bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the Conditions for the Synthesis of Poly(3,4-ethylenedioxythiophene) ATRP Macroinitiator
Authors: Božičević Marin , Fiket Lucija , Vujasinović Magdalena , Blažic Roko , Kovačić Marin , Katančić Zvonimir
Published: January 2023

One of the most widely used conductive polymers in the growing conductive polymer industry is poly(3,4-ethylenedioxythiophene) (PEDOT), whose main advantages are good thermal and chemical stability, a conjugated backbone, and ease of functionalization. The main drawback of PEDOT for use as wearable electronics is the lack of stretchable and self-healing properties. This can be overcome by grafting PEDOT with flexible side branches. As pure PEDOT is highly stable and grafting would not be possible, a new bromine-functionalized thiophene derivative, 2-(tiophen-3-yl) ethyl 2-bromo-2-methylpropanoate (ThBr), was synthesized and copolymerized with EDOT for the synthesis of a poly(EDOT-co-ThBr) ATRP macroinitiator. After the synthesis of the macroinitiator, flexible polymers could be introduced as side branches by atom-transfer radical polymerization (ATRP) to modify mechanical properties. Before this last synthesis step, the conditions for the synthesis of the ATRP macroinitiator should be investigated, as only functionalized units can function as grafting sites. In this study, nine new copolymers with different monomer ratios were synthesized to investigate the reactivity of each monomer. The ratios used in the different syntheses were ThBr:EDOT = 1:0.2, 1:0.4, 1:0.6, 1:0.8, 1:1, 0.8:1, 0.6:1, 0.4:1, and 0.2:1. In order to determine the effect of reaction time on the final properties of the polymer, macroinitiator synthesis at a 1:1 ratio was carried out at different time periods: 8 h, 16 h, 24 h, and 48 h. The obtained products were characterized by different techniques, and it was found that polymerizations longer than 24 h yielded practically insoluble macroinitiators, thus limiting its further application. Reactivity ratios of both monomers were found to be similar and close to 1, making the copolymerization reaction symmetrical and the obtained macroinitiators almost random copolymers.
Effect of Infill Parameters on the Compressive Strength of 3D-Printed Nylon-Based Material
Authors: Liu Jingjing , Naeem Muhammad Awais , Al Kouzbary Mouaz , Al Kouzbary Hamza , Shasmin Hanie Nadia , Arifin Nooranida , Abd Razak Nasrul Anuar , Abu Osman Noor Azuan
Published: January 2023

3D printing is the most suitable method to manufacture the frame parts of powered ankle-foot prostheses but the compressive strength of the 3D-printed part needs to be ensured. According to the compression test standard ASTM D695, the effect of infill pattern and density, which is transferred to the mass of the standard specimen, on the compressive strength is investigated with a carbon fiber-reinforced nylon material. With the same infill pattern, specimens with more mass have a higher compressive strength. With the same mass, specimens with triangular fill have a higher compressive strength than those with rectangular and gyroid fills. Compared with specimens with a solid fill, specimens with a triangular fill can also provide more compressive strength in a unit mass. According to the results of standard specimens, following the requirement of strength and lightweight, 41% triangular fill is selected to manufacture the supporting part of a powered ankle-foot prosthesis. Under a compressive load of 1225 N, the strain of the assembly of the standard adaptor and the 3D-printed part is 1.32 ± 0.04%, which can meet the requirement of the design. This study can provide evidence for other 3D-printed applications with the requirement of compressive strength.
Towards Tough Thermoplastic Adhesive Tape by Microstructuring the Tape Using Tailored Defects
Authors: Wagih Ahmed , Mahmoud Hassan A. , Tao Ran , Lubineau Gilles
Published: January 2023

