bluuebunny/crossref_metadata_2025 huggingface dataset:
Model of the Performance Based on Artificial Intelligence–Fuzzy Logic Description of Physical Activity
Authors: Szulc Adam , Prokopowicz Piotr , Buśko Krzysztof , Mikołajewski Dariusz
Published: January 2023

The aim of the study was to build a fuzzy model of lower limb peak torque in an isokinetic mode. The study involved 93 male participants (28 male deaf soccer players, 19 hearing soccer players and 46 deaf untraining male). A fuzzy computational model of different levels of physical activity with a focus on the lower limbs was constructed. The proposed fuzzy model assessing lower limb peak torque in an isokinetic mode demonstrated its effectiveness. The novelty of our research lies in the use of hierarchical fuzzy logic to extract computational rules from data provided explicitly and then to determine the corresponding physiological and pathological mechanisms. The contribution of our research lies in complementing the methods for describing physiology, pathology and rehabilitation with fuzzy parameters, including the so-called dynamic norm embedded in the model.
Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond
Authors: Homrighausen Jonas , Horsthemke Ludwig , Pogorzelski Jens , Trinschek Sarah , Glösekötter Peter , Gregor Markus
Published: January 2023

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
Fluorescence Detecting of Paraquat and Diquat Using Host–Guest Chemistry with a Fluorophore-Pendant Calix[6]arene
Authors: Vasca Ermanno , Siano Francesco , Caruso Tonino
Published: January 2023

Paraquat (PQ) and diquat (DQ), some of the most widely used herbicides in the world, both present a high mortality index after intentional exposure. In this paper, a fluorescence sensing method for PQ and DQ, based on host–guest molecular recognition, is proposed. Calix[6]arene derivatives containing anthracene or naphthalene as pendant fluorophore at their lower rim recognize DQ and PQ in hydroalcoholic solution with a broad linear response range at the μg L−1 level concentration. The linear response ranges were found from 1.0 to 18 μg L−1 with the detection limit of 31 ng L−1 for paraquat, and from 1.0 to 44 μg L−1 with the detection limit of 0.16 μg L−1 for diquat. The recognition process is detected by following the decrease in the fluorescence emission consequent to complexation. The proposed quenching method has been applied to the determination of paraquat in drinking water samples.
Enabling Secure Communication in Wireless Body Area Networks with Heterogeneous Authentication Scheme
Authors: Ullah Insaf , Khan Muhammad Asghar , Abdullah Ako Muhammad , Noor Fazal , Innab Nisreen , Chen Chien-Ming
Published: January 2023

Thanks to the widespread availability of Fifth Generation (5G) wireless connectivity, it is now possible to provide preventative or proactive healthcare services from any location and at any time. As a result of this technological improvement, Wireless Body Area Networks (WBANs) have emerged as a new study of research in the field of healthcare in recent years. WBANs, on the one hand, intend to gather and monitor data from the human body and its surroundings; on the other hand, biomedical devices and sensors interact through an open wireless channel, making them exposed to a range of cyber threats. However, WBANs are a heterogeneous-based system; heterogeneous cryptography is necessary, in which the transmitter and receiver can employ different types of public key cryptography. This article proposes an improved and efficient heterogeneous authentication scheme with a conditional privacy-preserving strategy that provides secure communication in WBANs. In the proposed scheme, we employed certificateless cryptography on the client side and Identity-Based Cryptography on the receiver side. The proposed scheme employs Hyperelliptic Curve Cryptography (HECC), a more advanced variation of Elliptic Curve Cryptography (ECC). HECC achieves the same level of security with a smaller key size and a more efficient approach than its counterpart methods. The proposed scheme not only meets the security and privacy standards of WBANs but also enhances efficiency in terms of computation and communication costs, according to the findings of the security and performance analysis.
Overview of Mobile Communications in Colombia and Introduction to 5G
Authors: Barrios-Ulloa Alexis , Cama-Pinto Dora , Arrabal-Campos Francisco Manuel , Martínez-Lao Juan Antonio , Monsalvo-Amaris José , Hernández-López Audomaro , Cama-Pinto Alejandro
Published: January 2023

