bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on a Traffic Sign Recognition Method under Small Sample Conditions
Authors: Zhang Xiao , Zhang Zhenyu
Published: May 2023

Traffic signs are updated quickly, and there image acquisition and labeling work requires a lot of manpower and material resources, so it is difficult to provide a large number of training samples for high-precision recognition. Aiming at this problem, a traffic sign recognition method based on FSOD (few-shot object learning) is proposed. This method adjusts the backbone network of the original model and introduces dropout, which improves the detection accuracy and reduces the risk of overfitting. Secondly, an RPN (region proposal network) with improved attention mechanism is proposed to generate more accurate target candidate boxes by selectively enhancing some features. Finally, the FPN (feature pyramid network) is introduced for multi-scale feature extraction, and the feature map with higher semantic information but lower resolution is merged with the feature map with higher resolution but weaker semantic information, which further improves the detection accuracy. Compared with the baseline model, the improved algorithm improves the 5-way 3-shot and 5-way 5-shot tasks by 4.27% and 1.64%, respectively. We apply the model structure to the PASCAL VOC dataset. The results show that this method is superior to some current few-shot object detection algorithms.
Energy-Efficient and Variability-Resilient 11T SRAM Design Using Data-Aware Read–Write Assist (DARWA) Technique for Low-Power Applications
Authors: Thirugnanam Sargunam , Soong Lim Way , Prabhu Chinnaraj Munirathina , Singh Ajay Kumar
Published: May 2023

The need for power-efficient devices, such as smart sensor nodes, mobile devices, and portable digital gadgets, is markedly increasing and these devices are becoming commonly used in daily life. These devices continue to demand an energy-efficient cache memory designed on Static Random-Access Memory (SRAM) with enhanced speed, performance, and stability to perform on-chip data processing and faster computations. This paper presents an energy-efficient and variability-resilient 11T (E2VR11T) SRAM cell, which is designed with a novel Data-Aware Read–Write Assist (DARWA) technique. The E2VR11T cell comprises 11 transistors and operates with single-ended read and dynamic differential write circuits. The simulated results in a 45 nm CMOS technology exhibit 71.63% and 58.77% lower read energy than ST9T and LP10T and lower write energies of 28.25% and 51.79% against S8T and LP10T cells, respectively. The leakage power is reduced by 56.32% and 40.90% compared to ST9T and LP10T cells. The read static noise margin (RSNM) is improved by 1.94× and 0.18×, while the write noise margin (WNM) is improved by 19.57% and 8.70% against C6T and S8T cells. The variability investigation using the Monte Carlo simulation on 5000 samples highly validates the robustness and variability resilience of the proposed cell. The improved overall performance of the proposed E2VR11T cell makes it suitable for low-power applications.
YOLOv7-Peach: An Algorithm for Immature Small Yellow Peaches Detection in Complex Natural Environments
Authors: Liu Pingzhu , Yin Hua
Published: May 2023

Using object detection techniques on immature fruits to find out their quantity and position is a crucial step for intelligent orchard management. A yellow peach target detection model (YOLOv7-Peach) based on the improved YOLOv7 was proposed to address the problem of immature yellow peach fruits in natural scenes that are similar in color to the leaves but have small sizes and are easily obscured, leading to low detection accuracy. First, the anchor frame information from the original YOLOv7 model was updated by the K-means clustering algorithm in order to generate anchor frame sizes and proportions suitable for the yellow peach dataset; second, the CA (coordinate attention) module was embedded into the backbone network of YOLOv7 so as to enhance the network’s feature extraction for yellow peaches and to improve the detection accuracy; then, we accelerated the regression convergence process of the prediction box by replacing the object detection regression loss function with EIoU. Finally, the head structure of YOLOv7 added the P2 module for shallow downsampling, and the P5 module for deep downsampling was removed, effectively improving the detection of small targets. Experiments showed that the YOLOv7-Peach model had a 3.5% improvement in mAp (mean average precision) over the original one, much higher than that of SSD, Objectbox, and other target detection models in the YOLO series, and achieved better results under different weather conditions and a detection speed of up to 21 fps, suitable for real-time detection of yellow peaches. This method could provide technical support for yield estimation in the intelligent management of yellow peach orchards and also provide ideas for the real-time and accurate detection of small fruits with near background colors.
Hardware-Efficient Scheme for Trailer Robot Parking by Truck Robot in an Indoor Environment with Rendezvous
Authors: G Divya Vani , Karumuri Srinivasa Rao , C Chinnaiah M , Lam Siew-Kei , Narambhatlu Janardhan , Dubey Sanjay
Published: May 2023

