bluuebunny/crossref_metadata_2025 huggingface dataset:
Bionic Design of a Winding Roller and Experiments for Cleaning Long Foreign Matter from Raw Cotton
Authors: Feng Zesen , Zhao Ling , Huangfu Zhongzheng , Liu Zongbin , Dong Zhihu , Yu Xin , Han Jialin , Zhou Guo , Wu Yanlong
Published: October 2022

Natural cotton fibers are inevitably mixed with long foreign matter (LFM) from the planting and ginning processes, which greatly deteriorate the quality of textile products. Therefore, how to design automated equipment that can efficiently remove LFM is very important for the cotton industry supply chain. Inspired by the comb-like gill rake structure of the carp (cyprinus carpio), a structural bionic method is first proposed for the development of automatic equipment. A series of key parameters of the proposed bionic design are presented and further optimized based on the orthogonal experimental design algorithm and the designed experimental automatic equipment through a sensitivity analysis between the factors (key parameters) and response (LFM removal efficiency). The design results show that the tooth length of the winding roller is the most significant factor effecting the response. Experimental results demonstrate that the designed modular comb-like winding device has great potential for application in the industrial cotton supply chain. Moreover, the structural bionic method is an applicable and promising method to develop this equipment compared with traditional design ideas.
Application of MMC-RPC in High-Speed Railway Traction Power Supply System Based on Energy Storage
Authors: Li Teng , Shi Yongbin
Published: October 2022

In order to effectively improve the power quality and utilize railway regenerative braking energy in high-speed railway traction power supply system, this paper adopts the Modular Multilevel Converter type Railway Power Conditioner (MMC-RPC) with distributed super-capacitor (SC) energy storage (ES) scheme. Firstly, the single-phase MMC mathematical model is established, and the power compensation characteristics of MMC-RPC are derived. Secondly, the Virtual Synchronous Generator (VSG) control strategy is adopted to provide inertial support. Compared with the double closed loop (DCL) control, it has better anti-disturbance and dynamic performance. Then, based on the VSG control, a two-stage circulating current suppression strategy is proposed, in which Quasi-Proportional Complex Integral (QPCI) control is used to suppress the bridge arm circulating current, and integrated Proportional Integral-Quasi Proportional Resonant (PI-QPR) control is an improvement of the VSG control to further suppress the circulating current inside the VSG. Furthermore, the virtual DC motor (VDCM) strategy was proposed to control the charge and discharge of the distributed SC connected to MMC-RPC to recycle railway braking energy. Finally, simulation results of the VSG small signal model and the MMC-RPC simulation model on the Matlab/Simulink platform verify the effectiveness, stability of VDCM and improved VSG controls in the MMC-RPC.
An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
Authors: Wang Bo , Shi Feng , Tie Guipeng , Song Ci , Guo Shuangpeng
Published: October 2022

There are strict requirements on the surface-shape accuracy, cracks, scratches, and subsurface damage of photomask glass substrates with the advancement of photo-etching technology. In this study, photomask quartz glass substrates were etched with the hydrofluoric (HF) acid etching method after chemical mechanical polishing (CMP), so that the polishing hydrolysis layer could be completely removed. In addition, the surface scratches caused by CMP were observed with a surface quality detection device and a white light interferometer. Second, the high-efficiency array-type magnetorheological polishing technology was employed to eliminate the surface-shape error on the surface of quartz photomask glass substrates. Finally, the removal function rotation angle based magnetorheological polishing technology was utilized to evenly remove the mid-frequency ripples and scratches caused in the preceding technological process. The experimental results demonstrated that these technologies can be used to realize rapid damage removal and high-quality processing of photomask glass substrates.
Deep Anomaly Detection for In-Vehicle Monitoring—An Application-Oriented Review
Authors: Caetano Francisco , Carvalho Pedro , Cardoso Jaime
Published: October 2022

