bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Calculation of Time Alignment Errors in Data Management Platforms for Distribution Grid Data
Authors: Schwefel Hans-Peter , Antonios Imad , Lipsky Lester
Published: October 2021

The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low- and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices.
5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle
Authors: Alkinani Monagi H. , Almazroi Abdulwahab Ali , Jhanjhi NZ , Khan Navid Ali
Published: October 2021

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.
A Secure Communication System for Constrained IoT Devices—Experiences and Recommendations
Authors: Goworko Michał , Wytrębowicz Jacek
Published: October 2021

The Internet of Things networks connect a large number of devices and can be used for various purposes. IoT systems collect and process vast amounts of often sensitive data. Information security should be the key feature of an IoT network. In this paper, we present the IoT-Crypto—secure communication system for the Internet of Things. It addresses IoT features, such as constrained abilities of devices, needs to reduce the volume of the transmitted data and be compatible with the Internet. IoT-Crypto introduces an innovative, lightweight certificate format and trust model based on real-world business relations. It also specifies secure communication protocol, which uses underlying encrypted DTLS connection. This paper presents IoT-Crypto in the context of comparable solutions, discusses its distinctive features and implementation details. Results of tests and experiments performed in the IoT-Crypto network confirm that it works correctly and securely. Test network was also used to ascertain the suitability of encoding standards and BLE IPSP profile for the IoT. Directions of future work were discussed based on those results.
Single-Use Fluidic Electrochemical Paper-Based Analytical Devices Fabricated by Pen Plotting and Screen-Printing for On-Site Rapid Voltammetric Monitoring of Pb(II) and Cd(II)
Authors: Soulis Dionysios , Trachioti Maria , Kokkinos Christos , Economou Anastasios , Prodromidis Mamas
Published: October 2021

This work reports the fabrication of integrated electrochemical fluidic paper-based analytical devices (ePADs) using a marker pen drawing and screen-printing. Electrodes were deposited on paper using screen-printing with conductive carbon ink. Then, the desired fluidic patterns were formed on the paper substrate by drawing with a commercial hydrophobic marker pen using an inexpensive computer-controlled x-y plotter. The working electrode was characterized by cyclic voltammetry and scanning electron microscopy. The analytical utility of the electrochemical PADs is demonstrated through electrochemical determination of Pb(II) and Cd(II) by anodic stripping voltammetry. For this purpose, the sample was mixed with a buffer solution and a Bi(III) solution, applied to the test zone of the PAD, the metals were preconcentrated as a bismuth alloy on the electrode surface and oxidized by applying an anodic potential scan. The proposed manufacturing approach enables the large-scale fabrication of fit-for-purpose disposable PADs at low cost which can be used for rapid on-site environmental monitoring.
A Step to Smart Fishways: An Autonomous Obstruction Detection System Using Hydraulic Modeling and Sensor Networks
Authors: Fuentes-Pérez Juan Francisco , García-Vega Ana , Bravo-Córdoba Francisco Javier , Sanz-Ronda Francisco Javier
Published: October 2021

Stepped fishways are structures that allow the free movement of fish in transversal obstacles in rivers. However, the lack of or incorrect maintenance may deviate them from this objective. To handle this problem, this research work presents a novel low-cost sensor network that combines fishway hydraulics with neural networks programmed in Python (Keras + TensorFlow), generating the first autonomous obstruction/malfunction detection system for stepped fishways. The system is based on a network of custom-made ultrasonic water level nodes that transmit data and alarms remotely and in real-time. Its performance was assessed in a field study case as well as offline, considering the influence of the number of sensing nodes and obstruction dimensions. Results show that the proposed system can detect malfunctions and that allows monitoring of the hydraulic performance of the fishway. Consequently, it optimizes the timing of maintenance on fishways and, thus, has the potential of automatizing and reducing the cost of these operations as well as augmenting the service of these structures. Therefore, this novel tool is a step forward to achieve smart fishway management and to increase their operability.
Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications
Authors: Vulpe Alexandru , Crăciunescu Răzvan , Drăgulinescu Ana-Maria , Kyriazakos Sofoklis , Paikan Ali , Ziafati Pouyan
Published: October 2021

Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.
Correlates of Balance and Aerobic Indices in Lower-Limb Prostheses Users on Arm Crank Exercise
Authors: Guerra Gary , Smith John D.
Published: October 2021

Background: The HUMAC Balance System (HBS) offers valid measurement of balance, and the arm crank exercise test (ACE) is a valid measure of physiological capacity. Neither have been used to evaluate associations between balance and physiological capacity in lower-limb amputees. Methods: Thirty-five participants with lower-limb amputations were recruited. Standing balance (center of pressure) was evaluated during eyes opened (EO) and eyes closed (EC) conditions using the HBS. Participants performed ACE graded exercise testing (GXT) to evaluate aerobic capacity. Spearman’s rho was used to identify relationships between variables. Cut-points for three groups were generated for time on ACE. Mann–Whitney U tests were used to explore significant differences in variables of balance and ACE between low and high performers. Results: Relationships between variables of eyes open displacement (EOD), eyes open velocity (EOV), eyes closed displacement (ECD), and eyes closed velocity (ECV) were significant (p < 0.05), and high performers with EO also performed best with EC. Longer exercise times were significantly associated with increased HRpeak, VO2peak, VEpeak, and RERpeak (p < 0.05). HRpeak (143.0 ± 30.6 b/min), VO2peak (22.7 ± 7.9 and 10.6 ± 4.7 mL/kg/min), VEpeak (80.2 ± 22.2 and 33.2 ± 12.7 L/min), and RERpeak (1.26 ± 0.08 and 1.13 ± 0.11) were significantly greater in high performers than low performers, respectively (p < 0.05). There was no significant association among VO2peak and any balance task variables; however, there were significant associations between some balance and physiological variables. Conclusions: Findings differentiated high and low performers; however, participants were still well below able-bodied norms of physical capacity. Training to mitigate deconditioning is suggested.
Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System
Authors: Yadav Rahul , Omrani Adel , Link Guido , Vauhkonen Marko , Lähivaara Timo
Published: October 2021

The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities.
Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
Authors: Mutlag Ammar Awad , Ghani Mohd Khanapi Abd , Mohammed Mazin Abed , Lakhan Abdullah , Mohd Othman , Abdulkareem Karrar Hameed , Garcia-Zapirain Begonya
Published: October 2021

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
Mineral Interpretation Discrepancies Identified between Infrared Reflectance Spectra and X-ray Diffractograms
Authors: Maghsoudi Moud Fardad , Deon Fiorenza , van der Meijde Mark , van Ruitenbeek Frank , Hewson Rob
Published: October 2021

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H–OH absorption feature at 1900 nm and the poorly defined Al–OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.
Determination of the Kinematic Excitation Originating from the Irregular Envelope of an Omnidirectional Wheel
Authors: Duda Sławomir , Dudek Olaf , Gembalczyk Grzegorz , Machoczek Tomasz
Published: October 2021

This paper describes a test stand for determining the kinematic excitation originating from the contact between a vehicle’s wheel and the ground, thus acting on the single suspension upright of the vehicle. This excitation is unique to the movement of omnidirectional wheels and originates from the irregular envelope of the wheel. The presented attitude enables the vertical displacement of the wheel’s axis rolling on a horizontal surface to be determined. This work includes experimental results considering different wheel orientations against the direction of movement.
EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization
Authors: Stancin Igor , Frid Nikolina , Cifrek Mario , Jovic Alan
Published: October 2021

Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features.
Severity Grading and Early Retinopathy Lesion Detection through Hybrid Inception-ResNet Architecture
Authors: Yasin Sana , Iqbal Nasrullah , Ali Tariq , Draz Umar , Alqahtani Ali , Irfan Muhammad , Rehman Abdul , Glowacz Adam , Alqhtani Samar , Proniewska Klaudia , Brumercik Frantisek , Wzorek Lukasz
Published: October 2021

Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the long run, it could be a permanent source of impaired vision, simply known as blindness in the advanced as well as in developing nations. This could be prevented if DR is identified early enough, but it can be challenging as we know the disease frequently shows rare signs until it is too late to deliver an effective cure. In our work, we recommend a framework for severity grading and early DR detection through hybrid deep learning Inception-ResNet architecture with smart data preprocessing. Our proposed method is composed of three steps. Firstly, the retinal images are preprocessed with the help of augmentation and intensity normalization. Secondly, the preprocessed images are given to the hybrid Inception-ResNet architecture to extract the vector image features for the categorization of different stages. Lastly, to identify DR and decide its stage (e.g., mild DR, moderate DR, severe DR, or proliferative DR), a classification step is used. The studies and trials have to reveal suitable outcomes when equated with some other previously deployed approaches. However, there are specific constraints in our study that are also discussed and we suggest methods to enhance further research in this field.
An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture
Authors: Abbas Qaisar , Qureshi Imran , Ibrahim Mostafa E. A.
Published: October 2021

The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.
Synthesis of Planar Circular Arrays with Quantized Amplitude Weights
Authors: He Zhiqiu , Chen Gang
Published: October 2021

A new stepwise and radially processed method for synthesizing uniformly distributed circular planar arrays with quantized weights is proposed in this paper. This method is based on a generalized analytical equation describing that for high directivity focusing arrays, minimizing the weighted mean square error between the reference pattern and the synthesized pattern is equivalent to minimizing the mean square error between the radial cumulative distributions of the reference distribution and the synthesized distribution. This principle has been successfully performed for designing large concentric ring arrays, and in this paper, we extend its use for synthesizing uniformly distributed planar circular arrays with quantized weights. Various numerical examples and comparisons with several reported statistical methods in terms of the lowest Maximum SideLobe Level (MSLL) demonstrate the effectiveness of the proposed method.
A Crack Size Quantification Method Using High-Resolution Lamb Waves
Authors: Li Xianjun , Yang Jinsong , Zhang Guangdong
Published: October 2021

Traditional tone burst excitation cannot attain a high output resolution, due to the time duration. The received signal is much longer than that of excitation during the propagation, which can increase the difficulty of signal processing, and reduce the resolution. Therefore, it is of significant interest to develop a general methodology for crack quantification through the optimal design of the excitation waveform and signal-processing methods. This paper presents a new crack size quantification method based on high-resolution Lamb waves. The linear chirp (L-Chirp) signal and Golay complementary code (GCC) signal are used as Lamb wave excitation signals. After dispersion removal, these excitation waveforms, based on pulse compression, can effectively improve the inspection resolution in plate-like structures. A series of simulations of both healthy plates and plates with different crack sizes are performed by Abaqus CAE, using different excitation waveforms. The first wave package of the S0 mode after pulse compression is chosen to extract the damage features. A multivariate regression model is proposed to correlate the damage features to the crack size. The effectiveness of the proposed crack size quantification method is verified by a comparison with tone burst excitation, and the accuracy of the crack size quantification method is verified by validation experiments.
Effect of Blue Light on Acne Vulgaris: A Systematic Review
Authors: Diogo Mara Lúcia Gonçalves , Campos Thalita Molinos , Fonseca Elsa Susana Reis , Pavani Christiane , Horliana Anna Carolina Ratto Tempestini , Fernandes Kristianne Porta Santos , Bussadori Sandra Kalil , Fantin Francisca Goreth Malheiro Moraes , Leite Diego Portes Vieira , Yamamoto Ângela Toshie Araki , Navarro Ricardo Scarparo , Motta Lara Jansiski
Published: October 2021