This paper presents a strategy towards achieving thermoplastic adhesive tapes with high toughness by microstructuring conventional tapes using tailored defects. Toughened tape was manufactured using two layers of a conventional tape where the bondline between the two adhesive layers was microstructured by embedding tailored defects with specific size and gap between them using PTFE film. Mode I toughness of the toughened tape was characterized experimentally. A high-fidelity finite element model was implemented to describe the toughening mechanisms using double cantilever beam simulations and end notch flexural tests. The model considers for the plasticity of the adhesive layer, the decohesion at the adherend–adhesive and adhesive–adhesive interfaces and progressive damage inside the adhesive layer. The adhesive–adhesive interface with the tailored defects inside the adhesive layer enables crack migration between adherend–adhesive interfaces, crack propagation at adhesive–adhesive interface, backward crack propagation under the defect, and plastic deformation of the adhesive ligament. The maximum toughness improvement of the tape with tailored defects of equal width and gap between two successive defects of 2 mm reached 278% and 147% for mode I and II, respectively, compared to conventional tape.
Seasonal Hypoxia Enhances Benthic Nitrogen Fixation and Shapes Specific Diazotrophic Community in the Eutrophic Marine Ranch
Authors: Yao Cheng , Zhang Qianqian , Lin Xianbiao , Zhao Jianmin , Zhang Xiaoli
Published: January 2023

Recently, a growing number of studies have confirmed that biological nitrogen fixation is also an important reactive nitrogen source in coastal regions. However, how benthic nitrogen fixation and diazotrophic community in coastal regions respond to seasonal hypoxia remains largely unknown. In this study, we investigated the spatiotemporal pattern of potential nitrogen fixation rate and diazotrophic abundance and community in sediments of a eutrophic marine ranch experiencing summer hypoxia using 15N tracing and high throughput sequencing techniques. The results showed that potential nitrogen fixation rates ranged from 0.013 to 10.199 μmol kg−1 h−1, and were significantly enhanced by summer hypoxia (ANOVA, p < 0.05). However, nifH gene abundance peaked in June. The diazotrophic community was dominated by Geobacteraceae (>60%), followed by Desulfobulbaceae (13.61%). Bottom water oxygen, pH, Chl-a concentration, and sediment NH4+ significantly regulated benthic nitrogen fixation, while the variation of diazotrophic community was explained by sediment TOC, TN, and Fe content (p < 0.05). This study highlighted that hypoxia stimulated benthic nitrogen fixation, which counteracted the nitrogen removal by denitrification and anammox, and could further aggregate eutrophication of the coastal marine ranch. Moreover, the result emphasized the importance of nitrogen fixation in coastal regions for the global N budget.
Experimental Study on the Acoustic Emission Characteristics of Fractured Granite after Repeated High Temperature-Water Cooling
Authors: Zhu Dong , Fan Yuqing , Bai Yang , Tao Xiangling , Miao Leigang , Jin Huiwu
Published: January 2023

Using the MTS816 rock mechanics servo tester, an acoustic emission monitoring system and high-speed digital photographic equipment, uniaxial compression tests were conducted on granite specimens containing single fracture slabs after repeated treatment (treatment times 1, 5, 10, 15 and 20) with three types of high temperature (250, 350 and 450 °C) water cooling, respectively, to analyze the basic mechanical parameters, acoustic emission change characteristics and fracture evolution of the specimens during the uniaxial compression process. It is shown that the heating temperature and the number of treatments not only have a deteriorating effect on the basic mechanical parameters of the specimens but also have an important effect on the changes in the basic parameters of acoustic emission at different compression stages. At 250 °C, the acoustic emission characteristics of the specimens at the initial tightening stage tended to decrease (N = 1 and 5 times) then, increase (N = 10 and 15 times) and then decrease (N = 20 times) as the number of treatments increased. At the same set temperature, the percentage of the bottom amplitude value of the acoustic emission of the specimen gradually decreases, and the percentage of the high amplitude value gradually increases as the number of treatments increases. After the specimen undergoes one and five treatments at 250 °C, the maximum acoustic emission energy value changes less, the maximum acoustic emission energy value decreases with the increase of treatment times in an approximately exponential function, the specimen is transformed from the brittle damage mode to the plastic damage mode and the effect of the prefabricated fracture on the damage of the specimen gradually disappears.
Bioremediation of Hazardous Wastes Using Green Synthesis of Nanoparticles
Authors: Singh Ayushi , Tyagi Parul , Ranjan Rajiv , Sushkova Svetlana N. , Minkina Tatiana , Burachevskaya Marina , Rajput Vishnu D.
Published: January 2023

