bluuebunny/crossref_metadata_2025 huggingface dataset:
Decontamination and Ecological Restoration Performance of a Bioretention Cell-Microbial Fuel Cell under Multiple-Antibiotics Stress
Authors: Yan Yuan , Wang Yajun , Chen Tianjing , Sun Hongwei , Cai Lijuan
Published: February 2023

Antibiotics are refractory pollutants that have been widely found in various environmental media such as soil and surface water. Existing sewage treatments perform poorly at preventing antibiotics in urban sewage from polluting natural environments. In this study, we designed a bioelectrically enhanced bioretention cell system (bioretention cell-microbial fuel cell, BRC-MFC) that utilizes the unique structure of the BRC system to improve the removal of sewage antibiotics. This new system can efficiently remove antibiotics by using a synergy of plant absorption, filler adsorption, filler filtration and microbial degradation. To study the influences of multiple-antibiotics stress on the decontamination performance of BRC-MFC, ofloxacin (OFLX) and tetracycline (TC) were selected as target antibiotics, and five BRC-MFCs were built to treat sewage containing antibiotics of different concentrations. The concentrations of pollutant in the influent and effluent were measured and the pollutant removal performance of BRC-MFC was studied. The diversity of rhizosphere microorganisms and the abundance of denitrifying functional genes were analyzed. Experimental results showed that over 90% of OFLX and TC in each BRC-MFC were removed, with the removal rates positively correlating with the concentration of antibiotics. In addition, the removal rates of chemical oxygen demand (COD) in BRC-MFC were both over 90%, while the removal rate of total nitrogen (TN) was around 70%. Meanwhile, antibiotics could significantly improve the removal of ammonia nitrogen (NH4+-N, p < 0.01). The microbial richness decreased, and we found that combined antibiotic stress on microorganisms was stronger than single antibiotic stress. The abundance of denitrifying functional genes was reduced by antibiotic stress. The results of this study provide reference values for other projects focusing on removing various antibiotics from domestic sewage using BRC-MFC.
Effect of the Lateral Bone Augmentation Procedure in Correcting Peri-Implant Bone Dehiscence Defects: A 7-Years Retrospective Study
Authors: Hadzik Jakub , Błaszczyszyn Artur , Gedrange Tomasz , Dominiak Marzena
Published: February 2023

Guided bone regeneration (GBR) is a well-documented and widely-used dental surgical procedure for the treatment of various types of alveolar bone defects. The aim of the study was to evaluate the long-term effectiveness of the GBR procedure in correcting small peri-implant bone dehiscence using the xenogeneic deproteinized bovine bone mineral material and a xenogeneic native bilayer collagen membrane. The present study was designed as a retrospective study. Seventy-five bone-level tapered two-piece dental implants Conelog®, Camlog (Biotechnologies AG, Switzerland) were divided into two groups G1—no bone augmentation (no GBR)—44 implants and G2—bone augmentation (GBR)—31 implants. For both groups, the closed healing protocol with a primary wound closure was used. The incidence of peri-implantits was evaluated, the quantitative assessment of soft tissue thickness was performed using the ultrasound (USG) device, quantitative assessment of marginal bone loss (MBL) was done. The prevalence of peri-implantitis was 5.3%, with a three-fold increase in peri-implantitis comparing the groups without and with bone augmentation (G1 = 2.27%, G2 = 9.7%). The average keratinized gingiva thickness was 1.87 mm and did not differ significantly between groups. No statistically significant differences in MBL between G1 and G2 were found. When dividing patients by gender, no statistically significant differences were observed. When dividing patients by age groups, statistically significant differences were observed between the youngest and oldest groups of patients. Within the limitations of this study, it can be concluded that the use of xenogeneic bone and a xenogeneic collagen membrane in a GBR procedure can be recommended to correct small peri-implant bone dehiscence.
The Effect of Custom Insoles on Muscle Activity in Diabetic Individuals with Neuropathy
Authors: Spolaor Fabiola , Guiotto Annamaria , Ciniglio Alfredo , Sawacha Zimi
Published: February 2023

