bluuebunny/crossref_metadata_2025 huggingface dataset:
Carriage Rate of Enterobacterales Resistant to Extended-Spectrum Cephalosporins in the Tunisian Population
Authors: Mahjoub Khachroub Ahlem , Souguir Meriem , Châtre Pierre , Elhouda Bouhlel Nour , Jaidane Nadia , Drapeau Antoine , El Kantaoui Marah , Azaiez Sana , Madec Jean-Yves , Mansour Wejdene , Haenni Marisa
Published: July 2024

Enterobacterales resistant to extended-spectrum cephalosporins (ESC) are a marker of the antimicrobial resistance (AMR) burden. They are infecting humans, but the intestinal microbiota can also be transiently colonized without developing symptoms. Healthy carriage can promote silent dissemination of resistant bacteria, and data on this colonization are often lacking. Between 2021 and 2023, a sampling of healthy Tunisian people was carried out. Fecal samples (n = 256) were plated on selective agar, and all collected isolates were characterized by phenotypic (antibiograms) and genomic (whole-genome sequencing) methods. A total of 26 (26/256, 10.2%) isolates were collected, including 24 Escherichia coli and 2 Klebsiella pneumoniae. In total, 17 isolates (15 E. coli and 2 K. pneumoniae) presented an ESBL phenotype conferred by the blaCTX-M-15 gene, and 9 E. coli isolates presented an AmpC phenotype conferred by the blaDHA-1 gene. K. pneumoniae belonged to ST1564 and ST313, while E. coli belonged to diverse STs including the pandemic ST131 clone. Clonally related ST349 E. coli isolates carrying the blaDHA-1 gene were found in nine individuals. In parallel, four blaCTX-M-15 -positive E. coli isolates carried this ESC-resistance gene on an epidemic plasmid IncF/F-:A-:B53 previously identified in Tunisian pigeons and fish. These findings highlight the spread of genetically diverse ESC-resistant Enterobacterales as well as an epidemic plasmid in Tunisia, emphasizing the need for antimicrobial stewardship to limit the transmission of these resistances in the Tunisian population.
Veterinary Hospital and Clinic Websites: Do They Reflect the Racial Demographics of Their Geographical Region?
Authors: Kogan Lori R. , Currin-McCulloch Jennifer , Brown Emma , Thompson Tori
Published: July 2024

Approximately 60% of the U.S. population own at least one pet, and many pet owners turn to the internet, including veterinary clinic websites, for pet health information. The pictures on these websites, and how accurately they reflect the racial diversity of their communities, have not been studied. This study assessed the perceived skin color of people depicted on veterinary websites to determine how well they represent their geographical area. Each photograph was rated, using the Fitzpatrick skin ratings, on a scale from 1 to 6, with 1 to 3 reflecting ‘White’ and 4 to 6 reflecting ‘Person of Color.’ The number of pictures categorized as ‘White’ was 638 (85.8%) and those categorized as ‘Person of Color’ was 106 (14.2%). For client/pet owner pictures, a total of 121 (80.2%) were categorized as ‘White’ and 30 (19.9%) were categorized as ‘Person of Color.’ The findings from this study reveal a glaring lack of racial diversity and representation among the images of people displayed on the websites of veterinary clinics that provide care within predominantly Black U.S. communities. One step that veterinary clinics could take to rectify this issue would be to partner with members of their communities to gather guidance on how to transform their social media presence to reflect their clients’ identities and needs more accurately.
Immune Checkpoint Inhibitors for Pediatric Cancers: Is It Still a Stalemate?
Authors: Jaing Tang-Her , Wang Yi-Lun , Chiu Chia-Chi
Published: July 2024

