bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Fault Diagnosis of Steel Surface Based on Improved YOLOV5
Authors: Liu Weimin , Xiao Yao , Zheng Aiyun , Zheng Zhi , Liu Xiaojie , Zhang Zhen , Li Chen
Published: November 2022

Steel is an important raw material of fluid components. The technological level limitation leads to the surface faults of the steel, thus the key to improving fluid components quality is to diagnose the faults in steel production. The complex shape and small size of steel surface faults result in the low accuracy of the diagnosis, and the large size of the network leads to poor real-time performance. Therefore, aiming at the problems, an improved YOLOV5 is proposed. Firstly, to reduce the feature information loss, coordinate attention is used to improve YOLOV5, thus the diagnosis ability can be improved. Secondly, to further reduce the loss, a new connection is constructed in YOLOV5, and the detection ability can also be further improved. Thirdly, to improve the real-time performance of the fault diagnosis, YOLOV5 is improved by the lightweight method ShuffleNetV2, and its size can be reduced. Lastly, to further improve the accuracy, the cosine annealing with warm restarts algorithm is used to optimize YOLOV5. The dataset of NEU-DET is verified and testified. The results show that improved YOLOV5 can diagnose steel surface faults with high efficiency and accuracy.
Simulation Analysis of Implementation Effects of Construction Waste Reduction Policies
Authors: Wang Qiufei , Li Siyu , Yang Ye
Published: November 2022

The development of the construction industry generates construction waste which could contribute to environmental issues. Construction waste reduction management plays an important role in directly reducing emissions and solving the environmental pollution caused by construction waste. The limited rationality hypothesis and an evolutionary game model are used to construct a simulation model for the effects of environmental policies’ influences on the behavior of government and construction enterprises in construction waste reduction activities. Simulation results show that: (1) The government and enterprises evolve in the same direction under the sewage fees system or the subsidy system. The relationship between the initial ratio of the two sides and the position of the saddle point determines the evolution direction of the system. (2) The government could adjust the sewage fees rate, the penalty ratio, and the upper limit of construction waste emission to obtain a superior effect under the sewage fees system. As the subsidy system, the government could adjust the unit subsidy and the upper limit of construction waste emissions by enterprises. (3) The evolution times of the different systems are different. The time required to evolve to a stable state is shorter under the sewage fees system. Under the subsidy system, the time to evolve to a non-reduced state is longer, and the time to evolve to a reduced state is about the same as the time for the government to evolve to a checked state. The time required to evolve to the reduced state is about the same as the time required for the government to evolve to the checked state. This study develops an evolutionary game model between the government and construction enterprises in construction waste reduction activities. This study helps the government analyze the influence of various policies on enterprises’ reduction behaviors. The findings could help the government formulate appropriate policies to guide enterprises in waste reduction.
Targeted Metabolic Analysis and MFA of Insect Cells Expressing Influenza HA-VLP
Authors: Murad Alexandre B. , Sousa Marcos Q. , Correia Ricardo , Isidro Inês A. , Carrondo Manuel J. T. , Roldão António
Published: November 2022

Virus-like particles (VLPs) are versatile vaccine carriers for conferring broad protection against influenza by enabling high-level display of multiple hemagglutinin (HA) strains within the same particle construct. The insect cell-baculovirus expression vector system (IC-BEVS) is amongst the most suitable platforms for VLP expression; however, productivities vary greatly with particle complexity (i.e., valency) and the HA strain(s) to be expressed. Understanding the metabolic signatures of insect cells producing different HA-VLPs could help dissect the factors contributing to such fluctuations. In this study, the metabolic traces of insect cells during production of HA-VLPs with different valences and comprising HA strains from different groups/subtypes were assessed using targeted metabolic analysis and metabolic flux analysis. A total of 27 different HA-VLP variants were initially expressed, with titers varying from 32 to 512 HA titer/mL. Metabolic analysis of cells during the production of a subset of HA-VLPs distinct for each category (i.e., group 1 vs. 2, monovalent vs. multivalent) revealed that (i) expression of group-2 VLPs is more challenging than for group-1 ones; (ii) higher metabolic rates are not correlated with higher VLP expression; and (iii) specific metabolites (besides glucose and glutamine) are critical for central carbon metabolism during VLPs expression, e.g., asparagine, serine, glycine, and leucine. Principal component analysis of specific production/consumption rates suggests that HA group/subtype, rather than VLP valency, is the driving factor leading to differences during influenza HA-VLPs production. Nonetheless, no apparent correlation between a given metabolic footprint and expression of specific HA variant and/or VLP design could be derived. Overall, this work gives insights on the metabolic profile of insect High Five cells during the production of different HA-VLPs variants and highlights the importance of understanding the metabolic mechanisms that may play a role on this system’s productivity.
Real-Time Temperature and Humidity Measurements during the Short-Range Distribution of Perishable Food Products as a Tool for Supply-Chain Energy Improvements
Authors: Aguiar Martim L. , Gaspar Pedro D. , Silva Pedro D. , Domingues Luísa C. , Silva David M.
Published: November 2022