Foot ulcers are amongst the most serious complications of diabetes. Guidelines recommend that people with diabetes wear appropriate footwear or insoles to reduce repetitive stresses. Excessive plantar pressure has been recognized as the major risk factor for plantar ulcers in diabetic individuals; custom insoles are indicated as the gold standard treatment to unload the foot structure. The aim of this study was to investigate the effect of custom insoles on biomechanical and neuromuscular functions in diabetic neuropathic individuals. Ten diabetic subjects walked with and without custom insoles at their preferred speed; ten controls were assessed for comparison. Data were captured through seven video cameras, plantar pressure insoles, and surface electromyography. The electrical activity of Rectus Femoris, Tibialis Anterior, Medius Gluteus and Gastrocnemius Lateralis were acquired bilaterally. The plantar pressure and surface electromyographic variables were determined, while videos were used to detect the gait cycle. The following comparisons were made across the variables through the non-parametric SPM1D test (p < 0.05): condition with vs. without insoles vs. controls. Custom insoles provided a reduction in plantar pressure through contact surface redistribution in association with a reduced electromyographic activity. Our results suggest optimizing the prevention approach by including personalized foot and ankle exercises.
A Scientometric Study of LCA-Based Industrialization and Commercialization of Geosynthetics in Infrastructures
Authors: Giglio Carlo , Vocaturo Gianluca Salvatore , Palmieri Roberto
Published: February 2023

This study analyzes the scientific literature on Life Cycle Assessment-based (LCA-based) industrialization and commercialization of geosynthetics for infrastructures in the field of Industry 4.0, by applying a scientometric study. A set of articles published in Scopus was analyzed through both a quantitative and a qualitative approach. The results are reported in a framework where the main keywords, themes, and topics are identified and discussed. Such results include the analysis of emerging trends and convergence among different themes and topics. In fact, results from the current literature in this area are still evolving and reveal increasingly new trends and themes, opening up new and challenging research perspectives in terms of innovative applications. Moreover, this study identifies the main affiliations and countries contributing to this area, as well as the main collaboration networks among the most prominent authors and geographical areas, thus providing scholars, namely, early career ones, with an indication of the most relevant authors to connect with for their future studies.
Determination of Harmonic Parameters in Pathological Voices—Efficient Algorithm
Authors: Fernandes Joana Filipa Teixeira , Freitas Diamantino , Junior Arnaldo Candido , Teixeira João Paulo
Published: February 2023

The harmonic parameters Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio are related to vocal quality, providing alternative measures of the harmonic energy of a speech signal. They will be used as input resources for an intelligent medical decision support system for the diagnosis of speech pathology. An efficient algorithm is important when implementing it on low-power devices. This article presents an algorithm that determines these parameters by optimizing the window type and length. The method used comparatively analyzes the values of the algorithm, with different combinations of window and size and a reference value. Hamming, Hanning, and Blackman windows with lengths of 3, 6, 12, and 24 glottal cycles and various sampling frequencies were investigated. As a result, we present an efficient algorithm that determines the parameters using the Hanning window with a length of six glottal cycles. The mean difference of Autocorrelation is less than 0.004, and that of HNR is less than 0.42 dB. In conclusion, this algorithm allows extraction of the parameters close to the reference values. In Autocorrelation, there are no significant effects of sampling frequency. However, it should be used cautiously for HNR with lower sampling rates.
Formulation of a Wort-Based Beverage with the Addition of Chokeberry (Aronia melanocarpa) Juice and Mint Essential Oil
Authors: Habschied Kristina , Nišević Jelena , Krstanović Vinko , Lončarić Ante , Valek Lendić Kristina , Mastanjević Krešimir
Published: February 2023

In Croatia, the production of non-alcoholic wort-based beverages is in its initial stages. The main goal of this research was to produce wort that could be then used in the production of a wort-based beverage with the addition of chokeberry (Aronia melanocarpa) juice. Sensory characteristics and consumer acceptance was also analyzed. Worts were prepared by the standard mashing process, using Munich and pale ale malt. Chokeberry juice was added to the cooled worts. For the purpose of this research, two versions of wort with different concentrations of chokeberry juice (10%, 20%, 30%) were formulated, and all of the beverages were subjected to sensory analysis. Sensory analysis showed that wort with the addition of 30% chokeberry juice was the most accepted. This concentration of chokeberry juice gave the beverage a pleasant taste, but needed some improvement. To address this, mint essential oil was added to the mixture and carbonation using gaseous CO2 was conducted. This beverage received better acceptance when carbonated and mixed with mint essential oil. In addition to the sensory analysis, the polyphenol content of the chosen beverage was also analyzed, along with its nutritional value. Polyphenol content was 2621.47 mg/L and antioxidative activity was 2.28 mmol Trolox/L. The energy value was determined to be 57 kcal. However, in order to optimize the production of this wort-based beverage, further research should be conducted.
A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
Authors: Peng Zhi , Zhao Yongli , Yin Jianxin , Peng Peng , Ba Fushuai , Liu Xiaolong , Guo Youmin , Rong Qian , Zhang Yafei
Published: February 2023