Acne is a dermatosis that affects almost 90% of the adolescent population worldwide and its treatment is performed with retinoids, antimicrobials, acids, and topical or systemic antibiotics. Side effects such as skin irritation in addition to microbial resistance to antibiotics are the main side effects found. Phototherapy with blue light is being used as an alternative treatment. Our objective was to analyze the use of blue light to treat inflammatory acne. We conducted a systematic literature review, following the recommendation PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses), including in the sample randomized clinical trial studies that compared blue light with another intervention as control. The research was carried out in the PUBMED and WEB of SCIENCE databases and the methodological quality of the studies evaluated were made by the Cochrane Collaboration Bias Risk Scale. After the exclusion of duplicates, the titles and abstracts of 81 articles were evaluated, and 50 articles were selected for full reading, including in the review at the end 8 articles. Studies have shown significant improvements in the overall picture of acne. It is concluded that despite the great potential in its use in the treatment of acne, there is a need for more detailed trials on the effect of blue light on the treatment of inflammatory acne.
Cost-Effective Multiplex Fluorescence Detection System for PCR Chip
Authors: Yun Sung-Hun , Park Ji-Sung , Koo Seul-Bit-Na , Park Chan-Young , Kim Yu-Seop , Kim Jong-Dae
Published: October 2021

The lack of portability and high cost of multiplex real-time PCR systems limits the device to be used in POC. To overcome this issue, this paper proposes a compact and cost-effective fluorescence detection system that can be integrated to a multiplex real-time PCR equipment. An open platform camera with embedded lens was used instead of photodiodes or an industrial camera. A compact filter wheel using a sliding tape is integrated, and the excitation LEDs are fixed at a 45° angle near the PCR chip, eliminating the need of additional filter wheels. The results show precise positioning of the filter wheel with an error less than 20 μm. Fluorescence detection results using a reference dye and standard DNA amplification showed comparable performance to that of the photodiode system.
Validation of a Unique Boxing Monitoring System
Authors: Menzel Tobias , Potthast Wolfgang
Published: October 2021

Much development work and scientific research has been conducted in recent years in the field of detecting human activity and the measurement of biomechanical performance parameters using portable sensor technologies, so-called wearable systems. Despite the fact that boxers participating in one of the most vigorous and complex disciplines of all sports, it is one of the disciplines where no noteworthy, advanced performance analytic tools are used for training or for competition purposes worldwide. This research aimed to develop and validate a comprehensive punch performance sensor system for the measurement and analysis of biomechanical parameters in the sport of boxing. A comprehensive validation study on linear regression was conducted following the development of the sensor system, between the gold standard of a Kistler force plate and Vicon motion capture system, to compare sensor-derived measurements with the gold standard-derived measurements. The developed sensor system demonstrated high accuracies ranging from R2 = 0.97 to R2 = 0.99 for punch force, acceleration, velocity and punch-time data. The validation experiments conducted demonstrated the significant accuracy of the sensor-derived measurements for predicting boxing-specific biomechanical movement parameters while punching in field use. Thus, this paper presents a unique sensor system for comprehensive measurements of biomechanical parameters using the developed mobile measurement system in the field of combat sports
Road Curb Detection: A Historical Survey
Authors: Romero Lucero M. , Guerrero Jose A. , Romero Gerardo
Published: October 2021

Curbs are used as physical markers to delimit roads and to redirect traffic into multiple directions (e.g., islands and roundabouts). Detection of road curbs is a fundamental task for autonomous vehicle navigation in urban environments. Since almost two decades, solutions that use various types of sensors, including vision, Light Detection and Ranging (LiDAR) sensors, among others, have emerged to address the curb detection problem. This survey elaborates on the advances of road curb detection problems, a research field that has grown over the last two decades and continues to be the ground for new theoretical and applied developments. We identify the tasks involved in the road curb detection methods and their applications on autonomous vehicle navigation and advanced driver assistance system (ADAS). Finally, we present an analysis on the similarities and differences of the wide variety of contributions.
A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme
Authors: Park Seo-Jeon , Kim Byung-Gyu , Chilamkurti Naveen
Published: October 2021