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot.
Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network
Authors: Cao Lin , Liang Song , Zhao Zongmin , Wang Dongfeng , Fu Chong , Du Kangning
Published: May 2023

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9–5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.
The Cryptographic Key Distribution System for IoT Systems in the MQTT Environment
Authors: Furtak Janusz
Published: May 2023

The Internet of Things (IoT) is a very abundant source of data, as well as a source of many vulnerabilities. A significant challenge is preparing security solutions to protect IoT nodes’ resources and the data exchanged. The difficulty usually stems from the insufficient resources of these nodes in terms of computing power, memory size, range energy resource, and wireless link performance. The paper presents the design and demonstrator of a system for symmetric cryptographic Key Generating, Renewing, and Distributing (KGRD). The system uses the TPM 2.0 hardware module to support cryptographic procedures, including creating trust structures, key generation, and securing the node’s exchange of data and resources. Clusters of sensor nodes and traditional systems can use the KGRD system to secure data exchange in the federated cooperation of systems with IoT-derived data sources. The transmission medium for exchanging data between KGRD system nodes is the Message Queuing Telemetry Transport (MQTT) service, which is commonly used in IoT networks.
Neutronics Simulations for DEMO Diagnostics
Authors: Luís Raul , Nietiadi Yohanes , Quercia Antonio , Vale Alberto , Belo Jorge , Silva António , Gonçalves Bruno , Malaquias Artur , Gusarov Andrei , Caruggi Federico , Perelli Cippo Enrico , Chernyshova Maryna , Bienkowska Barbara , Biel Wolfgang
Published: May 2023

One of the main challenges in the development of a plasma diagnostic and control system for DEMO is the need to cope with unprecedented radiation levels in a tokamak during long operation periods. A list of diagnostics required for plasma control has been developed during the pre-conceptual design phase. Different approaches are proposed for the integration of these diagnostics in DEMO: in equatorial and upper ports, in the divertor cassette, on the inner and outer surfaces of the vacuum vessel and in diagnostic slim cassettes, a modular approach developed for diagnostics requiring access to the plasma from several poloidal positions. According to each integration approach, diagnostics will be exposed to different radiation levels, with a considerable impact on their design. This paper provides a broad overview of the radiation environment that diagnostics in DEMO are expected to face. Using the water-cooled lithium lead blanket configuration as a reference, neutronics simulations were performed for pre-conceptual designs of in-vessel, ex-vessel and equatorial port diagnostics representative of each integration approach. Flux and nuclear load calculations are provided for several sub-systems, along with estimations of radiation streaming to the ex-vessel for alternative design configurations. The results can be used as a reference by diagnostic designers.
Research on the Directional Adaptability of a Self-Adaptive Energy Harvester
Authors: Han Minglei , Yang Xu , Guo Shimin
Published: May 2023

With the continuous development of wireless sensor networks (WSNs), multi-directional energy harvesting technology has received widespread attention from scholars. In order to evaluate the performance of multi-directional energy harvesters, this paper uses a directional self-adaptive piezoelectric energy harvester (DSPEH) as an example, defines the direction of the excitation in three-dimensional space, and studies the influence of excitations on the key parameters of the DSPEH. The rolling angle and pitch angle are used to define complex excitations in three-dimensional space, and the dynamic response of the excitation changes in a single direction and multiple directions is discussed. It is noteworthy that this work presents the concept of “Energy Harvesting Workspace” to describe the working ability of a multi-directional energy harvesting system. The workspace is expressed by the excitation angle and voltage amplitude, and energy harvesting performance is evaluated by the volume-wrapping method and area-covering method. The DSPEH exhibits good directional adaptability in two-dimensional space (rolling direction); in particular, when the mass eccentricity coefficient is r = 0 mm, 100% of the workspace in two-dimensional space is obtained. The total workspace in three-dimensional space depends entirely on the energy output in the pitch direction.
Ultra-Stable Temperature Controller-Based Laser Wavelength Locking for Improvement in WMS Methane Detection
Authors: Wang Fupeng , Wu Jinghua , Liang Rui , Wang Qiang , Wei Yubin , Cheng Yaopeng , Li Qian , Cao Diansheng , Xue Qingsheng
Published: May 2023