As the acquisition unit of gas information, the sensor array directly determines the overall performance of the electronic nose system (E-nose). This paper proposed a new method for optimizing the sensor array. Firstly, four evaluation indicators (sensitivity, selectivity, correlation, and repeatability) were selected to evaluate the sensor array. Subsequently, different evaluation indicators were assigned different weight values according to their contributions to the overall performance of the E-nose. Finally, a comprehensive evaluation model was established based on the EWM-TOPSIS algorithm to optimize the sensor array. In order to verify the effectiveness of the as-proposed model, it was applied to the optimization of the E-nose sensor array composed of 10 gas sensors, and the influence of the sensor array optimization on the gas recognition ability of the E-nose was investigated. The experimental results showed that the optimized sensor array can identify the CO-CH4 gas mixtures with an accuracy of 96.5%, which a significant improvement compared with the accuracy of 78.3% before the sensor array optimization.
An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
Authors: Li Peixian , Zhu Xiaoya , Ding Xingcheng , Zhang Tao
Published: February 2023

Engineering site and shaft pillars are excavated to prolong the life of collieries and excavate more underground coal. The Jinggezhuang colliery (‘JGZ’) is a resource-exhausted coal mine in eastern China. It was determined that the industrial site and shaft pillar of JGZ would be extracted in 2008. This study excavated an experimental panel to examine the effect of pillar excavation on surface buildings in complicated geological conditions. A new pillar design was proposed based on surface monitoring to increase the recovery ratio. To maintain the safety of the shaft and engineering facilities, panel 0091 was mined and surface deformation was monitored during the experiment. The deformation characteristics and parameters were obtained using a back analysis method. A new pillar was designed using the parameters measured from panel 0091. The design maintained the safety of the shaft but relaxed the restriction of the influence of constructions at the engineering site. The prediction results of the surface subsidence and the deformation of the main building were analyzed. The maximum subsidence of the surface was 7419 mm, but the surface subsidence of the shafts was less than 10 mm. The shafts were weakly influenced by the pillar excavation. The prediction results can be used as basic information for the monitoring and maintenance of buildings in the future. Using the new pillar design, 2.54 million tons of coal resources were mined. This study provides an engineering example and a reference for shaft pillar excavation in the future.
Study on Transverse Seismic Response Characteristics of Large Diameter Vertical Double-Layer Overlapping Pipe Jacking in the Soil-Rock Composite Stratum
Authors: Ding Lingxiao , Shao Guangbiao , Shang Jinhua , Han Jianyong
Published: February 2023

The sharp change of stiffness in the soil–rock combination stratum is the weak point in the seismic design of the pipe jacking structure. To study the seismic response characteristics of vertical double-layer overlapping pipe jacking, based on the typical soil–rock combination strata in Jinan, two electric power pipe jacking tunnels of 3.6 m diameter were studied as the research objects, where the upper pipe jacking is located in the soil and the lower pipe jacking is located in the composite stratum of half soil and half rock. The soil–rock-overlapping tunnel system was deemed as a plane strain problem. By using the dynamic time history method and considering the non-linearity of material, the seismic response characteristics of a vertical overlapping pipe jacking tunnel under seismic wave were discussed from five aspects, acceleration response, displacement response, stress response, soil interlayer response and the influence of soil–rock combination stratum. The results indicate that under the action of ground motion, the peak acceleration and relative horizontal displacement of the upper pipe jacking are greater than those of the lower pipe jacking; small pipe jacking spacing will lead to the aggravating earthquake failure effect; due to the stiffness difference, the relative horizontal displacement and stress of pipe jacking structure at the soil–rock interface change abruptly. The vertical double-layer arrangement of the pipe jacking increases the buried depth, and the stratum is prone to be uneven hardness. Therefore, the seismic design and relevant structural measures of large diameter vertical overlapping pipe jacking structure should be strengthened.
Prediction Model of Type 2 Diabetes Mellitus for Oman Prediabetes Patients Using Artificial Neural Network and Six Machine Learning Classifiers
Authors: Al Sadi Khoula , Balachandran Wamadeva
Published: February 2023

The early diagnosis of type 2 diabetes mellitus (T2DM) will provide an early treatment intervention to control disease progression and minimise premature death. This paper presents artificial intelligence and machine learning prediction models for diagnosing T2DM in the Omani population more accurately and with less processing time using a specially created dataset. Six machine learning algorithms: K-nearest neighbours (K-NN), support vector machine (SVM), naive Bayes (NB), decision tree, random forest (RF), linear discriminant analysis (LDA), and artificial neural networks (ANN) were applied in MATLAB. All data used were clinical data collected manually from a prediabetes register and the Al Shifa health system of South Al Batinah Province in Oman. The results were compared with the most widely used Pima Indian Diabetes dataset. Eleven clinical features were taken into consideration for predicting T2DM. The random forest and decision tree models performed better than all the other algorithms, providing an accuracy of 98.38% for Oman data. When the same model and number of features were used, the accuracy obtained with the Oman dataset exceeded PID by 9.1%. The analysis showed that T2DM diagnosis efficiency increased with more features, which is of help in the case of many missing values.
Investigation of the Structural Perfection of a LiNbO3:Gd3+(0.003):Mg2+(0.65 wt.%) Double-Doped Single Crystal Using the Raman Spectra Excited by Laser Lines in the Visible (532 nm) and Near-IR (785 nm) Regions
Authors: Sidorov Nikolay , Palatnikov Mikhail , Pyatyshev Alexander , Skrabatun Alexander
Published: February 2023

