bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Accuracy Positioning in GNSS-Blocked Areas by Using the MSCKF-Based SF-RTK/IMU/Camera Tight Integration
Authors: Xu Qiaozhuang , Gao Zhouzheng , Yang Cheng , Lv Jie
Published: June 2023

The integration of global navigation satellite system (GNSS) single-frequency (SF) real-time kinematics (RTKs) and inertial navigation system (INS) has the advantages of low-cost and low-power consumption compared to the multiple-frequency GNSS RTK/INS integration system. However, due to the vulnerability of GNSS signal reception, the application of the GNSS SF-RTK/INS integration is limited in complex environments. To improve the positioning accuracy of SF-RTK/INS integration in GNSS-blocked environments, we present a low-cost tight integration system based on BDS/GPS SF-RTK, a low-cost inertial measurement unit (IMU), and a monocular camera. In such a system, a multi-state constraint Kalman filter (MSCKF) is adopted to integrate the single-frequency pseudo-range, phase-carrier, inertial measurements, and visual data tightly. A wheel robot dataset collected under satellite signal-blocked conditions is used to evaluate its performance in terms of position, attitude, and run time, respectively. Results illustrated that the presented model can provide higher position accuracy compared to those provided by the RTK/INS tight integration system and visual-inertial tight integration system. Moreover, the average running time presents the potential of the presented method in real-time applications.
Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features
Authors: Trivedi Manushi B. , Marshall Michael , Estes Lyndon , de Bie C.A.J.M. , Chang Ling , Nelson Andrew
Published: June 2023

Mapping arable field areas is crucial for assessing agricultural productivity but poses challenges in sub-Saharan agroecosystems because of diverse crop calendars, small and irregularly shaped fields, persistent cloud cover, and lack of high-quality model training data. This study proposes several methodological improvements to overcome these challenges. Specifically, it utilizes long-term MODIS data to stratify finer Sentinel-2 reflectance and Sentinel-1 backscatter image features on a per-pixel basis. It also incorporates texture features and employs a machine learning approach with over 300,000 samples. The eastern region of Ghana was stratified into seven seasonal strata exhibiting distinct vegetation seasonality, capturing diversity in crop calendars, using long-term MODIS (2001–2009) normalized difference vegetation index phenology. Three years (2017–2019) of Sentinel-1 and Sentinel-2 original bands at 20 m were composited into dry and wet seasonal features according to the strata, from which spectral, polarimetric, and texture features were extracted. The field boundaries were digitized using PlanetScope images (2018–2019). Random Forest classifier with 10-fold cross-validation and recursive feature elimination was used for feature selection and model building. Including topographic variables, out of 137 image features, only 11 features were found important. Sentinel-2 SWIR-based spectral features were most important, followed by Sentinel-1 polarimetric (VV) and elevation features. Half of the 11 features were variance texture features, followed by spectral features. The Random Forest classifier produced a 0.78 AUC score with overall precision, recall, and F1-score of 0.96, 0.78, and 0.85, respectively. While the precision for both classes was >0.90, the recall rate for arable areas was half that of non-arable areas. Future studies could improve the technical workflow with reliable balanced sampling, narrowband hyperspectral images, and fully polarized SAR images.
ESarDet: An Efficient SAR Ship Detection Method Based on Context Information and Large Effective Receptive Field
Authors: Zhang Yimin , Chen Chuxuan , Hu Ronglin , Yu Yongtao
Published: June 2023

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on contextual information and a large effective receptive field (ERF), is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements to it. First, CAA-Net, which has a large ERF, is proposed to better merge the contextual and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.
A Comparative Study of VLF Transmitter Signal Measurements and Simulations during Two Solar Eclipse Events
Authors: Cheng Wen , Xu Wei , Gu Xudong , Wang Shiwei , Wang Qingshan , Ni Binbin , Lu Zilong , Xiao Binxiao , Meng Xiaoyu
Published: June 2023

To monitor the Very-Low-Frequency (VLF) environment, a VLF detection system has been installed in Suizhou, China, a location with the longitude almost identical to that of the NWC transmitter in Australia. In the years 2019 and 2020, two solar eclipses crossed the NWC–Suizhou path at different locations. Each solar eclipse event represents a naturally occurring controlled experiment, but these two events are unique in that similar levels of electron density variation occurred at different locations along the VLF propagation path. Therefore, we conducted a comparative study using the VLF measurements during these two eclipses. Previous studies mostly estimated a pair of the reflection height (h′) and sharpness parameter (β) using the Long Wavelength Propagation Capability code, whereas, in this study, we use the VLF amplitude and phase as constraints in order to find the electron density change that best explains the VLF measurements. The eclipse measurements could be best explained if the path-averaged β value was 0.56 and 0.62 km−1 for the 2019 and 2020 eclipse, respectively. The VLF reflection height increased from 71.5 to 73.3 km for the 2019 eclipse and from 71.1 to 72.8 km for the 2020 eclipse. The best-fit β values were consistent with the Faraday International Reference Ionosphere model and statistical studies, and the h′ change was also consistent with previous studies and theoretical calculations. Moreover, present results suggested that VLF signals collected by a single receiver were not sensitive to where the electron density change occurs along the propagation path but reflected the average path condition. Therefore, a network of VLF receivers is required in order to monitor in real time the spatial extent of the space weather events that disturb the lower ionosphere.
Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models
Authors: de Sousa Amanda Mendes , Viana Camila Duelis , Garcia Guilherme Pereira Bento , Grohmann Carlos Henrique
Published: June 2023