The deployment of 5G around the world continues to progress at a rapid pace, especially in North America and Asia. Its advantages and efficiency as a data transmission network have been widely demonstrated in different fields such as agriculture, education, health, and surveillance. However, this process does not have the same dynamics in Latin America, specifically in Colombia. The country is currently implementing actions aimed at facilitating the deployment of this technology in the short term, including pilot tests for the use of the radio spectrum, spectrum auctions, the planning of future auctions, and the review of spectrum caps. The results of this review allow us to conclude that despite the forecasts and the intentions of the Colombian government and mobile communication service operators, 5G in standalone mode will not be commercially available in Colombia before the end of 2023. The main failures in its deployment are related to the lack of available spectrum to support the ultrahigh-reliability and low-latency, enhanced mobile broadband, and massive machine-type communications scenarios, as well as the delay in the auction processes for its assignment.
A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism
Authors: Zhao Fan , Chao Yu , Li Linyun
Published: January 2023

With the wide application of computer vision technology and deep-learning theory in engineering, the image-based detection of cracks in structures such as pipelines, pavements and dams has received more and more attention. Aiming at the problems of high cost, low efficiency and poor detection accuracy in traditional crack detection methods, this paper proposes a crack segmentation network by combining a morphological network and a multiple-loss mechanism. First, for improving the identification of cracks with different resolutions, the U-Net network is used to extract multi-scale features from the crack image. Second, for eliminating the effect of polarized light on the cracks under different illuminations, the extracted crack features are further morphologically processed by a white-top hat transform and a black-bottom hat transform. Finally, a multi-loss mechanism is designed to solve the problem of the inaccurate segmentation of cracks on a single scale. Extensive experiments are carried out on five open crack datasets: Crack500, CrackTree200, CFD, AEL and GAPs384. The experimental results showed that the average ODS, OIS, AIU, sODS and sOIS are 75.7%, 73.9%, 36.4%, 52.4% and 52.2%, respectively. Compared with state-of-the-art methods, the proposed method achieves better crack segmentation performance. Ablation experiments also verified the effectiveness of each module in the algorithm.
Role of Defects of Carbon Nanomaterials in the Detection of Ovarian Cancer Cells in Label-Free Electrochemical Immunosensors
Authors: Runprapan Nattharika , Wang Fu-Ming , Ramar Alagar , Yuan Chiou-Chung
Published: January 2023

Developing label-free immunosensors to detect ovarian cancer (OC) by cancer antigen (CA125) is essential to improving diagnosis and protecting women from life-threatening diseases. Four types of carbon nanomaterials, such as multi-wall carbon nanotubes (MWCNTs), vapor-grown carbon fiber (VGCFs), graphite KS4, and carbon black super P (SP), have been treated with acids to prepare a carbon nanomaterial/gold (Au) nanocomposite. The AuNPs@carbon nanocomposite was electrochemically deposited on a glassy carbon electrode (GCE) to serve as a substrate to fabricate a label-free immunosensor for the detection of CA125. Among the four AuNPs@carbon composite, the AuNPs@MWCNTs-based sensor exhibited a high sensitivity of 0.001 µg/mL for the biomarker CA125 through the square wave voltammetry (SWV) technique. The high conductivity and surface area of MWCNTs supported the immobilization of AuNPs. Moreover, the carboxylic (COO-) functional groups in MWCNT improved to a higher quantity after the acid treatment, which served as an excellent support for the fabrication of electrochemical biosensors. The present method aims to explore an environmentally friendly synthesis of a layer-by-layer (LBL) assembly of AuNPs@carbon nanomaterials electrochemical immunoassay to CA125 in a clinical diagnosis at a low cost and proved feasible for point-of-care diagnosis.
Pleasant Stroke Touch on Human Back by a Human and a Robot
Authors: Ishikura Tomoki , Kitamura Yuki , Sato Wataru , Takamatsu Jun , Yuguchi Akishige , Cho Sung-Gwi , Ding Ming , Yoshikawa Sakiko , Ogasawara Tsukasa
Published: January 2023

