bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Properties of Single-Crystal Calcite and Their Temperature and Strain-Rate Effects
Authors: Luo Chaocai , Yang Xinhua , Li Jie
Published: July 2022

Calcite is the most stable crystalline phase of calcium carbonate. It is applied or found in composite products, the food industry, biomineralization, archaeology, and geology, and its mechanical properties have attracted more and more attention. In this paper, the mechanical behaviors of single-crystal calcite under uniaxial tension in different directions were simulated with the molecular dynamics method. The obtained elastic moduli are in good agreement with the experimental results. It has been found from further research that single-crystal calcite has typical quasi-brittle failure characteristics, and its elastic modulus, fracture strength, and fracture strain are all strongly anisotropic. The tensile failure is caused by dislocation emission, void formation, and phase transition along the [010] and [421] directions, but by continuous dislocation glide and multiplication along the [421¯] direction. The fracture strength, fracture strain, and elastic modulus are all sensitive to temperature, but only elastic modulus is not sensitive to strain rate. The effects of temperature and logarithmic strain rate on fracture strength are in good agreement with the predictions of fracture dynamics.
Investigation of Protein Corona Formed around Biologically Produced Gold Nanoparticles
Authors: Pourali Parastoo , Neuhöferová Eva , Dzmitruk Volha , Benson Veronika
Published: July 2022

Although there are several research articles on the detection and characterization of protein corona on the surface of various nanoparticles, there are no detailed studies on the formation, detection, and characterization of protein corona on the surface of biologically produced gold nanoparticles (AuNPs). AuNPs were prepared from Fusarium oxysporum at two different temperatures and characterized by spectrophotometry, Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDS). The zeta potential of AuNPs was determined using a Zetasizer. AuNPs were incubated with 3 different concentrations of mouse plasma, and the hard protein corona was detected first by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and then by electrospray liquid chromatography–mass spectrometry (LC-MS). The profiles were compared to AuNPs alone that served as control. The results showed that round and oval AuNPs with sizes below 50 nm were produced at both temperatures. The AuNPs were stable after the formation of the protein corona and had sizes larger than 86 nm, and their zeta potential remained negative. We found that capping agents in the control samples contained small peptides/amino acids but almost no protein(s). After hard protein corona formation, we identified plasma proteins present on the surface of AuNPs. The identified plasma proteins may contribute to the AuNPs being shielded from phagocytizing immune cells, which makes the AuNPs a promising candidate for in vivo drug delivery. The protein corona on the surface of biologically produced AuNPs differed depending on the capping agents of the individual AuNP samples and the plasma concentration.
Stress Component Decoupling Analysis Based on Large Numerical Aperture Objective Lens, an Impractical Approach
Authors: Chang Ying , Fu Donghui , Sun Mingyuan , He Saisai , Qiu Wei
Published: July 2022

Micro Raman spectroscopy is an effective method to quantitatively analyse the internal stress of semiconductor materials and structures. However, the decoupling analysis of the stress components for {100} monocrystalline silicon (c-Si) remains difficult. In the work outlined, physical and simulation experiments were combined to study the influence of the objective lens numerical aperture (NA) on the Raman stress characterization. The physical experiments and simulation experiments show that the spectral results obtained by using lenses with different NAs can accurately obtain the principal stress sum but cannot decouple the components of the in-plane stress. Even if the spectral resolution of the simulated experiment is ideal (The random errors of the polarization directions of less than ±1° and the systematic random errors of less than ±0.02 cm−1). The analysis based on the theoretical model demonstrates that the proportion of the principal stress sum in the Raman shift obtained in an actual experiment exceeded 98.7%, while the proportion of the principal stress difference part was almost negligible. This result made it difficult to identify the variable effects of different stress states from the experimental results. Further simulation experiments in this work verify that when the principal stress sum was identical, the differences in the Raman shifts caused by different stress states were much smaller than the resolution of the existing Raman microscope system, which was hardly possible to identify in the experimental results. It was proven that decoupling analysis of stress components using the large-NA objective lens lacked actual practicability.
Mass/Heat Transfer Analogy Method in the Research on Convective Fluid Flow through a System of Long Square Mini-Channels
Authors: Wilk Joanna , Grosicki Sebastian , Smusz Robert
Published: July 2022