In the wavelength modulation spectroscopy (WMS) gas detection system, the laser diode is usually stabilized at a constant temperature and driven by current injection. So, a high-precision temperature controller is indispensable in every WMS system. To eliminate wavelength drift influence and improve detection sensitivity and response speed, laser wavelength sometimes needs to be locked at the gas absorption center. In this study, we develop a temperature controller to an ultra-high stability level of 0.0005 °C, based on which a new laser wavelength locking strategy is proposed to successfully lock the laser wavelength at a CH4 absorption center of 1653.72 nm with a fluctuation of fewer than 19.7 MHz. For 500 ppm CH4 sample detection, the 1σ SNR is increased from 71.2 dB to 80.5 dB and the peak-to-peak uncertainty is improved from 1.95 ppm down to 0.17 ppm with the help of a locked laser wavelength. In addition, the wavelength-locked WMS also has the absolute advantage of fast response over a conventional wavelength-scanned WMS system.
Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
Authors: Hou Wenkui , Li Wanyu , Li Pengyu
Published: May 2023

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems.
Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks
Authors: AlQahtani Salman A.
Published: May 2023

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model’s performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.
Performance Tiers within a Competitive Age Group of Young Swimmers Are Characterized by Different Kinetic and Kinematic Behaviors
Authors: Santos Catarina C. , Garrido Nuno D. , Cuenca-Fernández Francisco , Marinho Daniel A. , Costa Mário J.
Published: May 2023

The present study aimed to analyze swimmers’ in-water kinetic and kinematic behaviors according to different swimming performance tiers within the same age group. An amount of 53 highly trained swimmers (girls and boys: 12.40 ± 0.74 years) were split up into 3 tiers based on their personal best performance (i.e., speed) in the 50 m freestyle event (short-course): lower-tier (1.25 ± 0.08 m·s−1); mid-tier (1.45 ± 0.04 m·s−1); and top-tier (1.60 ± 0.04 m·s−1). The in-water mean peak force was measured during a maximum bout of 25 m front crawl using a differential pressure sensors system (Aquanex system, Swimming Technology Research, Richmond, VA, USA) and defined as a kinetic variable, while speed, stroke rate, stroke length, and stroke index were retrieved and considered as kinematic measures. The top-tier swimmers were taller with a longer arm span and hand surface areas than the low-tier, but similar to the mid-tier. While the mean peak force, speed and efficiency differed among tiers, the stroke rate and stroke length showed mixed findings. Coaches should be aware that young swimmers belonging to the same age group may deliver different performance outcomes due to different kinetic and kinematic behaviors.
Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection
Authors: Xia Kewen , Lv Zhongliang , Zhou Chuande , Gu Guojun , Zhao Zhiqiang , Liu Kang , Li Zelun
Published: May 2023

Aiming at the problems of low detection efficiency and poor detection accuracy caused by texture feature interference and dramatic changes in the scale of defect on steel surfaces, an improved YOLOv5s model is proposed. In this study, we propose a novel re-parameterized large kernel C3 module, which enables the model to obtain a larger effective receptive field and improve the ability of feature extraction under complex texture interference. Moreover, we construct a feature fusion structure with a multi-path spatial pyramid pooling module to adapt to the scale variation of steel surface defects. Finally, we propose a training strategy that applies different kernel sizes for feature maps of different scales so that the receptive field of the model can adapt to the scale changes of the feature maps to the greatest extent. The experiment on the NEU-DET dataset shows that our model improved the detection accuracy of crazing and rolled in-scale, which contain a large number of weak texture features and are densely distributed by 14.4% and 11.1%, respectively. Additionally, the detection accuracy of inclusion and scratched defects with prominent scale changes and significant shape features was improved by 10.5% and 6.6%, respectively. Meanwhile, the mean average precision value reaches 76.8%, compared with the YOLOv5s and YOLOv8s, which increased by 8.6% and 3.7%, respectively.
The Use of Wearable Inertial Sensors and Workplace-Based Exercises to Reduce Lateral Epicondylitis in the Workstation of a Textile Logistics Center
Authors: Michaud Florian , Pazos Roberto , Lugrís Urbano , Cuadrado Javier
Published: May 2023