The knowledge surrounding the application of immune checkpoint inhibitors (ICIs) in the treatment of pediatric cancers is continuously expanding and evolving. These therapies work by enhancing the body’s natural immune response against tumors, which may have been suppressed by certain pathways. The effectiveness of ICIs in treating adult cancers has been widely acknowledged. However, the results of early phase I/II clinical trials that exclusively targeted the use of ICIs for treating different pediatric cancers have been underwhelming. The response rates to ICIs have generally been modest, except for cases of pediatric classic Hodgkin lymphoma. There seems to be a notable disparity in the immunogenicity of childhood cancers compared to adult cancers, potentially accounting for this phenomenon. On average, childhood cancers tend to have significantly fewer neoantigens. In recent times, there has been a renewed sense of optimism regarding the potential benefits of ICI therapies for specific groups of children with cancer. In initial research, individuals diagnosed with pediatric hypermutated and SMARCB1-deficient cancers have shown remarkable positive outcomes when treated with ICI therapies. This is likely due to the underlying biological factors that promote the expression of neoantigens and inflammation within the tumor. Ongoing trials are diligently assessing the effectiveness of ICIs for pediatric cancer patients in these specific subsets. This review aimed to analyze the safety and effectiveness of ICIs in pediatric patients with different types of highly advanced malignancies.
Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)
Authors: Venkatraman Vishwesh , Gaiser Jeremiah , Demekas Daphne , Roy Amitava , Xiong Rui , Wheeler Travis J.
Published: July 2024

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.
Clinically Relevant Characterization and Comparison of Ryaltris and Other Anti-Allergic Nasal Sprays
Authors: Patterlini Virginia , Guareschi Fabiola , D’Angelo Davide , Baldini Simone , Meto Suada , Mostafa Kamal Dalia , Fabrizzi Paolo , Buttini Francesca , Mösges Ralph , Sonvico Fabio
Published: July 2024

The deposition, residence time, and dissolution profile of nasal suspensions containing corticosteroids play a key role in their in vivo efficacy after administration. However, the conventional methods available to characterize nasal products appear to be unsuitable to exhaustively cover these aspects. The work aims to investigate technological aspects of Ryaltris (mometasone furoate and olopatadine hydrochloride nasal spray) compared to other commercial anti-allergic nasal products, namely, Dymista (azelastine hydrochloride and fluticasone propionate), Nasonex (mometasone furoate), and Avamys (fluticasone furoate). Innovative characterization methods were combined with more traditional approaches to investigate the anti-allergic nasal sprays. These methods applied together allowed to differentiate between the different products and provided a clear picture of the nasal product behavior in terms of drug dissolution and deposition. In particular, the dissolution tests were performed exploiting the Respicell® apparatus, an innovative technique that allows for the investigation of inhalation products. Then, formulation viscosities were considered along with a formulation flow test on an inclined plane. Finally, the intranasal deposition profile of the commercial formulations was determined using a silicon nasal cast. The results highlight in vitro significant differences in terms of viscosity as well as dissolution rate of the nasal products, with Ryaltris showing a higher viscosity and lower flow compared to other products, which, along with a corticosteroid faster dissolution rate than Dymista, suggest a potential advantage in terms of clinical behavior.
The Dual Role of Antimicrobial Proteins and Peptides: Exploring Their Direct Impact and Plant Defense-Enhancing Abilities
Authors: Farvardin Atefeh , González-Hernández Ana Isabel , Llorens Eugenio , Camañes Gemma , Scalschi Loredana , Vicedo Begonya
Published: July 2024

Plants face numerous environmental stresses that hinder their growth and productivity, including biotic agents, such as herbivores and parasitic microorganisms, as well as abiotic factors, such as cold, drought, salinity, and high temperature. To counter these challenges, plants have developed a range of defense strategies. Among these, plant antimicrobial proteins and peptides (APPs) have emerged as a promising solution. Due to their broad-spectrum activity, structural stability, and diverse mechanisms of action, APPs serve as powerful tools to complement and enhance conventional agricultural methods, significantly boosting plant defense and productivity. This review focuses on different studies on APPs, emphasizing their crucial role in combating plant pathogens and enhancing plant resilience against both biotic and abiotic stresses. Beginning with in vitro studies, we explore how APPs combat various plant pathogens. We then delve into the defense mechanisms triggered by APPs against biotic stress, showcasing their effectiveness against bacterial and fungal diseases. Additionally, we highlight the role of APPs in mitigating the abiotic challenges associated with climatic change. Finally, we discuss the current applications of APPs in agriculture, emphasizing their potential for sustainable agricultural practices and the need for future research in this area.
GmHs1-1 and GmqHS1 Simultaneously Contribute to the Domestication of Soybean Hard-Seededness
Authors: Yan Huifang , Tian Daicai , Zhang Qian , Wen Jiangqi , Wang Zeng-Yu , Chai Maofeng
Published: July 2024

