bluuebunny/crossref_metadata_2025 huggingface dataset:
Active Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) Films Containing Phenolic Compounds with Different Molecular Structures
Authors: La Fuente Arias Carla Ivonne , González-Martínez Chelo , Chiralt Amparo
Published: June 2024

To obtain more sustainable and active food packaging materials, PHBV films containing 5% wt. of phenolic compounds with different molecular structures (ferulic acid, vanillin, and catechin) and proved antioxidant and antimicrobial properties were obtained by melt blending and compression molding. These were characterized by their structural, mechanical, barrier, and optical properties, as well as the polymer crystallization, thermal stability, and component migration in different food simulants. Phenolic compounds were homogenously integrated within the polymer matrix, affecting the film properties differently. Ferulic acid, and mainly catechin, had an anti-plasticizing effect (increasing the polymer glass transition temperature), decreasing the film extensibility and the resistance to breaking, with slight changes in the elastic modulus. In contrast, vanillin provoked a plasticizing effect, decreasing the elastic modulus without notable changes in the film extensibility while increasing the water vapor permeability. All phenolic compounds, mainly catechin, improved the oxygen barrier capacity of PHBV films and interfered with the polymer crystallization, reducing the melting point and crystallinity degree. The thermal stability of the material was little affected by the incorporation of phenols. The migration of passive components of the different PHBV films was lower than the overall migration limit in every simulant. Phenolic compounds were released to a different extent depending on their thermo-sensitivity, which affected their final content in the film, their bonding forces in the polymer matrix, and the simulant polarity. Their effective release in real foods will determine their active action for food preservation. Catechin was the best preserved, while ferulic acid was the most released.
Freeze-Dried β-Glucan and Poly-γ-glutamic Acid: An Efficient Stabilizer to Strengthen Subgrades of Low Compressible Fine-Grained Soils with Varying Curing Periods
Authors: Vishweshwaran Muralidaran , Sujatha Evangelin Ramani , Baldovino Jair Arrieta
Published: June 2024

The freeze-drying of biopolymers presents a fresh option with greater potential for application in soil subgrade stabilization. A freeze-dried combination of β-glucan (BG) and γ-poly-glutamic acid (GPA) biopolymers was used to treat low compressible clay (CL) and low compressible silt (ML) soils in dosages of 0.5%, 1%, 1.5%, and 2%. The California bearing ratio (CBR) test for the treated specimens was performed under three curing conditions: (i) thermal curing at 60 °C, (ii) air-curing for seven days followed by submergence for 4 days, and (iii) no curing, i.e., tested immediately after mixing. To investigate the influence of shear strength on the freeze-dried biopolymer-stabilized soil specimens and their variations with aging, unconfined compressive strength (UCS) tests were conducted after thermal curing at 60 °C for 3 days, 7 days, and 7 days of thermal curing followed by 21 days of air curing. The maximum CBR of 125.3% was observed for thermally cured CL and a minimum CBR of 6.1% was observed under soaked curing conditions for ML soils. Scanning electron microscopy (SEM), infrared spectroscopy, average particle size, permeability, and adsorption tests revealed the pore filling, biopolymer adsorption and coating on the soil surface, and agglomeration of the soil along with the presence of hydrogen bonds, covalent amide bonds, and Van der Waals forces that contributed to the stiffening of the stabilized soil. Using three-dimensional (3D) finite element analysis (FEA) and layered elastic analysis (LEA), a mechanistic–empirical pavement design was carried out for the stabilized soil and a design thickness catalog was prepared for the maximum CBR. The cost reductions for a 1 km section of the pavement were expected to be 12.5%.
Synthesis of Polymer Brushes on Tannic Acid-Coated Copper Particles and Surface Co-Assembly
Authors: Wang Chen , Zhao Hanying
Published: June 2024