This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU Paz” (CP) and “Parque Santa Madalena I” (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork.
The Transformer Bridge Principle Circuit Using RF Admittance Technology
Authors: Liu Fanfan , Zhang Chaojie , Guo Wenyong , Pan Xinglong
Published: June 2023

To investigate the problem of the lag stability of the capacitance value during the level drop of the dirty U-shaped liquid level sensor, the equivalent circuit of the dirty U-shaped liquid level sensor was analyzed, and the transformer bridge’s principle circuit that uses RF admittance technology was designed accordingly. Using the method of controlling a single variable, the measurement accuracy of the circuit was simulated when the dividing capacitance and the regulating capacitance had different values. Then, the right parameter values for the dividing capacitance and the regulating capacitance were found. On this basis, the change of the sensor output capacitance and the change of the length of the attached seawater mixture were controlled separately under the condition of removing the seawater mixture. The simulation outcomes showed that the measurement accuracy was excellent under various situations, validating the transformer principle bridge circuit’s efficacy in minimizing the influence of the output capacitance value’s lag stability.
Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach
Authors: Xie Zaipeng , Ji Cheng , Xu Lifeng , Xia Mingyao , Cao Hongli
Published: June 2023

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.
A Hierarchical Siamese Network for Noninvasive Staging of Liver Fibrosis Based on US Image Pairs of the Liver and Spleen
Authors: Wang Xue , Song Ling , Zhuang Yan , Han Lin , Chen Ke , Lin Jiangli , Luo Yan
Published: June 2023

Due to the heterogeneity of ultrasound (US) images and the indeterminate US texture of liver fibrosis (LF), automatic evaluation of LF based on US images is still challenging. Thus, this study aimed to propose a hierarchical Siamese network that combines the information from liver and spleen US images to improve the accuracy of LF grading. There were two stages in the proposed method. In stage one, a dual-channel Siamese network was trained to extract features from paired liver and spleen patches that were cropped from US images to avoid vascular interferences. Subsequently, the L1 distance was used to quantify the liver–spleen differences (LSDs). In stage two, the pretrained weights from stage one were transferred into the Siamese feature extractor of the LF staging model, and a classifier was trained using the fusion of the liver and LSD features for LF staging. This study was retrospectively conducted on US images of 286 patients with histologically proven liver fibrosis stages. Our method achieved a precision and sensitivity of 93.92% and 91.65%, respectively, for cirrhosis (S4) diagnosis, which is about 8% higher than that of the baseline model. The accuracy of the advanced fibrosis (≥S3) diagnosis and the multi-staging of fibrosis (≤S2 vs. S3 vs. S4) both improved about 5% to reach 90.40% and 83.93%, respectively. This study proposed a novel method that combined hepatic and splenic US images and improved the accuracy of LF staging, which indicates the great potential of liver–spleen texture comparison in noninvasive assessment of LF based on US images.
Computational Integral Imaging Reconstruction via Elemental Image Blending without Normalization
Authors: Lee Eunsu , Cho Hyunji , Yoo Hoon
Published: June 2023

This paper presents a novel computational integral imaging reconstruction (CIIR) method using elemental image blending to eliminate the normalization process in CIIR. Normalization is commonly used in CIIR to address uneven overlapping artifacts. By incorporating elemental image blending, we remove the normalization step in CIIR, leading to decreased memory consumption and computational time compared to those of existing techniques. We conducted a theoretical analysis of the impact of elemental image blending on a CIIR method using windowing techniques, and the results showed that the proposed method is superior to the standard CIIR method in terms of image quality. We also performed computer simulations and optical experiments to evaluate the proposed method. The experimental results showed that the proposed method enhances the image quality over that of the standard CIIR method, while also reducing memory usage and processing time.
A Novel Strategy for Detecting Permittivity and Loss Tangent of Low-Loss Materials Based on Cylindrical Resonant Cavity
Authors: Zou Jin , Li Chuan-Jian , Zheng Chen , Wang Dong , Zhang Jian , Wang Xin , Zhang Jun-Ying , Hou Zhi-Ling
Published: June 2023