People whose jobs involve repetitive motions of the wrist and forearm can suffer from lateral epicondylitis, which is a significant burden on both the individual and the employer due to treatment costs, reduced productivity, and work absenteeism. This paper describes an ergonomic intervention to reduce lateral epicondylitis in the workstation of a textile logistics center. The intervention includes workplace-based exercise programs, evaluation of risk factors, and movement correction. An injury- and subject-specific score was calculated from the motion captured with wearable inertial sensors at the workplace to evaluate the risk factors of 93 workers. Then, a new working movement was adapted to the workplace, which limited the observed risk factors and took into account the subject-specific physical abilities. The movement was taught to the workers during personalized sessions. The risk factors of 27 workers were evaluated again after the intervention to validate the effectiveness of the movement correction. In addition, active warm-up and stretching programs were introduced as part of the workday to promote muscle endurance and improve resistance to repetitive stress. The present strategy offered good results at low cost, without any physical modification of the workplace and without any detriment to productivity.
Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments
Authors: Elkholy Mohamed , Elsheikh Mohamed , El-Sheimy Naser
Published: May 2023

Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle’s forward velocity, and the four units were used together to estimate the vehicle’s position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary’s urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage.
Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing
Authors: Reimer Tyson , Pistorius Stephen
Published: May 2023

This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast—other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.
Exploring the Applicability of Physiological Monitoring to Manage Physical Fatigue in Firefighters
Authors: Bustos Denisse , Cardoso Ricardo , Carvalho Diogo D. , Guedes Joana , Vaz Mário , Torres Costa José , Santos Baptista João , Fernandes Ricardo J.
Published: May 2023

Physical fatigue reduces productivity and quality of work while increasing the risk of injuries and accidents among safety-sensitive professionals. To prevent its adverse effects, researchers are developing automated assessment methods that, despite being highly accurate, require a comprehensive understanding of underlying mechanisms and variables’ contributions to determine their real-life applicability. This work aims to evaluate the performance variations of a previously developed four-level physical fatigue model when alternating its inputs to have a comprehensive view of the impact of each physiological variable on the model’s functioning. Data from heart rate, breathing rate, core temperature and personal characteristics from 24 firefighters during an incremental running protocol were used to develop the physical fatigue model based on an XGBoosted tree classifier. The model was trained 11 times with different input combinations resulting from alternating four groups of features. Performance measures from each case showed that heart rate is the most relevant signal for estimating physical fatigue. Breathing rate and core temperature enhanced the model when combined with heart rate but showed poor performance individually. Overall, this study highlights the advantage of using more than one physiological measure for improving physical fatigue modelling. The findings can contribute to variables and sensor selection in occupational applications and as the foundation for further field research.
Guided Wave Ultrasonic Testing for Crack Detection in Polyethylene Pipes: Laboratory Experiments and Numerical Modeling
Authors: Shah Jay , El-Hawwat Said , Wang Hao
Published: May 2023

The use of guided wave-based Ultrasonic Testing (UT) for monitoring Polyethylene (PE) pipes is mostly restricted to detecting defects in welded zones, despite its diversified success in monitoring metallic pipes. PE’s viscoelastic behavior and semi-crystalline structure make it prone to crack formation under extreme loads and environmental factors, which is a leading cause of pipeline failure. This state-of-the-art study aims to demonstrate the potential of UT for detecting cracks in non-welded regions of natural gas PE pipes. Laboratory experiments were conducted using a UT system consisting of low-cost piezoceramic transducers assembled in a pitch-catch configuration. The amplitude of the transmitted wave was analyzed to study wave interaction with cracks of different geometries. The frequency of the inspecting signal was optimized through wave dispersion and attenuation analysis, guiding the selection of third- and fourth- order longitudinal modes for the study. The findings revealed that cracks with lengths equal to or greater than the wavelength of the interacting mode were more easily detectable, while smaller crack lengths required greater crack depths for detection. However, there were potential limitations in the proposed technique related to crack orientation. These insights were validated using a finite element-based numerical model, confirming the potential of UT for detecting cracks in PE pipes.
A CMOS Temperature Sensor with a Smart Calibrated Inaccuracy of ±0.11 (3σ)
Authors: Wei Rongshan , Lin Huishan , Chen Qunchao , Huang Gongxing , Hu Wei
Published: May 2023

