bluuebunny/crossref_metadata_2025 huggingface dataset:
“Dispatcher, Can You Help Me? A Woman Is Giving Birth”. A Pilot Study of Remote Video Assistance with Smart Glasses
Authors: Aranda-García Silvia , Santos-Folgar Myriam , Fernández-Méndez Felipe , Barcala-Furelos Roberto , Pardo Ríos Manuel , Hernández Sánchez Encarna , Varela-Varela Lucía , San Román-Mata Silvia , Rodríguez-Núñez Antonio
Published: January 2023

Smart glasses (SG) could be a breakthrough in emergency situations, so the aim of this work was to assess the potential benefits of teleassistance with smart glasses (SG) from a midwife to a lifeguard in a simulated, unplanned, out-of-hospital birth (OHB). Thirty-eight lifeguards were randomized into SG and control (CG) groups. All participants were required to act in a simulated imminent childbirth with a maternal–fetal simulator (PROMPT Flex, Laerdal, Norway). The CG acted autonomously, while the SG group was video-assisted by a midwife through SG (Vuzix Blade, New York, NY, USA). The video assistance was based on the OHB protocol, speaking and receiving images on the SG. The performance time, compliance with the protocol steps, and perceived performance with the SG were evaluated. The midwife’s video assistance with SG allowed 35% of the SG participants to perform the complete OHB protocol. No CG participant was able to perform it (p = 0.005). All OHB protocol variables were significantly better in the SG group than in the CG (p < 0.05). Telemedicine through video assistance with SG is feasible so that a lifeguard with no knowledge of childbirth care can act according to the recommendations in a simulated, unplanned, uncomplicated OHB. Communication with the midwife by speaking and sending images to the SG is perceived as an important benefit to the performance.
Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging
Authors: Sun Miao , Zhuo Shenglong , Chiang Patrick Yin
Published: January 2023

LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this work, a probabilistic estimation-based super-resolution neural network for SPAD imaging that firstly uses temporal multi-scale histograms as inputs is proposed. To reduce the area and cost of on-chip histogram computation, only part of the histogram hardware for calculating the reflected photons is implemented on a chip. On account of the distribution rule of returned photons, a probabilistic encoder as a part of the network is first proposed to solve the depth estimation problem of SPADs. By jointly using this neural network with a super-resolution network, 16× up-sampling depth estimation is realized using 32 × 32 multi-scale histogram outputs. Finally, the effectiveness of this neural network was verified in the laboratory with a 32 × 32 SPAD sensor system.
Simultaneous Voltammetric Determination of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Using a Modified Carbon Paste Electrode and Chemometrics
Authors: Aguilar-Lira Guadalupe Yoselin , López-Barriguete Jesús Eduardo , Hernandez Prisciliano , Álvarez-Romero Giaan Arturo , Gutiérrez Juan Manuel
Published: January 2023

This work presents the simultaneous quantification of four non-steroidal anti-inflammatory drugs (NSAIDs), paracetamol, diclofenac, naproxen, and aspirin, in mixture solutions, by a laboratory-made working electrode based on carbon paste modified with multi-wall carbon nanotubes (MWCNT-CPE) and Differential Pulse Voltammetry (DPV). Preliminary electrochemical analysis was performed using cyclic voltammetry, and the sensor morphology was studied by scanning electronic microscopy and electrochemical impedance spectroscopy. The sample set ranging from 0.5 to 80 µmol L−1 was prepared using a complete factorial design (34) and considering some interferent species such as ascorbic acid, glucose, and sodium dodecyl sulfate to build the response model and an external randomly subset of samples within the experimental domain. A data compression strategy based on discrete wavelet transform was applied to handle voltammograms’ complexity and high dimensionality. Afterward, Partial Least Square Regression (PLS) and Artificial Neural Networks (ANN) predicted the drug concentrations in the mixtures. PLS-adjusted models (n = 12) successfully predicted the concentration of paracetamol and diclofenac, achieving correlation values of R ≥ 0.9 (testing set). Meanwhile, the ANN model (four layers) obtained good prediction results, exhibiting R ≥ 0.968 for the four analyzed drugs (testing stage). Thus, an MWCNT-CPE electrode can be successfully used as a potential sensor for voltammetric determination and NSAID analysis.
A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw
Authors: Wang Ye , Shan Gongbing , Li Hua , Wang Lin
Published: January 2023

Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.
Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment
Authors: Han Lintao , Lv Hengyi , Zhao Yuchen , Liu Hailong , Bi Guoling , Yin Zhiyong , Fang Yuqiang
Published: January 2023

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.
BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability
Authors: Kim Gyuwon , Hwang Donghyun
Published: January 2023

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75–150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.
Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers
Authors: Chang Ray-I , Tsai Chih-Yung , Chung Pu
Published: January 2023

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19 epidemic, brick-and-mortar businesses have been actively developing online channels where precision marketing has become the focus. This study proposed using the electrocardiography (ECG) recorded by wearable devices (e.g., smartwatches) to judge purchase intentions through deep learning. The method of this study included a long short-term memory (LSTM) model supplemented by collective decisions. The experiment was divided into two stages. The first stage aimed to find the regularity of the ECG and verify the research by repeated measurement of a small number of subjects. A total of 201 ECGs were collected for deep learning, and the results showed that the accuracy rate of predicting purchase intention was 75.5%. Then, incremental learning was adopted to carry out the second stage of the experiment. In addition to adding subjects, it also filtered five different frequency ranges. This study employed the data augmentation method and used 480 ECGs for training, and the final accuracy rate reached 82.1%. This study could encourage online marketers to cooperate with health management companies with cross-domain big data analysis to further improve the accuracy of precision marketing.
A Rapid Label-Free Disposable Electrochemical Salivary Point-of-Care Sensor for SARS-CoV-2 Detection and Quantification
Authors: Farsaeivahid Nadia , Grenier Christian , Nazarian Sheyda , Wang Ming L.
Published: January 2023

The coronavirus disease 2019 (COVID-19) pandemic has created an urgent need for accurate early diagnosis and monitoring. A label-free rapid electrochemical point-of-care (POC) biosensor for SARS-CoV-2 detection in human saliva is reported here to help address the shortcomings of traditional nucleic acid amplification methods and give a quantitative assessment of the viral load to track infection status anywhere, using disposable electrochemical sensor chips. A new chemical construct of gold nanoparticles (GNp) and thionine (Th) are immobilized on carboxylic acid functionalized carbon nanotubes (SWCNT-COOH) for high-performance biosensing. The sensor uses saliva with a one-step pretreatment and simple testing procedure as an analytical medium due to the user-friendly and non-invasive nature of its procurement from patients. The sensor has a response time of 5 min with a limit of detection (LOD) reaching 200 and 500 pM for the freely suspended spike (S) protein in phosphate buffer saline (PBS) and human saliva, respectively. The sensor’s performance was also proven for detecting a COVID-19 pseudovirus in an electrolyte solution with a LOD of 106 copies/mL. The results demonstrate that the optimized POC sensor developed in this work is a promising device for the label-free electrochemical biosensing detection of SARS-CoV-2 and different species of viruses.
Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring
Authors: Pham Quang-Quang , Ta Quoc-Bao , Kim Jeong-Tae
Published: January 2023

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.
Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul
Authors: Cho Minwoo , Kim Kitae , Cho Soohyun , Cho Seung-Mo , Chung Woojin
Published: January 2023

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.
A Bedside Method for Measuring Effects of a Sedative Drug on Cerebral Function in Newborn Infants
Authors: Nilsson Sofie , Tokariev Anton , Metsäranta Marjo , Norman Elisabeth , Vanhatalo Sampsa
Published: January 2023