Pleasant touching is an important aspect of social interactions that is widely used as a caregiving technique. To address the problems resulting from a lack of available human caregivers, previous research has attempted to develop robots that can perform this kind of pleasant touch. However, it remains unclear whether robots can provide such a pleasant touch in a manner similar to humans. To investigate this issue, we compared the effect of the speed of gentle strokes on the back between human and robot agents on the emotional responses of human participants (n = 28). A robot or a human stroked on the participants’ back at two different speeds (i.e., 2.6 and 8.5 cm/s). The participants’ subjective (valence and arousal ratings) and physiological (facial electromyography (EMG) recorded from the corrugator supercilii and zygomatic major muscles and skin conductance response) emotional reactions were measured. The subjective ratings demonstrated that the speed of 8.5 cm/s was more pleasant and arousing than the speed of 2.6 cm/s for both human and robot strokes. The corrugator supercilii EMG showed that the speed of 8.5 cm/s resulted in reduced activity in response to both human and robot strokes. These results demonstrate similar speed-dependent modulations of stroke on subjective and physiological positive emotional responses across human and robot agents and suggest that robots can provide a pleasant touch similar to that of humans.
A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
Authors: Tang Bin , Lu Yan , Li Qi , Bai Yueying , Yu Jie , Yu Xu
Published: January 2023

Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets.
Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold
Authors: Li Jiahui , Wang Zhihai , Liu Xiaoqin , Feng Zhengjiang
Published: January 2023

Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.
Development of Language Models for Continuous Uzbek Speech Recognition System
Authors: Mukhamadiyev Abdinabi , Mukhiddinov Mukhriddin , Khujayarov Ilyos , Ochilov Mannon , Cho Jinsoo
Published: January 2023

Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrences. We proposed the Uzbek language model named UzLM by examining the performance of statistical and neural-network-based language models that account for the unique features of the Uzbek language. Our Uzbek-specific linguistic representation allows us to construct more robust UzLM, utilizing 80 million words from various sources while using the same or fewer training words, as applied in previous studies. Roughly sixty-eight thousand different words and 15 million sentences were collected for the creation of this corpus. The experimental results of our tests on the continuous recognition of Uzbek speech show that, compared with manual encoding, the use of neural-network-based language models reduced the character error rate to 5.26%.
Online Fall Detection Using Wrist Devices
Authors: Marques João , Moreno Plinio
Published: January 2023

More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people’s movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector’s performance over time, achieving no single false positives or false negatives over four days.
Transmission Lines in Capacitance Measurement Systems: An Investigation of Receiver Structures
Authors: Flatscher Matthias , Neumayer Markus , Bretterklieber Thomas , Wegleiter Hannes
Published: January 2023

Dielectric sensing based on capacitive measurement technology is a favourable measurement approach in many industries and fields of application. From an electrical point of view, a coupling capacitance must be measured in the presence of stray capacitances. Different receiver circuit structures have been proposed for the underlying displacement current measurement. Ideally, the sensor assembly is directly connected to the sensor circuitry to minimize the influence with respect to these parasitic capacitances. However, under harsh operating conditions, e.g., at high temperatures, the sensor and the receiver circuit must be separated in order to protect the electronics. Consequently, the receiver circuit and the sensor have to be connected by cables, e.g., coaxial cables. The measurement setup differs significantly from the ideal design with a direct connection. In this paper, we investigate the behaviour of three common measurement circuits for capacitive measurements in instrumentations with cables. We study the interaction between the sensor and the electronics and analyse the operating behaviour of the circuit, as well as the operating states of the amplifiers used. We also address cross-sensitivities in the sensor design due to stray capacitances. The analyses are carried out for different cable lengths and measuring frequencies, and conditions for the usability of the circuit are deduced. In addition to the operational behaviour, we also evaluate the circuits by means of a noise analyses. Based on this analysis, we show a direct comparison of the circuits. The analysis is based on simulation studies, as well as collaborative measurements on test circuits where all circuit parameters are provided. The test circuits are realized with dedicated state-of-the-art circuit elements and, together with the analysis approach and the results, thus provide a basis for future developments.
Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data
Authors: Seol Mihye , Kim Taejoon
Published: January 2023

Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe performance degradation. We propose an efficient algorithm for enhancing the performance of federated learning by overcoming the negative effects of non-IID datasets. First, the intra-client class imbalance is reduced by rendering the class distribution of clients close to Uniform distribution. Second, the clients to participate in federated learning are selected to make their integrated class distribution close to Uniform distribution for the purpose of mitigating the inter-client class imbalance, which represents the class distribution difference among clients. In addition, the amount of local training data for the selected clients is finely adjusted. Finally, in order to increase the efficiency of federated learning, the batch size and the learning rate of local training for the selected clients are dynamically controlled reflecting the effective size of the local dataset for each client. In the performance evaluation on CIFAR-10 and MNIST datasets, the proposed algorithm achieves 20% higher accuracy than existing federated learning algorithms. Moreover, in achieving this huge accuracy improvement, the proposed algorithm uses less computation and communication resources compared to existing algorithms in terms of the amount of data used and the number of clients joined in the training.
Determination of Thin NiTi Wires’ Mechanical Properties during Phase Transformations
Authors: Hartwich Jonasz , Sławski Sebastian , Kciuk Marek , Duda Sławomir
Published: January 2023