The synthesis of polymer brushes on inorganic particles is an effective approach to surface modification. The polymer brushes on the surface endow the substrates with new surface properties. However, the lack of functional groups and the difficulty of surface modification have made it difficult to develop an effective method for the synthesis of polymer brushes on metal surfaces. Herein, a simple and versatile strategy for synthesizing polymer brushes on copper particles is reported. Tannic acid (TA) molecules are adsorbed onto the surfaces of copper particles, forming TA coatings. Quaternized poly(2-(dimethylamino)ethyl methacrylate)-block-polystyrene (qPDMAEMA-b-PS) block copolymer (BCP) chains are grafted on the TA coatings through hydrogen bonding and electrostatic interaction, and PS brushes are grafted on the copper particles. The effects of TA concentration on the adsorption of TA and PS brush synthesis are discussed. The PS brushes are able to form surface nanostructures on the copper particles through co-assembly with PDMAEMA-b-PS BCP chains. The effect of BCP concentration on the surface nanostructures is investigated. It is reasonable to expect that polymer brushes and surface nanostructures can be synthesized on different metal surfaces by using the TA-coating approach reported in this paper.
Investigating the Physical and Operational Characteristics of Manufacturing Processes for MFI-Type Zeolite Membranes for Ethanol/Water Separation via Principal Component Analysis
Authors: Chaouk Hamdi , Obeid Emil , Halwani Jalal , Abdelbaki Wiem , Dib Hanna , Mouhtady Omar , Gazo Hanna Eddie , Fernandes Célio , Younes Khaled
Published: June 2024

In this study, Principal Component Analysis (PCA) was applied to discern the underlying trends for 31 distinct MFI (Mobil No. 5)-zeolite membranes of 11 textural, chemical, and operational factors related to manufacturing processes. Initially, a comprehensive PCA approach was employed for the entire dataset, revealing a moderate influence of the first two principal components (PCs), which collectively accounted for around 38% of the variance. Membrane samples exhibited close proximity, which prevented the formation of any clusters. To address this limitation, a subset acquisition strategy was followed, based on the findings of the PCA for the entire dataset. This resulted in an enhanced overall contribution and the revelation of diverse patterns among the membranes and the considered manufacturing factors (total variance between 55% and 77%). The segmentation of the data unveiled a robust correlation between silica (SiO2) concentration and pervaporation conditions. Additionally, a notable clustering of the chemical compositions of the preparation solutions underscored their significant influence on the operational efficacy of MFI zeolite membranes. On the other hand, an exclusive chemical composition of the preparation solution was noticed. This highlighted the high influence of the chemical composition on the operational efficiency of MFI zeolite membranes. The coupling of PCA with experimental results can provide a data-driven enhancement strategy for the manufacturing of MFI-type zeolite membranes used for ethanol/water separation.
Erosive Wear Caused by Large Solid Particles Carried by a Flowing Liquid: A Comprehensive Review
Authors: Kang Can , Li Minghui , Teng Shuang , Liu Haixia , Chen Zurui , Li Changjiang
Published: June 2024

The erosive wear encountered in some industrial processes results in economic loss and even disastrous consequences. Hitherto, the mechanism of the erosive wear is not clear, especially when the erosive wear is caused by large particles (>3.0 mm) carried by a flowing liquid. Current approaches of predicting erosive wear need improvement, and the optimization of relevant equipment and systems lacks a sound guidance. It is of significance to further explore such a subject based on the relevant literature. The present review commences with a theoretical analysis of the dynamics of large particles and the fundamental mechanism of erosion. Then the characteristics of the erosion of various equipment are explicated. Effects of influential factors such as particle size and properties of the target material are analyzed. Subsequently, commonly used erosion models, measurement techniques, and numerical methods are described and discussed. Based on established knowledge and the studies reported, some expectations for future work are proposed.
Editorial on the Special Issue “Natural Compounds Applications in Drug Discovery and Development”
Authors: Bora Alina , Crisan Luminita
Published: June 2024

Nature is an amazing source of natural bioactive compounds derived from numerous species of plants, marine bacteria, and fungi [...]
New Psychoactive Substances: Health and Legal Challenges
Authors: Santos Inês C. , Maia Daniela , Dinis-Oliveira Ricardo Jorge , Barbosa Daniel José
Published: June 2024