Background: Data on the cerebral effects of analgesic and sedative drugs are needed for the development of safe and effective treatments during neonatal intensive care. Electroencephalography (EEG) is an objective, but interpreter-dependent method for monitoring cortical activity. Quantitative computerized analyses might reveal EEG changes otherwise not detectable. Methods: EEG registrations were retrospectively collected from 21 infants (mean 38.7 gestational weeks; range 27–42) who received dexmedetomidine during neonatal care. The registrations were transformed into computational features and analyzed visually, and with two computational measures quantifying relative and absolute changes in power (range EEG; rEEG) and cortico-cortical synchrony (activation synchrony index; ASI), respectively. Results: The visual assessment did not reveal any drug effects. In rEEG analyses, a negative correlation was found between the baseline and the referential frontal (rho = 0.612, p = 0.006) and parietal (rho = −0.489, p = 0.035) derivations. The change in ASI was negatively correlated to baseline values in the interhemispheric (rho = −0.753; p = 0.001) and frontal comparisons (rho = −0.496; p = 0.038). Conclusion: Cerebral effects of dexmedetomidine as determined by EEG in newborn infants are related to cortical activity prior to DEX administration, indicating that higher brain activity levels (higher rEEG) during baseline links to a more pronounced reduction by DEX. The computational measurements indicate drug effects on both overall cortical activity and cortico-cortical communication. These effects were not evident in visual analysis.
Multi-Scale Fusion Localization Based on Magnetic Trajectory Sequence
Authors: Jin Zhan , Kang Ruiqing , Su Hailu
Published: January 2023

Magnetic fingerprint has a multitude of advantages in the application of indoor positioning, but as a weak magnetic field, the dynamic range of the data is limited, which exerts direct influence on the positioning accuracy. Aiming at resolving the problem wherein the indoor magnetic positioning results tremendously rest with the magnetic characteristics, this paper puts forward a method based on deep learning to fuse the temporal and spatial characteristics of magnetic fingerprints, to fully explore the magnetic characteristics and to obtain stable and trustworthy positioning results. First and foremost, the trajectory of the acquisition area is extracted by adopting the ameliorated random waypoint model, and the simulation of pedestrian trajectory is completed. Then, the magnetic sequence is obtained by mapping the magnetic data. Aside from that, considering the scale characteristics of the sequence, a scale transformation unit is designed to obtain multi-scale features. At length, the neural network self-attention mechanism is adopted to fuse multiple features and output the positioning results. By probing into the positioning results of dissimilar indoor scenes, this method can adapt to diverse scenes. The average positioning error in a corridor, open area and complex area reaches 0.65 m, 0.93 m and 1.38 m respectively. The addition of multi-scale features has certain reference value for ameliorating the positioning performance.
The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice
Authors: Cavalli Rosa Maria
Published: January 2023

Following the success of the first hyperspectral sensor, the evaluation of hyperspectral image capability became a challenge in research, which mainly focused on improving image pre-processing and processing steps to minimize their errors, whereas in this study, the focus was on the weight of hyperspectral sensor characteristics on image capability in order to distinguish this effect from errors caused by image pre-processing and processing steps and improve our knowledge of errors. For these purposes, two satellite hyperspectral sensors with similar spatial and spectral characteristics (Hyperion and PRISMA) were compared with corresponding synthetic images, and the city of Venice was selected as the study area. After creating the synthetic images, the errors in the simulation of Hyperion and PRISMA images were evaluated (1.6 and 1.1%, respectively). The same spectral unmixing procedure was performed using real and synthetic images, and their accuracies were compared. The spectral accuracies in root mean square error were equal to 0.017 and 0.016, respectively. In addition, 72.3 and 77.4% of these values were related to sensor characteristics. The spatial accuracies in the mean absolute error were equal to 3.93 and 3.68, respectively. A total of 55.6 and 59.0% of these values were related to sensor characteristics, and 22.6 and 22.3% were related to co-localization and spatial resampling errors. The difference between the radiometric precision values of the sensors was 6.81 and 5.91% regarding the spectral and spatial accuracies of Hyperion image. In conclusion, the results of this study showed that the combined use of two or more real hyperspectral images with similar characteristics and their synthetic images quantifies the weight of hyperspectral sensor characteristics on their image capability and improves our knowledge regarding processing errors, and thus image capability.
Robust Adaptive Control Strategy for a Bidirectional DC-DC Converter Based on Extremum Seeking and Sliding Mode Control
Authors: Trinh Hoai-An , Nguyen Duc Giap , Phan Van-Du , Duong Tan-Quoc , Truong Hoai-Vu-Anh , Choi Sung-Jin , Ahn Kyoung Kwan
Published: January 2023