In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.
Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation
Authors: Fan Chao , Yin Zhenyu , Xu Fulong , Chai Anying , Zhang Feiqing
Published: October 2021

In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset.
Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
Authors: Cortese Lorenzo , Zanoletti Marta , Karadeniz Umut , Pagliazzi Marco , Yaqub M. Atif , Busch David R. , Mesquida Jaume , Durduran Turgut
Published: October 2021

Despite the wide range of clinical and research applications, the reliability of the absolute oxygenation measurements of continuous wave near-infrared spectroscopy sensors is often questioned, partially due to issues of standardization. In this study, we have compared the performances of 13 units of a continuous wave near-infrared spectroscopy device (PortaMon, Artinis Medical Systems, NL) to test their suitability for being used in the HEMOCOVID-19 clinical trial in 10 medical centers around the world. Detailed phantom and in vivo tests were employed to measure the precision and reproducibility of measurements of local blood oxygen saturation and total hemoglobin concentration under different conditions: for different devices used, different operators, for probe repositioning over the same location, and over time (hours/days/months). We have detected systematic differences between devices when measuring phantoms (inter-device variability, <4%), which were larger than the intra-device variability (<1%). This intrinsic variability is in addition to the variability during in vivo measurements on the forearm muscle resulting from errors in probe positioning and intrinsic physiological noise (<9%), which was also larger than the inter-device differences (<3%) during the same test. Lastly, we have tested the reproducibility of the protocol of the HEMOCOVID-19 clinical trial; that is, forearm muscle oxygenation monitoring during vascular occlusion tests over days. Overall, our conclusion is that these devices can be used in multi-center trials but care must be taken to characterize, follow-up, and statistically account for inter-device variability.
Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions
Authors: Papadimitrakis Myron , Stogiannos Marios , Sarimveis Haralambos , Alexandridis Alex
Published: October 2021

The field of automatic collision avoidance for surface vessels has been an active field of research in recent years, aiming for the decision support of officers in conventional vessels, or for the creation of autonomous vessel controllers. In this paper, the multi-ship control problem is addressed using a model predictive controller (MPC) that makes use of obstacle ship trajectory prediction models built on the RBF framework and is trained on real AIS data sourced from an open-source database. The usage of such sophisticated trajectory prediction models enables the controller to correctly infer the existence of a collision risk and apply evasive control actions in a timely manner, thus accounting for the slow dynamics of a large vessel, such as container ships, and enhancing the cooperation between controlled vessels. The proposed method is evaluated on a real-life case from the Miami port area, and its generated trajectories are assessed in terms of safety, economy, and COLREG compliance by comparison with an identical MPC controller utilizing straight-line predictions for the obstacle vessel.
Biomimetic Chaotic Sensor for Moderate Static Magnetic Field
Authors: Korneta Wojciech , Gomes Iacyel , Picos Rodrigo , Zábovský Michal
Published: October 2021

The effects of a static magnetic field on systems with chaotic dynamical behavior have attracted little attention so far. Here, Chua’s electronic circuit with an inductor placed in a static uniform magnetic field operating in a chaotic double-scroll regime is studied experimentally. The effect of the magnetic field on the duty cycle factor and the spike count rate, with spikes defined by crossings between the scrolls of the double-scroll attractor, is described. A slow monotonic variation in the duty cycle factor and constant spike count rate is observed for magnetic field intensities up to the threshold, where both these metrics change severely; the dynamic trajectory remains on one scroll and spikes disappear. The dependence of the static magnetic field intensity on Chua’s circuit resistivity at the threshold is given. Two biomimetic magnetic chaotic sensors are proposed: one based on one Chua’s circuit and another that can have various transfer functions and is composed of several independent Chua’s circuits.
A Medium-Frequency Fiber Bragg Grating Accelerometer Based on Flexible Hinges
Authors: Li Zichuang , Liang Lei , Wang Hui , Dai Shu , Jiang Ke , Song Zhiyuan
Published: October 2021