Advanced agronomic methods, urbanisation, and industrial expansion contaminate air, water and soil, globally. Agricultural and industrial activities threaten living biota, causing biodiversity loss and serious diseases. Strategies such as bioremediation and physiochemical remediation have not been effectively beneficial at treating pollutants. Metal-based nanoparticles (NPs) such as copper, zinc, silver, gold, etc., in various nanoformulations and nanocomposites are used more and more as they effectively resist the uptake of toxic compounds via plants by facilitating their immobilisation. According to studies, bio-based NP synthesis is a recent and agroecologically friendly approach for remediating environmental waste, which is effective against carcinogens, heavy metal contamination, treating marine water polluted with excessive concentrations of phosphorus, nitrogen and harmful algae, and hazardous dye- and pesticide-contaminated water. Biogenic resources such as bacteria, fungi, algae and plants are extensively used for the biosynthesis of NPs, particularly metallic NPs. Strategies involving green synthesis of NPs are nontoxic and could be employed for commercial scale production. Here, the focus is on the green synthesis of NPs for reduction of hazardous wastes to help with the clean-up process.
The Influence of Processing Conditions on Gas Transport and Thermal Properties of Graphite Foil Compressed from Exfoliated Graphite
Authors: Ivanov Andrei V. , Yurkov Andrey L. , Kalachev Igor L. , Maksimova Natalia V. , Malakho Artem P. , Volkova Svetlana I. , Avdeev Victor V.
Published: January 2023

Graphite foil (GF) compressed from exfoliated graphite (EG) is a sealing material, which is used in nuclear energy and the chemical industry. The preparation of graphite foil is a complex process, which includes the intercalation of graphite, water washing, thermal exfoliation and pressing of intermediate products. The preparation conditions significantly influence the structure of the material and its physicochemical properties. Thus, the aim of work was to reveal the correlation between GF processing conditions, its crystalline structure, porosity and gas permeability as well as thermal stability. Sealability of the material is connected with low value of gas permeability, while thermal stability allows use of the material in high-temperature processes. Optimization of these parameters allow for the obtaining of a reliable material and expanding of the areas of its application. Exfoliated graphite for GF was prepared at different temperatures of 600, 800 and 1000 °C from the H2SO4–graphite intercalation compound (GIC) of II, III, IV stages. The influence of the GF processing conditions (the GIC stage number and the EG preparation temperature) on the main properties (gas permeability and thermal oxidation stability) of the sealing materials was investigated. A decrease in GIC stage number leads to the formation of GF with lower macroporosity and lower nitrogen and hydrogen permeability. However, an increase in GF surface area leads to an increase in the rate of GF oxidation by air oxygen. An increase in the EG preparation temperature from 800 to 1000 oC results in the formation of EG with a developed micro- and mesoporosity and increasing GF gas permeability. A decrease in EG preparation temperature down to 600 °C promotes the formation of new transport macropores in GF. The change of the EG preparation temperature has little effect on GF oxidation stability.
Efficient Microwave Processing of Thin Films Based on Double-Ridged Waveguide
Authors: Wang Qiulin , Chen Hang , Huang Zihan , Yang Yang , Zhu Huacheng , Hong Tao
Published: January 2023

Microwave heating has a wide range of applications in the fields of industrial heating and drying. However, when microwave heating is applied to the thin film, it will be challenging due to its low loss and large heat dissipation area. In this paper, a double-ridged waveguide for thin-film heating is proposed. The double ridge structure is employed to enhance the electric field, thereby increasing the power-loss density in the thin film. Firstly, a double-ridged waveguide, in which the electric field strength can be about 2.5 times that of the conventional waveguide, was designed based on the transverse resonance method and the electromagnetic field simulation. Then, a multiphysics model was built to analyze the heating performance of the ridged waveguide, in which the electromagnetic field and heat transfer are coupled. The simulation results show that the heating performance of the proposed waveguide will be 35.0 times that of the conventional waveguide. An experiment was carried out to verify the proposed model, showing that the experimental results are in accordance with the simulation results. Finally, the influences of the thickness of the film, the permittivity, the distance between two ridges, and the working state on heating performance and heating uniformity were also discussed.
Numerical Analysis of Grouting of Water-Enriched Karst Highway Tunnel Based on Critical Water-Enriched Height
Authors: Hua Tao , Liu Shiyang , Zhang Xuefu , Meng Lianghua , Wang Pan
Published: January 2023