A compositionally homogeneous nonlinear optical single crystal of double-doped LiNbO3:Gd3+(0.003):Mg2+(0.65 wt.%) was obtained. Fine features of the LiNbO3:Gd3+(0.003):Mg2+(0.65 wt.%) crystal structure were studied from the Raman spectra of the first and second orders upon excitation by laser lines in the visible (532 nm) and near-IR (785 nm) regions. When the Raman spectrum was excited by a 785 nm laser line in the frequency range of 1000–2000 cm−1 for the first time, a number of low-intensity lines in the range of 900–2000 cm−1, corresponding to the second-order Raman spectrum, were discovered. The same lines also appear in the spectrum upon excitation by a laser line with a wavelength of 532 nm, but their intensities are significantly (by an order of magnitude or more) lower. It is shown that in the structure of the double-doped LiNbO3:Gd3+(0.003):Mg2+(0.65 wt.%), the crystal oxygen-octahedral clusters MeO6 (Me–Li, Nb, Gd, Mg) are slightly distorted, and in addition, the value R = [Li]/[Nb] ≈ 1 is close to that for a nominally pure stoichiometric crystal.
UnifiedFace: A Uniform Margin Loss Function for Face Recognition
Authors: Zhao Feng , Zhang Peng , Zhang Ran , Li Mengwei
Published: February 2023

Face recognition has achieved great success due to the development of deep convolutional neural networks (DCNNs) and loss functions based on margin. However, complex DCNNs bring a large number of parameters as well as computational effort, which pose a significant challenge to resource-constrained embedded devices. Meanwhile, the popular margin-based loss functions all introduce only one type of margin and cannot further introduce a larger margin to achieve tighter classification boundary. In contrast to the common approach, we believe that additive and multiplicative margins should be used jointly to introduce larger margins from the margin perspective. Therefore, we propose a new margin-based loss function called UnifiedFace. First, we introduce an additive margin in the target angle activation function. Second, we add a multiplicative margin in the non-target angle. UnifiedFace introduces both additive and multiplicative margins, allowing for the introduction of large margins to achieve more compact intra-class variance and closer separated inter-class variance. In addition, we specifically design efficient face recognition models called GhostFaceNet for resource-constrained embedded devices. Experimental results demonstrate that UnifiedFace achieves state-of-the-art performance or performance competed with popular methods in training datasets of different sizes. UnifiedFace achieves optimal performance in models of varying complexity. Moreover, competitive results are achieved in the large-scale test set IJBB/C, especially the state-of-the-art performance achieved in TAR (FAR=1e−6). GhostFaceNet can significantly improve operational efficiency without significantly degrading recognition performance, making it ideal for embedded devices with limited resources.
Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
Authors: Cheng Xingyuan , Wang Bo , Ma Longxiang , Xue Chenxi , Yu Yunxiang
Published: February 2023

Expanded anchor cables have been widely used in the rapid development of underground engineering. However, there are still some deficiencies in the computation of the ultimate bearing capacity of expanded anchor cables. Based on the upper-bound theorem of limit analysis, the upper-bound solution of the ultimate bearing capacity of an expanded anchor cable was derived. For this calculation, the instability mechanism of the soil at the front surface of the anchorage segment of the expanded anchor cable was assumed to satisfy the logarithmic spiral failure model, its 3D velocity discontinuity surface was generated using the spatial discretization technique, and the optimal solution was determined through the particle swarm optimization (PSO) algorithm. The breakage mechanism of the anchorage side surface was assumed to appear at the interface between the anchorage body and the soil, and its velocity field satisfied the requirements of the associated flow rule. The accuracy of the proposed analytical solution was well-verified through a comparison with three-dimensional numerical simulations, and its superiority was also well-demonstrated in comparison to the existing theoretical calculation method. Subsequently, the influence of the key parameters of the anchor cable on the ultimate lateral resistance, end resistance, and total pullout capacity was discussed. The results showed that: the anchorage segment diameter, anchorage segment length, and buried depth of the expanded anchor cable had a great influence on the ultimate lateral resistance, ultimate end resistance, and total ultimate pullout capacity; however, the anchorage segment length had little influence on the ultimate end resistance, and the inclination angle of the anchor cable had relatively little influence on the resistance.
Coefficient of Difficulty in Running a Sports Course in Alpine Skiing
Authors: Aschenbrenner Piotr , Erdmann Włodzimierz , Krawczyński Bartosz , Krawczyński Marcin
Published: February 2023