Food waste results in an increased need for production to compensate for losses. Increased production is directly related to an increase in the environmental impact of agriculture and in the energy needs associated with it. To reduce food waste, the supply chain should maintain ideal preservation conditions. In horticultural products, temperature, and relative humidity are two of the main parameters to be controlled. Monitoring these parameters can help decision-making in logistics and routes management, as well as to diagnose and timely prevent food losses. In the present work, eighteen wireless traceability devices with temperature and relative humidity sensors monitored crates with horticultural products along a short-range distribution route with five stops (4 h 30 m). Sensor data and a location tag were sent via GSM for real-time monitoring. The results showed fluctuations in temperature and relative humidity that reached up to 7.4 °C and 35.3%, respectively. These fluctuations happened mostly due to frequent door opening, operational procedures, and irregular refrigeration conditions. Furthermore, the results brought attention to a procedure that creates unnecessary temperature fluctuations and energy losses. This study highlights the importance of individual monitorization of goods, for quality control and optimization of energy efficiency along the supply chain.
Bioactive Compounds from Elderberry: Extraction, Health Benefits, and Food Applications
Authors: Pascariu Oana-Elena , Israel-Roming Florentina
Published: November 2022

Elderberries are appreciated for their antioxidant properties. Sambucus nigra L. is an extremely abundant plant in the wild flora of Romania, but it is underutilized. Elderberry is used in modern and traditional medicine due to the complex chemical composition of the fruit. The content of phenolic compounds is high (516–8974 mg/100 g DW), of which the most abundant are anthocyanins. Phenolic compounds are known for their beneficial effects on the body. Numerous studies have demonstrated the antioxidant capacity, antibacterial, antiviral, antidiabetic, and anticancer properties of the fruit. It is considered that most of the therapeutic properties of elderberries can be correlated with the antioxidant activity they have. S. nigra fruits are also used in the food industry. Some studies have shown that the therapeutic properties of elderberries can also be found in the products obtained from them. Therefore, this review aimed to describe the chemical composition of elderberries and products obtained from them, the positive effects on the body, and the methods by which the bioactive compounds can be extracted from the fruits and analyzed. This manuscript is useful for extraction optimization and characterization in order to valorize new functional foods, food supplements, and also in new pharmaceutical products.
Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials
Authors: Alqaed Saeed , Mustafa Jawed , Almehmadi Fahad Awjah , Alharthi Mathkar A. , Sharifpur Mohsen , Cheraghian Goshtasp
Published: November 2022

Considering the importance of environmental protection and renewable energy resources, particularly solar energy, the present study investigates the temperature control of a solar panel using a nanofluid (NFD) flow with eco-friendly nanoparticles (NPs) and a phase change material (PCM). The PCM was used under the solar panel, and the NFD flowed through pipes within the PCM. A number of straight fins (three fins) were exploited on the pipes, and the output flow temperature, heat transfer (HTR) coefficient, and melted PCM volume fraction were measured for different pipe diameters (D_Pipe) from 4 mm to 8 mm at various time points (from 0 to 100 min). Additionally, with the use of artificial intelligence and machine learning, the best conditions for obtaining the lowest panel temperature and the highest output NFD temperature at the lowest pressure drop have been determined. While the porosity approach was used to model the PCM melt front, a two-phase mixture was used to simulate NFD flow. It was discovered that the solar panel temperature and output temperature both increased considerably between t = 0 and t = 10 min before beginning to rise at varying rates, depending on the D_Pipe. The HTR coefficient increased over time, showing similar behavior to the panel temperature. The entire PCM melted within a short time for D_Pipes of 4 and 6 mm, while a large fraction of the PCM remained un-melted for a long time for a D_Pipe of 8 mm. An increase in D_Pipe, particularly from 4 to 6 mm, reduced the maximum and average panel temperatures, leading to a lower output flow temperature. Furthermore, the increased D_Pipe reduced the HTR coefficient, with the PCM remaining un-melted for a longer time under the panel.
An Improved Arc Flow Model with Enhanced Bounds for Minimizing the Makespan in Identical Parallel Machine Scheduling
Authors: Gharbi Anis , Bamatraf Khaled
Published: November 2022