The occurrence state of groundwater in a karst area has a significant impact on the tunnel lining structure. The impact on the groundwater is more significant during construction. This paper used numerical simulation to study the grouting reinforcement range of the Liaoshan Tunnel. This was based on the critical water rich height. Under Grade IV and Grade V surrounding rock, the critical points of grouting reinforcement were 230 m and 240 m deep, respectively. The vertical and horizontal stress and seepage flow of the tunnel arch crown and invert decreased as the circumferential grouting reinforcement range increased. In the case of no grouting reinforcement, 1/4 circumferential grouting reinforcement, 1/2 circumferential grouting reinforcement, and all circumferential grouting reinforcement, the seepage flow was 7 m3/dm, 4 m3/dm, 3 m3/dm, and 0 m3/dm, respectively. With Grade V surrounding rock, when the groundwater level was 260–270 m away from the tunnel arch crown, two schemes could be adopted: 1 m radial grouting reinforcement and 1/2 circumferential grouting reinforcement. The results have guiding significance for developing a construction scheme of a karst water rich tunnel.
Special Issue on “Food Processing Technologies Applied to Cereals, Legumes, Oilseeds, Alternative Food Raw Materials and Their Derivatives”
Authors: Păucean Adriana , Mureșan Vlad
Published: January 2023

A significant problem for the food industry is delivering high-quality, healthy food in response to the rising food demands brought on by the expanding worldwide population [...]
Benzene Reduction Process Simulation and Optimization in Catalytic Cracking Gasoline Distillation
Authors: Wang Zijian , Ke Ming , Song Zhaozheng , Li Jiahan , Sun Jinru
Published: January 2023

For countries where catalytic cracking gasoline is the primary source, the proposed technology consists in separating a benzene-rich fraction from catalytic cracking gasoline in order to be processed further together with reforming gasoline in a unit dedicated to aromatics extraction. In this way, two benefits are obtained: a benzene-rich fraction as raw material for extraction and the leftover fraction that satisfies the benzene content standards as a qualified product. It is established to use the divided wall distillation model, single-column distillation model, and double-column distillation model. Sensitivity analysis and SQP optimization are used to identify the ideal operating conditions and gasoline yield. Economic research shows that the divided wall and single-column distillation models have more potential for growth. It offers theoretical direction for businesses to design and optimize the pertinent process.
3D Printed Energy Return Elements for Upper Limb Sports Prosthetics
Authors: Park Jung Wook , Greenspan Ben , Tabb Taylor , Gallo Eric , Danielescu Andreea
Published: January 2023

Prosthetics are an extension of the human body and must provide functionality similar to that of a non-disabled individual to be effective. Sports prosthetics such as the Flex-Foot Cheetah from Össur have demonstrated the value of creating devices that both provide mechanical support and introduce passive energy return to mimic forces otherwise produced at joints. These energy return mechanisms have not yet been demonstrated for upper limb prosthetics but could improve their effectiveness and provide a greater range of motion and control. Using multi-material 3D printing technology, we extend energy return components to upper limb prosthetics by developing novel force-sensing springs and applying them to a basketball prosthetic. The 3D-printed springs compensate for the forces otherwise generated by wrist and finger flexion while measuring the mechanical deflection. We discuss design guidelines, methods for integrated 3D printed energy return within prosthetics, and broader applications in assistive technologies.
Catatonia Due to General Medical Conditions in Psychiatric Patients: Implications for Clinical Practice
Authors: Peritogiannis Vaios , Rizos Dimitrios V.
Published: January 2023