Accurate measurement of the permittivity and loss tangent of low-loss materials is essential due to their special applications in the field of ultra large scale integrated circuits and microwave devices. In this study, we developed a novel strategy that can accurately detect the permittivity and loss tangent of low-loss materials based on a cylindrical resonant cavity supporting the TE111 mode in X band (8–12 GHz). Based on an electromagnetic field simulation calculation of the cylindrical resonator, permittivity is precisely retrieved by exploring and analyzing the perturbation of the coupling hole and sample size on the cutoff wavenumber. A more precise approach to measuring the loss tangent of samples with various thicknesses has been proposed. The test results of the standard samples verify that this method can accurately measure the dielectric properties of samples that have smaller sizes than the high Q cylindrical cavity method.
Robust Detection, Segmentation, and Metrology of High Bandwidth Memory 3D Scans Using an Improved Semi-Supervised Deep Learning Approach
Authors: Wang Jie , Chang Richard , Zhao Ziyuan , Pahwa Ramanpreet Singh
Published: June 2023

Recent advancements in 3D deep learning have led to significant progress in improving accuracy and reducing processing time, with applications spanning various domains such as medical imaging, robotics, and autonomous vehicle navigation for identifying and segmenting different structures. In this study, we employ the latest developments in 3D semi-supervised learning to create cutting-edge models for the 3D object detection and segmentation of buried structures in high-resolution X-ray semiconductors scans. We illustrate our approach to locating the region of interest of the structures, their individual components, and their void defects. We showcase how semi-supervised learning is utilized to capitalize on the vast amounts of available unlabeled data to enhance both detection and segmentation performance. Additionally, we explore the benefit of contrastive learning in the data pre-selection step for our detection model and multi-scale Mean Teacher training paradigm in 3D semantic segmentation to achieve better performance compared with the state of the art. Our extensive experiments have shown that our method achieves competitive performance and is able to outperform by up to 16% on object detection and 7.8% on semantic segmentation. Additionally, our automated metrology package shows a mean error of less than 2 μm for key features such as Bond Line Thickness and pad misalignment.
Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features
Authors: Mamieva Dilnoza , Abdusalomov Akmalbek Bobomirzaevich , Kutlimuratov Alpamis , Muminov Bahodir , Whangbo Taeg Keun
Published: June 2023

Methods for detecting emotions that employ many modalities at the same time have been found to be more accurate and resilient than those that rely on a single sense. This is due to the fact that sentiments may be conveyed in a wide range of modalities, each of which offers a different and complementary window into the thoughts and emotions of the speaker. In this way, a more complete picture of a person’s emotional state may emerge through the fusion and analysis of data from several modalities. The research suggests a new attention-based approach to multimodal emotion recognition. This technique integrates facial and speech features that have been extracted by independent encoders in order to pick the aspects that are the most informative. It increases the system’s accuracy by processing speech and facial features of various sizes and focuses on the most useful bits of input. A more comprehensive representation of facial expressions is extracted by the use of both low- and high-level facial features. These modalities are combined using a fusion network to create a multimodal feature vector which is then fed to a classification layer for emotion recognition. The developed system is evaluated on two datasets, IEMOCAP and CMU-MOSEI, and shows superior performance compared to existing models, achieving a weighted accuracy WA of 74.6% and an F1 score of 66.1% on the IEMOCAP dataset and a WA of 80.7% and F1 score of 73.7% on the CMU-MOSEI dataset.
Optimized Unilateral Magnetic Resonance Sensor with Constant Gradient and Its Applications in Composite Insulators
Authors: Guo Pan , Yang Chenjie , Wu Jiamin , Xu Zheng
Published: June 2023

In this study, an optimized unilateral magnetic resonance sensor with a three-magnet array is presented for assessing the aging of composite insulators in power grids. The sensor’s optimization involved enhancing the static magnetic field strength and the homogeneity of the RF field while maintaining a constant gradient in the direction of the vertical sensor surface and maximizing homogeneity in the horizontal direction. The center layer of the target area was positioned 4 mm from the coil’s upper surface, resulting in a magnetic field strength of 139.74 mT at the center point of the area, with a gradient of 2.318 T/m and a corresponding hydrogen atomic nuclear magnetic resonance frequency of 5.95 MHz. The magnetic field uniformity over a 10 mm × 10 mm range on the plane was 0.75%. The sensor measured 120 mm × 130.5 mm × 76 mm and weighed 7.5 kg. Employing the optimized sensor, magnetic resonance assessment experiments were conducted on composite insulator samples utilizing the CPMG (Carr–Purcell–Meiboom–Gill) pulse sequence. The T2 distribution provided visualizations of the T2 decay in insulator samples with different degrees of aging.
A New Perspective on the Evaluation of Urbanization Sustainability: Urban Health Examination
Authors: Yan Hang , Liu Zhijiang
Published: June 2023