In this paper, an identical parallel machine problem was considered with the objective of minimizing the makespan. This problem is NP-hard in the strong sense. A mathematical formulation based on an improved arc flow model with enhanced bounds was proposed. A variable neighborhood search algorithm was proposed to obtain an upper bound. Three lower bounds from the literature were utilized in the improved arc flow model to improve the efficiency of the mathematical formulation. In addition, a graph compression technique was proposed to reduce the size of the graph. As a consequence, the improved arc flow model was compared with an arc flow model from the literature. The computational results on benchmark instances showed that the improved arc flow model outperformed the literature arc flow model at finding optimal solutions for 99.97% of the benchmark instances, with the overall percentage of the reduction in time reaching 87%.
Loyalty and Identity Formation: Muslim Perceptions of Loyalty in France
Authors: Belhaj Abdessamad
Published: November 2022

This paper sets out to study loyalty as identity formation through the cases of three Muslim leaders in France (T. Ramadan, A. Mamoun and M. Zenati). First, I will discuss the state of research on “Muslim loyalties” in the West. Afterwards, Ramadan’s concept of critical loyalties, Mamoun’s loyalty as gratitude, and Zenati’s human brotherhood as the basis of loyalty will be thoroughly examined. The main goal of the current study is to determine how the three Muslim leaders incorporate loyalty as an element of shaping the identity of French Muslim citizens while attempting to resolve the current tensions between the French state and Islam.
Narratives of Religious Landscape: Reading Gender and Chinese Buddhism in the Travel Writing of Christian Women
Authors: Baycroft Anne
Published: November 2022

This article explores the narrative descriptions of the Chinese religious landscape embedded within nineteenth century Christian missionary writings. I demonstrate the potential use of Protestant missionary writings as sources in the academic study of religion in China for both the physical descriptions of religious places that they contain and the narratives they express regarding the religious activities and identities of Chinese women. Of particular interest to this study are the religious encounters experienced between Christian and Buddhist women. My analysis of the travel writings of three Protestant women, Eliza Bridgeman (1805–1871), Helen Nevius (1833–1910), and Isabelle Williamson (d. 1886), illustrates that Chinese women were highly active within sacred spaces across China. This article contributes to discourses on the history of women and Chinese Buddhism, offers historiographical insights into the origins of Western academic studies of Buddhism in China, and provides alternate source material for information about religious continuity and change in early modern China.
Revisiting the Meaning of the Divine Preservation of the Qur’an: With Special References to Verse 15:9
Authors: Albayrak Ismail
Published: November 2022

In this article, I discuss the topic of Divine preservation of the Qur’an in the context of exegesis verse 15:9: ‘Indeed, it is We who sent down the reminder (dhikr) [i.e., the Qur’an], and indeed, We will be its guardian’. First, I will briefly examine Muslim and non-Muslim perceptions regarding authentic transmission of the Qur’an. I question some Western researchers, who problematise Muslims’ approaches to the history of the Qur’an with various polemics, and the Muslim perception, which compresses the notion of Divine preservation of the Qur’an into the narrow meaning of verse 15:9 with dogmatic and apologetic reflexes. Then, I chronologically analyse interpretations of the verse from linguistic, historical, theological and sociological aspects of the exegeses from the classical period to the modern times. The main argument of this article is centred on verse 15:9, which, in my opinion, deals with the first route of Qur’anic revelation from God to the Prophet through Angel Gabriel, and the Prophet’s perfect memorisation of it together with his flawless transmission to the Companions. The next processes (post-Prophetic developments in transmission of the revelation) were left to the foresight of the Muslim community, ummah. I believe, while the vertical process (revelation from arsh to farsh (heaven to earth) is guaranteed by God, responsibility for the horizontal process (from the Companions to the following generation and so on) is left to the Muslim community. The use of the same Qur’anic text by Sunnis, Shi’ites, Ibadis, etc., from northern Kazakhstan to West Africa, from Asia Pacific to the Balkans, is the clearest proof that Muslims are preserving the book entrusted to them. They demonstrate the reflection of their understanding of tawhid (Oneness of God) in their faith on the Qur’an by their consensus on a single mushaf.
Where Can IMERG Provide a Better Precipitation Estimate than Interpolated Gauge Data?
Authors: Hartke Samantha H. , Wright Daniel B.
Published: November 2022