Drug abuse represents a significant public health problem with a growing tendency. As a way of circumventing the strict national and international control of psychoactive substances by regulatory agencies, there is a market release of new substances with psychoactive activity, called New Psychoactive Substances (NPSs). This group of substances encompasses a diverse range of synthetic compounds designed to mimic the effects of traditional illicit substances. As NPSs show stronger psychoactive effects than classical drugs, they pose unique challenges to public health and regulatory frameworks. Additionally, some substances are considered NPSs in some countries but not in others. Therefore, based on a given legal definition, manufacturers can create an NPS that does not fall under that definition and thus is not prohibited. This review critically explores the multifaceted dimensions of the criminal and legal contexts associated with NPSs. It examines the trends of abuse, the intricate network of criminal and legal aspects surrounding these substances, and the crucial warning signs that indicate their emergence, highlighting the health risks posed by these substances. In conclusion, this manuscript addresses the intricate interplay between the pharmacology, risks, and regulatory responses. These multifaceted challenges associated with NPSs will likely provide valuable insights for future research.
Distributed Optical Fiber-Based Radiation Detection Using an Ultra-Low-Loss Optical Fiber
Authors: Weninger Luca , Morana Adriana , Ouerdane Youcef , Marin Emmanuel , Boukenter Aziz , Girard Sylvain
Published: June 2024

The combination of an ultra-low-loss optical fiber sensitive to ionizing radiation and an optical time domain reflectometer (OTDR) is investigated to explore the feasibility of a single-ended distributed radiation detector. The peculiarity of the tested fiber resides in its regenerative high radiation-induced attenuation (RIA) response in the infrared spectrum (1310 nm), which returns to a low value once the irradiation has ended, combined to its sensitivity, highly increasing with the dose rate. In this work, only some sections of the fiber line were irradiated with 100 kV X-rays at room temperature, to prove the spatially resolved radiation detection capabilities of the system. The transient RIA response of the fiber was characterized at different pre-irradiation doses. A pre-irradiation treatment was shown to stabilize the optical fiber response, improving its RIA vs. dose rate linearity and repeatability. This improved response, in terms of radiation quantification, comes at the cost of a lower detection threshold. This work lays the bases for a distributed radiation detector, with some capabilities in dose rate evaluation.
The Sanctification of the Disabled: A Study on the Images of Fortune Gods in Japanese Folk Beliefs
Authors: Liu Jianhua
Published: June 2024

Similarly to China, Japan has a long history of worshiping fortune gods. The act of making offerings and praying to these deities has been practiced since ancient times. Fortune gods are figures in Japanese folk religion that are believed to bring happiness, hope, and good luck. When speaking of fortune gods in Japan, people will first think of the Seven Lucky Gods. Apart from them, there are also some local fortune gods such as Fukusuke and Sendai Shiro. These gods share some common traits and also have connections with the Japanese folk belief in Fukuko (fortune child). This study adopts a comparative methodology to compare Japan’s Seven Lucky Gods with the local Japanese fortune gods as well as Fukuko, and then analyze their similarities. This article argues that the Japanese fortune gods have two major common characteristics: the super power to bring good fortune, and their distinctive appearance. By systematically analyzing the common features of Japanese fortune gods, this study will clarify the mechanism behind their deification as fortune deities and also help us to gain a better insight into the Japanese conceptions of deities and spirits.
The Development of the Thunder God’s Image from the Tang to the Song Dynasty: The Case of Deng Bowen
Authors: Yin Tianjie
Published: June 2024