This paper presents a new control strategy that combines classical control and an optimization scheme to regulate the output voltage of the bidirectional converter under the presence of matched and mismatched disturbances. In detail, a control-oriented modeling method is presented first to capture the system dynamics in a common canonical form, allowing different disturbances to be considered. To estimate and compensate for unknown disturbances, an extended state observer (ESO)-based continuous sliding mode control is then proposed, which can guarantee high tracking precision, fast disturbance rejection, and chattering reduction. Next, an extremum seeking (ES)-based adaptive scheme is introduced to ensure system robustness as well as optimal control effort under different working scenarios. Finally, comparative simulations with classical proportional-integral-derivative (PID) control and constant switching gains are conducted to verify the effectiveness of the proposed adaptive control methodology through three case studies of load resistance variations, buck/boost mode switching, and input voltage variation.
Graphene Oxide Thin Films for Detection and Quantification of Industrially Relevant Alcohols and Acetic Acid
Authors: Moura Pedro Catalão , Pivetta Thais Priscilla , Vassilenko Valentina , Ribeiro Paulo António , Raposo Maria
Published: January 2023

Industrial environments are frequently composed of potentially toxic and hazardous compounds. Volatile organic compounds (VOCs) are one of the most concerning categories of analytes commonly existent in the indoor air of factories’ facilities. The sources of VOCs in the industrial context are abundant and a vast range of human health conditions and pathologies are known to be caused by both short- and long-term exposures. Hence, accurate and rapid detection, identification, and quantification of VOCs in industrial environments are mandatory issues. This work demonstrates that graphene oxide (GO) thin films can be used to distinguish acetic acid, ethanol, isopropanol, and methanol, major analytes for the field of industrial air quality, using the electronic nose concept based on impedance spectra measurements. The data were treated by principal component analysis. The sensor consists of polyethyleneimine (PEI) and GO layer-by-layer films deposited on ceramic supports coated with gold interdigitated electrodes. The electrical characterization of this sensor in the presence of the VOCs allows the identification of acetic acid in the concentration range from 24 to 120 ppm, and of ethanol, isopropanol, and methanol in a concentration range from 18 to 90 ppm, respectively. Moreover, the results allows the quantification of acetic acid, ethanol, and isopropanol concentrations with sensitivity values of (3.03±0.12)∗104, (−1.15±0.19)∗104, and (−1.1±0.50)∗104 mL−1, respectively. The resolution of this sensor to detect the different analytes is lower than 0.04 ppm, which means it is an interesting sensor for use as an electronic nose for the detection of VOCs.
Analysis of Interpolation Methods in the Validation of Backscattering Coefficient Products
Authors: Jiao Yanan , Zhang Fengli , Huang Qiqi , Liu Xiaochen , Li Lu
Published: January 2023

Validation is the basis of synthetic aperture radar (SAR) image quantification applications. Based on the point target of the field site, the radiation characteristics of the backscattering coefficient image can be used to optimize the SAR imaging, and the product production system can be more closely targeted, to ensure the image product accuracy in the actual quantification application. In this study, the validation of the backscattering coefficient image was examined using calibrators, and the radiometric properties of the image were evaluated by extracting the radar cross-section of each point target. Bilinear interpolation and fast Fourier transform (FFT) interpolation methods were introduced for the local area interpolation of point targets, and the two methods were compared from the perspective of response function imaging and validation accuracy. The results show that the FFT interpolation method is more favorable for validating the backscattering coefficient.
A Width Measurement Method of Line Shape Based on Second Harmonic Peak and Modulation Amplitude
Authors: Lin Shan , Chang Jun , Sun Jiachen , Wang Zihan , Mao Minghui
Published: January 2023