Although rain gauges provide valuable point-based precipitation observations, gauge data is globally sparse, necessitating interpolation between often-distant measurement locations. Interpolated gauge data is subject to uncertainty just as other precipitation data sources. Previous studies have focused either on the effect of decreasing gauge density on interpolated gauge estimate performance or on the ability of gauge data to accurately assess satellite multi-sensor precipitation data as a function of gauge density. No previous work has directly compared the performance of interpolated gauge estimates and satellite precipitation data as a function of gauge density to identify the gauge density at which satellite precipitation data and interpolated estimates have similar accuracy. This study seeks to provide insight into interpolated gauge product accuracy at low gage densities using a Monte Carlo interpolation scheme at locations across the continental U.S. and Brazil. We hypothesize that the error in interpolated precipitation estimates increases drastically at low rain gauge densities and at high distances to the nearest gauge. Results show that the multisatellite precipitation product, IMERG, has comparable performance in precipitation detection to interpolated gauge data at very low gauge densities (i.e., less than 2 gauges/10,000 km2) and that IMERG often outperforms interpolated data when the distance to the nearest gauge used during interpolation is greater than 80–100 km. However, there does not appear to be a consistent relationship between this performance ‘break point’ and the geographical variables of elevation, distance to coast, and annual precipitation.
On the Quality Control of HY-2 Scatterometer High Winds
Authors: Lang Shuyan , Lin Wenming , Zhang Yi , Jia Yongjun
Published: November 2022

The operational wind processor for the Ku-band scatterometers onboard HY-2 satellite series uses a quality control (QC) scheme based on the maximum likelihood estimator (MLE). Since it is difficult to discriminate rain contamination from “true” high winds, the MLE-based wind QC is set in a conservative way, which rejects up to ~35% of high winds (w ≥ 20 m/s) in HY-2 scatterometers (HSCATs). In this paper, the sensitivity of MLE and its spatially averaged value (i.e., MLEm) to wind quality and rain is reconsidered by analyzing the collocated HSCAT observations and buoy data, as well as rain data from the global precipitation measurement satellite’s microwave imagers. It shows that MLEm is more effective than MLE in terms of flagging rain data. More interestingly, the HSCAT high winds are much less strongly affected by rain, compared to the prior Ku-band pencil-beam scatterometers (e.g., RapidScat). Consequently, a MLEm-based approach is proposed to improve the HSCAT wind QC, particularly for high winds. The new QC method results in ~8% rejections at 20 m/s and above. Compared to the collocated buoy winds, the HSCAT high winds preserved by the new QC (but rejected by the operational QC) are of fairly good quality.
Propionibacteriaium acidipropionici CP 88 Dose Alters In Vivo and In Vitro Ruminal Fermentation Characteristics
Authors: Levenson Jonah R. , Thompson Logan , Gonzalez-Murray Roderick , Gifford Ryan J. , Thorndyke Meghan P. , Guimaraes Octavio , Loh Huey Yi , Tangredi Briana V. , Hallmark Harrison , Goodall Richard , Wagner John J. , Engle Terry E.
Published: November 2022

Twelve 5-year-old beef steers, with an average weight of 2000 lbs., fitted with rumen canulae were used in a 4 × 4 incomplete Latin square design to examine the impact of the direct fed microbial Propionibacterium acidipropionici CP 88 (PA) on rumen fermentation characteristics, in vitro CH4, CO2, and N2 production, and in vivo CH4 and CO2 production. All steers were housed in the same pen equipped with eight GrowSafe feeding stations to monitor individual animal feed intake and one GreenFeed System to estimate individual animal CH4 and CO2 production. Steers were fed a corn-silage-based diet throughout the experiment. Treatments consisted of PA administered at: (1) control (0.0); (2) 1.0 × 108; (3) 1.0 × 109; and (4) 1.0 × 1010 cfu∙animal−1∙day (d)−1. Treatments were administered directly into the rumen as a single bolus dose daily. On day 7 and 14 of each period, rumen fluid was collected from each steer 2 h post treatment administration for VFA analysis and in vitro DM digestibility determination. Following a 14 d washout period, animal treatments were switched and the experiment repeated until the 4 × 4 Latin square was complete. In vivo propionic acid molar proportions and total VFA concentrations were greater (p < 0.05) in steers receiving PA when compared with controls. All other in vivo rumen fermentation characteristics were similar across treatments. In vitro DM disappearance (p < 0.05) and total VFA (p < 0.05) were greater and CH4 lesser (p < 0.04) in fermentation vessels incubated with rumen fluid from animals receiving PA when compared with controls. Dry matter disappearance (p < 0.03) and propionic acid molar proportions increased (p < 0.04) linearly as the dose of PA increased. In vitro total VFA tended (p < 0.08) to increase linearly and CH4 production per unit of DM digested tended (p < 0.09) to decrease quadratically in response to PA dose. All other in vitro rumen fermentation characteristics were similar across treatments. These data indicate that PA impacts in vivo and in vitro rumen fermentation characteristics.
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
Authors: Masood Farah , Sharma Milan , Mand Davleen , Nesathurai Shanker , Simmons Heather A. , Brunner Kevin , Schalk Dane R. , Sledge John B. , Abdullah Hussein A.
Published: November 2022