The paper presents the results of experimental investigations of mass transfer processes with the use of the limiting current technique. This experimental work analyzed the not fully developed entrance laminar region. The tested case refers to the convective fluid flow through a system of nine long, square mini-channels that are 2 mm wide and 100 mm long. The method used in the measurements allows one to determine mass transfer coefficients during the electrolyte flow by utilizing electrochemical processes. The received mass transfer coefficients were applied to the analogous heat transfer case. The Chilton–Colburn analogy between mass and heat transfer was applied. The obtained results, in the form of the dependence of Nusselt number within the function of Reynolds and Prandtl numbers, can be a useful formula in the design and analysis of heat transfer processes in mini heat exchangers.
Quantitative and Qualitative Assessment of Fluorescence in Aesthetic Direct Restorations
Authors: Bardocz-Veres Zsuzsanna , Székely Melinda , Salamon Pál , Bala Előd , Bereczki Előd , Kerekes-Máthé Bernadette
Published: July 2022

Currently available direct restoration materials have been developed to have improved optical properties to interact with light in the same manner as the natural tooth. The objective of this study was to investigate the fluorescence of different enamel resin composites. In the present study, nine brands of enamel composites were tested in vitro, some of which are cited by manufacturers as having color adjustment potential. Fluorescence spectra of the composite specimens and the human natural enamel were measured with a fluorescence spectrophotometer immediately after preparation and after 6 months. Qualitative data of the specimens were also collected. Statistical analyses were conducted by Kruskal–Wallis and Mann–Whitney U nonparametric tests (p < 0.05). Almost all tested resin composites presented a significant decrease in the fluorescence values after a period of 6 months. There was no significant decrease in fluorescence in the case of Harmonize™ resin composite samples, which presented the lowest initial fluorescence values. The highest value in the reduction of the initial fluorescence intensity after 6 months (22.95%) was observed for the Charisma® specimens. Composites with a color adjustment did not perform significantly better than other composites in terms of reduction in fluorescence intensity.
Gating System Optimization for EV31A Magnesium Alloy Engine Body Sand Casting
Authors: Kiełbus Andrzej , Jarosz Robert
Published: July 2022

The research presented in this paper aimed to change the existing gating system that would enable the engine body casting, from a new EV31A magnesium alloy, of the required quality. For this reason, the casting process simulations used the MAGMASoft software, followed by the experimental validation of the achieved results. The results achieved in the first stage of the cast computer simulation enabled the identification of potential problems and factors that reduce the casting quality. However, the proposed design modifications eliminated the inadequate delivery of liquid metal to the casting’s critical areas by adequately controlling the mold cavity filling and solidification process. The experiment validated the simulations of the computer casting defects at the various stages. The results enabled the new EV31A magnesium alloy to be implemented in industrial production.
Development of Multiscale Composite with Hybrid Natural Nanofibers
Authors: Tipu Javed A. K. , Rafiq Syed Usman , Arif Muhammad , Feroze Tariq , Ahmad Hafiz Waqar , Masood Chaudry Umer , Jun Tea-Sung , Aslam Noon Adnan
Published: July 2022

Natural nanofibers are widely used in the field of medicine, but the low strength of these nanofibers is one of the major concerns. A number of factors, importantly the composition, affect the strength of natural nanofibers. The purpose of the current study is to ascertain the effect of the composition of natural nanofibers on the strength of hybrid composites formed using these nanofibers. Hybrid composites formed using 32% volume glass fibre with optimized volume fraction of 0.5% of pure Cellulose Acetate (CA), and 0.5% CA + Hemp Seed (HS) for this study to carry out the analysis. Hybrid composites were produced with vacuum-assisted resin transfer molding (VARTM) by collecting natural nanofibers, produced using the electrospinning process, over glass fiber mats. The electrospinning process was carried out with 12 kV, 10 cm tip to the collector gap, and 12% concentration of the solution. The tensile strength of the hybrid composites was measured using the universal testing machine (UTM). The results showed that the diameter of the electrospun nanofiber varied between 50 and 1400 nm and was affected by solution concentration, voltage, tip-to-collector distance, flow rate, and inclusion of HS in CA. The inclusion of HS in CA, for all compositions, decreased the fiber diameter and caused the formation of beads prominently at higher concentrations. Hybrid composites formed from nanofibers produced using CA and HS showed higher elastic modulus (232 MPa) and tensile strength (20.4 GPa) as compared with nanofibers produced using CA only (elastic modulus = 110 MPa and 13.7 GPa).
Antimicrobial Activity of Composites-Based on Biopolymers
Authors: Bustamante-Torres Moises , Arcentales-Vera Belén , Estrella-Nuñez Jocelyne , Yánez-Vega Heidi , Bucio Emilio
Published: July 2022