Seed physical dormancy (hard-seededness) is an interesting ecological phenomenon and important agronomic trait. The loss of seed coat impermeability/hard-seededness is a key target trait during the domestication of leguminous crops which allows seeds to germinate rapidly and uniformly. In this study, we examined the mutation of quantitative trait locus (QTL) genes, GmHs1-1 and GmqHS1, in 18 wild soybean (G. soja) and 23 cultivated soybean (G. max) accessions. The sequencing results indicate that a G-to-T substitution in GmqHS1 and a C-to-T substitution in GmHs1-1 occurred in all 23 cultivated soybean accessions but not in any of the 18 wild soybean accessions. The mutations in the two genes led to increased seed coat permeability in cultivated soybean. Therefore, we provide evidence that two genes, GmHs1-1 and GmqHS1, simultaneously contribute to the domestication of hard-seededness in soybeans. This finding is of great significance for genetic analysis and improved utilization of the soybean hard-seededness trait.
Essential Oil Composition of Bupleurum praealtum and Bupleurum affine: New Natural Constituents
Authors: Nešić Milica D. , Nešić Milan S. , Dimitrijević Milan Ž. , Radulović Niko S.
Published: July 2024

This study explores the chemical composition of essential oils from two Serbian Bupleurum species (Apiaceae), Bupleurum praealtum L. and Bupleurum affine L., traditionally recognized in Chinese medicine for their therapeutic potential but less studied for their essential oils. Through GC-MS analysis, we identified 230 constituents, revealing distinct profiles between the species. Perillyl 2-methylbutanoate was identified in B. affine oil for the first time, confirmed using synthetic approaches and characterized by advanced spectroscopic techniques, including two-dimensional NMR and spin-simulation of 1H NMR spectra. Additionally, new natural compounds, including tentatively identified 4-decyl acetate and 4-undecyl acetate, were discovered. The study also reports five stereoisomeric esters of tetradeca-5,7,9,11-tetraen-1-ol. These findings significantly contribute to the understanding of the phytochemical diversity within the genus Bupleurum and underscore potential differences in ecological adaptations or biosynthetic pathways among species.
Genome-Wide Identification and Expression Analysis of the Casparian Strip Membrane Domain Protein-like Gene Family in Peanut (Arachis hypogea L.) Revealed Its Crucial Role in Growth and Multiple Stress Tolerance
Authors: Su Yating , Fang Jieyun , Zeeshan Ul Haq Muhammad , Yang Wanli , Yu Jing , Yang Dongmei , Liu Ya , Wu Yougen
Published: July 2024

Casparian strip membrane domain proteins (CASPs), regulating the formation of Casparian strips in plants, serve crucial functions in facilitating plant growth, development, and resilience to abiotic stress. However, little research has focused on the characteristics and functions of AhCASPs in cultivated peanuts. In this study, the genome-wide identification and expression analysis of the AhCASPs gene family was performed using bioinformatics and transcriptome data. Results showed that a total of 80 AhCASPs members on 20 chromosomes were identified and divided into three subclusters, which mainly localized to the cell membrane. Ka/Ks analysis revealed that most of the genes underwent purifying selection. Analysis of cis elements suggested the possible involvement of AhCASPs in hormonal and stress responses, including GA, MeJA, IAA, ABA, drought, and low temperature. Moreover, 20 different miRNAs for 37 different AhCASPs genes were identified by the psRNATarget service. Likewise, transcriptional analysis revealed key AhCASPs responding to various stresses, hormonal processing, and tissue types, including 33 genes in low temperature and drought stress and 41 genes in tissue-specific expression. These results provide an important theoretical basis for the functions of AhCASPs in growth, development, and multiple stress resistance in cultivated peanuts.
Impact of Silver Nanoparticle Treatment and Chitosan on Packaging Paper’s Barrier Effectiveness
Authors: Todorova Dimitrina , Yavorov Nikolay , Vrabič-Brodnjak Urška
Published: July 2024