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.
Multi-Irradiance: A Method for Simultaneous Measurement of the Temperature and Spectral Emissivity of High-Temperature Targets in SWIR
Authors: Sun Yehan , Pan Jun , Jiang Lijun , Hao Libo , Cao Yu , Wang Helin
Published: November 2022

The currently used energy methods in spectral emissivity measurement are susceptible to the difference in temperature between the target and the reference blackbody. It is also limited by the state of the observation target and observation. This paper introduces the irradiance condition, while using the correlation between the information of emission energy and reflected energy of the high-temperature target. Based on the principle of radiative transmission and energy conservation, the relationship between the emissivity and bidirectional reflectance factor (BRF) was used to perform the retrieval of emissivity and temperature. An experimental device was designed, and graphite and rock were considered to verify the feasibility of the experimental scheme. The error of emissivity and temperature of both targets were, respectively, less than 5% and 0.5%, due to the Lambertian assumption, and the systematic errors had negligible impact on the retrieval. This verifies that the experimental observation method and scheme is reasonable.
Acquisition and Analysis of Microcirculation Image in Septic Model Rats
Authors: Ye Chen , Kawasaki Mami , Nakano Kazuya , Ohnishi Takashi , Watanabe Eizo , Oda Shigeto , Nakada Taka-Aki , Haneishi Hideaki
Published: November 2022

Background: Microcirculation is a vital sign that supplies oxygen and nutrients to maintain normal life activities. Sepsis typically influences the operation of microcirculation, which is recovered by the administration of medicine injection. Objective: Sepsis-induced variation and recovery of microcirculation are quantitatively detected using microcirculation images acquired by a non-contact imaging setup, which might assist the clinical diagnosis and therapy of sepsis. Methods: In this study, a non-contact imaging setup was first used to record images of microcirculation on the back of model rats. Specifically, the model rats were divided into three groups: (i) the sham group as a control group; (ii) the cecum ligation and puncture (CLP) group with sepsis; and (iii) the CLP+thrombomodulin (TM) group with sepsis and the application of TM alfa therapy. Furthermore, considering the sparsity of red blood cells (RBCs), the blood velocity is estimated by robust principal component analysis (RPCA) and U-net, and the blood vessel diameter is estimated by the contrast difference between the blood vessel and tissue. Results and Effectiveness: In the experiments, the continuous degradation of the estimated blood velocity and blood vessel diameter in the CLP group and the recovery after degradation of those in the CLP+TM group were quantitatively observed. The variation tendencies of the estimated blood velocity and blood vessel diameter in each group suggested the effects of sepsis and its corresponding therapy.
xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning
Authors: Link Johannes , Schwinn Leo , Pulsmeyer Falk , Kautz Thomas , Eskofier Bjoern M.
Published: November 2022

With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis’ orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions’ accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor’s data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.
Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model
Authors: Adriaenssens Aurelien J. C. , Raveendranathan Vishal , Carloni Raffaella
Published: November 2022

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints.
Molecularly Imprinted Polymer-Modified Microneedle Sensor for the Detection of Imidacloprid Pesticides in Food Samples
Authors: Mugo Samuel M. , Lu Weihao , Robertson Scott V.
Published: November 2022

A portable, molecularly imprinted polymer (MIP)-based microneedle (MN) sensor for the electrochemical detection of imidacloprid (IDP) has been demonstrated. The MN sensor was fabricated via layer-by-layer (LbL) in-tube coating using a carbon nanotube (CNT)/cellulose nanocrystal (CNC) composite, and an IDP-imprinted polyaniline layer co-polymerized with imidazole-functionalized CNCs (PANI-co-CNC-Im) as the biomimetic receptor film. The sensor, termed MIP@CNT/CNC MN, was analyzed using both cyclic voltammetry (CV) and differential pulse voltammetry (DPV) and showed excellent electrochemical performance for the detection of IDP. The CV detection range for IDP was 2.0–99 µM, with limits of detection (LOD) of 0.35 µM, while the DPV detection range was 0.20–92 µM with an LOD of 0.06 µM. Additionally, the MIP@CNT/CNC MN sensor showed excellent reusability and could be used up to nine times with a 1.4 % relative standard deviation (% RSD) between uses. Lastly, the MIP@CNT/CNC MN sensor successfully demonstrated the quantification of IDP in a honey sample.
Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method
Authors: Wen Yu-Che , Wen Senfar , Hsu Long , Chi Sien
Published: November 2022

