , Ma Shuai
Published: May 2024

The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.
ICT in Nursing and Patient Healthcare Management: Scoping Review and Case Studies
Authors: Jayousi Sara , Barchielli Chiara , Alaimo Marco , Caputo Stefano , Paffetti Marzia , Zoppi Paolo , Mucchi Lorenzo
Published: May 2024

Over the past few decades, Information and Communication Technologies (ICT) have revolutionized the fields of nursing and patient healthcare management. This scoping review and the accompanying case studies shed light on the extensive scope and impact of ICT in these critical healthcare domains. The scoping review explores the wide array of ICT tools employed in nursing care and patient healthcare management. These tools encompass electronic health records systems, mobile applications, telemedicine solutions, remote monitoring systems, and more. This article underscores how these technologies have enhanced the efficiency, accuracy, and accessibility of clinical information, contributing to improved patient care. ICT revolution has revitalized nursing care and patient management, improving the quality of care and patient satisfaction. This review and the accompanying case studies emphasize the ongoing potential of ICT in the healthcare sector and call for further research to maximize its benefits.
Retention of Improved Plantar Sensation in Patients with Type II Diabetes Mellitus and Sensory Peripheral Neuropathy after One Month of Vibrating Insole Therapy: A Pilot Study
Authors: Ennion Liezel , Hijmans Juha M.
Published: May 2024

Sensory peripheral neuropathy is a common complication of diabetes mellitus and the biggest risk factor for diabetic foot ulcers. There is currently no available treatment that can reverse sensory loss in the diabetic population. The application of mechanical noise has been shown to improve vibration perception threshold or plantar sensation (through stochastic resonance) in the short term, but the therapeutic use, and longer-term effects have not been explored. In this study, vibrating insoles were therapeutically used by 22 participants, for 30 min per day, on a daily basis, for a month by persons with diabetic sensory peripheral neuropathy. The therapeutic application of vibrating insoles in this cohort significantly improved VPT by an average of 8.5 V (p = 0.001) post-intervention and 8.2 V (p < 0.001) post-washout. This statistically and clinically relevant improvement can play a role in protection against diabetic foot ulcers and the delay of subsequent lower-extremity amputation.
Workout Classification Using a Convolutional Neural Network in Ensemble Learning
Authors: Bang Gi-Seung , Park Seung-Bo
Published: May 2024

To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizing MediaPipe, the proposed system extracts the joint coordinates and angles of the human body, which the CNN uses to learn the complex patterns of various exercises. Additionally, this new approach enhances classification performance by combining predictions from multiple image frames using an ensemble learning method. Infinity AI’s Fitness Basic Dataset is employed for validation, and the experiments demonstrate high accuracy in classifying exercises such as arm raises, squats, and overhead presses. The proposed model demonstrated its ability to effectively classify exercise postures in real time, achieving high rates in accuracy (92.12%), precision (91.62%), recall (91.64%), and F1 score (91.58%). This indicates its potential application in personalized fitness recommendations and physical therapy services, showcasing the possibility for beneficial use in these fields.
Benchtop Performance of Novel Mixed Ionic–Electronic Conductive Electrode Form Factors for Biopotential Recordings
Authors: Colachis Matthew , Schlink Bryan R. , Colachis Sam , Shqau Krenar , Huegen Brittani L. , Palmer Katherine , Heintz Amy
Published: May 2024

Background: Traditional gel-based (wet) electrodes for biopotential recordings have several shortcomings that limit their practicality for real-world measurements. Dry electrodes may improve usability, but they often suffer from reduced signal quality. We sought to evaluate the biopotential recording properties of a novel mixed ionic–electronic conductive (MIEC) material for improved performance. Methods: We fabricated four MIEC electrode form factors and compared their signal recording properties to two control electrodes, which are electrodes commonly used for biopotential recordings (Ag-AgCl and stainless steel). We used an agar synthetic skin to characterize the impedance of each electrode form factor. An electrical phantom setup allowed us to compare the recording quality of simulated biopotentials with ground-truth sources. Results: All MIEC electrode form factors yielded impedances in a similar range to the control electrodes (all <80 kΩ at 100 Hz). Three of the four MIEC samples produced similar signal-to-noise ratios and interfacial charge transfers as the control electrodes. Conclusions: The MIEC electrodes demonstrated similar and, in some cases, better signal recording characteristics than current state-of-the-art electrodes. MIEC electrodes can also be fabricated into a myriad of form factors, underscoring the great potential this novel material has across a wide range of biopotential recording applications.
Atmospheric Light Estimation Using Polarization Degree Gradient for Image Dehazing
Authors: Liu Shuai , Li Hang , Zhao Jinyu , Liu Junchi , Zhu Youqiang , Zhang Zhenduo
Published: May 2024