Belief in the Thunder God has been important in China since ancient times. During the Tang Dynasty (618–906), the Thunder God was personified and incorporated into official rituals. Due to the increasing presence of Tantric Buddhism in China during the Tang Dynasty, in Daoism (particularly the Divine Empyrean lineage 神霄派), several Thunder Gods formed a military organization, among which Deng Bowen (鄧伯溫) was considered as a marshal who largely preserved the characteristics of the Thunder God from the Tang Dynasty, featuring traits of a half-human and half-bird. This study focuses on interreligious influences and aims to elucidate the logic that informs the evolution of the Chinese Thunder God’s image through Deng Bowen’s case. Through the analyses of iconography and text analysis, this study discusses how Deng’s image was blended with the image of the Hindu deity Garuda (迦樓羅), which was introduced to China during the Tang Dynasty through Tantric Buddhism. This study will also explore how Deng’s image evolved from before the Tang Dynasty to the period after the Song Dynasty and will indicate that the Tang Dynasty was a significant period for the development of Thunder God worship as well as its iconography.
Interdependency and Change: God in the Chinese Theology of Xie Fuya (1892–1991)
Authors: Chin Kenpa
Published: June 2024

Xie Fuya (N. Z. Zia, 1892–1991), a major Chinese Christian thinker, has contributed much to the development of Sino-theology. However, his work has yet to receive the recognition it deserves. As a thinker who is well-versed in both Chinese and Western philosophies while dedicating himself to the exploration of the philosophy of religion, Xie presents a dual feature in his writings. On the one hand, his work engages in a dialogical discourse between Eastern and Western philosophies. On the other hand, his writings represent an ambitious attempt to interpret traditional Chinese philosophical tenets within the context of Christian theology, transverse from the level of human nature to the level of ontological existence, representing an innovative model of contemplation in the field of Sino-theology. This contribution is of immense value to the development of Chinese philosophical thought. For this reason, this article attempts to illustrate, through Xie’s writings in various stages of his life, his relentless effort to promote the integration of Eastern and Western philosophies within the framework of Chinese thought. His most notable accomplishment in this East–West confluence effort is his unique assumption of God’s attributions as both zhonghe (literally “middle harmony”, connoted as interdependency by Xie) and bianyi (change).
DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models
Authors: Li Huadong , Wei Ying , Peng Han , Zhang Wei
Published: June 2024

Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance.
Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models
Authors: Osouli Mohammadreza , F. Samavati Faramarz
Published: June 2024

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.
Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection
Authors: Ghasemian Sorboni Nafiseh , Wang Jinfei , Najafi Mohammad Reza
Published: June 2024

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images.
Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching
Authors: Abubakar Aliyu , Zachariades Christos
Published: June 2024

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.
Visual and Quantitative Evaluation of Low-Concentration Bismuth in Dual-Contrast Imaging of Iodine and Bismuth Using Clinical Photon-Counting CT
Authors: Ataei Afrouz , Vasan Vasantha , Soesbe Todd C. , Brewington Cecelia C. , Zhou Zhongxing , Yu Lifeng , Hallam Kristina A. , Ren Liqiang
Published: June 2024

Simultaneous dual-contrast imaging of iodine and bismuth has shown promise in prior phantom and animal studies utilizing spectral CT. However, it is noted that in previous studies, Pepto-Bismol has frequently been employed as the source of bismuth, exceeding the recommended levels for human subjects. This investigation sought to assess the feasibility of visually differentiating and precisely quantifying low-concentration bismuth using clinical dual-source photon-counting CT (PCCT) in a scenario involving both iodinated and bismuth-based contrast materials. Four bismuth samples (0.6, 1.3, 2.5, and 5.1 mg/mL) were prepared using Pepto-Bismol, alongside three iodine rods (1, 2, and 5 mg/mL), inserted into multi-energy CT phantoms with three different sizes, and scanned on a PCCT system at three tube potentials (120, 140, and Sn140 kV). A generic image-based three-material decomposition method generated iodine and bismuth maps, with mean mass concentrations and noise levels measured. The root-mean-square errors for iodine and bismuth determined the optimal tube potential. The tube potential of 140 kV demonstrated optimal quantification performance when both iodine and bismuth were considered. Distinct differentiation of iodine rods with all three concentrations and bismuth samples with mass concentrations ≥ 1.3 mg/mL was observed across all phantom sizes at the optimal kV setting.
Using Spectroradiometry to Measure Organic Carbon in Carbonate-Containing Soils
Authors: Bartmiński Piotr , Siedliska Anna , Siłuch Marcin
Published: June 2024