The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by interpolation. This method is known as the look-up table (LUT) method. It is irradiance-dependent due to the dependence of the reconstructed spectrum shape on the sample irradiance. Since the irradiance can vary in field applications, an irradiance-independent LUT (II-LUT) method is required to recover spectral reflectance. This paper proposes an II-LUT method to interpolate the spectrum in the normalized signal space. Munsell color chips irradiated with D65 were used as samples. Example cameras are a tricolor camera and a quadcolor camera. Results show that the proposed method can achieve the irradiance independent spectrum reconstruction and computation time saving at the expense of the recovered spectral reflectance error. Considering that the irradiance variation will introduce additional errors, the actual mean error using the II-LUT method might be smaller than that of the ID-LUT method. It is also shown that the proposed method outperformed the weighted principal component analysis method in both accuracy and computation speed.
Self-Pulsations in Terahertz Quantum Cascade Lasers under Strong Optical Feedback: The Effect of Multiple Reflections in the External Cavity
Authors: Qi Xiaoqiong , Loh Hui Yi , Taimre Thomas , Bertling Karl , Indjin Dragan , Rakić Aleksandar D.
Published: November 2022

We have recently reported the self-pulsation phenomenon under strong optical feedback in terahertz (THz) quantum cascade lasers (QCLs). One important issue, however, we left open: the effect of multiple round trips in the external cavity on the laser response to feedback. Our current analysis also casts additional light on the phenomenon of self-pulsations. Using only one external cavity round trip (ECRT) in the model has been the common approach following the seminal paper by Lang–Kobayashi in 1980. However, the conditions under which the Lang–Kobayashi model, in its original single-ECRT formulation, is applicable has been rarely explored. In this work, we investigate the self-pulsation phenomenon under multiple ECRTs. We found that the self-pulsation waveform changes when considering more than one ECRT. This we attribute to the combined effect of the extended external cavity length and the frequency modulation of the pulsation frequency by the optical feedback. Our findings add to the understanding of the optical feedback dynamics under multiple ECRTs and provide a pathway for selecting the appropriate numerical model to study the optical feedback dynamics in THz QCLs and semiconductor lasers in general.
Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification
Authors: Wang Nanlan , Zeng Xiaoyong , Duan Yanjun , Deng Bin , Mo Yan , Xie Zhuojun , Duan Puhong
Published: November 2022

Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.
Intelligent Control of Groundwater in Slopes with Deep Reinforcement Learning
Authors: Biniyaz Aynaz , Azmoon Behnam , Liu Zhen
Published: November 2022

The occurrence of landslides has been increasing in recent years due to intense and prolonged rainfall events. Lowering the groundwater in natural and man-made slopes can help to mitigate the hazards. Subsurface drainage systems equipped with pumps have traditionally been regarded as a temporary remedy for lowering the groundwater in geosystems, whereas long-term usage of pumping-based techniques is uncommon due to the associated high operational costs in labor and energy. This study investigates the intelligent control of groundwater in slopes enabled by deep reinforcement learning (DRL), a subfield of machine learning for automated decision-making. The purpose is to develop an autonomous geosystem that can minimize the operating cost and enhance the system’s safety without introducing human errors and interventions. To prove the concept, a seepage analysis model was implemented using a partial differential equation solver, FEniCS, to simulate the geosystem (i.e., a slope equipped with a pump and subjected to rainfall events). A Deep Q-Network (i.e., a DRL learning agent) was trained to learn the optimal control policy for regulating the pump’s flow rate. The objective is to enable intermittent control of the pump’s flow rate (i.e., 0%, 25%, 50%, 75%, and 100% of the pumping capacity) to keep the groundwater close to the target level during rainfall events and consequently help to prevent slope failure. A comparison of the results with traditional proportional-integral-derivative-controlled and uncontrolled water tables showed that the geosystem integrated with DRL can dynamically adapt its response to diverse weather events by adjusting the pump’s flow rate and improve the adopted control policy by gaining more experience over time. In addition, it was observed that the DRL control helped to mitigate slope failure during rainfall events.
A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors
Authors: Fan Yun-Chieh , Tseng Yu-Hsuan , Wen Chih-Yu
Published: November 2022

Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities.
Modulation of Rab GDP-Dissociation Inhibitor Trafficking and Expression by the Transmembrane Protein 59 (TMEM59)
Authors: Wang Haifeng , Wen Tieqiao
Published: November 2022

Transmembrane protein 59 (TMEM59) is a type I transmembrane protein. However, the characterization and functions of TMEM59 in cells are not clear. Our results showed that TMEM59 localizes to vesicular structures. Further co-localization studies illustrated that TMEM59 is mainly distributed in the lysosome and acidic vesicular. TMEM59 movement between the nucleus and cell membrane was observed in living cells expressing TMEM59–EGFP fusion proteins. In addition, cell surface transport of amyloid precursor protein (APP) was significantly inhibited by TMEM59 and increased APP levels in HEK296T cells. TMEM59 also significantly inhibits transport of Rab GDP dissociation inhibitor alpha (GDI1) and Rab GDP dissociation inhibitor beta (GDI2), and further increases expression of GDI1 and GDI2 proteins in the cytoplasm. However, TMEM59 does not affect protein expression and localization of BACE2. These results suggest that TMEM59 may be involved in the packaging of acidic vesicles, modulated transport, and processing of APP, GDI1, and GDI2.
RP-HPLC-DAD Analysis of Mitragynine Content in Mitragyna speciosa Korth. (Ketum) Leaf Extracts Prepared Using Ultrasound Assisted Extraction Technique and Their Cytotoxicity
Authors: Karunakaran Thiruventhan , Goh Yong Sean , Santhanam Rameshkumar , Murugaiyah Vikneswaran , Abu Bakar Mohamad Hafizi , Ramanathan Surash
Published: November 2022

Mitragynine and its congeners are one of the major phytoconstituents present in Mitragyna speciosa Korth. (ketum) leaves and a well-known factor that contributes to ketum’s pharmacological activities. This study describes the usage of a green extraction method to yield botanical leaf extracts of ketum. The leaf extracts were assessed for mitragynine content, total phenolic and flavonoid content, and cytotoxicities. The Ultrasound Assisted Extraction (UAE) method showed a varying dry yield of the extracts (0.22–1.92 g) that were obtained with solvents of variable polarities. However, the mitragynine content was consistent among the organic solvent extracts (7.22–9.40%). This method calls for a minimal extraction solvent volume (solid to solvent ratio, 1:30) and a shorter extraction period (20 min). Of the solvents tested, the methanolic extract showed the highest content of total phenolic (419.50 ± 2.50 GAE mg/g) and flavonoids (177.33 ± 3.00 QE mg/g). The extract was nontoxic towards kidney (HEK-293) and Chang liver (HeLa) cell lines. Analysis via UHPLC-ESI-QTOF-MS/MS made it possible to identify mitragynine congeners, such as mitragynine, paynantheine, and speciociliatine, in the leaves extract. In conclusion, the UAE method using methanol as the extraction solvent provides a noncytotoxic ketum botanical extract for future preclinical and clinical studies.
Magnetic Technologies and Green Solvents in Extraction and Separation of Bioactive Molecules Together with Biochemical Objects: Current Opportunities and Challenges
Authors: Feng Xueting , Song Hang , Zhang Tenghe , Yao Shun , Wang Yan
Published: November 2022

Currently, magnetic technology and green solvents are widely used in chemical engineering, environmental engineering and other fields as they are environmentally friendly, easy to operate and highly efficient. Moreover, a magnetic field has positive effect on many physicochemical processes. However, related new methods, materials, strategies and applications in separation science still need to be developed. In this review, a series of meaningful explorations of magnetic technologies for the separation of natural products and biologic objects, including magnetic ionic liquids and other magnetic solvents and fluids, magnetic nanoparticles and magnetic fields, and the development of magnetic separators were reviewed. Furthermore, the difficulties in the application and development of magnetic separation technology were discussed on the basis of comparison and data analysis, especially for the selection of magnetic materials and magnetic field sources. Finally, the progress in the development of magnetic separators was also elaborated for researchers, mainly including that of the new high-efficiency magnetic separator through multi-technology integration and the optimization of traditional magnetic separators, which help current techniques break through their bottleneck as a powerful driving force.
Development and Validation of an HPLC-FLD Method for the Determination of NDMA and NDEA Nitrosamines in Lisinopril Using Pre-Column Denitrosation and Derivatization Procedure
Authors: Tsanaktsidou Eleni , Kanata Lamprini , Almpani Sofia , Zacharis Constantinos K. , Markopoulou Catherine K.
Published: November 2022