Anomaly detection has been an active research area for decades, with high application potential. Recent work has explored deep learning approaches to the detection of abnormal behaviour and abandoned objects in outdoor video surveillance scenarios. The extension of this recent work to in-vehicle monitoring using solely visual data represents a relevant research opportunity that has been overlooked in the accessible literature. With the increasing importance of public and shared transportation for urban mobility, it becomes imperative to provide autonomous intelligent systems capable of detecting abnormal behaviour that threatens passenger safety. To investigate the applicability of current works to this scenario, a recapitulation of relevant state-of-the-art techniques and resources is presented, including available datasets for their training and benchmarking. The lack of public datasets dedicated to in-vehicle monitoring is addressed alongside other issues not considered in previous works, such as moving backgrounds and frequent illumination changes. Despite its relevance, similar surveys and reviews have disregarded this scenario and its specificities. This work initiates an important discussion on application-oriented issues, proposing solutions to be followed in future works, particularly synthetic data augmentation to achieve representative instances with the low amount of available sequences.
Coastal Sargassum Level Estimation from Smartphone Pictures
Authors: Vasquez Juan Irving , Uriarte-Arcia Abril Valeria , Taud Hind , García-Floriano Andrés , Ventura-Molina Elías
Published: October 2022

Since 2011, significant and atypical arrival of two species of surface dwelling algae, Sargassum natans and Sargassum Fluitans, have been detected in the Mexican Caribbean. This massive accumulation of algae has had a great environmental and economic impact. Most works addressing this topic use high-resolution satellite imagery which is expensive or may be time delayed. We propose to estimate the amount of Sargassum based on ground-level smartphone photographs that, unlike previous approaches, is much less expensive and can be implemented to make predictions almost in real time. Another contribution of this work is the creation of a Sargassum images dataset with more than one thousand examples collected from public forums such as Facebook or Instagram, labeled into 5 categories of Sargassum level (none, low, mild, plenty, and excessive), a relevant difference with respect to previous works, which only detect the presence or not of Sargassum in a image. Several state-of-the-art convolutional networks: AlexNet, GoogleNet, VGG, and ResNet, were tested using this dataset. The VGG network trained under fine-tuning showed the best performance. The results of the carried out experiments show that convolutional neuronal networks are adequate for providing an estimate of the Sargassum level only from smartphone cameras images.
RobustATR: Substrate-Integrated Hollow Waveguide Coupled Infrared Attenuated Total Reflectance Sensors
Authors: Teuber Andrea , Mizaikoff Boris
Published: October 2022

Small and compact mid-infrared devices are of increasing importance, as there are several applications demanding on-site and real-time measurements in harsh real-world scenarios. The RobustATR, an innovative infrared attenuated total reflectance (IR-ATR) accessory, has been developed and tested with exemplary analytes integrating a single-wavelength Fabry–Pérot quantum cascade laser as light source for testing the feasibility of a potentially miniaturized overall sensor design. Successful direct coupling of the laser radiation via substrate-integrated hollow waveguide (iHWG) coupling elements to the sensor interface could be shown, whereby exemplary analytes of environmental and medical relevance were tested, revealing the future potential for real-world applications.
Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
Authors: Hao Shujia , Wen Mingyun , Cho Kyungeun
Published: October 2022

Three-dimensional (3D) reconstruction techniques are playing an increasingly important role in education and entertainment. Real and recognizable avatars can enhance the immersion and interactivity of virtual systems. In 3D face modeling technology, face texture carries vital face recognition information. Therefore, this study proposes a panoramic 3D face texture generation method for 3D face reconstruction from a single 2D face image based on a 3D Morphable model (3DMM). Realistic and comprehensive panoramic facial textures can be obtained using generative networks as texture converters. Furthermore, we propose a low-cost method for generating face texture datasets for data collection. Experimental results show that the proposed method can generate panoramic face textures for 3D face meshes from a single image input, resulting in the final generation of textured 3D models that look realistic from different viewpoints.
Empirical Safety Stock Estimation Using GARCH Model, Historical Simulation, and Extreme Value Theory: A Comparative Study
Authors: Derbel Mouna , Aljuaid Awad M. , Hachicha Wafik
Published: October 2022