Microorganisms have developed a resistance against some of the most conventional antibiotics. These microorganisms can be self-assembled, forming a microbial biofilm. A microbial biofilm formation is an inherent event on almost any surface, causing countless side effects on human health and the environment. Therefore, multiple scientific proposals have been developed based on renewable sources such as natural polymers. Natural polymers or biopolymers include cellulose, chitosan, starch, collagen, gelatin, hyaluronic acid, alginates, fibrin, and pectin, which are widely found in nature. The biopolymers have displayed many interesting properties, including biocompatibility and biodegradability. Nonetheless, these materials usually have no antimicrobial properties (except for the chitosan) by themselves. Therefore, antimicrobial agents have been incorporated into the natural polymeric matrix, providing an antimicrobial property to the biocomposite. Biocomposites consist of two different materials (one of natural origin) studied as biocompatible and biodegradable drug carriers of antimicrobial agents. In addition, due to the incorporation of antimicrobial agents, biocomposites can inhibit biofilm formation and bacteria proliferation on many surfaces. This review describes this using natural polymers as a platform of antimicrobial agents to form a biocomposite to eliminate or reduce biofilm formation on different surfaces.
Preparation and Characterization of Tilapia Collagen-Thermoplastic Polyurethane Composite Nanofiber Membranes
Authors: Wu Sijia , Yang Longhe , Chen Junde
Published: July 2022

Marine collagen is an ideal material for tissue engineering due to its excellent biological properties. However, the limited mechanical properties and poor stability of marine collagen limit its application in tissue engineering. Here, collagen was extracted from the skin of tilapia (Oreochromis nilotica). Collagen-thermoplastic polyurethane (Col-TPU) fibrous membranes were prepared using tilapia collagen as a foundational material, and their physicochemical and biocompatibility were investigated. Fourier transform infrared spectroscopy results showed that thermoplastic polyurethane was successfully combined with collagen, and the triple helix structure of collagen was retained. X-ray diffraction and differential scanning calorimetry results showed relatively good compatibility between collagen and TPU.SEM results showed that the average diameter of the composite nanofiber membrane decreased with increasing thermoplastic polyurethane proportion. The mechanical evaluation and thermogravimetric analysis showed that the thermal stability and tensile properties of Col-TPU fibrous membranes were significantly improved with increasing TPU. Cytotoxicity experiments confirmed that fibrous membranes with different ratios of thermoplastic polyurethane content showed no significant toxicity to fibroblasts; Col-TPU fibrous membranes were conducive to the migration and adhesion of cells. Thus, these Col-TPU composite nanofiber membranes might be used as a potential biomaterial in tissue regeneration.
Random Mutagenesis as a Promising Tool for Microalgal Strain Improvement towards Industrial Production
Authors: Trovão Mafalda , Schüler Lisa M. , Machado Adriana , Bombo Gabriel , Navalho Sofia , Barros Ana , Pereira Hugo , Silva Joana , Freitas Filomena , Varela João
Published: July 2022

