bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning
Authors: Ratnavel Rajalakshmi , Viswanath Shreya , Subramanian Jeyanthi , Selvaraj Vinoth Kumar , Prahasam Valarmathi , Siddharth Sanjay
Published: December 2022

3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry.
Effects of Different Doses, Forms, and Frequencies of Zinc Supplementation on Biomarkers of Iron and Zinc Status among Young Children in Dhaka, Bangladesh
Authors: Islam M. Munirul , Black Robert E. , Krebs Nancy F. , Westcott Jamie , Long Julie M. , Islam Kazi M. , Peerson Janet M. , Sthity Rahvia Alam , Khandaker Afsana Mim , Hasan Mehedi , El Arifeen Shams , Ahmed Tahmeed , King Janet C. , McDonald Christine M.
Published: December 2022

Young children in resource-constrained settings are susceptible to zinc deficiency and its deleterious health effects. The objective of this secondary analysis was to evaluate the effects of the following six interventions on biomarkers of iron and zinc status among a subgroup of young children in Dhaka, Bangladesh, who participated in the Zinc in Powders Trial (ZiPT): (1) standard micronutrient powders (MNPs) containing 4.1 mg zinc and 10 mg iron, daily; (2) high-zinc (10 mg) and low-iron (6 mg) (HiZn LoFe) MNP, daily; (3) HiZn (10 mg) and LoFe (6 mg)/HiZn (10 mg) and no-iron MNPs on alternating days; (4) dispersible zinc tablet (10 mg), daily; (5) dispersible zinc tablet (10 mg), daily for 2 weeks at enrollment and at 12 weeks; (6) placebo powder, daily. At the end of the 24 week intervention period, children in the daily dispersible zinc tablet group exhibited a mean serum zinc concentration (SZC) of 92.5 μg/dL, which was significantly higher than all other groups except the HiZn LoFe MNP alternating group (81.3 μg/dL). MNPs containing 10 mg and 6 mg of iron had a similar impact on biomarkers of iron status, with no evidence of an adverse interaction with zinc.
Thymoquinone: Hydroxypropyl-β-cyclodextrin Loaded Bacterial Cellulose for the Management of Wounds
Authors: Swingler Sam , Gupta Abhishek , Gibson Hazel , Kowalczuk Marek , Adamus Grazyna , Heaselgrave Wayne , Radecka Iza
Published: December 2022

The need for more advantageous and pharmaceutically active wound dressings is a pressing matter in the area of wound management. In this study, we explore the possibility of incorporating thymoquinone within bacterial cellulose, utilising cyclodextrins as a novel method of solubilising hydrophobic compounds. The thymoquinone was not soluble in water, so was incorporated within hydroxypropyl-β-cyclodextrin before use. Thymoquinone: hydroxypropyl-β-cyclodextrin inclusion complex produced was found to be soluble in water up to 7% (w/v) and was stable with no crystal formation for at least 7 days with the ability to be loaded within the bacterial cellulose matrix. The inclusion complex was found to be thermally stable up to 280 °C which is far greater than the production temperature of 80 °C and was stable in phosphate-buffered saline and extraction solvents in permeation and dose experiments. The adhesion properties of the Thymoquinone: hydroxypropyl-β-cyclodextrin loaded bacterial cellulose dressings were tested and found to be 2.09 N. Permeation studies on skin mimicking membrane Strat-M showed a total permeated amount (0–24 h) of 538.8 µg cm−2 and average flux after a 2 h lag of 22.4 µg h−1 cm−2. To the best of our knowledge, the methods outlined in this study are the first instance of loading bacterial cellulose with thymoquinone inclusion complex with the aim of producing a pharmaceutically active wound dressing.
Engineering of a Fully Human Anti-MUC-16 Antibody and Evaluation as a PET Imaging Agent
Authors: Babeker Hanan , Ketchemen Jessica Pougoue , Annan Sudarsan Arunkumar , Andrahennadi Samitha , Tikum Anjong Florence , Nambisan Anand Krishnan , Fonge Humphrey , Uppalapati Maruti
Published: December 2022