In recent decades, urbanization around the world has become an unavoidable trend. However, rapid urbanization has brought about a number of “urban diseases” which have considerable influence on sustainable urbanization. In order to diagnose urban problems, this study introduces a new perspective for the evaluation of urbanization sustainability named “urban health examination (UHE)” based on the urban lifeform theory which treats a city as a human body system. Then, an evaluation index system of UHE incorporating eight dimensions is constructed by referring to the existing authoritative indicator systems globally. Furthermore, a deviation maximization method and obstacle analysis method are integrated to evaluate urban development level and diagnose the urban diseases. Finally, in order to verify the feasibility of the UHE methodological framework, an empirical study was conducted in Wuhan, Central China. The results show that (1) the main urban diseases suffered by Wuhan City in 2010–2019 include traffic congestion, waterlogging, unsafe production, insufficient technological power, and excessive urban development; (2) the evolution mechanism of urban diseases in Wuhan has been explored. This study proposes a methodological framework of UHE which can successfully diagnose urban diseases, so that local urban managers adopt tailored strategies to prevent urban diseases and further achieve sustainable urban development goals.
Measurable Version of Spectral Decomposition Theorem for a Z2-Action
Authors: Kamarudin Nor Syahmina , Dzul-Kifli Syahida Che
Published: June 2023

In this article, we present a measurable version of the spectral decomposition theorem for a Z2-action on a compact metric space. In the process, we obtain some relationships for a Z2-action with shadowing property and k-type weak extending property. Then, we introduce a definition of measure expanding for a Z2-action by using some properties of a Borel measure. We also prove one property that occurs whenever a Z2-action is invariantly measure expanding. All of the supporting results are necessary to prove the spectral decomposition theorem, which is the main result of this paper. More precisely, we prove that if a Z2-action is invariantly measure expanding, has shadowing property and has k-type weak extending property, then it has spectral decomposition.
Bipolar b-Metric Spaces in Graph Setting and Related Fixed Points
Authors: Ahmad Haroon , Younis Mudasir , Abdou Afrah Ahmad Noman
Published: June 2023

In this article, we develop a new notion that combines fixed-point theory and graph theory: graphical bipolar b-metric spaces. We demonstrate fixed-point solutions in the framework of graphical bipolar b-metric spaces, employing covariant and contravariant mapping contractions, which is a new addition to this end. This article also features illustrative examples drawn from various contexts to further demonstrate our findings. This is a significant study since it melds ideas from graph theory with those from generalized bipolar metric spaces, and considers that the symmetry of the edges of the underlying graphs connected with the enunciated metric spaces is essential in the graphical metric spaces.
Path Loss and Auxiliary Communication Analysis of VANET in Tunnel Environments
Authors: Li Chunxiao , Jin Honghui , Wu Wen , Yang Mei , Wang Qingyue , Pei Yuanpeng
Published: June 2023

Vehicular ad hoc network (VANET) communications face severe fading problems due to the signal reflections and diffractions within tunnels. Unlike the open road, the space of a tunnel is very limited, so VANET communication performance in a tunnel is seriously affected. In the process of signal transmission, the reflected signal is symmetrical with the incident signal after it is reflected by the road and the wall. In this paper, we establish a mathematical model of path loss for V2V (Vehicle-to-Vehicle) communication based on the principle of signal reflection symmetry in tunnels and considering several factors, such as the tunnel surface and the color of the tunnel wall. In addition, we use cooperative communication to form a virtual multiple-input multiple-output (V-MIMO) system, to improve the communication quality in tunnels. In the proposed system, the OBU (On-Board-unit) and RSU (Road-Side-Unit) share each other’s antennas, so that wireless cooperative communication can be employed, without increasing the number of antennas in a one-way tunnel. Therefore, this multipath fading internal electromagnetic wave propagation model can be used to improve performance. A deep reinforcement learning algorithm was used to solve the pairing problem to obtain a more accurate OBU and RSU pair, to form a V-MIMO system. Here, the RSU is regarded as an agent and interacts with the OBU in the tunnel. The optimal strategy was learned in a real-time changing simulation environment, and the experiment verified the convergence of the algorithm. The simulation results showed that, compared with the Q-learning based scheme, the optimal matching algorithm based on V-MIMO and a DQN (Deep Q-network) could effectively reduce the probability of transmission outages and improve the communication efficiency in tunnels.
Seasonality of Polyaromatic Hydrocarbons (PAHs) and Their Derivatives in PM2.5 from Ljubljana, Combustion Aerosol Source Apportionment, and Cytotoxicity of Selected Nitrated Polyaromatic Hydrocarbons (NPAHs)
Authors: Drventić Ivana , Glumac Mateo , Carev Ivana , Kroflič Ana
Published: June 2023