The line width of different line shapes is a very important parameter in absorption spectroscopy sensing techniques. Based on the high sensitivity and low noise properties of wavelength modulation spectroscopy, we report a novel line width measurement method. After theoretically proving the relationship between line width, modulation amplitude and the amplitude of the second harmonic at the center frequency, the absorption lines of CH4 near 6046.96 cm−1 and CO2 4989.97 cm−1 were chosen for simulation, and the relative errors of the line width between our method and theoretical data were kept at about 1%. A distributed feedback laser diode operating near 1653 nm with three different concentrations of CH4 was used for experimental validation, and the results were consistent with the numerical simulation. Additionally, since only the peaks of second harmonic need to be measured, the advantages of wavelength modulation can be utilized while reducing the difficulty of data acquisition.
Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service
Authors: Harach Tomas , Simonik Petr , Vrtkova Adela , Mrovec Tomas , Klein Tomas , Ligori Joy Jason , Koreny Martin
Published: January 2023

This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level.
Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets
Authors: Abdallah Mustafa , Joung Byung-Gun , Lee Wo Jae , Mousoulis Charilaos , Raghunathan Nithin , Shakouri Ali , Sutherland John W. , Bagchi Saurabh
Published: January 2023

Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.
A Hybrid Software and Hardware SDN Simulation Testbed
Authors: Buzura Sorin , Peculea Adrian , Iancu Bogdan , Cebuc Emil , Dadarlat Vasile , Kovacs Rudolf
Published: January 2023

In recent years, the software-defined networking (SDN) paradigm has been deployed in various types of networks, including wireless sensor networks (WSN), wide area networks (WAN) and data centers. Given the wide range of SDN domain applicability and the large-scale environments where the paradigm is being deployed, creating a full real test environment is a complex and costly task. To address these problems, software-based simulations are employed to validate the proposed solutions before they are deployed in real networks. However, simulations are constrained by relying on replicating previously saved logs and datasets and do not use real time hardware data. The current article addresses this limitation by creating a novel hybrid software and hardware SDN simulation testbed where data from real hardware sensors are directly used in a Mininet emulated network. The article conceptualizes a new approach for expanding Mininet’s capabilities and provides implementation details on how to perform simulations in different contexts (network scalability, parallel computations and portability). To validate the design proposals and highlight the benefits of the proposed hybrid testbed solution, specific scenarios are provided for each design idea. Furthermore, using the proposed hybrid testbed, new datasets can be easily generated for specific scenarios and replicated in more complex research.
Analogical Assessment of Train-Induced Vibration and Radiated Noise in a Proposed Theater
Authors: Liu Xiangming , Xiao Yuchun , Jiang Huihuang , Guo Yunlong , Yu Mengwen , Tan Wanzhong
Published: January 2023

This study presents the analogical assessment of the train-induced vibration and radiated noise in a proposed theater. The theater is to be constructed in a region with crowded metro lines, and the assessment is implemented in an analogical building with comparable structural type and metro condition. Prior to the assessment, the comparability of the analogical building with the theater is validated using the train-induced ground vibration. With the same horizontal distance from the metro line, the train-induced vibration level in the analogical building is 9 dB higher than that in the construction site of the theater. Such results indicate that the lack of soil layers may lead to a dramatic increase in train-induced vibration in the building. In the staircase of the analogical building, the train-induced radiated noise reached 55 dB (A), which is 10 dB (A) higher than the daytime allowable level. As the most important indicator, the noise rating number in the cinema of the analogical building is NR-43, which put forward an enormous challenge on the construction of the theater with a denoise demand of 23 dB. The analogical method applied in this study provides an effective and practical way for the assessment of train-induced vibration and radiated noise in proposed vibration-sensitive buildings. The assessment results that provide necessary reference and support for the anti-vibration design will help guarantee the stage effect of the theater.
Thin Layer Chromatographic Method for Detection of Conventional Drug Adulterants in Herbal Products
Authors: Mwankuna Christopher Johnson , Mariki Eliapenda Elisante , Mabiki Faith Philemon , Malebo Hamisi Masanja , Styrishave Bjarne , Mdegela Robinson Hammerton
Published: January 2023