Antibodies that recognize cancer biomarkers, such as MUC16, can be used as vehicles to deliver contrast agents (imaging) or cytotoxic payloads (therapy) to the site of tumors. MUC16 is overexpressed in 80% of epithelial ovarian cancer (EOC) and 65% of pancreatic ductal adenocarcinomas (PDAC), where effective ‘theranostic’ probes are much needed. This work aims to develop fully human antibodies against MUC16 and evaluate them as potential immuno-PET imaging probes for detecting ovarian and pancreatic cancers. We developed a fully human monoclonal antibody, M16Ab, against MUC16 using phage display. M16Ab was conjugated with p-SCN-Bn-DFO and radiolabeled with 89Zr. 89Zr-DFO-M16Ab was then evaluated for binding specificity and affinity using flow cytometry. In vivo evaluation of 89Zr-DFO-M16Ab was performed by microPET/CT imaging at different time points at 24–120 h post injection (p.i.) and ex vivo biodistribution studies in mice bearing MUC16-expressing OVCAR3, SKOV3 (ovarian) and SW1990 (pancreatic) xenografts. 89Zr-DFO-M16Ab bound specifically to MUC16-expressing cancer cells with an EC50 of 10nM. 89Zr-DFO-M16Ab was stable in serum and showed specific uptake and retention in tumor xenografts even after 120 h p.i. (microPET/CT) with tumor-to-blood ratios > 43 for the SW1990 xenograft. Specific tumor uptake was observed for SW1990/OVCAR3 xenografts but not in MUC16-negative SKOV3 xenografts. Pharmacokinetic study shows a relatively short distribution (t1/2α) and elimination half-life (t1/2ß) of 4.4 h and 99 h, respectively. In summary, 89Zr-DFO-M16Ab is an effective non-invasive imaging probe for ovarian and pancreatic cancers and shows promise for further development of theranostic radiopharmaceuticals.
Nano and Microemulsions for the Treatment of Depressive and Anxiety Disorders: An Efficient Approach to Improve Solubility, Brain Bioavailability and Therapeutic Efficacy
Authors: Pires Patrícia C. , Paiva-Santos Ana Cláudia , Veiga Francisco
Published: December 2022

Most drugs used for the treatment of depression, anxiety and related disorders have low absorption, high metabolism, low brain targeting and/or low water solubility, which can make it hard to formulate them at high strength and can also lead to decreased bioavailability. Incorporating these drugs into nanometric emulsions can solve these issues. Hence, the aim of the present review was to assess the potential of nano and micro emulsions for the delivery of antidepressant and anxiolytic drugs. The results from several studies showed that nanometric emulsions were able to increase drug strength up to 20,270-fold (compared to aqueous solubility). Moreover, in general, the formulations showed droplet size, polydispersity index, zeta potential, viscosity, osmolality, pH, in vitro drug release and ex vivo drug permeation as adequate for the intended effect and administration route. In vivo animal pharmacokinetic experiments showed that nanometric emulsions improved systemic drug bioavailability and/or brain targeting, and in vivo pharmacodynamic studies showed that they had antidepressant and/or anxiolytic effects, also being apparently safe. Hence, the current review provides proof of the potential of nano and microemulsions for improving solubilization and increasing the overall bioavailability of antidepressant and/or anxiolytic drugs, providing evidence of a possible useful strategy for future therapies.
Study of Thermal Effect on the Mechanical Properties of Nylon 610 Nanocomposites with Graphite Flakes That Have Undergone Supercritical Water Treatment at Different Temperatures
Authors: Lim Jun-Ven , Bee Soo-Tueen , Sin Lee Tin , Ratnam Chantara Thevy , Bee Soo-Ling
Published: December 2022

This study investigates the thermal effect of supercritical water treatment at different temperatures (150, 175, 200 °C) and semi-vacuum state (−0.08 MPa) on graphite flakes which are then incorporated into nylon 610. The treatment is deemed to increase the surface activity of nanofillers through the formation of oxygen-containing functional groups. X-ray diffraction (XRD) analysis indicated that the crystal structure of the flakes remained similar before and after supercritical water treatment. Fourier transform infrared spectroscopy (FTIR) also showed the presence of hydrogen bonding between the flakes and the polymer matrix through the appearance of amide bands. The intensity of the amide peaks is higher for nanocomposites with treated flakes than untreated ones. Furthermore, scanning electron microscopy (SEM) showed that at higher wt%, aggregation will occur, which leads to a weakening in physical properties. The tensile strength of nanocomposites with treated flakes decreased with increasing wt%, while those with untreated flakes increased with increasing wt%. Young’s modulus of all the nanocomposites generally increased with increasing wt%. The highest tensile strength obtained is 967.02 kPa, while that of neat nylon 610 is 492.09 kPa. This enhancement in mechanical properties can be attributed to the intact structure of the graphite flakes and the interaction between the flakes and the nylon 610 matrix. A higher temperature of water treatment was discovered to cause higher oxidation levels on surface of the nanofillers but would result in some structural damage. The optimum nylon 610 nanocomposite synthesized was the one that was incorporated with 1.5 wt% graphite flakes treated at 150 °C and −0.08 MPa, as it has the highest tensile strength.
Effect of Wax Additives and Silanization of Diatom Surfaces on Thermomechanical Properties of Polylactide Composites
Authors: Dobrosielska Marta , Dobrucka Renata , Pajewska-Szmyt Martyna , Kozera Paulina , Gabriel Ewa , Głowacka Julia , Brząkalski Dariusz , Kurzydłowski Krzysztof J. , Przekop Robert E.
Published: December 2022