The modern industrial and consumer applications in accordance with the concepts of Industry 4.0 and the Internet of Things are characterized by autonomy and self-sufficiency. This has led to an increase in the interest for the so-called smart materials, capable of combining the functionalities of sensors, actuators and, in some applications, control systems. An important group of smart materials are shape-memory alloys, among which nickel–titanium (NiTi) alloys are the most known. In this article, the influence of phase transformation on the mechanical properties of thin NiTi alloy wires was investigated. During the test, the influence of the heating currents on the displacement and the force generated by the thin NiTi wires were analyzed. The temperature of the wires during heating was measured by a thermographic camera. This study proved the maximum value of the wires’ displacement was related to the value of the heating current. During the research, the dependence of the transformation dynamics on the value of the heating currents was also proved. In addition, the influence of the surface inhomogeneity of the thin NiTi alloy wires on the accuracy of the thermographic measurements was analyzed. For the experimental research described in this article, we used the NiTi alloy whose trade name is Flexinol, produced by DYNALLOY (Inc. 2801 McGaw Ave. Irvine, CA, USA).
Short-Training Damage Detection Method for Axially Loaded Beams Subject to Seasonal Thermal Variations
Authors: Berardengo Marta , Lucà Francescantonio , Vanali Marcello , Annesi Gianvito
Published: January 2023

Vibration-based damage features are widely adopted in the field of structural health monitoring (SHM), and particularly in the monitoring of axially loaded beams, due to their high sensitivity to damage-related changes in structural properties. However, changes in environmental and operating conditions often cause damage feature variations which can mask any possible change due to damage, thus strongly affecting the effectiveness of the monitoring strategy. Most of the approaches proposed to tackle this problem rely on the availability of a wide training dataset, accounting for the most part of the damage feature variability due to environmental and operating conditions. These approaches are reliable when a complete training set is available, and this represents a significant limitation in applications where only a short training set can be used. This often occurs when SHM systems aim at monitoring the health state of an already existing and possibly already damaged structure (e.g., tie-rods in historical buildings), or for systems which can undergo rapid deterioration. To overcome this limit, this work proposes a new damage index not affected by environmental conditions and able to properly detect system damages, even in case of short training set. The proposed index is based on the principal component analysis (PCA) of vibration-based damage features. PCA is shown to allow for a simple filtering procedure of the operating and environmental effects on the damage feature, thus avoiding any dependence on the extent of the training set. The proposed index effectiveness is shown through both simulated and experimental case studies related to an axially loaded beam-like structure, and it is compared with a Mahalanobis square distance-based index, as a reference. The obtained results highlight the capability of the proposed index in filtering out the temperature effects on a multivariate damage feature composed of eigenfrequencies, in case of both short and long training set. Moreover, the proposed PCA-based strategy is shown to outperform the benchmark one, both in terms of temperature dependency and damage sensitivity.
Simultaneous Measurement of Strain and Temperature Distributions Using Optical Fibers with Different GeO2 and B2O3 Doping
Authors: Hisada Shinsaku , Kodakamine Utanori , Wada Daichi , Murayama Hideaki , Igawa Hirotaka
Published: January 2023

Compensating for the effects of temperature is a crucial issue in structural health monitoring when using optical fiber sensors. This study focused on the change in sensitivity due to differences in GeO2 and B2O3 doping and then verified the accuracy when measuring the strain and temperature distributions simultaneously. Four types of optical fiber sensors were utilized to measure the strain and temperature in four-point bending tests, and the best combination of the sensors resulted in strain and temperature errors of 28.4 μϵ and 1.52 °C, respectively. Based on the results obtained from the four-point bending tests, we discussed the error factors via an error propagation analysis. The results of the error propagation analysis agreed well with the experimental results, thus indicating the effectiveness of the analysis as a method for verifying accuracy and error factors.
Image Vignetting Correction Using a Deformable Radial Polynomial Model
Authors: Bal Artur , Palus Henryk
Published: January 2023