In this study, a comparative analysis of silver nanoparticles treatment and chitosan coating on packaging paper barrier properties was carried out. In order to examine the water, grease, and antibacterial barrier properties of silver nanoparticle-treated and chitosan-coated laboratory-obtained paper samples, a mixture of bleached softwood and hardwood celluloses was used. In order to conduct the comparative analysis SEM, water contact angle, Cobb60, and Kit tests were carried out on a cellulose sample, and four paper samples (three of them treated with silver nanoparticles—1, 2, and 3 mL/20 cm2 or chitosan coated—0.5, 1, and 2 g/m2) together with the inhibition activity against nine Gram-positive and Gram-negative bacteria, yeast, and fungal strains. The study found out that increasing the silver nanoparticle treatment and chitosan coating led to improved water resistance, while grease resistance was improved only for chitosan coated paper samples. Additionally, paper treated with 3 mL/20 cm2 of silver nanoparticles had the highest antibacterial protection (81.6%) against the Gram-positive bacterium Staphylococcus aureus, followed by Gram-negative Escherichia coli (75.8%). For the rest of the studied microorganisms, the average efficiency of the treated paper was 40.79%. The treatment of the paper with 1 and 2 mL/20 cm2 of silver nanoparticles was less effective—27.13 and 39.83%, respectively. The antibacterial protection of 2 g/m2 chitosan-coated paper samples was the most effective (average 79%) against the tested bacterial, yeast, and fungal strains. At 1 and 0.5 g/m2 chitosan coatings, the efficiency was 72.38% and 54.67%, respectively. Gram-positive bacteria, yeasts, and fungal strains were more sensitive to chitosan supplementation.
Buddhism’s Knotted Thread (結縷法): Indian Origins and Chinese Adaptations
Authors: Yang Gang
Published: July 2024

The Buddhist “Knotted Thread” originates from the ancient Indian practice of venerating sacred threads. In Indian culture, sacred threads symbolize identity and status and possess functions such as exorcism, healing, disaster prevention, blessing, protection, and divination. Initially, Buddhism opposed the use of sacred threads by its followers. However, as Buddhism evolved, it began to adopt this and other practices. Buddhist threads could be monochromatic or multicolored, with five-colored threads (五色縷) having significant cultural connections to China. In Buddhism, the five colors typically consist of four primary colors (四本色; blue, yellow, red, and white) plus an additional color. The difference between the Buddhist and traditional Chinese five colors lies in the selection of the fifth color. The five-colored threads in Buddhism have various combinations, such as blue, yellow, red, white, and black; blue, yellow, red, white, and purple; and blue, yellow, red, white and green. With Buddhism’s spread into China, to align with traditional Chinese notions of five colors, the Buddhist five-colored threads gradually standardized to blue, yellow, red, white, and black. The evolution of the Buddhist five-colored threads reflects the cultural exchange between India and China.
Between “Jing 敬” and “Cheng 诚”: A Linguistic Study of the Internalization Process in the Pre-Qin Confucian Ethical System
Authors: Li Cong
Published: July 2024