In the present study, tests were conducted on high-filled composite samples on a polylactide matrix, modified with diatomaceous earth, three types of silanes, and natural and synthetic wax. The obtained samples were characterized in terms of the effect of modifications on mechanical properties (tensile strength, flexural strength, and impact resistance) or processing properties, e.g., melt flow rate (MFR). The study showed that the modification had a favorable effect on the processing properties of the composites, associated with up to an eight-fold increase in flow rate index compared with the reference sample, especially for samples treated with methyltrimethoxysilane (MTMOS), and up to a ten-fold increase under low shear-rate flow conditions. The effect of the addition of waxes of different origins (synthetic and natural) was also determined, and it was shown that beeswax tended to reduce the flow rate of the composites regardless of the silane used. The addition of synthetic wax to composites increased the tendency to agglomerate diatomaceous earth, while natural wax had a positive effect on filler dispersion.
TMDiMP: Temporal Memory Guided Discriminative Tracker for UAV Object Tracking
Authors: Yang Zheng , Han Bing , Chen Weiming , Gao Xinbo
Published: December 2022

Unmanned aerial vehicles (UAVs) have attracted increasing attention in recent years because of their broad range of applications in city security, military reconnaissance, disaster rescue, and so on. As one of the critical algorithms in the field of artificial intelligence, object tracking greatly improves the working efficiency of UAVs. However, unmanned aerial vehicle (UAV) object tracking still faces many challenges. UAV objects provide limited textures and contours for feature extraction due to their small sizes. Moreover, to capture objects continuously, a UAV camera must constantly move with the object. The above two reasons are usual causes of object-tracking failures. To this end, we propose an end-to-end discriminative tracker called TMDiMP. Inspired by the self-attention mechanism in Transformer, a novel memory-aware attention mechanism is embedded into TMDiMP, which can generate discriminative features of small objects and overcome the object-forgetting problem after camera motion. We also build a UAV object-tracking dataset with various object categories and attributes, named VIPUOTB, which consists of many video sequences collected in urban scenes. Our VIPUOTB is different from other existing datasets in terms of object size, camera motion speed, location distribution, etc. TMDiMP achieves competitive results on our VIPUOTB dataset and three public datasets, UAVDT, UAV123, and VisDrone, compared with state-of-the-art methods, thus demonstrating the effectiveness and robustness of our proposed algorithm.
JPSS-2 VIIRS Pre-Launch Reflective Solar Band Testing and Performance
Authors: Moyer David , Angal Amit , Ji Qiang , McIntire Jeff , Xiong Xiaoxiong
Published: December 2022

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board the Suomi National Polar-orbiting Partnership (S-NPP) and Joint Polar Satellite System (JPSS) spacecrafts 1 and 2 provides calibrated sensor data record (SDR) reflectance, radiance, and brightness temperatures for use in environment data record (EDR) products. The SDRs and EDRs are used in weather forecasting models, weather imagery and climate applications such as ocean color, sea surface temperature and active fires. The VIIRS has 22 bands covering a spectral range 0.4–12.4 µm with resolutions of 375 m and 750 m for imaging and moderate bands respectively on four focal planes. The bands are stratified into three different types based on the source of energy sensed by the bands. The reflective solar bands (RSBs) detect sunlight reflected from the Earth, thermal emissive bands (TEBs) sense emitted energy from the Earth and the day/night band (DNB) detects both solar and lunar reflected energy from the Earth. The SDR calibration uses a combination of pre-launch testing and the solar diffuser (SD), on-board calibrator blackbody (OBCBB) and space view (SV) on-orbit calibrator sources. The pre-launch testing transfers the National Institute of Standards and Technology (NIST) traceable calibration to the SD, for the RSB, and the OBCBB, for the TEB. Post-launch, the on-board calibrators track the changes in instrument response and adjust the SDR product as necessary to maintain the calibration. This paper will discuss the pre-launch radiometric calibration portion of the SDR calibration for the RSBs that includes the dynamic range, detector noise, calibration coefficients and radiometric uncertainties for JPSS-2 VIIRS.
Mobile Airborne Lidar for Remote Methane Monitoring: Design, Simulation of Atmospheric Measurements and First Flight Tests
Authors: Yakovlev Semyon V. , Sadovnikov Sergey A. , Romanovskii Oleg A.
Published: December 2022