Catatonic syndrome is frequently observed over the course of severe mental disorders and general medical conditions, but when catatonia occurs in psychiatric patients with co-morbid medical or neurologic conditions, diagnosis and management may be challenging. Several medical conditions may cause catatonia in psychiatric patients, but some, such as brain injury, infections, hyponatremia and critical illness, may be most relevant in this population. Alongside appropriate etiologic treatment, benzodiazepines and electroconvulsive therapy in refractory cases are effective and safe, and may resolve catatonic syndrome rapidly. When newly-onset psychotic symptoms in catatonic patients with established psychotic disorders occur, delirium should be suspected and appropriately managed. An extensive clinical and laboratory diagnostic workup to determine the underlying etiology of catatonic syndrome should be carried out. In cases of acute multi-morbidity, the exact cause of catatonic syndrome in psychiatric patients may be unclear. It is recommended to avoid antipsychotic drugs in acutely catatonic patients, because they may exacerbate the catatonic symptoms. The akinetic type of catatonia should be differentiated from hypoactive delirium, as treatments for these syndromes differ substantially. When a psychiatric patient presents with symptomatology of both catatonia and delirium, treatment is particularly challenging.
Assessment and Quantitative Evaluation of Loess Area Geomorphodiversity Using Multiresolution DTMs (Roztocze Region, SE Poland)
Authors: Siłuch Marcin , Kociuba Waldemar , Gawrysiak Leszek , Bartmiński Piotr
Published: January 2023

The geodiversity of loess areas is reflected in the characteristic dataset of loess landforms, with the dominance of several types of valley forms (mainly gullies). The availability of satellite elevation data and high-resolution aerial topography scanning data provides an opportunity for quantitative assessment of geomorphodiversity. This is done through the analysis of topographic texture, delimitation and statistical characterization of the topographic parameters of erosional landforms, such as volume and degree of dissection (density of valleys) or the degree of ‘coverage’ of the area by valley forms. An important factor affecting the accuracy of the estimation is the accuracy of the underlying digital terrain model (DTM). This study compares three digital terrain models, with cell sizes of 30, 10 and 1 m, generated from satellite altimetry data and airborne laser scanning (ALS) data. The subregion of Szczebrzeszyn Roztocze (Western Roztocze Region, SE Poland), i.e., one of the most typical loess relief regions in Poland, was selected as the study area. Selected topographic texture analyses were carried out using the SLRM (Simple Local Relief Model) algorithm. Delimitation of valleys was performed by delineating the extent of slope change in two key steps: (1) detection of areas below the average topographic surface; (2) delimitation using supervised classification of DTMs. The results of the study show that the accuracy of delimitation of valleys increases inversely proportional to the DTMs resolution. Automated topographic texture analysis allows delimitation and extraction, as well as statistical analysis of parameters of valleys. Finally, two indicators have been proposed, Relative Valley Area (RVA) and Area-normalised Valley Cubature (AVC), which can be used in geomorphodiversity studies of a geologically homogeneous area. The dimensionless RVA index can also be expressed as a percentage (%) of the area of valley forms in a basic field of 1 km2. Furthermore, the AVC index shows the dynamic character of the main relief features of the analysed area.
Feasibility of Early Yield Prediction per Coffee Tree Based on Multispectral Aerial Imagery: Case of Arabica Coffee Crops in Cauca-Colombia
Authors: Bolaños Julian , Corrales Juan Carlos , Campo Liseth Viviana
Published: January 2023

Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield.
Geodetic Monitoring for Land Deformation
Authors: Ng Alex Hay-Man , Ge Linlin , Chang Hsing-Chung , Du Zheyuan
Published: January 2023

Land deformation is a pervasive hazard that could lead to serious problems, for example, increasing risk of flooding in coastal areas, damaging buildings and infrastructures, destructing groundwater systems, generating tension cracks on land, and reactivating faults, to name only a few [...]
Detecting Deforestation Using Logistic Analysis and Sentinel-1 Multitemporal Backscatter Data
Authors: Dascălu Adrian , Catalão João , Navarro Ana
Published: January 2023