This paper presents a BJT-based smart CMOS temperature sensor. The analog front-end circuit contains a bias circuit and a bipolar core; the data conversion interface features an incremental delta-sigma analog-to-digital converter. The circuit utilizes the chopping, correlated double sampling, and dynamic element matching techniques to mitigate the effects of process bias and nonideal device characteristics on measurement accuracy. Furthermore, based on the principle of charge conservation, the dynamic range utilization of the ADC increases. We propose a neural network that uses a multilayer convolutional perceptron to calibrate the sensor output results. Using the algorithm, the sensor achieves an inaccuracy of ±0.11 °C (3σ), exceeding the accuracy of ±0.23 °C (3σ) achieved without calibration. We implement the sensor in a 0.18 µm CMOS process, occupying an area of 0.42 mm2. It achieves a resolution of 0.01 °C and has a conversion time of 24 ms.
An LC–MS/MS Analytical Method for Quantifying Tepotinib in Human Liver Microsomes: Application to In Vitro and In Silico Metabolic Stability Estimation
Authors: Attwa Mohamed W. , Mostafa Gamal A. E. , AlRabiah Haitham , Kadi Adnan A.
Published: May 2023

Tepotinib (MSC2156119) is a potent mesenchymal–epithelial transition (MET) factor inhibitor, a receptor tyrosine kinase that plays a crucial role in promoting cancer cell malignant progression. Adverse effects of tepotinib (TEP), such as peripheral edema, interstitial lung disease, nausea and diarrhea, occur due to drug accumulation and lead to termination of therapy. Therefore, the in silico and experimental metabolic susceptibility of TEP was investigated. In the current work, an LC-MS/MS analytical method was developed for TEP estimation with metabolic stability assessment. TEP and lapatinib (LTP) used as internal standards (ISs) were separated on a reversed-phase C18 column using the isocratic mobile phase. Protein precipitation steps were used to extract TEP from the human liver microsome (HLM) matrix. An electrospray ionization multi-reaction monitoring (MRM) acquisition was conducted at m/z 493→112 for TEP, at m/z 581→350, and 581→365 for the IS. Calibration was in the range of 5 to 500 ng/mL (R2 = 0.999). The limit of detection (LOD) was 0.4759 ng/mL, whereas the limit of quantification (LOQ) was 1.4421 ng/mL. The reproducibility of the developed analytical method (inter- and intra-day precision and accuracy) was within 4.39%. The metabolic stability of TEP in HLM was successfully assessed using the LC-MS/MS method. The metabolic stability assessment of TEP showed intermediate Clint (35.79 mL/min/kg) and a moderate in vitro t1/2 (22.65 min), proposing the good bioavailability and moderate extraction ratio of TEP. The in silico results revealed that the N-methyl piperidine group is the main reason of TEP metabolic lability. The in silico Star Drop software program could be used in an effective protocol to confirm and propose the practical in vitro metabolic experiments to spare resources and time, especially during the first stages for designing new drugs. The established analytical method is considered the first LC-MS/MS method for TEP estimation in the HLM matrix with its application to metabolic stability assessment.
Strategies for Natural Product Discovery by Unlocking Cryptic Biosynthetic Gene Clusters in Fungi
Authors: Hur Ji Yun , Jeong Eunju , Kim Young Chan , Lee Seoung Rak
Published: May 2023

Recent advancements in genome sequencing and bioinformatic analysis of fungal genomes have revealed that fungi possess cryptic or silent biosynthetic gene clusters (BGCs). This discovery suggests that our understanding of the universe of fungal secondary metabolomes is limited. In this review, we summarize recent strategies for activating cryptic BGCs in fungi, identify fungal secondary metabolites, and highlight their biological activities. We also briefly discuss the isolation and purification methods for these compounds. Our focus is on genetics-dependent and genetics-independent approaches for activating cryptic BGCs in fungi. Using the strategies discussed here, cryptic fungal natural products hold significant potential as a source for the discovery of new drug candidates.
Staying under the Radar? Immigration Effects on Overdose Deaths and the Impact of Sanctuary Jurisdictions
Authors: Pierce Kelly , Sun Diana , Feldmeyer Ben
Published: May 2023