From a semantic viewpoint, “Jing 敬” emphasizes an attitude of external respect and adherence to ritual propriety, whereas “Cheng 诚” signifies the true alignment between one’s internal attitudes and external actions. An exploration of “Jing” and “Cheng” in The Analects, The Great Learning, The Doctrine of the Mean, and The Works of Mencius from a linguistic perspective reveals the following: In the ethical system of The Analects, “Jing” represents an attitude towards others, and “Cheng” is rarely mentioned, with personal morality anchored in the social order; in The Great Learning and The Doctrine of the Mean, “Jing” is seldom discussed, while “Cheng” is emphasized as a requirement for individuals, highlighting the intrinsic nature and spontaneity of personal morality; The Works of Mencius, while inheriting Confucius’s concepts, also adopts the ideas from The Great Learning and The Doctrine of the Mean and reinterprets “Jing” internally, achieving a unity between personal morality and social ethics. The shift from “Jing” to “Cheng” and the reinterpretation of “Jing” reflect the concentrated embodiment of the internal reconstruction of the Pre-Qin Confucian ethical system.
Bioethics and the Human Body
Authors: Plöckinger Ursula , Ernst-Auga Ulrike
Published: July 2024

We discuss the concept of a ‘body’, the individual body as the lived experience of the body, the social body, shaped by the tensions between the demands of a social/moral order and the egocentric drives, and the body politic, as an institutionalized and disciplined body. We describe the body as it was perceived in Classical Greek Antiquity at the time when the Hippocratic Oath was first conceived, and any changes that may have occurred by Late Antiquity, using the concept of a body-world as represented by everyday life, the arts, politics, philosophy, and religion. This ‘recreated’ body-world elucidates how a person of Classical or Late Antiquity perceived her/his body via their ‘lived-in’ world and relates it to medical and philosophical thinking about the body as well as to concepts of health and disease. We demonstrate how the institutional structures of the Roman Empire and the Church influenced the way a body was understood, how the administrative and governmental needs led to the first developments of Public Health, and how the Christian understanding of the body as the body and spirit of Christ changed the attitude towards suicide, euthanasia, and abortion. These changes are reflected in the understanding of bioethical thinking and affected the interpretation of the Hippocratic Oath.
A Novel Modified Symmetric Nested Array for Mixed Far-Field and Near-Field Source Localization
Authors: Xiang Zheng , Jin Hanke , Wang Yinsheng , Ren Peng , Yang Long , Xu Baoyi
Published: July 2024

In the process of locating mixed far-field and near-field sources, sparse nonlinear arrays (SNAs) can achieve larger array apertures and higher degrees of freedom compared to traditional uniform linear arrays (ULAs) with the same number of sensors. This paper introduces a Modified Symmetric Nested Array (MSNA), which can automatically generate the optimal array structure with the maximum continuous lags for a given number of sensors. To effectively address mixed source localization, we designed an estimation algorithm based on high-order cumulants and the subarray partition method, applied to the MSNA. Firstly, a specialized fourth-order cumulant matrix, relevant only to Direction of Arrival (DOA) information, is constructed for the DOA estimation of mixed sources. Then, peak searching using the estimated DOA information enables the estimation of the distance parameters, effectively separating mixed sources. The algorithm has moderate computational complexity and provides high resolution and estimation accuracy. Numerical simulation results demonstrate that, with the same number of physical sensors, the proposed MSNA provides more continuous lags than existing arrays, offering higher degrees of freedom and estimation accuracy.
A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection
Authors: Selmer Patrick , Yorks John E. , Nowottnick Edward P. , Cresanti Amanda , Christian Kenneth E.
Published: July 2024

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.
Estimating the Depths of Normal Surface Notches Using Mode-Conversion Waves at the Bottom Tip
Authors: Pan Qianghua , Xu Jiawei , Li Wenbo , Li Haiyang , Li Zehui , Wang Pengfei
Published: July 2024