This paper presents a new approach for detecting deforestation using Sentinel-1 C-band backscattering data. It is based on the temporal analysis of the backscatter intensity and its correlation with the scattering behavior of deforested plots. The backscatter intensity temporal variability is modeled with a logistic function, whose lower and upper boundaries are, respectively, set based on the representative backscatter values for forest and deforested plots. The approach also enables the identification of the date of each deforestation event, corresponding to the inflection point of the logistic curve that best fits the backscatter intensity time series. The methodology was applied to two forest biomes, a tropical forest at Iguazu National Park in Argentina and a temperate forest in the Brăila region in Romania. The optimal flattening parameter was 0.12 for both sites, with an F1-score of 0.93 and 0.71 for the tropical and temperate forests, respectively. The temporal accuracy shows a bias on the estimated date, with a slight delay of 2 months. The results reveal that the Sentinel C-band data can be successfully used for deforestation detection over tropical forests; however, the accuracy for temperate forests might be 20 pp lower, depending on the environmental conditions, such as rainfall, snow and management after logging.
Rumen Fluid Amine/Phenol-Metabolome of Beef Steers with Divergent Residual Feed Intake Phenotype
Authors: Sidney Taylor , Taiwo Godstime , Idowu Modoluwamu , Amusan Ibukun , Pech Cervantes Andres , Ogunade Ibukun
Published: January 2023

The amine/phenol-metabolome of rumen fluid was analyzed to identify amino acid metabolism-related biomarkers associated with phenotypic selection for low or high residual feed intake (RFI) in beef cattle. Fourteen beef steers (most feed-efficient (HFE; RFI = −1.89 kg/d, n = 7) and least feed-efficient (LFE; RFI = +2.05 kg/d, n = 7)) were selected from a total of 56 crossbred growing beef steers (average BW = 261 ± 18.5 kg) after a 49-d feeding period in a dry lot equipped with two GrowSafe intake nodes. Rumen fluid samples were collected 4 h after feeding on d 56, 63, and 70 from the HFE and LFE beef steers. Metabolome analysis of the rumen fluid was performed using chemical isotope labeling/liquid chromatography-mass spectrometry to identify all metabolites containing amine/phenol chemical groups, which are mostly amino acid metabolites. A total of 493 metabolites were detected and identified in the rumen fluid. The partial least squares discriminant scores plot showed a slight separation between the two groups of steers, and a total of eight metabolites were found to be differentially abundant (FDR ≤ 0.05). Out of the eight differentially abundant metabolites, four metabolites (isomer 1 of cadaverine, baeocystin, 6-methyladenine, and N(6)-methyllysine) qualified as candidate biomarkers of divergent RFI phenotype based on area under the curve ≥ 0.70. The results of this study revealed that divergent RFI phenotype is associated with alteration in rumen amine/phenol-metabolome of beef steers.
Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model
Authors: Sirisha Uddagiri , Chandana Bolem Sai
Published: January 2023

Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.
Spatially-Resolved Study of the Electronic Transport and Resistive Switching in Polycrystalline Bismuth Ferrite
Authors: Abramov Alexander , Slautin Boris , Pryakhina Victoria , Shur Vladimir , Kholkin Andrei , Alikin Denis
Published: January 2023

Ferroelectric materials attract much attention for applications in resistive memory devices due to the large current difference between insulating and conductive states and the ability of carefully controlling electronic transport via the polarization set-up. Bismuth ferrite films are of special interest due to the combination of high spontaneous polarization and antiferromagnetism, implying the possibility to provide multiple physical mechanisms for data storage and operations. Macroscopic conductivity measurements are often hampered to unambiguously characterize the electric transport, because of the strong influence of the diverse material microstructure. Here, we studied the electronic transport and resistive switching phenomena in polycrystalline bismuth ferrite using advanced conductive atomic force microscopy (CAFM) at different temperatures and electric fields. The new approach to the CAFM spectroscopy and corresponding data analysis are proposed, which allow deep insight into the material band structure at high lateral resolution. Contrary to many studies via macroscopic methods, postulating electromigration of the oxygen vacancies, we demonstrate resistive switching in bismuth ferrite to be caused by the pure electronic processes of trapping/releasing electrons and injection of the electrons by the scanning probe microscopy tip. The electronic transport was shown to be comprehensively described by the combination of the space charge limited current model, while a Schottky barrier at the interface is less important due to the presence of the built-in subsurface charge.
Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption
Authors: Ronkin Mikhail , Bykhovsky Dima
Published: January 2023