In order to meet the analytical requirements of the European Medicines Agency (EMA), a new HPLC-FLD method was successfully developed using dansyl chloride for the derivatization and determination of the genotoxic impurities N-Nitrosodimethylamine (NDMA) and N-Nitrosodiethylamine (NDEA) in Lisinopril API and its final product. Samples’ pretreatment includes liquid–liquid microextraction, denitrosation, and derivatization steps. To optimize the process, the parameters contributing to high sensitivity and yielding reliable results were thoroughly studied and optimized using one-factor-at-a-time and experimental design approaches. The analytes were pre-column derivatized with Dansyl-Cl and analyzed by HPLC-fluorescence (λem/λem = 340/530) using a C18 column and a mixture of phosphate buffer (pH = 2.8; 20 mM)/acetonitrile 55:45 v/v as the mobile phase. The six-level concentration calibration was shown to be linear, with R equal to 0.9995 for both analytes. The limit of detection (LOD) was satisfactory and equal to 4.7 and 0.04 ng/mL for NDMA and NDEA, respectively. Precision was less than 13.4% in all cases, and the average recoveries were equal to 109.2 and 98.1% for NDMA and NDEA, respectively. The proposed procedure is relatively easy, rapid, and suitable for the determination of the two nitrosamines in routine analysis tests.
The Impact of Political Connections on Firm Performance: Evidence from Upstream Oil and Gas Companies
Authors: Ahmad Tarmizi Nur Farrahanie , Brahmana Rayenda Khresna
Published: November 2022

The purpose of this research is to emphasis the business and political linkages that are associated with performance. The inclusion of a politician or government official on the board is thought to vibrantly regard the firm as a resource value that leads to a competitive advantage. The engagement of these powerful figures verbally leads to the belief that a company with a resourceful person is very prominent in high-performing and high-risk industries that are economically advantageous to the country. The traditional perspective highlights that political involvement is particularly harmful for the firm due to control and ownership difficulties. The rhetorical engagement of these powerful people leads to the assumption that they are normally involved in high-performing and high-risk industries that are economically beneficial to the country. Contrary to the conventional view, some scholars suggest that political participation will continue to explore positive benefits for businesses such as profitability, subsidy, reputation, and investor trust for a long-term company strategy as proposed by the stakeholder theory and the helping hand theory. This paper examines the corporate performance in relation to political participation using 1218 datasets from Fortune Global 500 corporations, focusing mostly on oil and gas upstream activities from 2012 to 2017. All in all, our relationship hypothesis suggests that firms with strong political foundations are more likely to have an outsized impact on business performance and to be more market-sustained.
“Digging Deeper” Advocate Researchers’ Views on Advocacy and Inclusive Research
Authors: Hopkins Robert , Minogue Gerard , McGrath Joseph , Acheson Lisa Jayne , Skehan Pauline Concepta , McMahon Orla Marie , Hogan Brian
Published: November 2022

We are the Clare Inclusive Research Group (CIRG) a group of advocates with a learning disability, funded by the Irish support agency the Brothers of Charity (B.O.C.), Clare Services. As a long-established inclusive research group we were approached to reflect on our journey as advocates and researchers. In this article we talk about our work, challenging and helping repeal discriminating Irish law regarding intimate relationships. We then talk about our understanding of advocacy and inclusive research and make recommendations to make this work more effective. Method: As a group of members of CIRG, with the coordinator of the group, we developed this article using online Zoom discussion calls to identify themes, circulating online explanations of drafts followed by Zoom reflections and finally responding to academic reviews. The direct comments made by us as advocate researchers have been retained as they were expressed. Conclusions, Limitations and recommendations: One of our team remarked “advocacy and inclusive research are twins”. We concluded that they are very close but not identical. Our work together on this article led us to create a discussion paper, Manifesto for Inclusive Research. This was adopted as a touchstone for presentations at the first webinar roundtable of the newly formed Inclusive Research IASSID Special Interest Research Group in March 2022. In it we set out guidelines for creating inclusive research which require accessible information and valuing our input in terms that match our status as experts by experience in inclusive research. We challenge academic inclusive researchers who explore the world of intellectual disability to stand shoulder to shoulder with advocate inclusive researchers. Through our work together, we aim to create more fulfilling lives for us all.