Microalgae have become a promising novel and sustainable feedstock for meeting the rising demand for food and feed. However, microalgae-based products are currently hindered by high production costs. One major reason for this is that commonly cultivated wildtype strains do not possess the robustness and productivity required for successful industrial production. Several strain improvement technologies have been developed towards creating more stress tolerant and productive strains. While classical methods of forward genetics have been extensively used to determine gene function of randomly generated mutants, reverse genetics has been explored to generate specific mutations and target phenotypes. Site-directed mutagenesis can be accomplished by employing different gene editing tools, which enable the generation of tailor-made genotypes. Nevertheless, strategies promoting the selection of randomly generated mutants avoid the introduction of foreign genetic material. In this paper, we review different microalgal strain improvement approaches and their applications, with a primary focus on random mutagenesis. Current challenges hampering strain improvement, selection, and commercialization will be discussed. The combination of these approaches with high-throughput technologies, such as fluorescence-activated cell sorting, as tools to select the most promising mutants, will also be discussed.
Hematologic Malignancies Diagnosed in the Context of the mRNA COVID-19 Vaccination Campaign: A Report of Two Cases
Authors: Zamfir Maria-Alexandra , Moraru Liliana , Dobrea Camelia , Scheau Andreea-Elena , Iacob Simona , Moldovan Cosmin , Scheau Cristian , Caruntu Constantin , Caruntu Ana
Published: July 2022

Background: During the last two years, the COVID-19 pandemic led to millions of disease-related deaths worldwide. The efforts of the scientific community facing this global challenge resulted in outstanding achievements. Thus, within one year, new mRNA-based vaccines against SARS-CoV-2 viral infection were released, providing highly efficient protection and showing a very good safety profile in the general population. However, clinical data collection after vaccination is a continuous process for the long-term safety of any new medical product. The aim of our paper is to present two cases of hematological malignancies: diffuse large B-cell non-Hodgkin lymphoma and T/NK-cell lymphoma, diagnosed shortly after the administration of the mRNA COVID-19 vaccine. Methods and Results: Case 1: A female patient was admitted with a suspicious cervical mass that emerged within one week after the administration of second dose of the BNT162b2 COVID-19 vaccine. Surgical removal followed by pathology assessment of the specimen confirmed the diagnosis of diffuse large B-cell non-Hodgkin lymphoma. Case 2: A male patient was admitted with multiple ulcerative oral lesions arising on the third day after the initial dose of the BNT162b2 COVID-19 vaccine. These lesions had a progressive character and during the following months were complicated with repetitive episodes of heavy oral bleeding, requiring blood transfusions. The incisional biopsy of the lesions and pathological assessment of the specimens confirmed the diagnosis of T/NK-cell lymphoma. Conclusions: The safety profile of the mRNA-based vaccines is an undeniable fact. In most cases, suspicions of potentially aggressive side effects were ruled out, proving to be transient post-vaccine reactions. Clinicians should remain alert to report any potentially aggressive manifestations emerging in the context of mRNA COVID-19 vaccination, such as these cases of hematological malignancies, in order to promote additional investigations on the particular mechanisms of action of COVID-19 vaccines and to provide the best medical care to the patients.
Clinical Patterns of Traditional Chinese Medicine for Ischemic Heart Disease Treatment: A Population-Based Cohort Study
Authors: Wang Lung-Shuo , Yen Pei-Tzu , Weng Shih-Feng , Hsu Jong-Hau , Yeh Jwu-Lai
Published: July 2022

Background and objectives: Traditional Chinese medicines (TCMs) are widely prescribed to relieve ischemic heart disease (IHD); however, no cohort studies have been conducted on the use of TCMs for patients with IHD. The aim of the study was to analyze TCM prescription patterns for patients with IHD. Materials and Methods: The retrospective population-based study employed a randomly sampled cohort of 4317 subjects who visited TCM clinics. Data were obtained from the National Health Insurance Research Database (NHIRD) of Taiwan for the period covering 2000 to 2017. Data analysis focused on the top ten most commonly prescribed formulae and single TCMs. We also examined the most common two- and three-drug combinations of TCM in single prescriptions. Demographic characteristics included age and sex distributions. Analysis was performed on 22,441 prescriptions. Results: The majority of TCM patients were male (53.6%) and over 50 years of age (65.1%). Zhi-Gan-Cao-Tang (24.76%) was the most frequently prescribed formulae, and Danshen (28.89%) was the most frequently prescribed single TCM for the treatment of IHD. The most common two- and three-drug TCM combinations were Xue-Fu-Zhu-Yu-Tang and Danshen” (7.51%) and “Zhi-Gan-Cao-Tang, Yang-Xin-Tang, and Gua-Lou-Xie-Bai-Ban-Xia-Tang” (2.79%). Conclusions: Our results suggest that most of the frequently prescribed TCMs for IHD were Qi toning agents that deal with cardiovascular disease through the promotion of blood circulation. The widespread use of these drugs warrants large-scale, randomized clinical trials to investigate their effectiveness and safety.
A Framework for Interactive Development of Simulation Models with Strategical–Tactical–Operational Layering Applied to the Logistics of Bulk Commodities
Authors: Guiguet Andres , Pons Dirk
Published: July 2022