Growing political and public rhetoric claim that immigration has contributed to drug crime and the overdose crisis of the 21st century. However, research to date has given little attention to immigration–overdose relationships, and almost no work has examined the ways that the sanctuary status of locales influences these connections. The current study draws on the immigrant revitalization perspective and Brayne’s (2014) systems avoidance theory to examine the connections between immigrant concentration, sanctuary status, and overdose mortality across MSAs for the 2015 period, overall and across races/ethnicities. The analysis uses data on overdose deaths drawn from the CDC’s Restricted Access Multiple Cause of Death Mortality files, combined with data on characteristics of MSAs drawn from the U.S. Census and other macro-level data sources. Findings reveal that the percent Latinx foreign-born is related to lower levels of overdose deaths overall and for White and Black populations but higher levels of Latinx overdose mortality. Contrary to expectations, sanctuary status has little effect on overdose deaths across most groups, and it does not significantly condition immigration–overdose relationships.
Assessment of the Impact of Ceftriaxone on the Functional Profile of Soil Microbiota Using Biolog EcoPlateTM
Authors: da Silva Freitas Livia , de Lima Brum Rodrigo , da Silva Bonifácio Alícia , Volcão Lisiane Martins , da Silva Júnior Flavio Manoel Rodrigues , Ramos Daniela Fernandes
Published: May 2023

Background: Antibiotics are essential to the treatment of diseases, but they have also brought about concerns in terms of their environmental, economic, and health impacts. Antibiotics can be excreted in unchanged form or as metabolites, which can cause toxicity by contaminating different environmental compartments, including soil. Soil is a critical compartment due to the numerous functions it performs and its direct impact on the communities of microorganisms, plants, and animals that make up the soil ecosystem. The functional profile of soil microbiota has emerged as a promising tool to assess soil quality. This study aimed to evaluate the functional profile of soil microbiota and the gut microbiota of earthworms in ceftriaxone-contaminated soil using Biolog EcoPlate. Methods: Soil samples contaminated with varying concentrations of ceftriaxone (0, 1, and 10 mg/kg) were incubated for 14 days in the presence or absence of the earthworm Eisenia andrei. After exposure, the physiological profile of the soil microbiota and the gut microbiota of the earthworms were evaluated using Biolog EcoPlate. Results: No significant differences were observed in the parameters evaluated using different concentrations of the antibiotic. The functional profile of the microbiota in the soil with and without earthworms was found to be similar, but interestingly, it differed from the profile of the intestinal microbiota of the earthworms. Conclusions: The findings of this study indicate that the presence of earthworms did not significantly alter the functional profile of the soil microbiota in ceftriaxone-contaminated soil. Further studies are necessary to investigate the potential impact of ceftriaxone and other antibiotics on soil microbiota and the role of earthworms in this regard.
A Knowledge-Based Engineering System for the Planning of Networked Rainwater Harvesting and Distribution Systems
Authors: Gembarski Paul Christoph , Melching Jan , Plappert Stefan
Published: May 2023

Rainwater harvesting attracts growing interest from the field of municipal planning. When considering a rainwater harvesting system as a design object, questions include whether the system is designed for a single property or for a local water network serving multiple properties, what allows for the inclusion of buffer tanks and resource balancing among participants in the network, how to size the tanks, and how robust the system is in the face of changing demands. Knowledge-based engineering provides methods and a tool set for such planning objects. For this article, the authors applied techniques based on model-based and resource-based configuration and Bayesian decision networks to propose a knowledge-based engineering system for residential, networked rainwater harvesting and distribution systems. This enables designers to investigate the effects of different catchment areas, adjust or minimize the storage tank sizes in the grid and evaluate their effect on the individual harvest and the exchange with a central network buffer, evaluate the demands within a neighborhood based on a detailed consumer model also over time, and test the sensitivities of the single sinks and sources to the water grid. For urban planners, this offers the possibility, for example, to make design obligations for housing construction or for the refurbishment of settlements.
Effect of Chemical Pre-Treatment on the Catalytic Performance of Oil Palm EFB Fibre Supported Magnetic Acid Catalyst
Authors: Krishnan Shamala Gowri , Pua Fei Ling , Fan Zhang
Published: May 2023