Image vignetting is one of the major radiometric errors that occur in lens-camera systems. In many applications, vignetting is an undesirable effect; therefore, when it is impossible to fully prevent its occurrence, it is necessary to use computational methods for its correction. In probably the most frequently used approach to the vignetting correction, that is, the flat-field correction, the use of appropriate vignetting models plays a pivotal role. The radial polynomial (RP) model is commonly used, but for its proper use, the actual vignetting of the analyzed lens-camera system has to be a radial function. However, this condition is not fulfilled by many systems. There exist more universal models of vignetting; however, these models are much more sophisticated than the RP model. In this article, we propose a new model of vignetting named the Deformable Radial Polynomial (DRP) model, which joins the simplicity of the RP model with the universality of more sophisticated models. The DRP model uses a simple distance transformation and minimization method to match the radial vignetting model to the non-radial vignetting of the analyzed lens-camera system. The real-data experiment confirms that the DRP model, in general, gives better (up 35% or 50%, depending on the measure used) results than the RP model.
Imbedding Pd Nanoparticles into Porous In2O3 Structure for Enhanced Low-Concentration Methane Sensing
Authors: Zuo Xiaoyang , Yang Zhengyi , Kong Jing , Han Zejun , Zhang Jianxin , Meng Xiangwei , Hao Shuyan , Wu Lili , Wu Simeng , Liu Jiurong , Wang Zhou , Wang Fenglong
Published: January 2023

Methane (CH4), as the main component of natural gas and coal mine gas, is widely used in daily life and industrial processes and its leakage always causes undesirable misadventures. Thus, the rapid detection of low concentration methane is quite necessary. However, due to its robust chemical stability resulting from the strong tetrahedral-symmetry structure, the methane molecules are usually chemically inert to the sensing layers in detectors, making the rapid and efficient alert a big challenge. In this work, palladium nanoparticles (Pd NPs) embedded indium oxide porous hollow tubes (In2O3 PHTs) were successfully synthesized using Pd@MIL-68 (In) MOFs as precursors. All In2O3-based samples derived from Pd@MIL-68 (In) MOFs inherited the morphology of the precursors and exhibited the feature of hexagonal hollow tubes with porous architecture. The gas-sensing performances to 5000 ppm CH4 were evaluated and it was found that Pd@In2O3-2 gave the best response (Ra/Rg = 23.2) at 370 °C, which was 15.5 times higher than that of pristine-In2O3 sensors. In addition, the sensing materials also showed superior selectivity against interfering gases and a rather short response/recovery time of 7 s/5 s. The enhancement in sensing performances of Pd@In2O3-2 could be attributed to the large surface area, rich porosity, abundant oxygen vacancies and the catalytic function of Pd NPs.
How Industry 4.0 and Sensors Can Leverage Product Design: Opportunities and Challenges
Authors: Rosário Albérico Travassos , Dias Joana Carmo
Published: January 2023

The fourth industrial revolution, also known as Industry 4.0, has led to an increased transition towards automation and reliance on data-driven innovations and strategies. The interconnected systems and processes have significantly increased operational efficiency, enhanced organizational capacity to monitor and control functions, reduced costs, and improved product quality. One significant way that companies have achieved these benefits is by integrating diverse sensor technologies within these innovations. Given the rapidly changing market conditions, Industry 4.0 requires new products and business models to ensure companies adjust to the current and future changes. These requirements call for the evolutions in product design processes to accommodate design features and principles applicable in the current dynamic business environment. Thus, it becomes imperative to understand how these innovations can leverage product design to maximize benefits and opportunities. This research paper employs a Systematic Literature Review with Bibliometric Analysis (SLBA) methodology to explore and synthesize data on how Industry 4.0 and sensors can leverage product design. The results show that various product design features create opportunities to be leveraged to guarantee the success of Industry 4.0 and sensor technologies. However, the research also identifies numerous challenges that undermine the ongoing transition towards intelligent factories and products.
Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique
Authors: Vimala Baiju Babu , Srinivasan Saravanan , Mathivanan Sandeep Kumar , Muthukumaran Venkatesan , Babu Jyothi Chinna , Herencsar Norbert , Vilcekova Lucia
Published: January 2023

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.
A Review of the Biological Properties of Purple Corn (Zea mays L.)
Authors: Kim Hee Yeon , Lee Ki Yeon , Kim Minju , Hong Minji , Deepa Ponnuvel , Kim Songmun
Published: January 2023