CONTEXT–Simulation modelling provides insight into hidden dynamics underlying business processes. However, an accurate understanding of operations is necessary for fidelity of the model. This is challenging because of the need to extract the tacit nature of operational knowledge and facilitate the representation of complex processes and decision-making patterns that do not depend on classes, objects, and instantiations. Commonly used industrial simulation, such as Arena®, does not natively support the object-oriented constructs available for software development. OBJECTIVE–This paper proposes a method for developing simulation models that allow process-owners and modellers to jointly build a series of evolutionary models that improve conceptual validity of the executable computer model. APPROACH-Software and Systems Engineering principles were adapted to develop a framework that allows a systematic transition from conceptual to executable model, which allows multiple perspectives to be simultaneously considered. The framework was applied to a logistics case study in a bulk commodities distribution context. FINDINGS–The method guided the development of a set of models that served as scaffolds to allow the natural flow of ideas from a natural language domain to Arena® code. In doing so, modeller and process-owners at strategic, tactical, and operational levels developed and validated the simulation model. ORIGINALITY—This work provides a framework for structuring the development of simulation models. The framework allows the use of non-object-oriented constructs, making it applicable to SIMAN-based simulation languages and packages as Arena®.
Nano-Titanium Oxide in Polymeric Contact Lenses: Short Communication
Authors: Shaker Lina Mohammed , Alamiery Ahmed A. , Takriff Mohd , Wan Isahak Wan Nor Roslam
Published: July 2022

Many individuals suffer from myopia or hyperopia and astigmatism owing to the refractive defects of the eye optics or because of the use of inappropriate contact lenses. This study dealt with three polymers Poly(methyl methacrylate) (PMMA), Poly(Hydroxyl methacrylate) (PHEMA), and Poly(glycidyl methacrylate) (PGMA) and doping them with TiO2 nanoparticles to evaluate the difference between the effect of each lens on the human eye. The TiO2 NPs were prepared in this work by the sol–gel method to obtain 70–90 nm sized particles. Modulation transfer (MTF) and spot diagram were assessed to measure ocular performance. The PGMA-TiO2 contact lens provided the highest image quality at the lowest probability (P) of about p < 0.0001 when inserted on an aberrated eye system because of its ability to eliminate the chromatic aberrations created inside the eyes having a smaller spot size.
Validation of a Saliency Map for Assessing Image Quality in Nuclear Medicine: Experimental Study Outcomes
Authors: Hosokawa Shota , Takahashi Yasuyuki , Inoue Kazumasa , Nagasawa Chimo , Watanabe Yuya , Yamamoto Hiroki , Fukushi Masahiro
Published: July 2022

Recently, the use of saliency maps to evaluate the image quality of nuclear medicine images has been reported. However, that study only compared qualitative visual evaluations and did not perform a quantitative assessment. The study’s aim was to demonstrate the possibility of using saliency maps (calculated from intensity and flicker) to assess nuclear medicine image quality by comparison with the evaluator’s gaze data obtained from an eye-tracking device. We created 972 positron emission tomography images by changing the position of the hot sphere, imaging time, and number of iterations in the iterative reconstructions. Pearson’s correlation coefficient between the saliency map calculated from each image and the evaluator’s gaze data during image presentation was calculated. A strong correlation (r ≥ 0.94) was observed between the saliency map (intensity) and the evaluator’s gaze data. This trend was also observed in images obtained from a clinical device. For short acquisition times, the gaze to the hot sphere position was higher for images with fewer iterations during the iterative reconstruction. However, no differences in iterations were found when the acquisition time increased. Saliency by flicker could be applied to clinical images without preprocessing, although compared with the gaze image, it increased slowly.
To Tend or to Subdue? Technology, Artificial Intelligence, and the Catholic Ecotheological Tradition
Authors: Labrecque Cory Andrew
Published: July 2022