A number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting the effectiveness of dehazing. This article presents a novel approach for estimating atmospheric light using the polarization state and polarization degree gradient of the sky. We utilize this approach to enhance the outcomes of image dehazing by applying it to pre-existing dehazing algorithms. Our study and development of a real-time dehazing system has shown that the approach we propose has a clear advantage over previous methods for estimating ambient light. After incorporating the proposed approach into existing defogging methods, a significant improvement in the effectiveness of defogging was noted through the assessment of various criteria such as contrast, PSNR, and SSIM.
Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression
Authors: Shao Yiting , Gao Wei , Liu Shan , Li Ge
Published: May 2024

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.
Exploring the Role of Video Playback Visual Cues in Object Retrieval Tasks
Authors: Qin Yechang , Su Jianchun , Qin Haozhao , Tian Yang
Published: May 2024

Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object’s last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users’ search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object’s destination to the object’s final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object’s last appearance further reduced search time and cognitive load.
A Novel High-Precision Railway Obstacle Detection Algorithm Based on 3D LiDAR
Authors: Nan Zongliang , Zhu Guoan , Zhang Xu , Lin Xuechun , Yang Yingying
Published: May 2024

This article presents a high-precision obstacle detection algorithm using 3D mechanical LiDAR to meet railway safety requirements. To address the potential errors in the point cloud, we propose a calibration method based on projection and a novel rail extraction algorithm that effectively handles terrain variations and preserves the point cloud characteristics of the track area. We address the limitations of the traditional process involving fixed Euclidean thresholds by proposing a modulation function based on directional density variations to adjust the threshold dynamically. Finally, using PCA and local-ICP, we conduct feature analysis and classification of the clustered data to obtain the obstacle clusters. We conducted continuous experiments on the testing site, and the results showed that our system and algorithm achieved an STDR (stable detection rate) of over 95% for obstacles with a size of 15 cm × 15 cm × 15 cm in the range of ±25 m; at the same time, for obstacles of 10 cm × 10 cm × 10 cm, an STDR of over 80% was achieved within a range of ±20 m. This research provides a possible solution and approach for railway security via obstacle detection.
Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework
Authors: Yan Zhenguo , Qin Zhixin , Fan Jingdao , Huang Yuxin , Wang Yanping , Zhang Jinglong , Zhang Longcheng , Cao Yuqi
Published: May 2024

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.
Advancing Transdermal Delivery by Zn/Ag-Electrode-Printed Iontophoretic Patch with Self-Generating Microcurrents
Authors: Peng Cheng-Liang , Lee Pei-Chi , Liu Hsin-Tung , Lai Ping-Shan
Published: May 2024

This study aimed to evaluate Zn/Ag-electrode-printed patches for the transdermal delivery of small molecules through iontophoresis. The Zn/Ag-electrode-printed patches interact with biological liquid electrolytes and generate suitable microcurrents for the iontophoretic delivery of small molecules across the skin. In fluorescein permeation studies, Zn/Ag-electrode-printed patches increased the transdermal depth of fluorescein into the dermis, while the permeation of fluorescein was limited when Zn/C-electrode-printed patches were tested. Further permeation experiments were conducted with 3D skin models, which showed a similar trend to the above, indicating that Zn/Ag-electrode-printed patches had a higher penetration rate compared to the blank. Studies using acetyl hexapeptide-8 as a peptide drug model and sodium ascorbyl phosphate (SAP) as a hydrophilic derivative of ascorbic acid showed that the iontophoretic patch with Zn/Ag electrodes promoted more penetration of drugs than unprinted patches. The permeation of SAP exhibited a two-phase profile with a relatively rapid permeation followed by a sustained, slower permeation. The permeation of acetyl hexapeptide-8 was slower due to its higher molecular weight, but the iontophoretic patch increased the permeation up to 1.5 times more than the unprinted patch. The microcurrent generated by the patch drives the transport of small molecule components through the skin, for the controlled and efficient delivery of therapeutic agents. The flexible design, efficient microcurrent generation, and stable electrodes make the Zn/Ag-electrode-printed patch a promising tool for transdermal drug delivery.
Feasibility of Different Methods for Separating n-Hexane and Ethanol
Authors: Sander Aleksandra , Petračić Ana , Rogošić Marko , Župan Mirela , Frljak Leonarda , Cvetnić Matija
Published: May 2024