Safety stock (SS) is an appropriate tactic to deal with demand and supply uncertainty with the aim of preventing inventory shortages. In the literature, previous work on SS estimation assumes that the forecast error distributions (FED) are independent and identically distributed (i.i.d) following the normal distribution. In order to assess violations of this assumption, there are many solution methods in the recent literature that include the following: (1) Consider the FED as other distribution models, such as gamma distribution or log-normal distribution, etc. (2) Use the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model to consider the Heteroskedasticity phenomena, (3) Use the extreme value theory (EVT) to take into consideration the occurrence of extreme demands, etc. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Indeed, the estimation of SS is based on the approximation of quantiles of the FED. Such quantiles are related to the cycle service levels (CSL) that are important to achieve company goals. Accordingly, the aim of this research is to propose two combined empirical methods to determine the SS in a more robust fashion and compare them with traditional methods under different supply chain parameters. The first combined method, named Filtered Historical Simulation (FHS), consists of combining the GARCH model with the simulation method. The second combination named Conditional Extreme Value Theory (CEVT) is the GARCH model with EVT. To validate these proposed combined methods, the SS is also estimated using traditional methods, such as simple exponential smoothing (SES), simulation, and kernel density estimation (KDE). The methodology is illustrated with both simulation data and real case study data for different lead times. For the FED, two cases are studied: lognormal distribution and gamma distribution. The results show the superiority of the two proposed combination methods with respect to the tick loss function (TLF) for the different CSL targets and for shorter and longer lead times. Results are confirmed using the ANOVA test.
A Federated Incremental Learning Algorithm Based on Dual Attention Mechanism
Authors: Hu Kai , Lu Meixia , Li Yaogen , Gong Sheng , Wu Jiasheng , Zhou Fenghua , Jiang Shanshan , Yang Yi
Published: October 2022

Federated incremental learning best suits the changing needs of common Federal Learning (FL) tasks. In this area, the large sample client dramatically influences the final model training results, and the unbalanced features of the client are challenging to capture. In this paper, a federated incremental learning framework is designed; firstly, part of the data is preprocessed to obtain the initial global model. Secondly, to help the global model to get the importance of the features of the whole sample of each client, and enhance the performance of the global model to capture the critical information of the feature, channel attention neural network model is designed on the client side, and a federated aggregation algorithm based on the feature attention mechanism is designed on the server side. Experiments on standard datasets CIFAR10 and CIFAR100 show that the proposed algorithm accuracy has good performance on the premise of realizing incremental learning.
Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China
Authors: Zhao Bo , Zhang Dehui , Zhang Rongzhen , Li Zhu , Tang Panpan , Wan Haoming
Published: October 2022

This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe2O3, etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection.
Asymmetric Etalon Effect in Fold-Type Optical Feedback Cavity-Enhanced Absorption Spectroscopy
Authors: Wang Yunzheng , Guan Shiyu , Cao Huilin , Tan Zhongqi
Published: October 2022

To further improve the performance of cavity-enhanced spectroscopy systems, a high-quality U-cavity system was established. In the process of the experiment, an asymmetric ripple effect, which is different from the previous etalon effect, was found, which seriously affects the performance of the spectral system. This unique phenomenon mainly manifests in the different amplitudes of the fluctuations of the spectral curves measured by the folding mirror and the end mirror in the U-cavity system. Based on multi-beam interference theory, we analyzed the characteristics of the transmission spectrum of each mirror in the presence of the etalon effect at the end mirror, and obtained the following conclusions: for the U-cavity system, the strength of the etalon effect of each mirror is inversely proportional to its transmission loss value, that is, the larger the loss, the smaller the ripple of the transmission spectrum, and vice versa. In order to eliminate this effect, the most effective way is to eliminate the etalon effect caused by the light feedback of the end mirror. After improving the system, the minimum detectable absorption coefficient of αmin=8.33×10−9cm−1 is obtained with this U-shape Optical Feedback Cavity-Enhanced Absorption Spectroscopy. These works are valuable references for the design of folded Cavity-Enhanced Absorption Spectroscopy systems and have potential for laser wavelength calibration and measurement of a mirror’s reflectance.
Identification of Mobility Patterns in Rural Areas of Low Demographic Density through Stated Preference Surveys
Authors: Jiménez-Espada Montaña , Naranjo Juan Miguel Vega , García Francisco Manuel Martínez
Published: October 2022