Mediumfrequency fiber Bragg grating (FBG) acceleration sensors are used in important applications in mechanical, aerospace and weapon equipment, and have strict requirements in terms of resonance frequency and sensitivity. A novel medium-frequency accelerometer, based on fiber Bragg grating and flexible hinges, is proposed in this paper. The differential structure doubles the sensitivity of the sensor while avoiding temperature effects. The structure model and principle for the sensor are introduced, the sensor’s sensing characteristics are theoretically analyzed, and the structure parameters for the sensor are determined through numerical analysis. The sensing experiments show that the resonance frequency of the sensor is approximately 2800 Hz, the sensitivity is 21.8 pm/g in the flat frequency range of 50–1000 Hz, and the proposed sensor has a good temperature self-compensation function and lateral anti-interference capability.
From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art
Authors: Paepae Thulane , Bokoro Pitshou , Kyamakya Kyandoghere
Published: October 2021

Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.
A Study on Dining-Out Trends Using Big Data: Focusing on Changes since COVID-19
Authors: Jung Hyo-Sun , Yoon Hye-Hyun , Song Min-Kyung
Published: October 2021

This study examined consumers’ emotions and needs related to dining-out experiences before and during the COVID-19 crisis. This study identifies words closely associated with the keyword “dining-out” based on big data gleaned from social media and investigates consumers’ perceptions of dining-out and related issues before and after COVID-19. The research findings can be summarized as follows: In 2019, frequently appearing dining-related words were dining-out, family, famous restaurant, recommend, and dinner. In 2020, they were dining-out, family, famous restaurant, and Corona. The analysis results for the dining-out sentimental network based on 2019 data revealed discourses revolving around delicious, nice, and easily. For the 2020 data, discourses revolved around struggling, and, cautious. The analysis of consumers’ dining-out demand network for 2019 data showed discourses centered around reservation, famous restaurant, meal, order, and coffee. However, for 2020 data, discourses were formed around delivery, price, order, take-out, and social distance. In short, with the outbreak of the pandemic, delivery, takeout, and social distance emerged as new search words. In addition, compared with before the COVID-19 pandemic, a weakening trend in positive emotions and an increasing trend in negative emotions were detected after the outbreak of the COVID-19 pandemic; specifically, fear was found to be the fear emotion.
Tactical Urbanism in Italy: From Grassroots to Institutional Tool—Assessing Value of Public Space Experiments
Authors: Cariello Alessandro , Ferorelli Rossella , Rotondo Francesco
Published: October 2021

The paper aims to evaluate the value that the experimentation of tactical urban planning activities can assume for the city, through the critical account of some practices in three Italian cities of large (Milan), medium-large (Bari), and medium size (Taranto), which in recent years, in some cases unknowingly, have experienced its effects, also forced by the thrust offered by the need to respond to the consequences of the pandemic. The authors reflect on how short-term interventions started by tactical urbanism movement are inspiring planning institutions to implement short-term place-making initiatives. The contribution moves within the context of new generation urban regeneration in which the transformation of existing spaces is a process of community reconstruction through the redevelopment of public spaces increasingly open to multiple and temporary uses. First through a process of rereading the state of the art of the project of public spaces in Italy and its transformation caused by the pandemic, then through a comparative look between the three case studies, conclusions are drawn on the urban value of the experiments conducted and, on their ability, to identify a new reference point for the sustainable urban regeneration of public spaces.
A Review on Snowmelt Models: Progress and Prospect
Authors: Zhou Gang , Cui Manyi , Wan Junhong , Zhang Shiqiang
Published: October 2021