In February 2020, the president of Microsoft, the executive vice president of IBM, the director general of the Food and Agriculture Organization (FAO) of the United Nations, and the former Italian Minister of Innovation joined the president of the Vatican’s Pontifical Academy for Life in Rome to sign The Rome Call for AI Ethics. In doing so, they promoted a shared sense of responsibility and commitment—by industry, government, and Church—to uphold certain ethical standards in the areas of digital innovation, artificial intelligence, and technological progress. In this article, I discuss The Rome Call for AI Ethics in conjunction with Pope Francis’ rendering of integral ecology and the technocratic paradigm in Laudato Si’. My aim here is to link Catholic teaching on technology (using AI as a starting point) to the environment and the ecological crisis.
The Effect of Time Window Length on EEG-Based Emotion Recognition
Authors: Ouyang Delin , Yuan Yufei , Li Guofa , Guo Zizheng
Published: July 2022

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.
Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data
Authors: Ivanenko Yevhen , Vu Viet T. , Batra Aman , Kaiser Thomas , Pettersson Mats I.
Published: July 2022

Time-domain backprojection algorithms are widely used in state-of-the-art synthetic aperture radar (SAR) imaging systems that are designed for applications where motion error compensation is required. These algorithms include an interpolation procedure, under which an unknown SAR range-compressed data parameter is estimated based on complex-valued SAR data samples and backprojected into a defined image plane. However, the phase of complex-valued SAR parameters estimated based on existing interpolators does not contain correct information about the range distance between the SAR imaging system and the given point of space in a defined image plane, which affects the quality of reconstructed SAR scenes. Thus, a phase-control procedure is required. This paper introduces extensions of existing linear, cubic, and sinc interpolation algorithms to interpolate complex-valued SAR data, where the phase of the interpolated SAR data value is controlled through the assigned a priori known range time that is needed for a signal to reach the given point of the defined image plane and return back. The efficiency of the extended algorithms is tested at the Nyquist rate on simulated and real data at THz frequencies and compared with existing algorithms. In comparison to the widely used nearest-neighbor interpolation algorithm, the proposed extended algorithms are beneficial from the lower computational complexity perspective, which is directly related to the offering of smaller memory requirements for SAR image reconstruction at THz frequencies.
Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
Authors: Noguer Josep , Contreras Ivan , Mujahid Omer , Beneyto Aleix , Vehi Josep
Published: July 2022

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models.
Digital Twin for Human–Robot Interactions by Means of Industry 4.0 Enabling Technologies
Authors: Gallala Abir , Kumar Atal Anil , Hichri Bassem , Plapper Peter
Published: July 2022

There has been a rapid increase in the use of collaborative robots in manufacturing industries within the context of Industry 4.0 and smart factories. The existing human–robot interactions, simulations, and robot programming methods do not fit into these fast-paced technological advances as they are time-consuming, require engineering expertise, waste a lot of time in programming and the interaction is not trivial for non-expert operators. To tackle these challenges, we propose a digital twin (DT) approach for human–robot interactions (HRIs) in hybrid teams in this paper. We achieved this using Industry 4.0 enabling technologies, such as mixed reality, the Internet of Things, collaborative robots, and artificial intelligence. We present a use case scenario of the proposed method using Microsoft Hololens 2 and KUKA IIWA collaborative robot. The obtained results indicated that it is possible to achieve efficient human–robot interactions using these advanced technologies, even with operators who have not been trained in programming. The proposed method has further benefits, such as real-time simulation in natural environments and flexible system integration to incorporate new devices (e.g., robots or software capabilities).
Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force
Authors: Qian Qianqian , Cheng Ke , Qian Wei , Deng Qingchang , Wang Yuanquan
Published: July 2022

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.
Mobile Edge Computing Task Offloading Strategy Based on Parking Cooperation in the Internet of Vehicles
Authors: Shen Xianhao , Chang Zhaozhan , Niu Shaohua
Published: July 2022