Conventional distillation methods cannot effectively separate the components of an azeotropic mixture since both phases have the same composition, thereby preventing further separation. Additional techniques such as pressure swing distillation or distillation with entrainers are often employed to overcome this limitation and achieve separation. The aim of this investigation was to select the most effective method for separating n-hexane and ethanol. The feasibility of three methods was analyzed: reduced pressure distillation, extractive distillation, and liquid–liquid extraction. The mutual solubility of n-hexane and prepared deep eutectic solvents (DESs) (nine hydrophilic: choline chloride with glycerol, ethylene glycol, or carboxylic acid (malic, citric, glycolic); tetramethylammonium chloride with glycolic acid; lactic acid with glycerol; K2CO3 with glycerol or ethylene glycol; two hydrophobic: menthol with decanoic or dodecanoic acid) was experimentally determined. Extraction experiments were conducted to test the solubility of DESs in the feed mixture. The effect of changing DES-to-feed mass ratio was further investigated with choline chloride–glycerol (1:2). The same DES and both hydrophobic DESs were able to increase the relative volatility and enhance the separation of ethanol and n-hexane. Based on the obtained results, extraction was selected as the most effective method for the separation of n-hexane and ethanol.
Influence of Agro-Industrial Waste Composts on Soil Characteristics, Growth Dynamics, and Yield of Red Cabbage and Broccoli
Authors: Maffia Angela , Marra Federica , Battaglia Santo , Oliva Mariateresa , Mallamaci Carmelo , Muscolo Adele
Published: May 2024

In this work, environmentally sound technologies for converting organic wastes into fertilizers to improve soil sustainability and crop yield have been identified and assessed. Wet wastes were combined with 50% wood sawdust and 50% wet wastes (Compost 1) or (10% Straw + 90% wet wastes) (Compost 2) to produce soil improvers with a balanced level of nutrients, and their effectiveness on soil ecosystem functioning have been tested and compared to horse manure (HM) and nitrogen–phosphorous–potassium (NPK) fertilizers. Unfertilized soil was used as a control. Soil chemical and biological properties have been detected after the harvesting of broccoli and red cabbage (90 days from the initial treatments). Three independent experiments have been conducted in an open field in a randomized complete block design with three replications (n = 9). The results showed that Compost 1 had the highest C/N ratio and cation exchange capacity (CEC), indicating a better humification of the wet material. Compost 1, even if it contained a minor amount of organic carbon, as well as less activity of fluorescein diacetate (FDA) and dehydrogenase (DHA) than Compost 2, was the most effective in improving soil quality, significantly increasing the labile fraction of organic matter, the oxidative enzyme (DHA), microbial biomass, and crop yield. Both composts increased crop productivity.
New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa
Authors: Daniel-Durandt Francisca Muriel , Rix Arnold Johan
Published: May 2024

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.
Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence
Authors: Si Tong , Wang Yunge , Zhang Lingling , Richmond Evan , Ahn Tae-Hyuk , Gong Haijun
Published: May 2024

Change-point detection is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between different states, each characterized by its distinct data distribution. Precise identification of change points in time series omics data can provide insights into the dynamic and temporal characteristics inherent to complex biological systems. Many change-point detection methods have traditionally focused on the direct estimation of data distributions. However, these approaches become unrealistic in high-dimensional data analysis. Density ratio methods have emerged as promising approaches for change-point detection since estimating density ratios is easier than directly estimating individual densities. Nevertheless, the divergence measures used in these methods may suffer from numerical instability during computation. Additionally, the most popular α-relative Pearson divergence cannot measure the dissimilarity between two distributions of data but a mixture of distributions. To overcome the limitations of existing density ratio-based methods, we propose a novel approach called the Pearson-like scaled-Bregman divergence-based (PLsBD) density ratio estimation method for change-point detection. Our theoretical studies derive an analytical expression for the Pearson-like scaled Bregman divergence using a mixture measure. We integrate the PLsBD with a kernel regression model and apply a random sampling strategy to identify change points in both synthetic data and real-world high-dimensional genomics data of Drosophila. Our PLsBD method demonstrates superior performance compared to many other change-point detection methods.
Evaluating the Performance and Practicality of a Multi-Parameter Assessment System with Design, Comparative Analysis, and Future Directions
Authors: Zlatev Zlatin , Todorov Apostol , Karadzhova Dzheni , Vasilev Miroslav , Veleva Petya
Published: May 2024

This study introduces a developed environmental quality assessment system, detailing its hardware, software, and comparative analysis against a publicly available system. While showing larger deviations in particulate matter and air humidity parameters, the proposed system demonstrates sufficient accuracy in other environmental characteristics. It establishes a standardized operating procedure and evaluates uncertainty and quality assurance measures, ensuring reliability in environmental measurements. The system offers comprehensive capabilities, measuring parameters like total volatile organic compounds, carbon dioxide, temperature, humidity, particulate matter, noise, nitrogen oxides, sulfur oxides, ozone, and carbon monoxide, with real-time monitoring functions for detecting environmental changes. Its user-friendly interfaces, scalability, and potential integration with existing systems enhance its versatility and cost-effectiveness across diverse settings. This study underscores the need for future research to enhance accuracy, reliability, and real-time operability and explore integration with smart city initiatives and environmental management systems. Overall, the proposed system represents a promising advancement in environmental monitoring technology, facilitating comprehensive environmental quality assessment and management.