The development of heterogenous catalysts using renewable materials has received wide attention. A heterogenous catalyst has been a preferred choice as it evades the disadvantages of homogeneous catalysts, nevertheless, heterogenous catalysts has limited activity and a longer separation process. The current study emphasises the preparation of a new magnetic catalyst using oil palm empty fruit bunch (EFB) fibre as a carbon-based support material. The effect of different alkaline pre-treatments over the methyl ester conversion rate were investigated. The catalyst preparation parameters were studied by using the single factor optimisation approach, including the fibre loading, impregnation time, calcination temperature, and calcination time. Their effects in the esterification of oleic acid were investigated in this study. The optimisation study shows that the Na2CO3-treated(T)-EFBC magnetic catalyst had the highest esterification rate of 93.5% with 7 g EFB fibre loading, a 2 h impregnation time and a calcination temperature of 500 °C for 2 h. The catalyst possessed a good acidity of 3.5 mmol/g with excellent magnetism properties. This study showed that the catalysts are magnetically separable and exhibited good stability with 82.1% after five cycles. The oil palm EFB supported magnetic acid catalyst indicates it as a potential option to the existing solid catalysts that is economical and environmentally friendly for the esterification process.
International Students’ Perceptions towards Their Learning Experience in an International Network Seminar in Japan: During and Post the COVID-19 Pandemic
Authors: Søndergaard Mikael , Takita Fuyuko , Van Rompay-Bartels Ingrid
Published: May 2023

The COVID-19 pandemic has struck educational experience systems around the globe. This paper investigates and evaluates the student participants’ perceptions who joined the international exchange seminar on global citizenship and peace held at a University in Hiroshima, Japan. Approximately seventy students and faculty members from nine to ten different universities from around the globe joined this summer program in August 2021 (online) and 2022 (face-to-face). This study is a mixed-method study. The first part consists of a quantitative analysis of BEVI data obtained from the students in the seminar before COVID-19 and after. The research concludes that there are no changes in the effects of what students learn. The second part consists of qualitative data. The data shows the perceptions of students of online teaching versus hybrid teaching. It compares the differences in participants’ perceptions reported in students’ feedback on the programs during and post-COVID-19. Our results confirm prominent differences exist in the students’ perceptions of their learning experience during the pre-pandemic and post-pandemic periods. The findings indicate based on lessons learned post-pandemic, universities need to strive and define the meaning and purpose of international seminars, which enable students to experience a high level of intercultural social interaction online and face-to-face. As the world becomes more interconnected, virtual environments, such as the ones presented within the International Seminar in Hiroshima, Japan, are vital to facilitating intercultural teaching environments and the implications within this paper indicate that these virtual mediums can promote inclusion, leading to a more sustainable world.
Synergistic Effect of Micro-Silica and Recycled Tyre Steel Fiber on the Properties of High-Performance Recycled Aggregate Concrete
Authors: Amir Muhammad Talha , Riaz Sobia , Ahmed Hawreen , Raza Syed Safdar , Shohan Ahmed Ali A. , Alsulamy Saleh
Published: May 2023

The present research investigates the mechanical and physical properties of recycled aggregate concrete (RAC) modified with micro-silica (MS) and recycled tire steel fiber (RTSF). Natural coarse aggregates (NCA) were completely replaced by recycled coarse aggregates (RCA) to prepare RAC. High-strength RAC mixes were prepared by replacing 5% and 10% of Portland cement with MS. With each level of MS, RTSF was incorporated as 0%, 0.5%, 1 and 2% by volume fraction. In addition to mechanical properties, ultrasonic pulse velocity (UPV), electrical resistivity (ER), and water absorption (WA) of the mixes were also evaluated. The performance of modified RAC mixtures was also compared with plain natural aggregate concrete (PNAC). The experimental investigation revealed that RTSF substantially increased the tensile strength of RAC, whereas MS improved the durability of RTSF-reinforced RAC. RAC made with 1% RTSF and 10% MS showed 54% more splitting-tensile strength compared to the PNAC. The WA capacity of RAC incorporating 10% MS was 15–22% lower than that of the PNAC.
Corporate Governance, Media Coverage, and Corporate Environmental Protection Investment: Empirical Evidence from Listed Companies in China’s High-Pollution Industries
Authors: Wang Yuan , Zhang Junrui
Published: May 2023