Commercially available conventional drugs have been used to adulterate herbal products. Considering the rapid growth of herbal products’ market, it is essential to screen herbal products for the presence of conventional drugs. Simple analytical methods are needed for the rapid screening of conventional drugs that are likely to be adulterated in herbal products. Thin layer chromatography (TLC) methods for screening twelve conventional drugs in herbal products have been developed and applied. The analytes were extracted from herbal products using acetonitrile:methanol:acetic acid:water (4:4:1:1, v/v). Solvent mixture of dichloromethane:ethyl acetate:methanol (75:15:10, v/v) separated well trimethoprim, sildenafil, paracetamol, and sulfamethoxazole while pyrimethamine, metronidazole, and sulfadoxine were well separated by dichloromethane:ethyl acetate:methanol (77.5:12.5:10, v/v). In addition, acetyl salicylic acid, ibuprofen, diclofenac, quinine, and lumefantrine were well separated by ethyl acetate:methanol:30% ammonia (75:22.5:2.5, v/v). Chromatographic separations were found to be highly reproducible, and more than 10 samples can be analysed in one run. The method was applied in the screening of 229 herbal products. Consequently, 24.0% of the samples contained one adulterant, while 21.4% contained at least two adulterants. All conventional drugs detected in herbal products were not mentioned on the labels and therefore the consumers are kept unaware of their side effects and health problems. Further studies for confirming and quantitatively determining the adulterants in a wide range of products as well as a systematic toxicological analysis of the adulterants in herbal products are recommended.
Lithium Extraction from Salt Lake Brine with High Mass Ratio of Mg/Li Using TBP-DIBK Extraction System
Authors: Li Huifang , Li Lijuan , Li Wu
Published: January 2023

Lithium extraction from salt lake brine with a high mass ratio of Mg/Li by counter-current method using TBP-DIBK extraction system was investigated in this study. In the extraction process, TBP and DIBK acted as extractant, kerosene was used as diluent, and FeCl3 was used as co-extractant. The operation conditions of extraction, scrubbing, stripping, and regeneration were investigated. In this study, 5.5 mol/L LiCl + 0.5 mol/L HCl was used as a scrubbing solution for purifying products, and 6 mol/L HCl solution was used as a stripping agent to transfer Li+ from organic phase to aqueous phase. Furthermore, 2 mol/L NaOH and saturated MgCl2 were used as the regeneration agent to remove H+ from organic phase. The counter-current extraction experiments of 3-1-1-3 stages were carried out. After 10 cycles, the lithium extraction efficiency reached 99.90%; the separation factor of Li/Mg, Li/K, Li/Na, and Li/Ca was 5.215 × 104, 2.159 × 103, 3.659 × 102, and 3.104 × 102, respectively. The results indicated that the TBP-DIBK extraction system was effective at extracting lithium from salt lake brine with a high mass ratio of Mg/Li. Furthermore, the study of extraction thermodynamics suggested that lithium extraction was an exothermic process and that lower temperature promoted lithium extraction.
Volunteering: A Tool for Social Inclusion and Promoting the Well-Being of Refugees? A Qualitative Study
Authors: Sveen Silje , Anthun Kirsti Sarheim , Batt-Rawden Kari Bjerke , Tingvold Laila
Published: January 2023