In alpine ski disciplines, it is common for competitors to record incomplete runs. In some instances, up to 40% of competitors have failed to complete a course. Possible reasons include incorrect preparation of skiers (lack of adequate motor preparation) and poor tactical approaches. However, there are also some errors in the setting of gates on a course. The aim of this paper is to determine the objective difficulty coefficient of alpine skiing competition courses, based on course geometry and the kinematic parameters of the run. This research monitored alpine skiers during the FIS World Cup. All four alpine skiing disciplines (i.e., downhill, super giant, giant slalom, and slalom) were considered. The average value of the coefficient of difficulty (W.tr) was the highest for the downhill style runs and amounted to 1.20, the super giant and the giant had comparable values of 1.13–1.14, and for the slalom, the W.tr value was the lowest at 0.97. Based on the determined coefficients of route difficulty, it is possible to derive a quantifiable metric to estimate the degree of risk associated with different sections of a course.
Cognition Test Battery Survey: Development of a Single Alertness and Mood Score for Short- and Long-Duration Spaceflight
Authors: Pundyavana Anish , Gilad Yoni , Stahn Alexander C. , Basner Mathias
Published: February 2023

Astronaut behavioral and mental health are key factors for space mission success. NASA’s Cognition test battery is often preceded by a brief 18-item Alertness and Mood Survey (AMS) adding subjective assessments to Cognition’s 10 objective neuropsychological tests. Therefore, the main objective of this study was to generate a single AMS summary score for short (<6 months) and long (>6 months) space missions based on the relevance of individual items. With the paired comparison (PC) method, 19 subject matter experts (SMEs) were asked to rate the relevance of 14 AMS items for astronaut behavioral health. Boredom (22.1% of comparisons), monotony (23.1%), and sleepiness (35.6%) were considered the least relevant, and health (74.3%), depression (76.5%), and crew conflicts (77.9%) were considered the most relevant by SMEs. Six of the fourteen items differed statistically significantly between PCs for short- and long-duration missions: sleepiness, tiredness, energy level, and mental status were considered more relevant for short-duration missions, while monotony and loneliness were considered more relevant for long-duration missions (all adjusted p < 0.05). We also demonstrated systematic changes in AMS summary scores during a 60-day 6° head-down tilt bed rest (HDBR) study, with increased alertness and mood disturbance during and after the HDBR period relative to pre-HDBR levels. This analysis identified the AMS domains considered most relevant for space mission success by SMEs, and highlighted differences between long- and short-duration missions. The resulting AMS summary scores were based on item relevance and will be useful for monitoring astronaut behavioral health on short- and long-duration space missions.
A SPICE-Oriented Method for Finding Multiple DC Solutions in Nonlinear Circuits
Authors: Hałgas Stanisław
Published: February 2023

Finding multiple operating points of nonlinear circuits is a fundamental issue in electronic circuit analysis and design. Methods that guarantee finding all DC solutions are very time-consuming and can be used to analyze only rather small-sized circuits. On the other hand, there exist approaches that can find multiple operating points, but they do not guarantee finding all solutions. The methods are less time-consuming, do not require much computing power, and allow for analyzing more complex circuits. This paper proposes an efficient method for determining multiple and, in most cases, all DC solutions. The approach uses a deflation technique, and the SPICE simulator is applied to the calculations. For this purpose, the original nonlinear circuit during the computational process was modified appropriately after successive solutions were determined. The method was verified on benchmark nonlinear circuits from the literature, made in various technologies and using various models. The results of the DC analysis of several circuits are provided.
Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
Authors: Xu Jieming , Ding Xuefeng , Ke Hanyu , Xu Cong , Zhang Hanlun
Published: February 2023

Students’ mental health has always been the focus of social attention, and mental health prediction can be regarded as a time-series classification task. In this paper, an informer network based on a two-stream structure (TSIN) is proposed to calculate the interdependence between students’ behaviors and the trend of time cycle, and the intermediate features are integrated layer by layer to realize the prediction of mental health by a gating mechanism. Through experiments on a real campus environment dataset (STU) and an open dataset (MTS), it is verified that the proposed algorithm can obtain higher accuracy than existing methods.
The Impact of Virtual Reality-Based Products on Mild Cognitive Impairment Senior Subjects: An Experimental Study Using Multiple Sources of Data
Authors: Tian Mi , Cai Yuchao , Zhang Jie
Published: February 2023