In this study, regarding listed companies of high-pollution industries in China’s A share as the research object and media coverage as the moderator variable, corporate governance data from 2015 to 2019 were selected. Studied the impact of corporate governance and media coverage on corporate environmental protection investment. Corporate governance was divided into four dimensions: shareholding structure, characteristics of the independent directors, characteristics of the board of supervisors, and management characteristics. A multiple regression model and monitoring model were constructed to study the influencing factors of the environmental protection investment behavior of enterprises, and the relationship between relevant variables was empirically tested. The results show the following: (1) The equity structure is expressed by the degree of separation of cash flow rights and shareholders’ control rights. There is a significant negative correlation between shareholding structure and enterprise environmental protection investment. The characteristics of the board of supervisors and management are measured by executive compensation. The characteristics of the board of supervisors have a significant positive impact on an enterprise’s environmental protection investment. Management characteristics have a significant positive impact on enterprise environmental protection investment. (2) Media coverage as a moderator variable is measured by the data reported by important Chinese newspapers. In the robustness test, media coverage is measured by the number of Chinese financial newspaper reports and the number of online media reports. Media coverage positively regulates the relationships among the ownership structure, the characteristics of the board of supervisors, management characteristics, and enterprise environmental protection investment. (3) Positive media reports have a more significant moderating effect than negative media reports.
The Impact of Renewable Energy Consumption on Economic Growth: Evidence from Countries along the Belt and Road
Authors: Jia Hongwen , Fan Shugang , Xia Miao
Published: May 2023

To mitigate the adverse effects of climate change, the structure of global energy consumption has changed, and renewable energy consumption has increased rapidly, which may have a new impact on sustainable economic development. Against this backdrop, this paper investigates the direct and indirect effects of renewable energy consumption on economic growth, utilizing panel data from 90 countries along the Belt and Road between 2000 and 2019. Employing Granger causality tests and mediating effect models, we detect a bidirectional causal relationship between renewable energy consumption and economic growth, further affirming the feedback hypothesis. Our findings show that renewable energy consumption directly contributes to economic growth. Additionally, we found that renewable energy consumption has an indirect influence on economic growth via its impact on gross capital formation and trade. Drawing on these findings, we offer practical recommendations for the Belt and Road countries to implement appropriate countermeasures.
Symbiosis-Evolution Game and Scenario-Simulation Analysis of Advanced Manufacturing Enterprises from the Perspective of an Innovation Ecosystem
Authors: Chen Zhenghan , Tang Tianzhen , Zhang Fan , Deng Mingran
Published: May 2023

In recent years, the importance of advanced manufacturing in the innovation and sustainability of nations and regions has been demonstrated. Several studies have highlighted the need to maintain and build an advanced manufacturing-enterprise-innovation ecosystem (AMEIE). Thus, in this paper, the symbiosis-evolution model of AMEIE is established according to ecological theory. On the basis of analyzing the evolution equilibrium and its stability conditions, different symbiosis-evolution scenarios were examined through a computer simulation. The results showed the following; (1) AMEIE is a complex system composed of advanced manufacturing enterprises, new-generation information-technology enterprises, and innovation-service organizations that create value cooperatively around resource acquisition and innovation interaction in a given symbiotic environment; (2) in this innovation ecosystem, symbiosis-evolution results depend on the symbiotic coefficient between the subjects; (3) the synergy of the subjects can be maximized in mutualism symbiosis evolution, while system members should strive to switch from parasitic symbiosis evolution or partial symbiosis evolution to mutualism symbiosis evolution. Therefore, countermeasures and suggestions are proposed, such as balancing the dynamics of population symbiosis evolution, optimizing the symbiotic environment, and formulating and improving efficient collaborative innovation mechanisms, which can provide theoretical support to the promotion of the evolution and development of AMEIE, along with mutualism symbiosis evolution.