Airborne particulate matter (PM) is a vector of many toxic pollutants, including polyaromatic hydrocarbons (PAHs) and their derivatives. Especially harmful is the fine fraction (PM2.5), which penetrates deep into the lungs during inhalation and causes various diseases. Amongst PM2.5 components with toxic potential are nitrated PAHs (NPAHs), knowledge of which is still rudimentary. Three of the measured NPAHs (1-nitropyrene (1-nP), 9-nitroanthracene (9-nA), and 6-nitrochrysene (6-nC)) were detected in ambient PM2.5 from Ljubljana, Slovenia, along with thirteen non-nitrated PAHs. The highest concentrations of pollutants, which are closely linked with incomplete combustion, were observed in the cold part of the year, whereas the concentrations of NPAHs were roughly an order of magnitude lower than those of PAHs throughout the year. Further on, we have evaluated the toxicity of four NPAHs, including 6-nitrobenzo[a]pyrene (6-nBaP), to the human kidney cell line, HEK293T. The most potent was 1-nP (IC50 = 28.7 µM), followed by the other three NPAHs, whose IC50 was above 400 or 800 µM. According to our cytotoxicity assessment, atmospheric 1-nP is the most harmful NPAH among the investigated ones. Despite low airborne concentrations of NPAHs in ambient air, they are generally considered harmful to human health. Therefore, systematic toxicological assessment of NPAHs at different trophic levels, starting with cytotoxicity testing, is necessary in order to accurately evaluate their threat and adopt appropriate abatement strategies.
Bioaccumulation of Lead, Cadmium, and Arsenic in a Mining Area and Its Associated Health Effects
Authors: Surenbaatar Ulziikhishig , Lee Seungho , Kwon Jung-Yeon , Lim Hyunju , Kim Jeong-Jin , Kim Young-Hun , Hong Young-Seoub
Published: June 2023

Soil contamination is associated with a high potential for health issues. This study aimed to investigate the bioaccumulation of heavy metals and its associated health impact among residents near a mining area. We performed environmental monitoring by analyzing lead (Pb), cadmium (Cd), and arsenic (As) levels in soil and rice samples, as well as biomonitoring by analyzing blood and urine samples from 58 residents living near the mine. Additionally, concentration trends were investigated among 26 participants in a 2013 study. The Cd and As levels in the soil samples and Cd levels in the rice samples exceeded the criteria for concern. The geometric mean blood Cd level (2.12 μg/L) was two times higher than that in the general population aged > 40 years. The blood Cd level showed decreasing trends from the previous measurements of 4.56–2.25 μg/L, but was still higher than that in the general population. The blood and urine Cd levels were higher in those with a low estimated glomerular filtration rate (eGFR) than in those with normal eGFR. In conclusion, heavy metals from mining areas can accumulate in soil and rice, adversely impacting human health. Continuous environmental monitoring and biomonitoring are required to ensure the safety of residents.
Ionic Liquids as Environmental Pollutants—Analysis of the Effect of Tetrabutylammonium Chloride on the Growth and Development of Wheat and Cucumber
Authors: Pawłowska Barbara , Wojtala Dagmara , Biczak Robert
Published: June 2023

Ionic liquids are a huge group of chemical compounds that have found, or may, in the future, find, applications in various industries. These compounds are characterized by excellent physical, chemical, and biological properties, but a big problem is their environmental impact. One of the representatives of this group of compounds is tetrabutylammonium chloride ([TBA][Cl]). In this present study, the effects of [TBA][Cl] were evaluated on two popular plant species—a monocotyledonous plant—wheat (Triticum aestivum L.) and a dicotyledonous plant—cucumber (Cucumis sativus L.). The results showed that the compound caused a pronounced inhibition of plant growth and roots, as well as plant fresh weight yield. An increase in plant dry weight was observed at the same time. Despite the decrease in the content of photosynthetic pigments, no major changes were observed in chlorophyll fluorescence. All observed changes were strongly related to the applied concentration of the compound.
Treatment Outcomes and Associated Factors among Tuberculosis Patients from Selected Rural Eastern Cape Hospitals: An Ambidirectional Study
Authors: Faye Lindiwe M. , Hosu Mojisola C. , Iruedo Joshua , Vasaikar Sandeep , Nokoyo Kolisa A. , Tsuro Urgent , Apalata Teke
Published: June 2023

An essential metric for determining the efficacy of tuberculosis (TB) control programs is the evaluation of TB treatment outcomes; this study was conducted to investigate treatment outcomes and associated factors among tuberculosis patients in rural areas of Eastern Cape, South Africa. Assessing treatment outcomes is fundamental to facilitating the End TB Strategy’s set target. Clinic records from 457 patients with DR-TB were examined for data collection while 101 patients were followed up prospectively. Data were analyzed using Stata version 17.0. The odds ratio and 95% confidence interval were calculated to check the association between variables. p ≤ 0.05 was considered statistically significant. Of the 427 participants, 65.8% had successful treatment whilst 34.2% had unsuccessful TB treatment. A total of 61.2% and 39% of the HIV-positive and HIV-negative participants had a successful TB treatment whilst 66% and 34% of both HIV-negative and positive participants had unsuccessful TB treatment. From the 101 patients that were followed up, smokers took longer to have treatment outcomes compared to non-smokers. In the study with HIV/TB co-infection, men predominated. HIV and tuberculosis co-infection made therapy difficult with unfavorable effects on TB management. The treatment success rate (65.8%) was lower than the WHO threshold standard with a high proportion of patients being lost to the follow up. The co-infection of tuberculosis and HIV resulted in undesirable treatment outcomes. Strengthening TB surveillance and control is recommended.
Targeted Antimicrobial Prophylaxis with Cefmetazole Based on Presence of Fluoroquinolone-Resistant Isolates to Prevent Post-Prostate Biopsy Infectious Complications
Authors: Higashi Shinichiro , Yoshio Yuko , Kanda Hideki , Nishikawa Taketomo , Kato Momoko , Sugino Yusuke , Sasaki Takeshi , Kato Manabu , Masui Satoru , Nishikawa Kouhei , Inoue Takahiro
Published: June 2023