This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate.
A Wearable Visually Impaired Assistive System Based on Semantic Vision SLAM for Grasping Operation
Authors: Fei Fei , Xian Sifan , Yang Ruonan , Wu Changcheng , Lu Xiong
Published: June 2024

Because of the absence of visual perception, visually impaired individuals encounter various difficulties in their daily lives. This paper proposes a visual aid system designed specifically for visually impaired individuals, aiming to assist and guide them in grasping target objects within a tabletop environment. The system employs a visual perception module that incorporates a semantic visual SLAM algorithm, achieved through the fusion of ORB-SLAM2 and YOLO V5s, enabling the construction of a semantic map of the environment. In the human–machine cooperation module, a depth camera is integrated into a wearable device worn on the hand, while a vibration array feedback device conveys directional information of the target to visually impaired individuals for tactile interaction. To enhance the system’s versatility, a Dobot Magician manipulator is also employed to aid visually impaired individuals in grasping tasks. The performance of the semantic visual SLAM algorithm in terms of localization and semantic mapping was thoroughly tested. Additionally, several experiments were conducted to simulate visually impaired individuals’ interactions in grasping target objects, effectively verifying the feasibility and effectiveness of the proposed system. Overall, this system demonstrates its capability to assist and guide visually impaired individuals in perceiving and acquiring target objects.
An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks
Authors: Rahmani Mahsan , Mohajelin Fatemeh , Khaleghi Nastaran , Sheykhivand Sobhan , Danishvar Sebelan
Published: June 2024

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications.
Using Physiological Markers to Assess Comfort during Neuromuscular Electrical Stimulation Induced Muscle Contraction in a Virtually Guided Environment: Pilot Study for a Path toward Combating ICU-Acquired Weakness
Authors: Abou-Hamde Ahmad , Philippi Lauren , Jones Eric , Martin Christian , Wu Kingsley , Kundell Michael , Mathur Sunita , Sadeghian Alireza , Davoudpour Maryam , Batt Jane , Ieraci Adriana , Gabison Sharon
Published: June 2024

We assessed the feasibility of implementing a virtually guided Neuromuscular Electrical Stimulation (NMES) protocol over the tibialis anterior (TA) muscle while collecting heart rate (HR), Numeric Pain Rating Scale (NPRS), and quality of contraction (QoC) data. We investigated if HR, NPRS, and QoC differ ON and OFF the TA motor point and explored potential relationships between heart rate variability (HRV) and the NPRS. Twelve healthy adults participated in this cross-sectional study. Three NMES trials were delivered ON and OFF the TA motor point. HR, QoC, and NPRS data were collected. There was no significant difference in HRV ON and OFF the motor point (p > 0.05). The NPRS was significantly greater OFF the motor point (p < 0.05). The QoC was significantly different between motor point configurations (p < 0.05). There was no correlation between the NPRS and HRV (p > 0.05, r = −0.129). We recommend non-electrical methods of measuring muscle activity for future studies. The NPRS and QoC can be administered virtually. Time-domain HRV measures could increase the validity of the protocol. The variables should be explored further virtually to enhance the protocol before eventual ICU studies.
Localized Path Planning for Mobile Robots Based on a Subarea-Artificial Potential Field Model
Authors: Lv Qiang , Hao Guoqiang , Huang Zhen , Li Bin , Fu Dandan , Zhao Huanlong , Chen Wei , Chen Sheng
Published: June 2024

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm.
A Sensor Fusion Approach to Observe Quadrotor Velocity
Authors: Meza-Ibarra José Ramón , Martínez-Ulloa Joaquín , Moreno-Pacheco Luis Alfonso , Rodríguez-Cortés Hugo
Published: June 2024