Within the multiple urban–rural interactions that make up the territorial dynamics, this article addresses and identifies how mobility relations are produced between neighbouring municipalities that share services. The aim of this research is to carry out a diagnosis of the current mobility situation in an area of low population density in order to identify the needs and possible shortcomings in this area. The initial identification of weaknesses is essential in order to propose solutions for rural mobility. The methodology adopted is based on two distinct lines of work: (1) analysis of information in open data from public repositories using geographic information system tools (GIS), and (2) surveys of citizens living in the study area. The results allude to the fact that the most transcendental problem in the study area is the lack of a quality collective public transport service that meets minimum utility requirements for users, a fact that generates a transfer towards the use of private vehicles. No serious parking, noise, pollution or road safety problems are observed; however, similar dynamics to other rural areas with low demographic density are confirmed, such as the age of the mobile fleet and an aging population with accessibility problems. The presence of new modes of transport (electric bicycles, personal mobility vehicles, and even electric vehicles) is practically insignificant. Both teleworking and the new consumer habits associated with online shopping have not yet had a strong impact. Political decision making by public administrations is identified as a direct application of this research.
Finite Element Analysis of the Reduction in Stress Concentration Factors in Shrink Fits by Using Contact Rings
Authors: Izard Eulalia , Garcia Roberto , Rodriguez-Martín Manuel , Lorenzo Miguel
Published: October 2022

As it is well known, shrink fits exhibit a stress concentration at the hub edges that can cause the failure of such mechanical components. A method for reducing such a stress concentration is placing a contact ring between the hub and the shaft. To achieve the desired effect, the Young Modulus of the material used for the contact ring must be lower than the one used in the hub and the shaft. Unfortunately, there are no design methods for estimating the optimal dimensions or materials of the contact ring. To fill this gap, in this study, diverse numerical simulations by the finite elements method (FEM) were carried out considering different geometries and materials in order to obtain recommendations that allow mechanical designers to significantly reduce the stress concentrations in these components. According to the obtained results, a contact ring of 25% of the hub thickness allows to significantly reduce up to 40% of the stress concentration. In addition, a linear influence of the stress reduction with the Young modulus was found thereby, and the most recommendable material for the contact ring is the one with the lowest Young modulus. On the other hand, according to the obtained results, the influence of the Poisson coefficient can be considered negligible.
GC–MS Analysis of A Helichrysum italicum Hydrosol: Sensitivity, Repeatability and Reliability of Solvent Extraction versus Direct Hydrosol Analysis
Authors: Kokalj Ladan Meta , Kočevar Glavač Nina
Published: October 2022

Hydrosols have been increasingly used in the food industries as drinks and as ingredients for seasoning and flavoring. Research has shown they have antimicrobial, antioxidative and anti-inflammatory effects and therefore have potential as food preservative agents and sanitizing agents for tools used in the food industry. The composition analysis of hydrosols is commonly conducted using extraction with organic solvents. However, some hydrophilic compounds are lost with this procedure, leading to issues in identifying native composition and potential adulteration. In this paper, different methods for hydrosol analysis are studied. The methods differ in sample preparation, number of detected compounds, repeatability and reliability. Direct hydrosol analysis and 1:1 extraction with organic solvent were determined to be the most appropriate in our experiments. However, the method implementation depends on the application. Direct hydrosol analysis is simple and does not change ratios among compounds; its drawback is poor repeatability. Using 1:1 extraction has good repeatability; however, some hydrophilic compounds are lost, and adulterations with cosolvents and the addition of solubilizers cannot be discovered.
Distinction of Human and Mechanical Vibrations within Similar Frequency Bands Based on Wavelet Entropy Using Ultrawideband Radar
Authors: Xue Huijun , Ma Yangyang , Zhang Yang , Zhang Ziqi , Shi Gang , Wang Jianqi , Lv Hao
Published: October 2022

Construction machinery is necessary in postdisaster emergency rescue missions involving the destruction of ruins. However, their mechanical vibrations can interfere with the detection of human survivors using ultrawideband (UWB) radar. Traditional methods detect and identify humans by determining maximum energy and checking respiratory frequency. However, they lose effectiveness because mechanical vibration is associated with a frequency band which is similar to the human respiration band, but it has higher energy. This study proposes a novel method to distinguish human vibrations from mechanical vibrations. After preprocessing, wavelet entropy decomposition was implemented on the radar data. An improved, censored mean-level detector, constant false-alarm rate algorithm was utilized to automatically identify the position of human and mechanical vibrations. A novel feature is then extracted by calculating the half-height width of the target’s wavelet entropy. Finally, the results of two independent sample t-tests prove that there is a significant statistical difference between the feature values of humans and the mechanical vibrations (p < 1.9 × 10−6), thus proving the effectiveness of the method. We envisage that the proposed method can be used in postdisaster rescue missions to improve the accuracy and speed of identifying human targets. Therefore, more survivors may be rescued.
Dynamic Characteristic Analysis of Permanent Magnet Brushless DC Motor System with Rolling Rotor
Authors: Wan Yi , Li Zhengyang , Xia Yan , Gong Fangbin , Chen Fei
Published: October 2022