The frequency and intensity of flood events have been increasing recently under the warming climate, with snowmelt floods being a significant part. As an effective manner of simulating snowmelt flood, snowmelt models have attracted more and more attention. Through comprehensive analysis of the literature, this paper reviewed the characteristics and current status of different types of snowmelt models, as well as the different coupling methods of models for runoff generation and confluence. We then discussed key issues in snowmelt modelling, including blowing snow model, frozen ground model, and rain-on-snow model. Finally, we give some perspectives from four aspects: data, model structure, forecast and early warning, and forecast and estimation. At present, most of the snowmelt models do not have blowing snow or frozen ground modules. Explicit consideration of blowing snow and soil freezing/thawing processes can improve the accuracy of snowmelt runoff simulations. With climate warming, rain-on-snow events have increased, but the mechanism of enhanced rain and snow mixed flooding is still unclear, particularly for the mechanism of rain-snow-ice mixed runoff generation. The observation and simulation of rain and snow processes urgently need further study. A distributed physical snowmelt model based on energy balance is an advanced tool for snowmelt simulation, but the model structure and parameter schemes still need further improvements. Moreover, the integration of satellite-based snow products, isotopes, and terrestrial water storage change, monitored by gravity satellites, can help improve the calibration and validation of snowmelt models.
Estimation of Current and Future Suitable Areas for Tapirus pinchaque in Ecuador
Authors: Mestanza-Ramón Carlos , Herrera Feijoo Robinson J. , Chicaiza-Ortiz Cristhian , Gaibor Isabel Domínguez , Mateo Rubén G.
Published: October 2021

At present, climate change is a direct threat to biodiversity and its effects are evidenced by an increasingly accelerated loss of biodiversity. This study identified the main threats presently facing the Tapirus pinchaque species in Ecuador, generated predictive models regarding its distribution, and analyzed the protected areas as a conservation tool. The methodology was based on a literature review and the application of binary predictive models to achieve these objectives. The main results indicate that the T. pinchaque is seriously threatened, mainly by changes in land use. In addition, three models were selected that show current and future suitable areas for the conservation of the species. Its current distribution amounts to 67,805 km2, 33% (22,872 km2) of which is located in 31 of the 61 protected areas. Finally, it is important to take timely actions focused on biodiversity conservation, considering the importance of balance in ecosystems to the humans dependent thereof, and the results regarding the changes in the current and future distribution areas of the mountain tapir are a great contribution to be used as a management tool for its conservation.
How Does Digital Transformation Improve Organizational Resilience?—Findings from PLS-SEM and fsQCA
Authors: Zhang Jichang , Long Jing , von Schaewen Alexandra Martina Eugenie
Published: October 2021

Digital transformation has become a critical path for enterprises to improve organizational resilience, and has been widely considered by both academia and business practice. However, the extant literature focuses on the concepts and antecedents of digital transformation and the outcomes of digital transformation, lacking of exploring the effect mechanism of digital transformation of enterprises on organizational resilience. Based on the perspective of dynamic capacity and the theoretical path of “digital transformation—ambidextrous innovation—organizational resilience”, this study constructs a theoretical model to explore a path where digital transformation affects both exploitative innovation and exploratory innovation, and further affects the organizational resilience of enterprises. By performing a questionnaire investigation with 339 Chinese enterprises, this study adopted both a fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling (SEM) to explore the relationships among digital transformation, ambidextrous innovation, and organizational resilience. The results show that the digital transformation of enterprises helps to improve organizational resilience. Additionally, digital transformation has a positive impact on the organizational resilience of enterprises mediated by both exploitative innovation and exploratory innovation. Finally, both exploitative innovation and exploratory innovation of enterprises have a positive impact on organizational resilience, and there is a complementary relationship between exploitative innovation and exploratory innovation. Further qualitative comparative analysis also shows that there are three types of configurations for achieving organizational resilience: digital transformation and exploitative innovation, digital transformation and exploratory innovation, and exploitative innovation and exploratory innovation. The paper is concluded by highlighting the importance of the practical significance for enterprises to effectively carry out digital transformation and further achieve organizational resilience.