Mild Cognitive Impairment (MCI) in pensioners has become an important concern in the aging population, and there are an increasing number of products, especially virtual reality (VR)-based products, to assist in the identification, intervention, and treatment of MCI older adults. Multiple studies on the efficacy and usability of VR products are also receiving increased attention from designers. However, issues such as the efficacy testing of VR products still face serious challenges. In this study, we evaluated VR products looking at the interactive responsiveness of MCI older adults when using VR products, and analyzed the brain activation status and behavioral conditions of MCI older adults. Multisource data were generated by a functional near-infrared spectroscopy (fNIRS) device with high spatial resolution and a behavioral recording device reflecting motor abilities. Small-wave amplitudes were selected as indicators of brain activation to analyze six brain areas: LPFC, RPFC, LOL, ROL, LMC, and RMC. Eight aspects, such as overall upper-limb speed, upper-limb global acceleration, and median velocity, were selected as indicators for behavioral recording. The differences were observed by comparing the level of completion of interactive responses by MCI older adults between the two groups. The results showed that MCI older adults showed different levels of activation in brain regions when performing VR product-based tasks. The higher the level of cognition, the better the interactive response in the task and the stronger the activation of brain regions. Meanwhile, the level of interaction response had a significant correlation with the motor performance of MCI older adults, with stronger motor functions leading to a more effective interaction response to the product. This study proposes a new method to evaluate the feasibility of monitoring the interaction between the MCI elderly and VR-based products using fNIRS with Kinect, which provides a new way to evaluate the effectiveness of VR-based product-assisted treatment.
Assessment of the Rock Elasticity Modulus Using Four Hybrid RF Models: A Combination of Data-Driven and Soft Techniques
Authors: Li Chuanqi , Dias Daniel
Published: February 2023

The determination of the rock elasticity modulus (EM) is an indispensable key step for the design of rock engineering problems. Traditional experimental analysis can accurately measure the rock EM, but it requires manpower and material resources, and it is time consuming. The EM estimation of new rocks using former published empirical formulas is also a possibility but can be attached of high uncertainties. In this paper, four types of metaheuristic optimization algorithms (MOA), named the backtracking search optimization algorithm (BSA), multi-verse optimizer (MVO), golden eagle optimizer (GEO) and poor and rich optimization algorithm (PRO), were utilized to optimize the random forest (RF) model for predicting the rock EM. A data-driven technology was used to generate an integrated database consisting of 120 rock samples from the literature. To verify the predictive performance of the proposed models, five common machine-learning models and one empirical formula were also developed to predict the rock EM. Four popular performance indices, including the root-mean-square error (RMSE), mean absolute error (MAE), the coefficient of determination (R2) and Willmott’s index (WI), were adopted to evaluate all models. The results showed that the PRO-RF model has obtained the most satisfactory prediction accuracy. The porosity (Pn) is the most important variable for predicting the rock EM based on the sensitive analysis. This paper compares the performance of the RF models optimized by using four MOA for the rock EM prediction. It provides a good example for the subsequent application of soft techniques on the EM and other important rock parameter estimations.
Composites Containing Felt Wastes from the Automotive Industry
Authors: Zegardło Bartosz , Maraveas Chrysanthos , Kaleszko Sylwester , Bombik Antoni
Published: February 2023

(One) Background: Using textile waste materials in composites is a well-known problem and is frequently addressed by various scientific teams. Most of this work concerns textile waste introduced into composites as yarn strands. The present work focuses on adding textile wastes prepared in the form of single filaments of yarn spun to fluff, which was produced from waste felt materials from the automotive industry. (Two) Methods: The material was extracted from the bulkheads of worn-out vehicles, serving as thermal and acoustic insulation. The waste was shredded to form single yarn fibres with a fibre diameter of 0.08–0.3 mm and a 2–8 cm length. The shredded waste was used as a filler and modifier for composites. Four test batches were produced with different recyclate contents. A traditional cementitious composite without additives was used as a comparison material. (Three) Results: Composites filled with 3% felt waste have 23.31% lower density (1.71 g/cm3), 71.03% higher absorbability (21.58%), 49.58% lower tensile strength (19.86 MPa), and 53.55% lower compressive strength (3.64 MPa) than traditional composites. Partitions made of these composites had much higher thermal insulation than traditional composites. Composite made of 1% waste was resistant to the phenomena of thermal spalling. Moreover, the spot flame loading did not damage the composite, and there were no scratches or defects. (Four) Conclusions: The tests proved that the waste felt materials could potentially be used as fillers and modifiers in lightweight composites with higher thermal insulation. The addition of felt fibres improves the resistance of the composite to local spalling.
Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine
Authors: Ye Qing , Liu Changhua
Published: February 2023