In the food and beverage industries, replacing synthetic colorants with plant-based colorants has become popular in recent times. Purple corn (Zea mays L.) is an important source of natural colorants due to its range in color from orange to purple. The whole plant of purple corn has a high amount of anthocyanin content. Anthocyanin is the water-soluble pigment found in various fruits and vegetables. The color pigments are chiefly found in the pericarp or kernels, in addition to corn cobs. Purple corn is rich in various health-promoting compounds, mainly anthocyanins such as cyanidin-3-O-glucoside, perlagonidin-3-O-glucoside, peonidin 3-O-glucoside, and their malonylated forms. This review emphasized recent updates regarding the in vitro and in vivo biological properties of extracts and compounds from purple corn. Purple corn color extracts possess a variety of biological properties, including antioxidant, anti-inflammatory, anticancer, anti-diabetic, anti-obesity, etc. The results of in vitro and in vivo studies of the biological properties of purple corn could lead to the development of different health-promoting products in the near future.
Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia
Authors: Jiménez-García Williams Gilberto , Arenas-Valencia Wilson , Bohorquez-Bedoya Natalia
Published: January 2023

The drug/violence relationship has been a recurrent topic of interest in criminology and sociology. This interest began with the crack epidemic in the 1980s and continues due to the consolidation and functioning of the cocaine markets of Latin American drug trafficking organizations. We approach the drug/violence relationship in Pereira (Colombia) which is strategic for the global cocaine market and for the Colombian domestic market. This city has an average homicide rate of 38.5 per 100,000 inhabitants (2010–2019). To test the drug/violence relationship, we used Goldstein’s systemic violence theory and Zimring and Hawkins’ contingent causality theory. We analyze the influence of socioeconomic and drug trafficking variables on homicides between the years 2010 and 2019. Our dataset comes from official Colombian government sources. We performed multivariate regression modeling through structural equation modeling with partial least squares (PLS-SEM). Our model was consistent and obtained statistical significance for all years, resulting in a good approximation to the study of the phenomenon. Based on the evidence, we can affirm that there is a relationship among violence/drug trafficking/socioeconomic disadvantages, thus confirming the contingent causation theory.
New Environmental Controversies: Towards a Typology of Green Conflicts
Authors: Eskjær Mikkel Fugl , Horsbøl Anders
Published: January 2023

As the ecological crisis deepen, new environmental controversies emerge. Whereas traditional environmental conflicts mostly concern socio-economic interests clashing with environmental protection, recent conflicts are increasingly pitting different environmental considerations against each other. These green conflicts have received scattered attention in the scholarly literature, mostly in the form of case studies in relation to renewable energy plants, such as wind turbines and solar panels. However, there is a need for more systematic approaches to conceptualize the green conflicts. This article embarks on that task by developing a typology of green conflicts as they appear in public discourse and mediated communication. We test the model on public debates on four different topics: national parks, organic farming, wind turbines, and nuclear energy. Our data suggests that green conflicts can increasingly be found across a wide range of environmental and climate change issues. However, green conflicts are not simply replacing traditional environmental conflicts, but are rather adding new layers to environmental controversies by reconfiguring conflict lines, actor positions, spatial scales, and temporalities.
Acknowledgment to the Reviewers of Symmetry in 2022
Authors:   Symmetry Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Differential Energy Criterion of Brittle Fracture as a Criterion for Wood’s Transition to the Plastic Deformation Stage
Authors: Kolesnikov Gennady , Gavrilov Timmo , Zaitseva Maria
Published: January 2023

An experimental study and modeling of the behavior of wood during compression along the fibers was carried out. The nonlinear analytical dependence of the load on the strain was considered. Attention was focused on the post-peak stage of deformation in order to determine the load and displacement at which the transition to the stage of plastic deformation occurs. The work was aimed at substantiating the application of the energy criterion of brittle fracture as a criterion for the transition to the stage of plastic deformation. To achieve this goal, methods of mathematical modeling and analysis of test results were used. As an upshot, a simple and practical procedure was developed to predict the transition point to the above stage of plastic deformation. The simulation results were consistent with laboratory tests of samples and fragments of structures. The practical significance of this criterion lies in its possible use as an additional tool for analyzing the condition of some wooden structures. Energy criteria, including the one mentioned above, belong to fairly universal criteria. Accordingly, the research methodology can be adapted to analyze the behavior of, for example, composites under other types of loads in further studies.
Influence of Symmetry of Tightened Parts on the Force in a Bolted Joint
Authors: Zacal Jiri , Folta Zdenek , Struz Jiri , Trochta Miroslav
Published: January 2023