The growing use of Unmanned Aerial Vehicles (UAVs) raises the need to improve their autonomous navigation capabilities. Visual odometry allows for dispensing positioning systems, such as GPS, especially on indoor flights. This paper reports an effort toward UAV autonomous navigation by proposing a translational velocity observer based on inertial and visual measurements for a quadrotor. The proposed observer complementarily fuses available measurements from different domains and is synthesized following the Immersion and Invariance observer design technique. A formal Lyapunov-based observer error convergence to zero is provided. The proposed observer algorithm is evaluated using numerical simulations in the Parrot Mambo Minidrone App from Simulink-Matlab.
Activity-Based Prospective Memory in Insomniacs
Authors: Occhionero Miranda , Tonetti Lorenzo , Giudetti Federica , Natale Vincenzo
Published: June 2024

Objective: To investigate the activity-based prospective memory performance in patients with insomnia, divided, on the basis of actigraphic evaluation, into sleep onset, maintenance, mixed and negative misperception insomnia. Methods: A total of 153 patients with insomnia (I, 83 females, mean age + SD = 41.37 + 16.19 years) and 121 healthy controls (HC, 78 females, mean age + SD = 36.99 + 14.91 years) wore an actigraph for one week. Insomnia was classified into sleep onset insomnia (SOI), maintenance insomnia (MaI), mixed insomnia (MixI) and negative misperception insomnia (NMI). To study their activity-based prospective memory performance, all the participants were required to push the actigraph event marker button twice, at bedtime (task 1) and at get-up time (task 2). Results: Only patients with maintenance and mixed insomnia had a significantly lower accuracy in the activity-based prospective memory task at get-up time compared with the healthy controls. Conclusion: The results show that maintenance and mixed insomnia involve an impaired activity-based prospective memory performance, while sleep onset and negative misperception insomnia do not seem to be affected. This pattern of results suggests that the fragmentation of sleep may play a role in activity-based prospective memory efficiency at wake-up in the morning.
Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia
Authors: Trabassi Dante , Castiglia Stefano Filippo , Bini Fabiano , Marinozzi Franco , Ajoudani Arash , Lorenzini Marta , Chini Giorgia , Varrecchia Tiwana , Ranavolo Alberto , De Icco Roberto , Casali Carlo , Serrao Mariano
Published: June 2024

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.
Modeling and Control of a Two-Axis Stabilized Gimbal Based on Kane Method
Authors: Huang Qixuan , Zhou Jiaxing , Chen Xiang , Yao Youxin , Chen Yuhao , Chen Wei , Chen Runjing , Lv Zhisheng
Published: June 2024

A two-axis stabilizing gimbal is a device that ensures a sensor is working properly on a moving platform. When classical mechanics (Newton–Euler and Lagrange) is employed to model a two-axis stable gimbal, its limitations can complicate the modeling process. To address this issue, a method for establishing a dynamic model for a two-axis stabilizing platform based on the Kane method is proposed in this paper. The Kane method offers the advantage of a simple model structure and computational efficiency. Initially, utilizing a generalized coordinate system, expressions of the generalized velocities, deflection velocities and angular velocities are derived. Subsequently, the generalized active forces and inertial forces acting on the two-axis stabilized gimbal are analyzed. Finally, by combining force and velocity with the Kane equation, the dynamic model of the two-axis stable platform is obtained, demonstrating the validity of the Kane method for establishing the two-axis stable platform model. To ensure the pointing accuracy stability of the two-axis stabilizing platform, a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller is designed using the PSO algorithm. It is then simulated in MATLAB/Simulink and compared with a classical PID controller. Simulation results demonstrate that NPSO-PID exhibits superior object tracking performance compared to classical PID controllers and better optimization of control parameters compared to traditional PSO-PID controllers.
The General Theory of Scientific Variability for Technological Evolution
Authors: Coccia Mario
Published: June 2024