Fluoroquinolones (FQs) have been traditionally used for prophylaxis against bacterial infection. However, the rapid emergence of FQ-resistant Escherichia coli due to overuse and misuse have resulted in an increase in post-biopsy infections. We requested 723 patients undergoing transrectal or transrectal plus transperineal targeted prostate biopsy to provide preprocedure rectal swabs. The rectal swabs were plated onto deoxycholate hydrogen sulfate lactose agar culture and FQ resistance tests were conducted using the disc diffusion method following the guidelines of the Clinical and Laboratory Standards Institute. All patients undergoing biopsy were given a 1.0 g intravenous injection of cefmetazole (CMZ) 30 min before and 12 h after biopsy. Patients with FQ-resistant organisms received an additional 1.0 g intravenous injection of CMZ every 12 h for an additional 1.5 days, while those without FQ-resistant organisms received levofloxacin 500 mg for 4 days. We evaluated infectious symptoms during the 30 days after the biopsy. We also evaluated the incidence of acute prostatitis within 7 days after the biopsy and isolation rates of FQ-resistant strains. A total of 289 patients (40%) had FQ-resistant isolates on rectal swabs. The overall infectious complication rate was 0.69%. Two patients with FQ-resistant isolates and three patients without them experienced infectious episodes. One patient with FQ-resistant isolates and two patients without them suffered acute prostatitis. The difference in the rates of infectious complication and acute prostatitis rates between FQ-resistant and FQ-susceptible carriers were not significant (p = 1.0 and 1.0, respectively). Post-biopsy sepsis was identified in one patient (0.14%) who had FQ-resistant Escherichia coli. Targeted antimicrobial prophylaxis with cefmetazole based on presence of FQ-resistant isolates on rectal swabs may prevent post-prostate biopsy infectious complications, especially in geographic lesions with a high incidence of FQ-resistant strains in rectal flora.
Framing Effects on Willingness and Perceptions towards COVID-19 Vaccination among University Students in Italy: An Exploratory Study
Authors: Cesareo Massimo , Tagliabue Marco , Lopes Magdalena Edyta , Moderato Paolo
Published: June 2023

Vaccine hesitancy is a diffused psychological phenomenon that has been increasingly addressed in several studies since the COVID-19 pandemic. Communication campaigns play a pivotal role influencing recipients’ perceptions and may affect the likelihood to vaccinate or to show hesitancy. In the context of communicating risks during the COVID-19 pandemic, we hypothesized that highlighting different aspects of data on the effectiveness of vaccines would influence people’s willingness and attitudes to vaccinate. In this exploratory study, we administered two versions of a survey to a convenience sample of students from three universities in Italy. In the first version, salience was placed on the effectiveness of the vaccine in terms of reducing the probability of infection. In the second version, salience was placed on the effectiveness of the vaccine in terms of reducing the probability of hospitalization after being infected by COVID-19. The results confirmed our hypothesis: participants reported that they were more willing to become vaccinated when exposed to the hospitalization frame (main dimension). Conversely, we found mixed effects of the frame on the following sub-dimensions: reliability, trust, protection, safety, and confidence. Taken together, we show that it is possible to influence, to some extent, university students’ attitudes and perceptions toward COVID-19 vaccination by acting on how information is framed. We discuss the implications of these findings for the development of behaviorally informed policies.
Plasma and Urine Pharmacokinetics of Oral Fosfomycin Tromethamine in Dogs
Authors: Jariyapamornkoon Nattha , Patthanachai Koranis , Suanpairintr Nipattra
Published: June 2023

Fosfomycin is a broad-spectrum, bactericidal antibiotic with low toxicity. It has been used in human medicine and is a promising candidate for treating infections in veterinary medicine. Different Fosfomycin salts exhibit various degrees of bioavailability. Tromethamine salt is the most commonly used oral form due to its improved bioavailability. However, information regarding its use with dogs is limited. Therefore, this study aimed to investigate the pharmacokinetics of oral Fosfomycin tromethamine in canine plasma and urine using liquid chromatography tandem mass spectrometry (LC-MS/MS). Six healthy male beagles underwent a three-period three-treatment study: treatment 1 and 2 with single oral Fosfomycin tromethamine at 40 and 80 mg/kg (the total doses with tromethamine salt were 75 and 150 mg/kg, respectively), and treatment 3 with intravenously Fosfomycin disodium at 57 mg/kg (the total dose with disodium salt was 75 mg/kg). Dogs receiving oral Fosfomycin tromethamine at 75 and 150 mg/kg, maximal drug concentration (Cmax) in plasma produced results of 34.46 ± 12.52 and 66.40 ± 12.64 µg/mL, oral bioavailability (F) was approximately 38 and 45%, while urine Cmax was 4463.07 ± 2208.88 and 8784.93 ± 2303.46 µg/mL, respectively. No serious adverse effects were reported, except loose stool in some dogs. The tremendously high urine Fosfomycin concentrations indicate that oral Fosfomycin tromethamine is suitable as an alternative treatment for bacterial cystitis in dogs.
UN ESTUDIO DE LOS POLINOMIOS DE LAGUERRE
Authors: Loor Bermeo Elvin Eduardo , Infante Linares Adrián Ramón
Published: June 2023