In this work, a two-parameter inversion problem is analyzed, related to surface crack widths for measuring depths of normal surface notches, based on a laser-based ultrasonic measurement method in the time domain. In determining the depth measurement formulas, the main technique is the time delay between reflected and scattered waves. Scattered waves are generated by two reflections along the bottom and three mode transformations at the surface of the crack tips. Moreover, the scattering angle of the mode-conversion waves is 30°. These two key factors lead to corrected item “2wβ” in the depth measurement formula. A laser-based ultrasonic experimental platform is built to generate and receive surface waves in a non-contact manner on aluminum and steel specimens with surface cracks. The depth measurement method proposed in this paper has been validated through theoretical, simulation, and experimental methods. Finally, in this paper, an effective approach for quantitatively measuring crack depths, based on laser ultrasound, using the time-domain properties of surface wave propagation is provided.
Integration of Tracking, Re-Identification, and Gesture Recognition for Facilitating Human–Robot Interaction
Authors: Lee Sukhan , Lee Soojin , Park Hyunwoo
Published: July 2024

For successful human–robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human–robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS.
Mo-Doped LaFeO3 Gas Sensors with Enhanced Sensing Performance for Triethylamine Gas
Authors: Shen Chenyu , Liang Hongjian , Zhao Ziyue , Guo Suyi , Chen Yuxiang , Tan Zhenquan , Song Xue-Zhi , Wang Xiaofeng
Published: July 2024

Triethylamine is a common volatile organic compound (VOC) that plays an important role in areas such as organic solvents, chemical industries, dyestuffs, and leather treatments. However, exposure to triethylamine atmosphere can pose a serious threat to human health. In this study, gas-sensing semiconductor materials of LaFeO3 nano materials with different Mo-doping ratios were synthesized by the sol–gel method. The crystal structures, micro morphologies, and surface states of the prepared samples were characterized by XRD, SEM, and XPS, respectively. The gas-sensing tests showed that the Mo doping enhanced the gas-sensing performance of LaFeO3. Especially, the 4% Mo-doped LaFeO3 exhibited the highest response towards triethylamine (TEA) gas, a value approximately 11 times greater than that of pure LaFeO3. Meantime, the 4% Mo-doped LaFeO3 sensor showed a remarkably robust linear correlation between the response and the concentration (R2 = 0.99736). In addition, the selectivity, stability, response/recovery time, and moisture-proof properties were evaluated. Finally, the gas-sensing mechanism is discussed. This study provides an idea for exploring a new type of efficient and low-cost metal-doped LaFeO3 sensor to monitor the concentration of triethylamine gas for the purpose of safeguarding human health and safety.
Subjective and Objective Day-to-Day Performance Measures of People with Essential Tremor
Authors: Roth Navit , Salih Adham , Rosenblum Sara
Published: July 2024

This paper aims to map the daily functional characteristics of people diagnosed with essential tremor (ET) based on their subjective self-reports. In addition, we provide objective measurements of a cup-drinking task. This study involved 20 participants diagnosed with ET who completed the Columbia University Assessment of Disability in Essential Tremor (CADET) questionnaire that included five additional tasks related to digital equipment operation we wrote. Participants also described task-performance modifications they implemented. To create objective personal performance profiles, they performed a cup-drinking task while being monitored using a sensor measurement system. The CADET’s subjective self-report results indicate that the most prevalent tasks participants reported as having difficulty with or requiring modifications were writing, threading a needle, carrying a cup, using a spoon, pouring, and taking a photo or video on a mobile phone. Analysis of participants’ modifications revealed that holding the object with two hands or with one hand supporting the other were the most prevalent types. No significant correlation was found between the CADET total scores and the cup drinking objective measures. Capturing patients’ perspectives on their functional disability, alongside objective performance measures, is envisioned to contribute to the development of custom-tailored interventions aligned with individual profiles, i.e., patient-based/smart healthcare.
HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images
Authors: Yao Guangzhen , Zhu Sandong , Zhang Long , Qi Miao
Published: July 2024

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model’s focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach’s efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.
Linguistic-Driven Partial Semantic Relevance Learning for Skeleton-Based Action Recognition
Authors: Chen Qixiu , Liu Yingan , Huang Peng , Huang Jiani
Published: July 2024