The proposed general theory of scientific variability for technological evolution explains one of the drivers of technological change for economic progress in human society. Variability is the predisposition of the elements in systems to assume different values over time and space. In biology, the variability is basic to explaining differences and development in organisms. In economics of technical change, the effects of variability within research fields on evolutionary dynamics of related technologies are unknown. In a broad analogy with the principles of biology, suggested theoretical framework here can clarify a basic driver of technological evolution: the variability within research fields can explain the dynamics of scientific development and technological evolution. The study sees whether statistical evidence supports the hypothesis that the rate of growth of scientific and technological fields can be explained by the level of variability within scientific fields. The validation is based on emerging research fields in quantum technologies: quantum imaging, quantum meteorology, quantum sensing, and quantum optics. Statistical evidence seems in general to support the hypothesis stated that the rate of growth can be explained by the level of scientific variability within research fields, measured with the relative entropy (indicating the dispersion of scientific topics in a research field underlying a specific technology). Nonparametric correlation with Spearman’s rho shows a positive coefficient of 0.80 between entropy measures and rates of growth between scientific and technological fields. The linear model of the relation between rate of growth and scientific variability reveals a coefficient of regression equal to 1.63 (R2 = 0.60). The findings here suggest a general law that variability within research fields positively drives scientific development and technological evolution. In particular, a higher variability within research fields can support a high rate of growth in scientific development and technological evolution. The proposed general theory of scientific variability is especially relevant in turbulent environments of technology-based competition to clarify a basic determinant of technological development to design strategies of technological forecasting and management of promising innovations.
Cassia alata and Its Phytochemicals: A Promising Natural Strategy in Wound Recovery
Authors: Keng Jing-Wen , Lee Sue-Kei , Sang Sze-Huey , Liew Kai-Bin , Teo Swee-Sen , Mossadeq Wan Mastura Shaik Mohamed , Chow Sek-Chuen , Akowuah Gabriel Akyirem , Lee Siew-Keah , Mai Chun-Wai , Chew Yik-Ling
Published: June 2024

Cassia alata, a traditional herb with a global presence, is renowned for its anti-inflammatory, antibacterial, and antifungal properties, making it a go-to remedy for skin ailments. While it has demonstrated wound healing capabilities in both in vitro and in vivo studies, the precise mechanisms remain elusive. This review aims to highlight its key phytochemicals, their effects, and the mechanism of action. The compounds that have been reviewed and discussed include kaempferol, apigenin, quercetin, rhein, and rutin. These polyphenols play important roles in normal and impaired wound healing processes, encompassing hemostasis, inflammation, proliferation, and tissue remodeling.
Mechanical Alloying and Concentration Quenching of the Luminescence of Pr3+ Ions in Chalcogenide Glass
Authors: Tverjanovich Andrey , Mikhaylova Alexandra , Bychkov Eugene
Published: June 2024

The disadvantage of chalcogenide glasses containing rare earth ions as luminescent materials for the IR optical range is the strong concentration quenching of luminescence due to the non-uniform distribution of rare earth ions in the glass matrix. This study investigates the effect of grinding chalcogenide glass containing Pr3+ ions in a planetary ball mill on its luminescent properties in the near-IR range, as well as its optical properties and structure. The results indicate that milling, under certain conditions, leads to a decrease in the concentration quenching of the luminescence of Pr3+ ions. This finding suggests that milling can be used in the development of glassy materials with the increased efficiency of luminescence of rare earth ions. However, it is essential to consider that high-energy milling may result in the formation of areas with increased pressure in the obtained material, leading to structural changes in the glass.
Risk and Energy Based Optimization for Fire Monitoring System in Utility Tunnel Using Cellular Automata
Authors: Zhang Ying , Bai Jitao , Diao Yu , Chen Zhonghao , Wang Chu , Yang Kun , Gao Zeng , Wei Huajie
Published: June 2024