En este artículo se presentan los polinomios de Laguerre complejo L(α−k)(z), junto con algunas expresiones que resultan k
a partir de ellos, que aparecen utilizando definiciones mencionadas en el texto. Además, se realiza una revisión de las
propiedades de los polinomios de Laguerre y su convergencia en media, estudiada por varios autores a lo largo de la

historia. La convergencia de los polinomios de Laguerre empieza con los estudios de Pollard, que plantea que para que se

Rb PN pcumpla limN−→∞ a f(x) − 0 anPn(x) dF(x) = 0, entonces p = 2, luego Askey y Wainger plantean una función

de Laguerre Lαn (x) = xα/2 rn exp(−x/2)Lαn (x) que converge si 4/3 < p < 4. En la siguiente investigacion, Muckenhoupt
indica que en los polinomios de Laguerre, los términos no convergerán a 0 en la media si p no está entre 4/3 y 4, pero esta
vez probando que p es un número fijo que satisface 1 ≤ p ≤ 4/3 o 4 ≤ p ≤ ∞. Luego, el mismo Muckenhoupt generaliza
los resultados de Askey y Wainger para la convergencia media con 1 < p < ∞. Los resultados mejoran en términos de las

funciones de peso, su investigación se basa en desigualdades que requerían una función de ponderación mayor en el lado

derecho que en el izquierdo: R ∞ |Sα(f, x)U(x)|pdx ≤ C R ∞ |f(x)V (x)|pdx. Mas adelante, Poiani prueba inecuaciones 0n0

de la forma ∥σn(f,x)W(x)∥p ≤ ∥f(x)W(x)∥p donde σn es la enésima (C,1) convergencia de la serie de Laguerre de f , W (x) la funcion peso de una forma particular y la norma Lp es tomada sobre (0, ∞), aqui solo se utiliza una función de peso. Por último, se encuentra la investigación realizada por Mario Riera, quien estudia dicha convergencia con deltas de Dirac, en este caso para Laguerre con una delta en el cero.
SOBRE EL ANILLO DEL ESPACIO DE FUNCIONES UNIFORMEMENTE CONTINUA
Authors: VERA MENDIETA INGRID MARIA , Infante Linarez Ramón Adrián
Published: June 2023

Es bien conocido que el producto puntual de dos funciones uniformente continua no necesariamente es uniformemente continua. En este artículo se hace una revisión sobre los trabajos que estudian las funciones uniformente continuas y las características para que una función continua sobre un espacio siempre sea uniformemente continua y su producto puntual sea uniformente continuo. En la revisión se pudo verificar condiciones suficientes para que un espacio uniforme sea uniformemente continuo. Además, se incluirá las demostraciones de resultados más importante unificando la notación entre un artículo y otro.
CKD Associates with Cognitive Decline in Middle-Aged and Older Adults with Long-Standing Type 1 Diabetes
Authors: Khatri Minesh , Ryan Christopher M. , Gao Xiaoyu , de Boer Ian H. , Braffett Barbara H. , Molitch Mark , Karger Amy B. , Lorenzi Gayle M. , Lee Pearl , Trapani Victoria R. , Lachin John M. , Jacobson Alan M.
Published: June 2023

Key Points

We found that development of both albuminuria and reduced eGFR was associated with clinically significant cognitive decline, particularly in the psychomotor and mental efficiency domain.

There was also a significant interaction between worsened albuminuria and eGFR, the combination of which augmented cognitive deficits.

A more comprehensive longitudinal phenotype of albuminuria showed that regressed albuminuria did not associate with worsened cognitive decline, as opposed to persistent albuminuria.

Background

Individuals with CKD or type 1 diabetes (T1D) are at risk for cognitive decline, but it is unclear whether these associations are with albuminuria, eGFR, or both.

Methods

We examined the longitudinal relationships between CKD and change in cognition in 1051 participants with T1D in the Diabetes Control and Complications Trial and its follow-up, the Epidemiology of Diabetes Interventions and Complications study. Albumin excretion rate and eGFR were measured every 1–2 years. Three cognitive domains were assessed repeatedly over a 32-year period: immediate memory, delayed memory, and psychomotor and mental efficiency. Associations between cognitive function and CKD were assessed: (1) longitudinally and (2) in models using eGFR and albuminuria measurements over the first 15–20 years with subsequent change in cognitive function over the ensuing 14 years (when decline in cognition was greatest).