Background: The Norwegian government’s increased expectations that volunteering can be used as a means of integration and the scarce research regarding refugees’ experiences with volunteering is taken as the background for this study. Our purpose is to adopt a salutogenic perspective to investigate whether and how formal volunteering contributes to developing a sense of social inclusion and well-being among refugees in Norway. Methods: Qualitative in-depth interviews were conducted with 12 volunteers with refugee backgrounds in a semi-rural district in Norway. Stepwise deductive induction was used for analysis. Results: Three themes were identified as a result of the analysis: (1) feeling safer due to increased knowledge regarding cultures, values, and systems and achieving mutual acceptance; (2) feeling more confident when communicating in Norwegian and contributing to society, and (3) feeling more connected via social relations. Conclusions: Our study indicates that participation in volunteering may contribute to social inclusion and that the participants’ resources and volunteering experiences may have a health-promotive impact under certain conditions.
Youth’s Poverty and Inequality of Opportunities: Empirical Evidence from Morocco
Authors: Yassine Abderrahman , Bakass Fatima
Published: January 2023

Youth is an important and critical transition stage towards adulthood, during which time individuals are supposed to prepare in the best possible conditions for adulthood. Moroccan youth are facing unequal opportunities to develop because of the circumstances of their household background and childhood deprivation. This paper measures the level of poverty and the equality of opportunities among Moroccan youth aged 18–29 years utilizing the Human Opportunity Index (HOI). It analyzes poverty from a multidimensional perspective. The findings demonstrate that younger youth are at substantially higher risk of poverty than older young. Poor youth have low educational attainments. The unemployment rate for the non-poor youth is lower than for the poor. Unemployment rates continue to be high for secondary and university graduates, particularly for the poor. Furthermore, the analysis reveals that education deprivation followed by healthcare deprivation is the most prevalent severe deprivation among youth. The results from both the logit regression and the descriptive analyses show that youths of illiterate parents are more likely to have poorer health, drop out of school themselves, and work rather than attend school. Youth in rural areas are least likely to have the opportunity to complete secondary and university education as well as to attend school/university than those in urban areas. Moreover, the decomposition of the total difference of HOI between urban and rural areas into access and equality of opportunities indicates that the differences are mainly due to the coverage effect.
Intersections of Women as Survivors: Disclosures of Violence and Global Research Standards in Guyana and Trinidad and Tobago
Authors: Rodney Ruth , Bobbili Sireesha , Hosein Gabrielle , Cummings Emmanuelle
Published: January 2023

Global guidelines on VAWG research prioritize safety-first approaches to discuss experiences of violence with survivors. Guidelines recommend that survivors only be interviewed in confidential and private interviews. However, little is known about why and how women choose to disclose experiences of violence in focus group settings. Utilizing survivor quotes and reflexive notes from the qualitative components of the first national prevalence surveys on VAWG in Guyana and Trinidad and Tobago, we reflect on women’s decisions to disclose experiences of violence in focus groups. Our results illustrate that women’s choice to speak out about previous experiences of violence and in some cases, irrespective of the guidelines provided for focus group discussions, aligns with the unapologetic nature of Caribbean feminist organizing in the region. Identifying as a survivor of gender-based violence holds different meanings for women based on when the relationship occurred and the sense of insight and empowerment they gained and therefore guides which information and where women feel comfortable disclosing. Researchers should ensure safety-first approaches are followed and also support women who choose to disclose experiences of violence outside of confidential interviews. Focus groups can be areas where women disclose violence and should be addressed through preparation rather than as an error in ethical research practices.
Apparel Consumer Behavior and Circular Economy: Towards a Decision-Tree Framework for Mindful Clothing Consumption
Authors: Patwary Sarif , Haque Md Ariful , Kharraz Jehad A. , Khanzada Noman Khalid , Farid Muhammad Usman , Kumar Nallapaneni Manoj
Published: January 2023