The results of modernization of a mobile lidar for the airborne monitoring of the methane content in the atmosphere are presented. The modernization was carried out on the basis of in situ tests, several engineering solutions, and preliminary numerical simulations. The in situ tests showed a possibility of sounding background tropospheric methane concentrations along a 500 m surface path. During the modernization, the airborne lidar for methane monitoring was supplemented with an off-axis mirror collimator, which made it possible to reduce the divergence of laser radiation by a factor of 4. The overlapping function was simulated for a biaxial scheme of the mobile lidar with radii of the light-sensitive zone of the receiving optics of 0.1, 0.3, 0.5, 0.8 and 1 mm. The dimensions of the light-sensitive zone were found to provide complete coverage of the field of view of the telescope and a laser beam; the length of the “dead” zone was estimated when a laser beam propagated parallel to the optical axis of the telescope. Airborne methane monitoring in the atmosphere in the informative wavelength range (2916.55–2917 cm−1 on-line and 2915.00 cm−1 off-line) was numerically simulated for midlatitude and Arctic summer. Thus, on the basis of the work carried out, the design of the mobile airborne lidar is substantiated, which is to operate as a part of the Tu-134 “Optik” aircraft laboratory of IAO SB RAS and to perform methane monitoring vertically downwards. The airborne lidar was tested during test flights and the Arctic expedition in 2022. The first experimental results of lidar measurements of the averaged methane concentration vertically downwards from sounding altitudes of 2000–3000, 380, and 270 m were obtained for mid-latitude summer and Arctic summer.
Ground Subsidence Monitoring in a Mining Area Based on Mountainous Time Function and EnKF Methods Using GPS Data
Authors: Zhang Shifang , Zhang Jin
Published: December 2022

Ground subsidence is an important geomorphological phenomenon in mining areas. It is difficult to monitor and predict ground subsidence with high precision, especially in mountainous mining areas. Taking the mining workface of a mountainous coalfield in Taiyuan City, in the Shanxi Province of China as an example, this research selects five typical points from GPS observation data along the strike section. Based on the materials, the ground subsidence processes at these typical points are monitored and predicted using the mountainous time function method. Acquired from the mountains time function is a recurrence equation, which is regarded as the state equation, and the Ensemble Kalman (EnKF) method is conducted accordingly. Finally, the performance of the two methods is evaluated and compared using error curves and indexes. This research presents a recurrence equation based on the mountainous time function method and establishes the EnKF method for ground subsidence monitoring and prediction. Meanwhile, compared to the mountainous time function method, the values of the ME, MAE, RMSE and MAPE indexes are largely improved for the EnKF method. Hence, this research not only presents an effective method for ground subsidence monitoring in mountainous mining areas, but also provides theoretical support for safe coal mining and environmental protection.
Correction: De Angeli et al. Newly Developed Tool for the Post-Processing of GPR Time-Slices in a GIS Environment. Remote Sens. 2022, 14, 3459
Authors:   Remote Sensing Editorial Office
Published: December 2022

There was an error in the original publication [...]
Fluorescent Imprinted Nanoparticles for Sensing of Chlorogenic Acid in Coffee Extracts
Authors: Gutiérrez-Ortiz Anggy Lusanna , Vida Veronica , Peterka Matjaž , Tušar Jasmina , Berti Federico , Navarini Luciano , Forzato Cristina
Published: December 2022

Green coffee beans are particularly rich in chlorogenic acids (CGAs), and their identification and quantification are usually performed by HPLC, coupled with mass spectrometry (LC-MS). Although there are a few examples of molecularly imprinted polymers (MIPs) for chlorogenic acid (5-CQA) recognition present in the literature, none of them are based on optical fluorescence, which is very interesting given its great sensitivity. In the present manuscript, fluorescent polymeric imprinted nanoparticles were synthetized following the non-covalent approach using hydrogenated 5-O-caffeoylquinic acid (H-5-CQA) as the template. The capability of the polymer to bind 5-CQA was evaluated by HPLC and fluorescence. A real sample of coffee extract was also analyzed to verify the selectivity of the polymer. Polymer fMIP01, containing 4-vinylpyridine and a naphtalimide derivative as monomers, showed a good response to the fluorescence quenching in the range 39 μM–80 mM. In the real sample, fMIP01 was able to selectively bind 5-CQA, while caffeine was not recognized. To demonstrate this, there is a promising system that can be exploited in the design of an optical sensor for 5-CQA detection. Polymer fMIP01 was immobilized by physical entrapment on a functionalized glass surface, showing a quenching of fluorescence with an increase of the CGA concentration between 156 μM and 40 mM.
Imputing Missing Data in Hourly Traffic Counts
Authors: Shafique Muhammad Awais
Published: December 2022

Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.
Towards Building a Distributed Virtual Flow Meter via Compressed Continual Learning
Authors: Arief Hasan Asy’ari , Thomas Peter James , Constable Kevin , Katsaggelos Aggelos K.
Published: December 2022

A robust–accurate estimation of fluid flow is the main building block of a distributed virtual flow meter. Unfortunately, a big leap in algorithm development would be required for this objective to come to fruition, mainly due to the inability of current machine learning algorithms to make predictions outside the training data distribution. To improve predictions outside the training distribution, we explore the continual learning (CL) paradigm for accurately estimating the characteristics of fluid flow in pipelines. A significant challenge facing CL is the concept of catastrophic forgetting. In this paper, we provide a novel approach for how to address the forgetting problem via compressing the distributed sensor data to increase the capacity of the CL memory bank using a compressive learning algorithm. Through extensive experiments, we show that our approach provides around 8% accuracy improvement compared to other CL algorithms when applied to a real-world distributed sensor dataset collected from an oilfield. Noticeable accuracy improvement is also achieved when using our proposed approach with the CL benchmark datasets, achieving state-of-the-art accuracies for the CIFAR-10 dataset on blurry10 and blurry30 settings of 80.83% and 88.91%, respectively.
Interactive Lane Keeping System for Autonomous Vehicles Using LSTM-RNN Considering Driving Environments
Authors: Jeong Yonghwan
Published: December 2022

This paper presents an interactive lane keeping model for an advanced driver assistant system and autonomous vehicle. The proposed model considers not only the lane markers but also the interaction with surrounding vehicles in determining steering inputs. The proposed algorithm is designed based on the Recurrent Neural Network (RNN) with long short-term memory cells, which are configured by the collected driving data. A data collection vehicle is equipped with a front camera, LiDAR, and DGPS. The input features of the RNN consist of lane information, surrounding targets, and ego vehicle states. The output feature is the steering wheel angle to keep the lane. The proposed algorithm is evaluated through similarity analysis and a case study with driving data. The proposed algorithm shows accurate results compared to the conventional algorithm, which only considers the lane markers. In addition, the proposed algorithm effectively responds to the surrounding targets by considering the interaction with the ego vehicle.
Building a COVID-Safe Navigation App Using a Meta-Model Based Context Server
Authors: Wojciechowski Manfred , Pogscheba Patrick
Published: December 2022

Building context-aware applications is an already widely researched topic. It is our belief that context awareness has the potential to supplement the Internet of Things, when a suitable methodology including supporting tools will ease the development of context-aware applications. We believe that a meta-model based approach can be key to achieving this goal. In this paper, we present our meta-model based methodology, which allows us to define and build application-specific context models and the integration of sensor data without any programming. We describe how that methodology is applied with the implementation of a relatively simple context-aware COVID-safe navigation app. The outcome showed that programmers with no experience in context-awareness were able to understand the concepts easily and were able to effectively use it after receiving a short training. Therefore, context-awareness is able to be implemented within a short amount of time. We conclude that this can also be the case for the development of other context-aware applications, which have the same context-awareness characteristics. We have also identified further optimization potential, which we will discuss at the conclusion of this article.
Improving Inertial Sensor-Based Activity Recognition in Neurological Populations
Authors: Celik Yunus , Aslan M. Fatih , Sabanci Kadir , Stuart Sam , Woo Wai Lok , Godfrey Alan
Published: December 2022

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued.
Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals
Authors: Li Dezhao , Ruan Yangtao , Zheng Fufu , Su Yan , Lin Qiang
Published: December 2022

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.
Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives
Authors: Hagos Desta Haileselassie , Rawat Danda B.
Published: December 2022