Due to the limited computing capacity of onboard devices, they can no longer meet a large number of computing requirements. Therefore, mobile edge computing (MEC) provides more computing and storage capabilities for vehicles. Inspired by a large number of roadside parking vehicles, this paper takes the roadside parking vehicles with idle computing resources as the task offloading platform and proposes a mobile edge computing task offloading strategy based on roadside parking cooperation. The resource sharing and mutual utilization among roadside vehicles, roadside units (RSU), and cloud servers (cloud servers) were established, and the collaborative offloading problem of computing tasks was transformed into a constraint problem. The hybrid genetic algorithm (HHGA) with a mountain-climbing operator was used to solve the multi-constraint problem, to reduce the delay and energy consumption of computing tasks. The simulation results show that when the number of tasks is 25, the delay and energy consumption of the HHGA algorithm is improved by 24.1% and 11.9%, respectively, compared with Tradition. When the task size is 1.0 MB, the HHGA algorithm reduces the system overhead by 7.9% compared with Tradition. Therefore, the proposed scheme can effectively reduce the total system cost during task offloading.
Modelling of Land Use/Cover and LST Variations by Using GIS and Remote Sensing: A Case Study of the Northern Pakhtunkhwa Mountainous Region, Pakistan
Authors: Rehman Akhtar , Qin Jun , Shafi Sedra , Khan Muhammad Sadiq , Ullah Siddique , Ahmad Khalid , Rehman Nazir Ur , Faheem Muhammad
Published: July 2022

Alteration in Land Use/Cover (LULC) considered a major challenge over the recent decades, as it plays an important role in diminishing biodiversity, altering the macro and microclimate. Therefore, the current study was designed to examine the past 30 years (1987–2017) changes in LULC and Land Surface Temperature (LST) and also simulated for next 30 years (2047). The LULC maps were developed based on maximum probability classification while the LST was retrieved from Landsat thermal bands and Radiative Transfer Equation (RTE) method for the respective years. Different approaches were used, such as Weighted Evidence (WE), Cellular Automata (CA) and regression prediction model for the year 2047. Resultantly, the LULC classification showed increasing trend in built-up and bare soil classes (13 km2 and 89 km2), and the decreasing trend in vegetation class (−144 km2) in the study area. In the next 30 years, the built-up and bare soil classes would further rise with same speed (25 km2 and 36.53 km2), and the vegetation class would further decline (−147 km2) until 2047. Similarly for LST, the temperature range for higher classes (27 -< 30 °C) increased by about 140 km2 during 1987–2017, which would further enlarge (409 km2) until 2047. The lower LST range (15 °C to <21 °C) showed a decreasing trend (−54.94 km2) and would further decline to (−20 km2) until 2047 if it remained at the same speed. Prospective findings will be helpful for land use planners, climatologists and other scientists in reducing the increasing LST associated with LULC changes.
Democratization of PV Micro-Generation System Monitoring Based on Narrowband-IoT
Authors: Paredes-Parra José Miguel , Jiménez-Segura Raquel , Campos-Peñalver David , Mateo-Aroca Antonio , Ramallo-González Alfonso P. , Molina-García Angel
Published: July 2022

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called ’prosumers’ and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms.
Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
Authors: Elmezughi Mohamed K. , Salih Omran , Afullo Thomas J. , Duffy Kevin J.
Published: July 2022

Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.
Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining
Authors: Gurazada Sai Gayatri , Gao Shijia (Caddie) , Burstein Frada , Buntine Paul
Published: July 2022

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.
Improved Dual Attention for Anchor-Free Object Detection
Authors: Xiang Ye , Zhao Boxuan , Zhao Kuan , Wu Lifang , Wang Xiangdong
Published: July 2022