With the wide application of permanent magnet brushless DC motors (BLDCMs) in home appliances and electric vehicles, there is increasing demand for BLDCMs with low vibration and noise. This paper aims to study the dynamic characteristics of a type of BLDCM with a rolling rotor. Firstly, a dynamic model of a BLDCM with eighteen degrees of freedom (18 DOFs) is built, for which the electromagnetic force and the oil-film force of the sliding bearing are considered. Then, the system responses are solved by Runge–Kutta numerical method, and the effects of the rotational speed, bearing backlash and eccentric distance of the rolling rotor on the dynamic response are analyzed in detail. The time history, frequency plot, axis trajectory diagram and phase portrait are introduced to discuss the dynamic behavior of the motor system. Analysis results show that eccentric force increases obviously with increasing rotational speed or eccentric distance, which can change the dynamic response through suppressing the electromagnetic force. The effect of bearing clearance on the rotor and stator is negatively correlated. Therefore, system parameters should be determined properly to improve the running performance of the motor system. Numerical results can provide a useful guide for the design and vibration control of such motor systems.
Shearer-Positioning Method Based on Non-Holonomic Constraints
Authors: Song Danyang , Yang Jinheng , Song Jiancheng
Published: October 2022

In the traditional shearer-positioning method, an odometer is used to assist the forward velocity correction of the inertial navigation system, but it cannot restrain the system’s error divergence. For this reason, this paper proposes a shearer positioning method based on non-holonomic constraints. In this method, an inertial measurement unit and odometer are installed in the middle of the shearer body and on the traction gear, respectively, the shearer attitude, speed, and position information are calculated through the inertial measurement-unit mechanization, and the shearer’s instantaneous velocity is calculated through the output of the odometer. The mechanization and error transfer process of the inertial navigation system are used to establish a Kalman filtering-state equation. The Kalman filtering observation equation is established through the difference between the projected velocity of the inertial navigation system at the joint and the output velocity of the odometer as the observation vector, and the non-holonomic constraint is introduced. Finally, the error feedback is derived from the results processed by the Kalman filtering algorithm, and the output of the inertial navigation system is corrected to obtain the optimal estimation of the shearer’s attitude, speed and position. The experiment shows that compared with the traditional inertial navigation and odometer combined positioning method, the degree of divergence in the positioning results over time is significantly reduced after adding the non-holonomic constraint. The positioning method has good tracking ability for the trajectory of the shearer. The error of the positioning results in the forward direction is reduced by 66%, the lateral direction is reduced by 62%, and the vertical direction is reduced by 67%.
A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
Authors: Yang Qi , Li Fang , Tian Hong , Li Hua , Xu Shuai , Fei Jiyou , Wu Zhongkai , Feng Qiang , Lu Chang
Published: October 2022

Aiming to assess the problems of low detection accuracy, poor reliability, and high cost of the manual inspection method for conveyor-belt-surface defect detection, in this paper we propose a new method of conveyor-belt-surface defect detection based on knowledge distillation. First, a data enhancement method combining GAN and copy–pasting strategies is proposed to expand the dataset to solve the problem of insufficient and difficult-to-obtain samples of conveyor-belt-surface defects. Then, the target detection network, the YOLOv5 model, is pruned to generate a mini-network. A knowledge distillation method for fine-grained feature simulation is used to distill the lightweight detection network YOLOv5n and the pruned mini-network YOLOv5n-slim. The experiments show that our method significantly reduced the number of parameters and the inference time of the model, and significantly improves the detection accuracy, up to 97.33% accuracy, in the detection of conveyor belt defects.
Chinese Character Component Deformation Based on AHP
Authors: Chen Tian , Yang Fang , Gao Xiang
Published: October 2022