This paper presents the findings of detailed and comprehensive technical literature aimed at identifying the current and future research challenges of tactical autonomy. It discusses in great detail the current state-of-the-art powerful artificial intelligence (AI), machine learning (ML), and robot technologies, and their potential for developing safe and robust autonomous systems in the context of future military and defense applications. Additionally, we discuss some of the technical and operational critical challenges that arise when attempting to practically build fully autonomous systems for advanced military and defense applications. Our paper provides the state-of-the-art advanced AI methods available for tactical autonomy. To the best of our knowledge, this is the first work that addresses the important current trends, strategies, critical challenges, tactical complexities, and future research directions of tactical autonomy. We believe this work will greatly interest researchers and scientists from academia and the industry working in the field of robotics and the autonomous systems community. We hope this work encourages researchers across multiple disciplines of AI to explore the broader tactical autonomy domain. We also hope that our work serves as an essential step toward designing advanced AI and ML models with practical implications for real-world military and defense settings.
Greening and Celebrification: The New Dimension of Celebrities through Green Production Advocacy
Authors: Jiménez-Morales Manel , Lopera-Mármol Marta
Published: December 2022

Screen culture and conglomerates are starting to echo the green shooting phenomena; roles such as sustainability director, eco-manager, eco-consultant, and eco-assistant are taking a more prominent space in the entertainment and cultural industry to achieve the goal of creating sustainable productions. In this current context, there seems to be a need for an agent to catch the attention of the audience to make a claim about green policies and contribute to a green literacy fabric. This opinion article recognizes that there are two types of voices, internal (scholars and practitioners) and external (celebrities and audiences), that have arisen in the audiovisual industry from different perspectives. Hence, through a theoretical approach, it tackles the particularities, typologies, and the role celebrities play as hot spots to push both viewers and creators into better decision-making models. The results show two main typologies: celebrification, in which a person becomes famous due to their sustainable actions, provoking a metonymic effect, and recelebrification, when famous people or well-known figures redefine their status by acting sustainable, producing a synecdoche effect. In conclusion, it is difficult to define what goes before and what goes after: whether it is the celebrity who passes the attributes onto production or whether it is the production that, by its characteristics, passes its attributes onto the celebrity.
Iron Sulfate (FeSO4) Improved Physiological Attributes and Antioxidant Capacity by Reducing Oxidative Stress of Oryza sativa L. Cultivars in Alkaline Soil
Authors: Saleem Ammara , Zulfiqar Asma , Ali Baber , Naseeb Manal Ahmed , Almasaudi Arwa Saad , Harakeh Steve
Published: December 2022

Rice ranks second among cereals in dietary uses around the world. Rice is deficient in iron (Fe), and these are important micronutrients for infants, men, and women. Fortification of rice with iron would help to minimize nutrient deficiency disorders among humans. The current study aims to introduce nutrient-rich rice. The effects of iron on germination, growth, photosynthetic pigment, antioxidant activity, and reduction of oxidative stress were investigated in four Oryza sativa L. cultivars. O. sativa of four different cultivars (Basmati-515, PK-386, KSK-133, and Basmati-198) were grown under five treatments (100, 200, 300, 400, and 500 mM) of iron sulphate (FeSO4) in soil of pH 7.5, along with control, by using six replicates. The result revealed that Fe treatment significantly affected seed germination percentage, plant growth parameters, biomass, photosynthetic pigments (chl a, chl b, total chlorophyll, and carotenoids), antioxidant enzymatic and non-enzymatic activity, and reduced oxidative stress. The findings also showed that Fe application reduced the oxidative stress including malondialdehyde content and hydrogen peroxide, by increasing the antioxidant enzymatic activity, i.e., catalase, ascorbate peroxidase, superoxide dismutase, peroxidase, glutathione peroxidase, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), and non-enzymatic antioxidant compounds (proline, amino acid, total soluble protein, phenolics, flavonoids, reducing-non-reducing sugar, and carbohydrates) in all cultivars of O. sativa. Furthermore, FeSO4 induced a significant increase in proline, free amino acid, and total carbohydrates in the leaves of all O. sativa cultivars, but Basmati-198 showed the significantly highest content by 169, 88, and 110%, respectively, at concentration of 500 mM. The present research work showed that soil application of FeSO4 improved the seed germination, plant growth, and antioxidants enzymatic and non-enzymatic activity, denatured the ROS (reactive oxygen species) in alkaline soil. In order to understand the underlying mechanisms, long-term field investigations should be carried out at the molecular level to examine patterns of iron uptake and plant growth.
Soil Heterogeneity Effects on Bridge Piles Deformation under Shield Tunnelling Disturbance
Authors: Chen Shi , Guo Panpan , Ma Jingjing , Li Xian , Lin Hang , Zhao Yanlin , Wang Yixian
Published: December 2022