In anchor-free object detection, the center regions of bounding boxes are often highly weighted to enhance detection quality. However, the central area may become less significant in some situations. In this paper, we propose a novel dual attention-based approach for the adaptive weight assignment within bounding boxes. The proposed improved dual attention mechanism allows us to thoroughly untie spatial and channel attention and resolve the confusion issue, thus it becomes easier to obtain the proper attention weights. Specifically, we build an end-to-end network consisting of backbone, feature pyramid, adaptive weight assignment based on dual attention, regression, and classification. In the adaptive weight assignment module based on dual attention, a parallel framework with the depthwise convolution for spatial attention and the 1D convolution for channel attention is applied. The depthwise convolution, instead of standard convolution, helps prevent the interference between spatial and channel attention. The 1D convolution, instead of fully connected layer, is experimentally proved to be both efficient and effective. With the adaptive and proper attention, the correctness of object detection can be further improved. On public MS-COCO dataset, our approach obtains an average precision of 52.7%, achieving a great increment compared with other anchor-free object detectors.
Selection of the Best Set of Features for sEMG-Based Hand Gesture Recognition Applying a CNN Architecture
Authors: Sandoval-Espino Jorge Arturo , Zamudio-Lara Alvaro , Marbán-Salgado José Antonio , Escobedo-Alatorre J. Jesús , Palillero-Sandoval Omar , Velásquez-Aguilar J. Guadalupe
Published: July 2022

The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.
E-Taste: Taste Sensations and Flavors Based on Tongue’s Electrical and Thermal Stimulation
Authors: Ullah Asif , Liu Yifan , Wang You , Gao Han , Wang Hengyang , Zhang Jin , Li Guang
Published: July 2022

Taste is a key sense that helps identify different food types and most of this work was carried out on primary tastes rather than generating different flavors. In this work, we proposed a plan to create other flavors rather than primary tastes, adjusted the electrical (40–180 µA) and thermal stimulation (20–38 °C and 38–20 °C), and revealed the digital coding for multi-flavors. Our results showed that different combinations of digital coding could generate different flavors and that tastes related to different stimuli are easy to develop. The novelty of this work is to design other types of flavors and primary tastes. The experimental results demonstrated that the novel method proposed for digital taste coding could realize primary tastes (sweet, sour, salty, spicy, and mint) and mixed flavors. Furthermore, some innovative sensations have been realized, which are sprite, soda water, sweet-sour, salty-sweet, and salty-mint sensations. We presume that this innovation could digitally enhance various flavors.
Analysis of Major Polyphenolic Compounds of Cydonia oblonga Miller (Quince) Fruit Extract by UPLC-MS/MS and Its Effect on Adipogenesis in 3T3-L1 Cells
Authors: Khan Nawaz , Maihemuti Nulibiya , Nuer Muhadaisi , Abudurousuli Kayisaier , Simayi Jimilihan , Talihati Ziruo , Han Mengyuan , Hailati Sendaer , Zhou Wenting , Wumaier Ainiwaer
Published: July 2022

Cydonia oblonga miller (quince) plant serves as a potential folk medicine for treating hypertension and cardiovascular diseases in China. However, to the best of our knowledge, no study has been conducted on the polyphenolic profile and anti-adipogenic effect of quince fruit grown in China. In the current study, we aimed to investigate the quince fruit extract’s major phenolic compounds, evaluate their antioxidant activity, and examine their effect on adipogenesis in 3T3-L1 cells. A rapid and sensitive analytical method was established for the simultaneous determination of major polyphenolic compounds by using ultra-pressure liquid chromatography coupled with a triple quadrupole mass spectrometer (UPLC-MS/MS). Among the 10 compounds, the cryptochlorgenic acid was noticed as the most abundant compound of both purified (242.44 ± 0.73 µg/mg dw) and unpurified extract (3.37 ± 0.01 µg/mg dw) followed by quercetin 3-rutinoside and chlorogenic acid. Alternatively, both extracts possessed a high quantity of phenolic acids (purified extract = 483.10 ± 5.16 µg/mg dw and unpurified extract = 7.89 ± 0.02 µg/mg dw). The purified extract exhibited a strong antioxidant capacity (DPPH: EC50 = 3.316 µg/mL, ABTS: EC50 = 36.38 µg/mL) as compared to the unpurified extract. Additionally, our results also showed that the extract at 100 µg/mL significantly suppressed the preadipocyte differentiation and decreased the lipid droplets up to 69% in mature adipocytes. The present study highlights an accurate and fast detection method for quince fruit extract polyphenolic compounds with its antioxidant and antiadipogenic effects. The study also provides the necessary information for the rational development and utilization of quince fruit extract as a source of phytochemicals.