Since Chinese characters are composed of components, deforming the components in a small number of existing calligraphy characters to generate new characters is an effective method to produce a Chinese character library in the same style. Usually, the component deformation is achieved by affine transformation. However, when calculating the parameters in affine transformation, existing methods usually have the problems of a large amount of manual participation or complicated calculation. In this paper, we proposed an Analytic Hierarchy Process (AHP)-based Chinese character component deformation method, which is simple in calculation and can effectively realize the deformation of Chinese character components on the basis of reducing manual intervention. We first determined the factors that affect the selection of control points in affine transformation, then used AHP to calculate the weights of feature points and select the control points according to the weights. As a prerequisite for affine transformation, a matching method of Chinese character feature points based on the Chinese character skeleton map and neighborhood information is also proposed, which helps to achieve more efficient deformation. Experimental results on different fonts demonstrate the effectiveness and generality of our method.
Vibration Energy Comparison Helps Identify Formation Time of New Free Surface in Urban Tunnel Blasting
Authors: Wu Haojun , Gong Min , Zhao Qing , Wu Xiaodong , Liu Xiangyu
Published: October 2022

Cut blasting creates new free surfaces that facilitate rock breaking and reduces blast vibration. However, the time when the new free surface is formed is not clear in on-site blasting practices. This paper proposes a method to identify this formation time through a vibration energy comparison. Firstly, a variational mode decomposition method identified the initiation time sequence of high-precision detonators from the observed blast-induced vibration wave. Based on the superposition of the single-hole waves extracted from the single-free-surface blasting test, we constructed a predicted wave that shared the initiation time sequence with the observed one. The Hilbert transform found the accumulated energy curves of the two waves separately. By comparing the linear correlation of the two curves, we identified the new free surface’s formation time to improve the blast design. The tunnel-blasting case showed that 64.5 ms was required to form the new free surface. In the actual blasting, each cut hole used 1.0 kg of explosives. The maximum vibration velocity was 0.90 cm·s−1, which met the control target of less than 1.0 cm·s−1.
Factorial Design to Stimulate Biomass Development with Chemically Modified Starch
Authors: Popa Olga , Rosu Ana-Maria , Nicuta Daniela , Voicu Roxana Elena , Zichil Valentin , Nistor Ileana Denisa
Published: October 2022

The present study is focused on mathematical modeling by testing the benefits of modified potato starch in the biomass production of microorganisms, such as the fungus type. Microorganisms need a carbon source for the biomass development. In different industries, microorganisms, such as the Penicillium type, are used for the extraction of different important compounds utilized in biotechnologies. The aim of this study is to establish some important parameters in order to stimulate the biomass production in the presence of chemically modified starch. The carbon sources used in this research are glucose, native potato starch, and chemically modified potato starch. The chemical modification of potato starch was realized with green chemical compounds in order to not influence biomass development. The chemical characterization of starch and modified starch was important in order to confirm the chemical modification of starch. The response function in mathematical modeling is the amount of biomass developed when there are varied parameters. The varied parameters for the factorial design are as follows: time of biomass development, mass report of glucose:starch (G:S), and mass report of glucose:modified starch (G:MS). The results obtained for the optimal values are as follows: 6 days for the biomass development, 1:1.35 for the mass report of G:S, and 1:1.27 for the report of G:MS.
Reduction of Bacterial Pathogens in a Single-Stage Steel Biodigester Co-Digesting Saw Dust and Pig Manure at Psychrophilic Temperature
Authors: Manyi-Loh Christy , Lues Ryk
Published: October 2022

The experiment was conducted using a single-stage steel biodigester of 75 L working volume, charged with 75% pig manure and 25% pine wood sawdust and operated under batch mode at a psychrophilic temperature range (13.16–24.69 °C). The total viable count was determined via the spread plate method on selective microbiological media to determine viable numbers of the selected bacterial pathogens in samples collected from the biodigesting chamber every seven or fourteen days over the duration of study. Multiple linear regression models including the log bacterial counts (response) and number of days, pH, and average daily temperature as predictors were developed using Matlab for each bacterium. The reduction (90–99.9%) in numbers of isolates of E. coli, Salmonella, Yersinia, Campylobacter, and Listeria varied with time (days) from their initial respective counts of 2 × 106, 7 × 104, 3 × 105, 9 × 105, and 1 × 104 cfu/g to concentrations lower than the detection limit (DL = 102 cfu/g substrate). E. coli demonstrated the least resistance to the environmental conditions in the biodigester and survived only for 77 days, unlike L. monocytogenes that lasted for 175 days and was the most resistant bacterium. From the models, the number of days and temperature were directly and inversely related to log Listeria counts, respectively, contrary to the others. The predictors, number of days, pH, and average daily temperature, were described as either primary or secondary factors based on the bacteria via the reliefF test.
Analysis of Contaminants and Residues in Food
Authors: Tsochatzis Emmanouil D. , Begou Olga
Published: October 2022