This article discusses the issue of force behavior in a pre-tensioned bolt that is part of a symmetrical flange connection, loaded with an operating load parallel to the bolt axis. The first part describes the issue being dealt with in detail. The next part offers a brief description of the generally known theoretical approach to the solution, and also outlines the limitations of this approach. This part is followed by a theoretical analysis of working load behavior in the bolt of the analyzed device, using Finite Element Method (FEM). In this article, a comparison of several different approaches to the solution can be also found, followed by a description of the experiment preparation and its results. The obtained values also serve as a subject for reverse iterative analysis carried out using ANSYS software. The aim is to find a proper modification to the symmetric calculation model that would be consistent with the results of measurements from technical practice. All results achieved are described in the final part of the article.
Compound-Combination Synchronization for Fractional Hyperchaotic Models with Different Orders
Authors: Mahmoud Gamal M. , Althemairi Asma , Abed-Elhameed Tarek M. , Farghaly Ahmed A.
Published: January 2023

In this paper, we introduce a new type of synchronization for the fractional order (FO) hyperchaotic models with different orders called compound-combination synchronization (CCS). Using the tracking control method, a theorem to calculate the analytical controllers which achieve our proposed synchronization is described and proved. We introduce, also, the FO hyperchaotic complex Lü, Chen, and Lorenz models with complex periodic forcing. The symmetry property is found in the FO hyperchaotic complex Lü, Chen, and Lorenz models. These hyperchaotic models are found in many areas of applied sciences, such as physics and secure communication. These FO hyperchaotic models are used as an example for our proposed synchronization. The numerical simulations show a good agreement with the analytical results. The complexity and existence of additional variables mean that it is safer and interesting to transmit and receive signals in communication theory. The proposed scheme of synchronization is considered a generalization of many types in the literature and other examples can be found in similar studies.
Acknowledgment to the Reviewers of Thermo in 2022
Authors:   Thermo Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Bivalve Shellfish Safety in Portugal: Variability of Faecal Levels, Metal Contaminants and Marine Biotoxins during the Last Decade (2011–2020)
Authors: Braga Ana Catarina , Rodrigues Susana Margarida , Lourenço Helena Maria , Costa Pedro Reis , Pedro Sónia
Published: January 2023

Bivalves are a high-value product whose production has markedly increased, reaching 9863 tonnes in Portugal in 2021. Bivalves’ habitats—lagoons, estuaries and coastal waters—are exposed to biological and anthropogenic contaminants, which can bioaccumulate in these organisms and pose a significant public health risk. The need to obtain a safe product for human consumption led to the implementation of standardised hygiene regulations for harvesting and marketing bivalve molluscs, resulting in routine monitoring of bivalve production areas for microbial quality, metal contaminants, and marine biotoxins. While excessive levels of biotoxins and metal contamination lead to temporary harvesting bans, high faecal contamination leads to area reclassification and impose post-harvest treatments. In this study, the seasonal and temporal variability of these parameters were analysed using historical data generated by the monitoring programme during the last decade. Moreover, the impact of the monitoring program on bivalve harvesting from 2011 to 2020 was assessed. This program presented a considerable improvement over time, with an increase in the sampling effort and the overall program representativeness. Finally, contamination risk, revising control measures, and defining recommendations for risk mitigation measures are given in the light of ten years’ monitoring.
Intravesical Injection of Botulinum Toxin Type A in Patients with Refractory Overactive Bladder—Results between Young and Elderly Populations, and Factors Associated with Unfavorable Outcomes
Authors: Ou Yin-Chien , Kao Yao-Lin , Ho Yi-Hui , Wu Kuan-Yu , Kuo Hann-Chorng
Published: January 2023