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as “brush teeth” and “brush hair” are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results.
Robotic Grasping of Unknown Objects Based on Deep Learning-Based Feature Detection
Authors: Khor Kai Sherng , Liu Chao , Cheah Chien Chern
Published: July 2024

In recent years, the integration of deep learning into robotic grasping algorithms has led to significant advancements in this field. However, one of the challenges faced by many existing deep learning-based grasping algorithms is their reliance on extensive training data, which makes them less effective when encountering unknown objects not present in the training dataset. This paper presents a simple and effective grasping algorithm that addresses this challenge through the utilization of a deep learning-based object detector, focusing on oriented detection of key features shared among most objects, namely straight edges and corners. By integrating these features with information obtained through image segmentation, the proposed algorithm can logically deduce a grasping pose without being limited by the size of the training dataset. Experimental results on actual robotic grasping of unknown objects over 400 trials show that the proposed method can achieve a higher grasp success rate of 98.25% compared to existing methods.
Social Type-Aware Navigation Framework for Mobile Robots in Human-Shared Environments
Authors: Kang Sumin , Yang Sungwoo , Kwak Daewon , Jargalbaatar Yura , Kim Donghan
Published: July 2024

As robots become increasingly common in human-populated environments, they must be perceived as social beings and behave socially. People try to preserve their own space during social interactions with others, and this space depends on a variety of factors, such as individual characteristics or their age. In real-world social spaces, there are many different types of people, and robots need to be more sensitive, especially when interacting with vulnerable subjects such as children. However, the current navigation methods do not consider these differences and apply the same avoidance strategies to everyone. Thus, we propose a new navigation framework that considers different social types and defines appropriate personal spaces for each, allowing robots to respect them. To this end, the robot needs to classify people in a real environment into social types and define the personal space for each type as a Gaussian asymmetric function to respect them. The proposed framework is validated through simulations and real-world experiments, demonstrating that the robot can improve the quality of interactions with people by providing each individual with an adaptive personal space. The proposed costmap layer is available on GitHub.
Self-Assembled TiN-Metal Nanocomposites Integrated on Flexible Mica Substrates towards Flexible Devices
Authors: Liu Juncheng , Zhang Yizhi , Dou Hongyi , Tsai Benson Kunhung , Choudhury Abhijeet , Wang Haiyan
Published: July 2024

The integration of nanocomposite thin films with combined multifunctionalities on flexible substrates is desired for flexible device design and applications. For example, combined plasmonic and magnetic properties could lead to unique optical switchable magnetic devices and sensors. In this work, a multiphase TiN-Au-Ni nanocomposite system with core–shell-like Au-Ni nanopillars embedded in a TiN matrix has been demonstrated on flexible mica substrates. The three-phase nanocomposite film has been compared with its single metal nanocomposite counterparts, i.e., TiN-Au and TiN-Ni. Magnetic measurement results suggest that both TiN-Au-Ni/mica and TiN-Ni/mica present room-temperature ferromagnetic property. Tunable plasmonic property has been achieved by varying the metallic component of the nanocomposite films. The cyclic bending test was performed to verify the property reliability of the flexible nanocomposite thin films upon bending. This work opens a new path for integrating complex nitride-based nanocomposite designs on mica towards multifunctional flexible nanodevice applications.
Spectral Features Analysis for Print Quality Prediction in Additive Manufacturing: An Acoustics-Based Approach
Authors: Olowe Michael , Ogunsanya Michael , Best Brian , Hanif Yousef , Bajaj Saurabh , Vakkalagadda Varalakshmi , Fatoki Olukayode , Desai Salil
Published: July 2024

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques.
Electrohydrodynamic-Jet-Printed SnO2-TiO2-Composite-Based Microelectromechanical Systems Sensor with Enhanced Ethanol Detection
Authors: Wang Danyang , Yu Dongqi , Xu Menghan , Chen Xue , Gu Jilin , Huang Lei
Published: July 2024