The food supply chain contains several steps that start with the farm and end with the forks of consumers [...]
MST-VAE: Multi-Scale Temporal Variational Autoencoder for Anomaly Detection in Multivariate Time Series
Authors: Pham Tuan-Anh , Lee Jong-Hoon , Park Choong-Shik
Published: October 2022

In IT monitoring systems, anomaly detection plays a vital role in detecting and alerting unexpected behaviors timely to system operators. With the growth of signal data in both volumes and dimensions during operation, unsupervised learning turns out to be a great solution to trigger anomalies thanks to the feasibility of working well with unlabeled data. In recent years, autoencoder, an unsupervised learning technique, has gained much attention because of its robustness. Autoencoder first compresses input data to lower-dimensional latent representation, which obtains normal patterns, then the compressed data are reconstructed back to the input form to detect abnormal data. In this paper, we propose a practical unsupervised learning approach using Multi-Scale Temporal convolutional kernels with Variational AutoEncoder (MST-VAE) for anomaly detection in multivariate time series data. Our key observation is that combining short-scale and long-scale convolutional kernels to extract various temporal information of the time series can enhance the model performance. Extensive empirical studies on five real-world datasets demonstrate that MST-VAE can outperform baseline methods in effectiveness and efficiency.
Mitigation of Structural Vibrations of MDOF Oscillators by Modal Coupling Due to Hysteretic Dampers
Authors: Casini Paolo , Vestroni Fabrizio
Published: October 2022

In civil engineering, structural elements characterized by hysteresis are often encountered, such as materials with limited elastic fields, microsliding friction and elastomeric absorbers. Hysteretic nonlinearities produce a wide variety of dynamical phenomena, such as significant modal coupling, bifurcations and superabundant modes. This paper investigates nonlinear modal interactions in the dynamic response of a two-degree-of-freedom system (2DOF) with hysteretic elements. These phenomena are notably important in internal resonance conditions, where modal interactions produce strong modifications in the response with possible beneficial effects. In specific conditions, the transfer of energy between the two modes leads to a notable reduction in the maximum response amplitude; the exploitation of this feature to achieve vibration mitigation of the forced response is the main goal of the paper. Two configurations are investigated: the hysteretic element at the top (vibration damper) and the hysteretic element at the base (isolator). In both cases, several internal resonance conditions occur since, by increasing the excitation intensity, the frequencies of the hysteretic system change, as well as their ratio. Qualitative similar results are obtained, characterized by a transfer of energy between the two modes. For both configurations, the usefulness of exploiting these nonlinear phenomena in vibration mitigation has been shown.
Acute Moderate-Intensity Aerobic Exercise under High PM2.5 Levels Does Not Influence the Pulmonary Function and Lung Diffusion Capacity in Healthy Young Men
Authors: Kim Jin-Su , Lee Do Gyun , Wang Lin , Kang Heechan , Hwang Moon-Hyon
Published: October 2022

Exposure to fine particulate matter (PM2.5) impairs the respiratory system and increases the morbidity and mortality of respiratory diseases. Even though aerobic exercise is known to improve pulmonary function in diverse populations, it can lead to an increase in the inhalation of PM2.5 in polluted environments. We aimed to investigate the effects of aerobic exercise under high PM2.5 conditions on the pulmonary function in young adults. Nine healthy young men performed indoor treadmill running in both high and low PM2.5 conditions (59.0 ± 2.1 vs. 7.8 ± 1.0 μg/m3) by a crossover study design. Pulmonary function was assessed by spirometry and diffusing capacity for carbon monoxide (DLCO) at pre- and 1-h post-exercise. There was no difference in the response of pulmonary function and lung diffusion capacity to the acute aerobic exercise in high and low PM2.5 conditions (p ≥ 0.09). These findings indicate that aerobic exercise in high PM2.5 conditions may not adversely affect pulmonary function in healthy young adults.
Designing and Modeling of a Dual-Band High-Efficiency Rectenna Using Dielectric Resonant Antenna Array
Authors: Li Jianxing , Li Ziyue , Jiang Cheng , Wei Tong , Liu Zan
Published: October 2022