Fire is one of the biggest threats to the safety of utility tunnels, and establishing camera-based monitoring systems is conducive to early fire finding and better understanding of the evolution of tunnel fires. However, conventional monitoring systems are being faced with the challenge of high energy consumption. In this paper, the camera operation in a utility tunnel was optimized considering both fire risk and energy consumption. Three design variables were investigated, namely the camera sight, the number of cameras in simultaneous operation, and the duration of camera operation. Cellular automata were used as a simple but effective method to simulate the spread of fire in a utility tunnel. Results show that as the number of cameras in simultaneous operation increases, the probability of fire capture also increases, but the energy consumption decreases. A shorter duration of camera operation can lead to a higher probability of fire capture, and meanwhile, lower energy consumption. For the duration of camera operation shorter than or equal to the allowable time, the probability of fire capture is significantly higher than that for the duration longer than the allowable time. Increasing the camera sight will significantly increase the probability of fire capture and lower the total energy consumption when a blind monitoring area exists. The total energy consumption of a camera-based monitoring system roughly satisfies hyperbolic correlation with the duration of camera operation, while the probability of fire capture can be predicted based on the number of cameras in simultaneous operation through a power model. The optimal design for the modeled tunnel section is two cameras in simultaneous operation with a tangent monitoring area. The duration of camera operation should be as short as possible, at least shorter than the allowable time. The study is expected to provide a reference for the sustainable design of energy-saving utility tunnels with lower fire risk.
Assessing Reusable Packaging: The Importance of Methodological Choices in Carbon Footprint Calculation
Authors: Zimmermann Till , Hauschke Fynn
Published: June 2024

The reliability and clarity of environmental assessments represent an important prerequisite for measures towards the sustainable transformation of our economic system. Studies examining the environmental performance of reusables are often used to derive arguments for and against their use. Accordingly, it is important to have clarity about the influence of methodological decisions on the results of such studies. This paper analyses possible approaches to the allocation of environmental impacts of transport processes to reusable shipping packaging in the context of parcel deliveries. A model was developed to conduct comparative analyses of carbon emissions (carbon footprint) from the use of single-use vs. reusable shipping packaging and was subsequently applied to two reusable shipping packaging systems currently available on the market. The results showed that using different allocation models led to significant variations in the results for the carbon footprint of the analysed packaging (single-use and reusable), while at the same time, the calculated environmental break-even point in the comparison between the single-use and reusable options remained rather stable. The results highlight the importance of a clear and standardized methodological framework for the communication of footprint information for reusable shipping packaging. Moreover, for determining the environmental break-even point, the results suggest that aspects like the comparison scenario (i.e., the selection of the single-use packaging) are more important than the methodological choice of the allocation model for transportation processes.
Towards Sustainable Urban Mobility: Voronoi-Based Spatial Analysis of EV Charging Stations in Bangkok
Authors: Boonprong Sornkitja , Punturasan Nathapat , Varnakovida Pariwate , Prechathamwong Wichien
Published: June 2024

This study leverages the efficacy of Voronoi diagram theory within a mixed-methods approach to thoroughly examine the spatial distribution, service coverage, and optimal locations for expanding electric vehicle (EV) charging infrastructure in Bangkok. Drawing on data from field surveys and public data providers, our analysis unfolds in four key stages. Firstly, we delve into the spatial distribution of charging stations, scrutinizing density, proximity to various road types, and land use through the lens of Voronoi diagrams. Secondly, the application of Voronoi diagrams informs the evaluation of service boundaries. Thirdly, utilizing this effective tool, we assess population density per parking slot or charging head to anticipate future EV adoption. Finally, the study introduces an approach to identify suitable locations for new charging stations through the application of overlapping Voronoi circles. Findings reveal a clustered distribution of charging stations along roads, particularly in the central business district, showcasing the efficiency of Voronoi diagrams in spatial analysis. Residential areas and urban commercial zones also host significant charging station concentrations. Notably, service coverage in inner Bangkok surpasses that of middle and outer areas, highlighting underserved regions. Prospective areas for new charging stations, identified through Voronoi analysis, include Bang Khae, Phra Khanong, Min Buri, and Huai Khwang. This research, rooted in the application of Voronoi diagram theory, offers vital insights for various stakeholders involved in urban infrastructure planning. By employing Voronoi diagrams within Geographic Information Systems (GIS), the study contributes to strategically placing charging stations, optimizing spatial understanding, and enhancing resource allocation. This GIS-based approach not only supports the rise of electric vehicles but also promotes sustainable urban development practices through the efficient utilization of spatial data and analysis techniques.