Intravesical botulinum toxin type A (BoNT-A) injection has been recognized as the standard treatment for refractory overactive bladder (OAB). However, its therapeutic efficacy and safety have not been thoroughly reviewed in elderly patients. This study aims to provide treatment outcomes for patients aged ≥75 years, and to identify factors associated with unfavorable outcomes. Patients receiving intradetrusor injections of 100 U onabotulinumtoxinA for refractory OAB between 2011 and 2021 were retrospectively reviewed. Urodynamic parameters, underlying comorbidities, subjective success, and unfavorable outcomes were assessed. A total of 192 patients were included, and 65 of them were classified into the elderly group. For the elderly group, 60.0% experienced subjective dryness, and 84.6% remained subjective success at 6 months after the injections. The prevalence rates of common unfavorable outcomes, including urinary tract infections, large post-void residual urine volume, and urinary retention, were 9.2%, 27.7%, and 12.3%, respectively. Multivariate analysis revealed that female, baseline urodynamic parameters, and diabetes mellitus were associated with unfavorable outcomes in the elderly group. Intravesical BoNT-A injections provide comparable therapeutic efficacy and safety concerns in elderly patients with refractory OAB. A thorough consultation for treatment benefits and possible adverse events is mandatory before the procedure.
High Plasma Levels of Fibroblast Growth Factor 23 Are Associated with Increased Risk of COVID-19 in End-Stage Renal Disease Patients on Hemodialysis: Results of a Prospective Cohort
Authors: Toro Luis , Michea Luis , Parra-Lucares Alfredo , Mendez-Valdes Gabriel , Villa Eduardo , Bravo Ignacio , Pumarino Catalina , Ayala Patricia , Sanhueza María Eugenia , Torres Ruben , Elgueta Leticia , Chavez Sebastian , Rojas Veronica , Alvo Miriam
Published: January 2023

End-stage renal disease (ESRD) patients are a population with high rates of COVID-19 and mortality. These patients present a low response to anti-SARS-CoV-2 immunization, which is associated with immune dysfunction. ESRD patients also present high plasma titers of Fibroblast Growth Factor 23 (FGF23), a protein hormone that reduces immune response in vivo and in vitro. Increased FGF23 levels associate with higher infection-related hospitalizations and adverse infectious outcomes. Thus, we evaluated whether ESRD patients with high FGF23 titers have an increased rate of SARS-CoV-2 infection. Methods: We performed a prospective cohort of ESRD patients in hemodialysis who had measurements of plasma intact FGF23 in 2019. We determined COVID-19 infections, hospitalizations, and mortality between January 2020 and December 2021. Results: We evaluated 243 patients. Age: 60.4 ± 10.8 years. Female: 120 (49.3%), diabetes: 110 (45.2%). During follow-up, 45 patients developed COVID-19 (18.5%), 35 patients were hospitalized, and 12 patients died (mortality rate: 26.6%). We found that patients with higher FGF23 levels (defined as equal or above median) had a higher rate of SARS-CoV-2 infection versus those with lower levels (18.8% versus 9.9%; Hazard ratio: 1.92 [1.03–3.56], p = 0.039). Multivariate analysis showed that increased plasma FGF23 was independently associated with SARS-CoV-2 infection and severe COVID-19. Discussion: Our results suggest that high plasma FGF23 levels are a risk factor for developing COVID-19 in ESRD patients. These data support the potential immunosuppressive effects of high circulating FGF23 as a factor implicated in the association with worse clinical outcomes. Further data are needed to confirm this hypothesis.
Emerging Issues on Tropane Alkaloid Contamination of Food in Europe
Authors: de Nijs Monique , Crews Colin , Dorgelo Folke , MacDonald Susan , Mulder Patrick P. J.
Published: January 2023

The occurrence of tropane alkaloids (TAs), toxic plant metabolites, in food in Europe was studied to identify those TAs in food most relevant for human health. Information was extracted from the literature and the 2016 study from the European Food Safety Authority. Calystegines were identified as being inherent TAs in foods common in Europe, such as Solanum tuberosum (potato), S. melongena (eggplant, aubergine), Capsicum annuum (bell pepper) and Brassica oleracea (broccoli, Brussels sprouts). In addition, some low-molecular-weight tropanes and Convolvulaceae-type TAs were found inherent to bell pepper. On the other hand, atropine, scopolamine, convolvine, pseudotropine and tropine were identified as emerging TAs resulting from the presence of associated weeds in food. The most relevant food products in this respect are unprocessed and processed cereal-based foods for infants, young children or adults, dry (herbal) teas and canned or frozen vegetables. Overall, the occurrence data on both inherent as well as on associated TAs in foods are still scarce, highlighting the need for monitoring data. It also indicates the urge for food safety authorities to work with farmers, plant breeders and food business operators to prevent the spreading of invasive weeds and to increase awareness.