In this paper, we proposed a high-efficiency 2.45 GHz and 5.8 GHz dual-band rectenna for wireless energy harvesting (WEH). First, a dual-band dielectric resonant antenna (DRA) was designed. A 4-element DRA array was further developed using a compact feeding network. The measured gains of the DRA array were 5.5 dBi at 2.45 GHz and 12.9 dBi at 5.8 GHz. Then, a new type of impedance regulation stub (IRS) was introduced that significantly improved the conversion efficiency of the rectenna. The result showed that the conversion efficiency of the rectifier was 66% and 62% at 2.45 GHz and 5.8 GHz, respectively, when the input power level was 10 dBm. Furthermore, the proposed rectenna was validated to activate a standard BQ25504 DC-to-DC boost converter and charge a supercapacitor. The results demonstrated that the proposed rectenna could be an appropriate solution for WEH applications.
A Resilient Cyber-Physical Demand Forecasting System for Critical Infrastructures against Stealthy False Data Injection Attacks
Authors: Gheyas Iffat , Epiphaniou Gregory , Maple Carsten , Lakshminarayana Subhash
Published: October 2022

The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.
Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
Authors: Sousa João C. , Bernardo Hermano
Published: October 2022

As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day—ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naïve model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.
The Simulation of the Dynamic Characteristics of Friction Stir Welding and the Structural Deflection of Base Materials
Authors: Cha Woon-Yong , Im Se-Bin , Kim Jae-Won , Kang Dae-Gwan
Published: October 2022

Friction stir welding requires an optimized process because the quality of the weld can vary depending on the dynamic characteristics of the welding tool. In addition, controlling the structural deflection of the base material according to the load of the welding tool is important. In this study, the structural deflection of thin base material according to the dynamic characteristics of the welding tool was confirmed using dynamic simulation. The generated deflection was controlled through the fixing clamp, and the results according to the rotation and progress speed of the welding tool were compared and analyzed through finite volume analysis. Thus, factors of the welding tools were obtained based on computational data without accompanying experimental data, and a process guide for efficient friction stir welding for thin base materials was presented.
Evaluation of the Microbiological Quality of Water in the Rice-Growing System of the Lis Valley, Portugal
Authors: Duarte Isabel Maria , Marques Helena , Amador Paula , Vasconcelos Teresa , Gonçalves José Manuel
Published: October 2022

Society’s demand for the preservation of water resources has stimulated technological advances and improved agricultural management, to limit its negative impacts, favor socio-economic development and promote the conservation of natural resources. This study assessed the impact of a rice farming system in the Mediterranean area on the microbiological quality of water, representing the irrigation and cultivation conditions of the Lis Valley Irrigation District (LVID). Indicators used include the counting of total coliforms (TC), fecal coliforms (FC), fecal enterococci (FE) and ampicillin-resistant Enterobacteriaceae (Ampr), and a phenotypic characterization of antibiotic (AB) resistances. The results show that the water at the LVID inlets contained high microbiological contamination due to sources outside the perimeter. The rice paddy agroecosystem had a beneficial impact on the load of TC, FC and FE of the respective drainage water, with a decrease of two orders of magnitude between the values of these counts at the inlets and at the outlets of the rice fields. However, the relative frequency of Enterobacteriaceae Ampr increased in the rice paddy soil, possibly as a result of the favorable conditions provided by this wetland ecosystem for the transfer of AB resistance genes. The analysis of AB resistance patterns revealed high relative resistance percentages for both β-lactams and non-β-lactams in all samples. This study made it possible to assess the risks of microbiological contamination of irrigation water and the beneficial effect of the rice-growing system in controlling the applied indicators.
Parameter Sensitivity Analysis of SWAT Modeling in the Upper Heihe River Basin Using Four Typical Approaches
Authors: Xiang Xin , Ao Tianqi , Xiao Qintai , Li Xiaodong , Zhou Li , Chen Yao , Bi Yao , Guo Jingyu
Published: October 2022

Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.