This research examines the impact of soil heterogeneity on the bridge piles beneath a nearby tunnel excavation using Monte-Carlo stochastic analysis. Sensitivity analysis is specifically used to the variation of stratum range, variation coefficient (COV), and fluctuation distance of the soil Young’s modulus. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of COV on the likelihood of a pile failing. The findings suggest that more consideration should be given to the degree and range of geological parameter variations in the strata surrounding the tunnel. The horizontal and vertical fluctuation distances in this project are predicted to be around 18 m and 4.5 m, respectively. The fluctuation range influences the frequency of low stiffness zones in the soil. Additionally, the variation coefficient has a significant effect on the pile deformation, presenting a positive association. The pile deformation exhibits an increasing tendency in the wake of the growing variation coefficient. More significantly, the increase of the COV will directly lead to a rising failure probability of the pile settlement. According to extensive Monte-Carlo simulation calculations, the simulation results considering the variability of soil parameters have a certain deviation from the deterministic in the perspective of probability statistics. It is quite necessary to attach importance to the soil heterogeneity effects in the pile foundation stability under construction disturbance.
A Review of Recycling Methods for Fibre Reinforced Polymer Composites
Authors: Qureshi Jawed
Published: December 2022

This paper presents a review of waste disposal methods for fibre reinforced polymer (FRP) materials. The methods range from waste minimisation, repurposing, reusing, recycling, incineration, and co-processing in a cement plant to dumping in a landfill. Their strength, limitations, and key points of attention are discussed. Both glass and carbon fibre reinforced polymer (GFRP and CFRP) waste management strategies are critically reviewed. The energy demand and cost of FRP waste disposal routes are also discussed. Landfill and co-incineration are the most common and cheapest techniques to discard FRP scrap. Three main recycling pathways, including mechanical, thermal, and chemical recycling, are reviewed. Chemical recycling is the most energy-intensive and costly route. Mechanical recycling is only suitable for GFRP waste, and it has actually been used at an industrial scale by GFRP manufacturers. Chemical and thermal recycling routes are more appropriate for reclaiming carbon fibres from CFRP, where the value of reclaimed fibres is more than the cost of the recycling process. Discarding FRP waste in a sustainable manner presents a major challenge in a circular economy. With strict legislation on landfill and other environmental limits, recycling, reusing, and repurposing FRP composites will be at the forefront of sustainable waste-management strategies in the future.
Numerical Simulation Study on Deformation Characteristics of Surrounding Rock during Construction and Operation of Large Underground Gas Storage Structures
Authors: Peng Zhenhua , Ding Hao , Jiang Xinghong , Hu Xuebing , Cheng Liang
Published: December 2022

Underground gas storage is an important technical measure for future natural gas storage. The stability of the surrounding rock during excavation and under ultra-high gas storage pressure is the key to the stable operation of gas storage reservoirs. A numerical calculation model for different surrounding rock conditions, different depth-span ratios, and different buried depth conditions was conducted to study the stability of surrounding rock after large section underground gas storage excavation and under an ultra-high gas storage pressure of 20 MPa. The results show that after construction is completed, the deformation of the rock surrounding the cavern increases with a decrease in the surrounding rock grade, and the deformation of the rock surrounding the cavern increases as the burial depth increases. In addition, the maximum vertical deformation of the surrounding rock decreases with the increase in the depth-span ratio of the cavern, and the maximum horizontal displacement increases with the increase in the depth-to-span ratio. While operating at 20 MPa gas storage pressure, the displacement of the rock surrounding the chamber tends to increase with the decrease in the surrounding rock grade and the deformation of the surrounding rock of the chamber decreases as the burial depth increases. Furthermore, the vertical displacement of the rock surrounding the chamber decreases with the increase in the depth-span ratio, while the horizontal displacement of the surrounding rock increases with the increase in the depth-span ratio. Considering the stability of the surrounding rock during construction and operation, gas storage chambers should be built in areas with better conditions, such as Grade II and Grade III surrounding rocks within a burial depth range of 200 m. Moreover, the stability of the surrounding rocks is better when the chamber depth-span ratio is 2.5~3.0. These research results can provide a theoretical reference for the design of large underground gas storage structures.
Waste to Energy from Municipal Wastewater Treatment Plants: A Science Mapping
Authors: De la Torre Bayo Juan Jesús , Martín Pascual Jaime , Torres Rojo Juan Carlos , Zamorano Toro Montserrat
Published: December 2022