The apparel consumer, one of the vital stakeholders in the apparel supply chain, has a significant role to play in moving the clothing industry in a sustainable direction. From purchasing and care practice to donation and disposal, every step of their decisions impacts the environment. Various internal and external variables influence those decisions, including culture, customs, values, beliefs, norms, assumptions, economy, gender, education and others. Therefore, we believe having a scientific understanding is very important, because consumers need to be aware of what makes eco-conscious apparel behavior; only then will the circular transition be eased. However, the key concern is whether the apparel consumers are aware of this knowledge or not. Therefore, we formulated a prospective study from a life cycle thinking point of view with a key focus on synthesizing apparel consumer behavior concerning clothing acquisition, maintenance and disposal through the circular economy lens. Hence, a circular economy lens framework is proposed, followed by three research questions’ (RQ) formulation: RQ1. What is the current norm of clothing acquisition, maintenance and disposal behavior?; RQ2. Is apparel consumer clothing acquisition, maintenance and disposal behavior circular-driven?; RQ3. What is the sustainable way of clothing acquisition, maintenance and disposal? These questions are followed by circular economy lens framework development for apparel consumers. Second, following the research questions, state-of-the-art literature-driven decisions were gathered to form constructive consumer-centric decisions over the apparel lifecycle. Third, building on this synthesis, a critical discussion is offered, following the decision-tree approach to inform relevant behavioral guidelines for consumers and other stakeholders in the apparel supply chain. Overall, our findings on apparel consumer behavior through the circular economy lens could serve as new guidelines for consumers to exercise mindful clothing consumption behavior.
A Machine Learning-Based Approach to Estimate Energy Flows of the Mangrove Forest: The Case of Panama Bay
Authors: Brooks Jefferson , Rivera Ana , Chen Austin Miguel , Tejedor-Flores Nathalia
Published: January 2023

Two models were developed to simulate energy flows in a mangrove area of A. germinans and A. bicolor in the Bay of Panama, considering the importance of these areas in CO2 fixation. The first model (black box) consisted of the use of artificial neural networks for estimation, using meteorological data and energy flows calculated by the Eddy Covariance method for model training. The second model (grey box) used the RC circuit theory, considering a non-steady state model for the flow of water from the ground to the atmosphere. A methodology was developed to reduce the uncertainty of the data collected by the sensors in the field. The black box model managed to predict the fluxes of latent heat (R2 > 0.91), sensible heat (R2 > 0.86), CO2 (R2 > 0.88), and the potential of water in the air (R2 > 0.88) satisfactorily, while the grey box model generated R2 values of 0.43 and 0.37, indicating that it requires further analysis regarding the structuring of the equations and parameters used. The application of the methodology to filter the data improved the effectiveness of the model during the predictions, reducing the computational capacity necessary for the resolution of the iterations.
COVID-19 Struggle and Post-COVID-19 Recovery: Exploring the Governance, Success, and Digital Transition in Construction Projects in Serbia
Authors: Petojević Zorana , Savić Milica , Parezanović Aleksandra , Nadaždi Ana
Published: January 2023

Construction, one of the largest global economic sectors, has been severely challenged by the economic uncertainties brought on by COVID-19. Since 2020, pandemic-related disruptions and remedial measures have made its historically low performance even more difficult. As a result, recent research mainly addressed these negative consequences on the construction sector. In contrast, this paper aims to identify mitigation strategies recognised as good practices on construction projects in Serbia, in addition to detecting disruptions and quantifying their effects on cost and time overruns. A particular emphasis is given to how the pandemic hastened digital transition and encouraged the adoption of modern project management practices. The research was carried out through a survey of two rounds, conducted one year apart, to obtain an in-depth overview. The findings indicated that, although it had an impact on construction projects, the pandemic was not particularly harmful, because of widely used remedial measures and modern project management techniques. However, the pandemic did not modernise project implementation substantially nor significantly increase the use of cutting-edge digital technologies. Nevertheless, it encouraged project managers to think about introducing new approaches in project management, where digitisation is the new normal. The research findings may indicate to academia and practitioners what strategies may assure a project’s implementation even in enormously changed conditions, such as during a pandemic.