One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify slight variations in the consumed current under steady-state conditions. An experimental database of current consumption signals of two similar groups containing 20 same-model computer displays was collected. The resulting signals were classified using various state-of-the-art time-series classification (TSC) methods. We successfully identified 40 similar (same-model) electrical devices with about 94% precision, while most errors were concentrated in confusion between a small number of devices. A simplified empirical wavelet transform (EWT) paired with a linear discriminant analysis (LDA) classifier was shown to be the recommended classification method.
Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements
Authors: Pasinetti Simone , Nuzzi Cristina , Luchetti Alessandro , Zanetti Matteo , Lancini Matteo , De Cecco Mariolino
Published: January 2023

Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras’ error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device.
DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips
Authors: Ahmed Khaled R.
Published: January 2023

Automatic defects inspection and classification demonstrate significant importance in improving quality in the steel industry. This paper proposed and developed DSTEELNet convolution neural network (CNN) architecture to improve detection accuracy and the required time to detect defects in surface steel strips. DSTEELNet includes three parallel stacks of convolution blocks with atrous spatial pyramid pooling. Each convolution block used a different dilation rate that expands the receptive fields, increases the feature resolutions and covers square regions of input 2D image without any holes or missing edges and without increases in computations. This work illustrates the performance of DSTEELNet with a different number of parallel stacks and a different order of dilation rates. The experimental results indicate significant improvements in accuracy and illustrate that the DSTEELNet achieves of 97% mAP in detecting defects in surface steel strips on the augmented dataset GNEU and Severstal datasets and is able to detect defects in a single image in 23ms.
Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal
Authors: González-González Carina S. , Muñoz-Cruz Vanesa , Toledo-Delgado Pedro Antonio , Nacimiento-García Eduardo
Published: January 2023

A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student–player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach’s implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully.
Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory
Authors: Le Ngoc Thien , Thwe Chit Mya Myet , Truong Thanh Le , Siritantikorn Atchasai , Kongruttanachok Narisorn , Asdornwised Widhyakorn , Chaitusaney Surachai , Benjapolakul Watit
Published: January 2023

In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box’s GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital.
Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes
Authors: Garcia Pedro P. , Santos Telmo G. , Machado Miguel A. , Mendes Nuno
Published: January 2023

The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of human labor, making such solutions uninteresting. In this study, a different concept of the HRC system is introduced, consisting of an HRC framework for managing assembly processes that are executed simultaneously or individually by humans and robots. This HRC framework based on deep learning models uses only one type of data, RGB camera data, to make predictions about the collaborative workspace and human action, and consequently manage the assembly process. To validate the HRC framework, an industrial HRC demonstrator was built to assemble a mechanical component. Four different HRC frameworks were created based on the convolutional neural network (CNN) model structures: Faster R-CNN ResNet-50 and ResNet-101, YOLOv2 and YOLOv3. The HRC framework with YOLOv3 structure showed the best performance, showing a mean average performance of 72.26% and allowed the HRC industrial demonstrator to successfully complete all assembly tasks within a desired time window. The HRC framework has proven effective for industrial assembly applications.
A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles
Authors: Panigrahy Saroj Kumar , Emany Harika
Published: January 2023

The Internet of Things (IoT) has risen from ubiquitous computing to the Internet itself. Internet of vehicles (IoV) is the next emerging trend in IoT. We can build intelligent transportation systems (ITS) using IoV. However, overheads are imposed on IoV network due to a massive quantity of information being transferred from the devices connected in IoV. One such overhead is the network connection between the units of an IoV. To make an efficient ITS using IoV, optimization of network connectivity is required. A survey on network optimization in IoT and IoV is presented in this study. It also highlights the backdrop of IoT and IoV. This includes the applications, such as ITS with comparison to different advancements, optimization of the network, IoT discussions, along with categorization of algorithms. Some of the simulation tools are also explained which will help the research community to use those tools for pursuing research in IoV.
VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm
Authors: Gou Pingzhang , Guo Baoyong , Guo Miao , Mao Shun
Published: January 2023