Energy recovery, according to circular economy and sustainable principles, has established itself as an inevitable field of action in wastewater treatment plants (WWTPs). Energy costs are forcing the optimization of processes and increases in the development of applicable waste-to-energy (WtE) technologies. This study aims to analyze the existing knowledge on WtE research in municipal WWTPs using a systematic literature review and a bibliometric analysis from 1979 to 2021. For this purpose, Science Mapping Analysis Tool (SciMAT) and VosViewer, two softwares for analyzing performance indicators and visualizing scientific maps, were used to identify the most relevant figures in the research. The results show an exponential increase in the number of publications over time, which has yet to reach a stage of maturity. The analysis of the evolution of the topics exposes variability in the keywords over the years. The main field of WtE research has focused on sludge treatment, with technologies ranging from anaerobic digestion to more recently-emerging ones such as microalgae or membrane technologies. The analysis also identified the need for more publications on other wastes in WWTPs, which are necessary to achieve zero waste.
Identification of the Quality Gaps in the Services Offered in Accommodation Facilities in Rural Areas: The Case of the Lublin Region
Authors: Wojciechowska-Solis Julia , Śmiglak-Krajewska Magdalena , Viti Domenico
Published: December 2022

The quality of services offered in accommodation facilities is one of the factors determining the development of this business activity. Therefore, it is necessary to constantly examine the level of customer satisfaction, their expectations and needs. The systematic collection and analysis of the opinions of tourists make it possible to learn about their changing expectations and allows the service providers to adapt to them. The purpose of the research that constituted the basis of this article was to identify the quality gaps in the services provided in the accommodation facilities in rural areas of the region of Lublin. The Servqual method was used in the study, thanks to which it was possible to assess the quality criteria and the validity of the provided service. A total of 699 respondents took part in the research. According to the study, the reliability and timeliness of the service, the adherence to the terms and conditions of the service, as well as the willingness to solve customers’ problems, were of the greatest importance for the clients, while the completeness and reliability of the advertising materials turned out to be the least important. With the obtained results, it can be concluded that the expectations of the customers in the accommodation facilities in rural areas exceed the actual state of the offered services. The areas that need to be improved, such as reliability and empathy, were identified.
A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty
Authors: Wang Shilei , Ji Ying , Wahab M. I. M. , Xu Dan , Zhou Changbao
Published: December 2022

In order to realize the “dual carbon” goal proposed for the world and to seek the low-carbon and sustainable development of the economy and society, the green supply chain management problem faced by Chinese enterprises and governments is particularly important. At the same time, how to quickly and efficiently select the suitable green supplier is the most basic and critical link in green supply chain management, as well as an important issue that Chinese government and enterprises must face in the process of green material procurement. In addition, there are various uncertainties emerging in the current market environment that hinder the green suppliers and the buyer to make the efficient decisions. Therefore, in order to find a more suitable and efficient method for green supplier selection, from the standpoint of the buyer, a new decision framework of online multi-sourcing, multi-attribute reverse auction (OMSMARA), which effectively improves the procurement efficiency and reduces procurement costs and risks, is proposed under the mixed uncertainty. Specifically, the main innovation work includes three aspects: Firstly, the trapezoidal fuzzy numbers are applied to describe the uncertain bidding attribute values by the green suppliers. Secondly, the hesitant fuzzy sets theory is introduced to characterize the buyer’s satisfaction degrees of the bidding evaluation attribute information, and the attribute weights are determined by using the hesitant fuzzy maximizing deviation method. Thirdly, a fuzzy multi-objective mixed integer programming model is proposed to solve the green supplier selection and quantity allocation question in OMSMARA. A numerical example is given to demonstrate the feasibility and effectiveness of the proposed decision framework, and the sensitivity analysis and comparison analysis further show the robustness and reliability of the proposed solution method.
Data Marketplaces: A Solution for Personal Data Control and Ownership?
Authors: Mahajan Sachit
Published: December 2022

Data sharing is critical for advancing research, commercializing technologies, and informing people. However, data owners have been reluctant to share data due to concerns about data control, access, and a lack of secure data storage solutions. Frequent data breaches have also led to a lack of user trust in businesses. Despite widespread recognition of user concerns, few steps have been taken to empower users. While there are models for decentralized data sharing, the question of how to incentivize these structures to enable data sharing at scale is largely unexplored. This work discusses the potential of blockchain-based data marketplaces to empower users by giving them more control over their data and bringing transparency to the data-driven ecosystem.