This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs the probability of each fault mode occurring. First, multiple two-class sub-classifiers based on SBELM are trained by using single-fault samples to extract the correlation between various pairs of single-fault, and the sub-classifiers are integrated with the proposed hierarchical multi-label classification (HMLC) strategy to form the diagnostic model based on HMLC-SBELM. Then, samples of single faults and simultaneous faults are used to generate the optimal discriminative thresholds by using optimization algorithms. Finally, the probabilistic output generated by the HMLC-SBELM-based model is transformed into the final fault modes by using the optimal discriminative threshold. The model performance is evaluated by using actual vibration signals of the main reducer and is compared with several classical models. The contrastive results indicate that the proposed model is more accurate, efficient, and stable.
COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
Authors: Kim Sera , Baek Ji-Young , Lee Seok-Pil
Published: February 2023

Contrary to expectations that the coronavirus pandemic would terminate quickly, the number of people infected with the virus did not decrease worldwide and coronavirus-related deaths continue to occur every day. The standard COVID-19 diagnostic test technique used today, PCR testing, requires professional staff and equipment, which is expensive and takes a long time to produce test results. In this paper, we propose a feature set consisting of four features: MFCC, Δ2-MFCC, Δ-MFCC, and spectral contrast as a feature set optimized for the diagnosis of COVID-19, and apply it to a model that combines ResNet-50 and DNN. Crowdsourcing datasets from Cambridge, Coswara, and COUGHVID are used as the cough sound data for our study. Through direct listening and inspection of the dataset, audio recordings that contained only cough sounds were collected and used for training. The model was trained and tested using cough sound features extracted from crowdsourced cough data and had a sensitivity and specificity of 0.95 and 0.96, respectively.
Review on Recent Applications of Cold Plasma for Safe and Sustainable Food Production: Principles, Implementation, and Application Limits
Authors: Cherif Mohamed Majdi , Assadi Imen , Khezami Lotfi , Ben Hamadi Naoufel , Assadi Aymen Amine , Elfalleh Walid
Published: February 2023

The food and agricultural industries have numerous practical advantages to be gained from the use of cold plasma technology. This paper attempts to showcase the possible uses of cold plasma in the food sector, while also highlighting the most recent developments and market trends. The efficiency of cold plasma in enhancing food products’ quality and shelf life has been demonstrated in several investigations. This review has concentrated on current research into how this technology affects various food chain production stages. Cold plasma has become a cutting-edge non-thermal technique that can be used to ensure food safety. The precise mechanism underlying the effectiveness of cold plasma is still unclear. Understanding these mechanisms and potential elements that can restrict or increase their effectiveness and results is crucial to further enhancing and implementing cold plasma treatment in food processing. The main objective of this review is to investigate the use of plasma, its exceptional characteristics, and its advantages in safe, sustainable food production. In particular, this review summarizes recent studies on the use of cold plasma for microorganisms and pesticides treatment, compiling them and discussing their content. As reported in the literature, a critical point has also been reviewed about some diverse plasma configurations. A comparative study of the efficacy of cold plasma in environmental applications (microorganisms/pesticides) has also been reviewed from the literature.
Retrofitting Historic Buildings for Future Climatic Conditions and Consequences in Terms of Artifacts Conservation Using Hygrothermal Building Simulation
Authors: Coelho Guilherme B. A. , de Freitas Vasco Peixoto , Henriques Fernando M. A. , Silva Hugo Entradas
Published: February 2023

Climate change will greatly influence the world at several levels and will have consequences on the interior climate of historic buildings and artifacts conservation. Buildings are responsible for a large slice of the overall emissions, which is due both to the greenhouse gases that are released during their construction as well as the activities that are performed therewithin. One way of counteracting this trend is to design more efficient retrofit buildings and predict their behavior using simulation software, which can thoroughly assess the performance of new buildings or the impact of each retrofit measure for existing buildings. In this study, a calibrated computational model of high thermal inertia building was used to assess the performance of passive retrofits in mitigating the effects of climate change concerning artifact decay mechanisms. In addition, a methodology that aims to reduce the amount of time spent to perform these studies is also presented, in which time savings reach up to 63%.
Increasing the Editing Efficiency of the MS2-ADAR System for Site-Directed RNA Editing
Authors: Li Jiarui , Oonishi Tomoko , Fan Guangyao , Sakari Matomo , Tsukahara Toshifumi
Published: February 2023