Ethanol sensors have found extensive applications across various industries, including the chemical, environmental, transportation, and healthcare sectors. With increasing demands for enhanced performance and reduced energy consumption, there is a growing need for developing new ethanol sensors. Micro-electromechanical system (MEMS) devices offer promising prospects in gas sensor applications due to their compact size, low power requirements, and seamless integration capabilities. In this study, SnO2-TiO2 nanocomposites with varying molar ratios of SnO2 and TiO2 were synthesized via ball milling and then printed on MEMS chips for ethanol sensing using electrohydrodynamic (EHD) printing. The study indicates that the two metal oxides dispersed evenly, resulting in a well-formed gas-sensitive film. The SnO2-TiO2 composite exhibits the best performance at a molar ratio of 1:1, with a response value of 25.6 to 50 ppm ethanol at 288 °C. This value is 7.2 times and 1.8 times higher than that of single SnO2 and TiO2 gas sensors, respectively. The enhanced gas sensitivity can be attributed to the increased surface reactive oxygen species and optimized material resistance resulting from the chemical and electronic effects of the composite.
An Application of Uncertainty Quantification to Efficiency Measurements and Validating Requirements through Correlating Simulation and Physical Testing Results
Authors: Leighton Michael , Akasapu Uday
Published: July 2024

Validation is a critical aspect of product development for meeting design goals and mitigating risk in the face of considerable cost and time commitments. In this research article, uncertainty quantification (UQ) for efficiency testing of an Electric Drive Unit (EDU) is demonstrated, considering confidence in simulations with respect to the validation campaign. The methodology used for UQ is consistent with the framework mentioned in the guide to the expression of uncertainty in measurement (GUM). An analytical evaluation of the measurement chain involved in EDU efficiency testing was performed and elemental uncertainties were derived, later to be propagated to the derived quantity of efficiency. When uncertainties were associated with measurements, the erroneous measurements made through sensors in the measurement chain were highlighted. These results were used for the assessment of requirement coverage and the validation of test results.
Revealing the Ground Deformation and Its Mechanism in the Heihe River Basin by Interferometric Synthetic Aperture Radar and Optical Images
Authors: Cui Qunpeng , Wang Yuedong , Wang Pengkun , Tan Ke , Feng Guangcai
Published: July 2024

The Heihe River Basin (HRB), located on the northeast margin of the Qilian Mountains, is China’s second largest inland river basin. It is a typical oasis-type agricultural area in northwest China’s arid and semiarid areas. It is important to monitor and investigate the spatiotemporal distribution characteristics and mechanisms of surface deformation in HRB for the ecology of inland river basins. In recent years, research on HRB has mainly focused on hydrology, meteorology, geology, or biology. Few studies have conducted wide-area monitoring and mechanism analysis of the surface stability of HRB. In this study, an improved interferometric point target analysis InSAR (IPTA-InSAR) technique is used to process 101 Sentinel-1 SAR images from two adjacent track frames covering the HRB from 2019 to 2020. The wide-area deformation of the HRB is obtained first for this period. The results show that most of the surface around the HRB is relatively stable. There are six areas with an extensive deformation range and magnitude in the plain oasis area. The maximum deformation rate is more than 50 mm/year. The maximum seasonal subsidence and uplift along the satellites’ line-of-sight (LOS) direction can be up to −70 mm and 60 mm, respectively. Moreover, we use the Google Earth Engine platform to process the multisource optical images and analyze the deformation areas. The remote sensing indicators of the deformation areas, such as the normalized difference vegetation index (NDVI), soil moisture (SMMI), and precipitation, are obtained during the InSAR monitoring period. We combine these integrated remote sensing results with soil type and precipitation to analyze the surface deformations of the HRB. The spatiotemporal relationships between soil moisture, vegetation cover, and surface deformation of the HRB are revealed. The results will provide data support and reference for the healthy and sustainable development of the inland river basin economic zone.
Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition
Authors: Sondej Tadeusz , Bednarczyk Mariusz
Published: July 2024

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.