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes’ energy consumption and lengthen the network’s lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network’s lifetime.
Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data
Authors: Kim Jehyun , Bang JongIl , Choi Anseop , Moon Hyeun Jun , Sung Minki
Published: January 2023

Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ) management. Air quality can be managed simply by alleviating the source of infection or pollution, but the person within a space can be the source of infection or pollution, thus necessitating an estimation of the exact number of people occupying the space. Generally, management plans for mitigating the spread of infections and maintaining the IAQ, such as ventilation, are based on the number of people occupying the space. In this study, carbon dioxide (CO2)-based machine learning was used to estimate the number of people occupying a space. For machine learning, the CO2 concentration, ventilation system operation status, and indoor–outdoor and indoor–corridor differential pressure data were used. In the random forest (RF) and artificial neural network (ANN) models, where the CO2 concentration and ventilation system operation modes were input, the accuracy was highest at 0.9102 and 0.9180, respectively. When the CO2 concentration and differential pressure data were included, the accuracy was lowest at 0.8916 and 0.8936, respectively. Future differential pressure data will be associated with the change in the CO2 concentration to increase the accuracy of occupancy estimation.
Financial Feasibility of Harvesting Rainwater from Permeable Pavements: A Case Study in a City Square
Authors: Klein Caio Wolf , Maykot Jéssica Kuntz , Ghisi Enedir , Thives Liseane Padilha
Published: January 2023

The objective of this study was to carry out the financial feasibility analysis of harvesting rainwater from permeable pavements in a city square. A case study was carried out in a square close to the beach in the city of Florianópolis, Brazil. Questionnaires were applied to pedestrians who circulate within the area. The square is to be implemented to promote sustainability and improve the user’s quality of life. From the rainfall data and the average daily water demand for irrigation of the square vegetation, the volume of rainwater to be harvested from the permeable pavement was calculated. The rainwater demand was estimated as 662 L/day. The implementation and operation costs of the pavement and irrigation systems were evaluated. The potential for potable water savings was 89.8%. The payback period was estimated as 347 months. This study showed that rainwater collected from permeable pavements is financially feasible and represents a promising technique.
Advantageous Effects of Sumac Usage in Meatball Preparation on Various Quality Criteria and Formation of Heterocyclic Aromatic Amines
Authors: Savaş Adem , Ekiz Elif , Elbir Zeynep , Savaş Burcunur , Proestos Charalampos , Elobeid Tahra , Khan Mohammad , Oz Fatih
Published: January 2023

Heterocyclic aromatic amines (HAAs) are mutagenic/carcinogenic compounds that can be formed during the cooking process of proteinaceous foods such as meat. Therefore, it is needed to inhibit or reduce their formations in cooked meats. Hereby, the effects of sumac usage (0.5%, w/w) in beef meatball preparation on the formation of HAAs and some quality parameters (water, pH, cooking loss, and lipid oxidation values) of meatballs cooked at 150 and 250 °C were investigated. The sumac usage caused a reduction in pH (p < 0.01), cooking loss (p < 0.05), lipid oxidation level (TBARS, p < 0.01), and total HAA amount (p < 0.05) of the samples. In addition, increasing the cooking temperature significantly decreased the pH value (p < 0.01) and increased the cooking loss (p < 0.05) of the samples. Only one compound, 2-amino-3,8-dimethylimidazo [4,5-ƒ]quinoxaline (MeIQx), from nine different HAAs studied in this study, could be determined, and the levels of the other HAAs studied were lower than their detection limits. On the other hand, MeIQx was not detected in the samples cooked at 150 °C, it was only determined in the control group samples cooked at 250 °C. The sumac usage completely inhibited MeIQx formation in the samples. Due to its positive effect on cooking loss value, lipid oxidation level, and MeIQx formation, it can be suggested to use sumac powder in meatball preparation.