Results

In fully adjusted longitudinal analyses, the magnitude of decline in the psychomotor and mental efficiency domain score was associated with eGFR <60 ml/min per 1.73 m2 (β −0.449; 95% confidence interval [CI], −0.640 to −0.259) and sustained albumin excretion rate 30 to <300 mg/24 hours (β −0.148; 95% CI, −0.270 to −0.026). This was equivalent to a decrease associated with approximately 11 and 4 years of aging, respectively. In analyses focused on changes in cognition between study years 18 and 32, eGFR <60 ml/min per 1.73 m2 was associated with reduced psychomotor and mental efficiency (β −0.915; 95% CI, −1.613 to −0.217).

Conclusions

In T1D, development of CKD was associated with a subsequent reduction on cognitive tasks requiring psychomotor and mental efficiency. These data highlight the need for increased recognition of risk factors for neurologic sequelae in patients with T1D, as well as preventive and treatment strategies to ameliorate cognitive decline.
Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients
Authors: Raza Abidi Syed Sibte , Naqvi Asil , Worthen George , Vinson Amanda , Abidi Samina , Kiberd Bryce , Skinner Thomas , West Kenneth , Tennankore Karthik K.
Published: June 2023

Key Points

An unsupervised machine learning clustering algorithm identified distinct deceased kidney donor phenotypes among older recipients.

Recipients of certain donor phenotypes were at a relatively higher risk of all-cause graft loss even after accounting for recipient factors.

The use of unsupervised clustering to support kidney allocation systems may be an important area for future study.

Background

Older transplant recipients are at a relatively increased risk of graft failure after transplantation, and some of this risk may relate to donor characteristics. Unsupervised clustering using machine learning may be a novel approach to identify donor phenotypes that may then be used to evaluate outcomes for older recipients. Using a cohort of older recipients, the purpose of this study was to (1) use unsupervised clustering to identify donor phenotypes and (2) determine the risk of death/graft failure for recipients of each donor phenotype.

Methods

We analyzed a nationally representative cohort of kidney transplant recipients aged 65 years or older captured using the Scientific Registry of Transplant Recipients between 2000 and 2017. Unsupervised clustering was used to generate phenotypes using donor characteristics inclusive of variables in the kidney donor risk index (KDRI). Cluster assignment was internally validated. Outcomes included all-cause graft failure (including mortality) and delayed graft function. Differences in the distribution of KDRI scores were also compared across the clusters. All-cause graft failure was compared for recipients of donor kidneys from each cluster using a multivariable Cox survival analysis.

Results

Overall, 23,558 donors were separated into five clusters. The area under the curve for internal validation of cluster assignment was 0.89. Recipients of donor kidneys from two clusters were found to be at high risk of all-cause graft failure relative to the lowest risk cluster (adjusted hazards ratio, 1.86; 95% confidence interval, 1.69 to 2.05 and 1.73; 95% confidence interval, 1.61 to 1.87). Only one of these high-risk clusters had high proportions of donors with established risk factors (i.e., hypertension, diabetes). KDRI scores were similar for the highest and lowest risk clusters (1.40 [1.18–1.67] and 1.37 [1.15–1.65], respectively).

Conclusions

Unsupervised clustering can identify novel donor phenotypes comprising established donor characteristics that, in turn, may be associated with different risks of graft loss for older transplant recipients.
Caracterización de los niveles de desempeño de la competencia informacional: El caso de la Universidad José Vasconcelos, México
Authors: Espinoza Colón Jorge
Published: June 2023

Ante un conocimiento cada vez más relativo e interdisciplinar, el desarrollo de la competencia informacional se ha posicionado como un asunto crítico en la enseñanza universitaria. En este contexto, el objetivo de la investigación fue recuperar las experiencias de docentes universitarios que imparten asignaturas relacionadas con la alfabetización informacional, con el fin de caracterizar los niveles de desempeño que evalúan en la competencia informacional y las situaciones de aprendizaje que implementan para este propósito. En el estudio se asumió el marco conceptual de la evaluación de competencias y se desplegó una metodología cualitativa mediante entrevistas semiestructuradas aplicadas a siete docentes de la Universidad José Vasconcelos, Oaxaca, México. Los resultados apuntan a que el aprendizaje de esta competencia se concibe como un proceso gradual y dinámico que puede caracterizarse a partir de tres niveles de desempeño: bajo, medio y alto. Se concluye que el conocimiento condicional es el indicador clave para verificar la transición por estos niveles, es decir, la manera en que los estudiantes integran los conocimientos y habilidades a partir de la toma de decisiones estratégicas y éticas para la búsqueda y uso de información, y cómo esto se expresa en la elaboración de escritos argumentativos.