Site-directed RNA editing (SDRE) technologies have great potential in gene therapy. Our group has developed a strategy to redirect exogenous adenosine deaminases acting on RNA (ADARs) to specific sites by making editable structures using antisense RNA oligonucleotides. Improving the editing efficiency of the MS2-ADAR system is important in treating undesirable G-to-A point mutations. This work demonstrates an effective strategy to enhance the editing efficiency of this SDRE system. The strategy involves changing the number of MS2 stem-loops on both sides of the antisense RNA and the mismatch base on the antisense part. The enhanced green fluorescent protein (EGFP) with W58X mutation is used as the reporter gene. Subsequently, we adjusted the amount of plasmids for transfection to tune the expression level of the guide RNA, and finally, we observed the fluorescence signal after transfection. After equalizing number of MS2 stem-loops at both sides of the antisense RNA, high editing efficiency was achieved. In the same level of guide RNA expression, when the paired base position was the target uridine, the editing efficiency was higher than cytidine, adenosine, and guanosine.
Experimental Investigation of High-Viscosity Conductive Pastes and the Optimization of 3D Printing Parameters
Authors: Zhang Jinyu , Wu Shixiong , Wang Zedong , Chen Yuanfen , You Hui
Published: February 2023

Traditional contact printing technology is primarily controlled by the shape of the mask to form the size, while for the more popular non-contact printing technologies, in recent years, adjusting the print parameters has become a direct way to control the result of the printing. High-viscosity conductive pastes are generally processed by screen printing, but this method has limited accuracy and wastes material. Direct-write printing is a more material-efficient method, but the printing of high-viscosity pastes has extrusion difficulties, which affects the printed line width. In this paper, we addressed these problems by studying the method of printing high-viscosity conductive paste with a self-made glass nozzle. Then, by parameter optimization, we achieved the minimum line width printing. The results showed that the substrate moving speed, the print height, and the feed pressure were the key factors affecting the line width and stability. The combination of the printing parameters of 0.6 MPa feed pressure, 200 mm/s substrate moving speed, and 150 μm print height can achieve a line width of approximately 30 μm. In addition, a mathematical model of the line width and parameters was established, and the prediction accuracy was within 5%. The results and the prediction model of the parameters provide an important reference for the printing of high-viscosity pastes, which have immense potential applications in electronics manufacturing and bioprinting.
Patient Mortality Prediction and Analysis of Health Cloud Data Using a Deep Neural Network
Authors: Alourani Abdullah , Tariq Kinza , Tahir Muhammad , Sardaraz Muhammad
Published: February 2023

Cloud computing plays a vital role in healthcare as it can store a large amount of data known as big data. In the current emerging era of computing technology, big data analysis and prediction is a challenging task in the healthcare industry. Healthcare data are very crucial for the patient as well as for the respective healthcare services provider. Several healthcare industries adopted cloud computing for data storage and analysis. Incredible progress has been achieved in making combined health records available to data scientists and clinicians for healthcare research. However, big data in health cloud informatics demand more robust and scalable solutions to accurately analyze it. The increasing number of patients is putting high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. Predicting mortality among patients with a variety of symptoms and complications is difficult, resulting inaccurate and slow prediction of the disease. This article presents a deep learning based model for the prediction of patient mortality using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Different parameters are used to analyze the proposed model, i.e., accuracy, F1 score, recall, precision, and execution time. The results obtained are compared with state-of-the-art models to test and validate the proposed model. Moreover, this research suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate.
Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
Authors: Wang Chen , Zhuang Jiayan , Ye Sichao , Liu Wei , Yuan Yaoyao , Zhang Hongman , Xiao Jiangjian
Published: February 2023

Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder–decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics.
Comparison of CW NUV and Pulse NIR Laser Influence on PbSe Films Photosensitivity
Authors: Olkhova Anastasiia A. , Patrikeeva Alina A. , Dubkova Maria A. , Kuzmenko Natalia K. , Nikonorov Nikolai V. , Sergeev Maksim M.
Published: February 2023

This paper shows the laser irradiation effect on optical characteristics on PbSe chalcogenide films as a result of irreversible structural modification. The features of film structure and property modification under the action of a continuous wave (CW) laser with a 405 nm wavelength and nanosecond laser pulses with a 1064 nm wavelength are studied. The valence and conduction bands boundaries displacement as a laser radiation result of photothermal action on the film until it darkens and bleaches, is demonstrated. Under CW near ultraviolet (NUV) laser action, the film was modified at a power density of 0.74 to 1.09 kW/cm2. The near-infrared (NIR) laser pulses used ensured the film structure modification at a power density of 1.45 kW/cm2 and a pulse duration of 4 to 20 ns. Scanning with a laser spot in these modes provides the desired change in the film’s optical characteristics, and this becomes a serious alternative to the technology of heat treatment in an oven.