bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time–Space Analysis
Authors: Ge Zhao , Wu Hao , Zhao Can , Tang Ming
Published: March 2022

Distributed optical fiber vibration sensing (DVS) can measure vibration information along with an optical fiber. Accurate classification of vibration events is a key issue in practical applications of DVS. In this paper, we propose a convolutional neural network (CNN) to analyze DVS data and achieve high-accuracy event recognition fully. We conducted experiments outdoors and collected more than 10,000 sets of vibration data. Through training, the CNN acquired the features of the raw DVS data and achieved the accurate classification of multiple vibration events. The recognition accuracy reached 99.9% based on the time–space data, a higher than used time-domain, frequency–domain, and time–frequency domain data. Moreover, considering that the performance of the DVS and the testing environment would change over time, we experimented again after one week to verify the method’s generalization performance. The classification accuracy using the previously trained CNN is 99.2%, which is of great value in practical applications.
Influence of Engine Electronic Management Fault Simulation on Vehicle Operation
Authors: Šarkan Branislav , Loman Michal , Synák František , Richtář Michal , Gidlewski Mirosław
Published: March 2022

The preparation of the fuel mixture of a conventional internal combustion engine is currently controlled exclusively electronically. In order for the electrical management of an internal combustion engine to function properly, it is necessary that all its electronic components work flawlessly and fulfill their role. Failure of these electronic components can cause incorrect fuel mixture preparation and also affect driving safety. Due to the effect of individual failures, it has a negative impact on road safety and also negatively affects other participants. The task of the research is to investigate the effect of the failure of electronic engine components on the selected operating characteristics of a vehicle. The purpose of this article is to specify the extent to which a failure of an electronic engine component may affect the operation of a road vehicle. Eight failures of electronic systems (sensors and actuators) were simulated on a specific vehicle, with a petrol internal combustion engine. Measurements were performed in laboratory conditions, the purpose of which was to quantify the change in the operating characteristics of the vehicle between the faulty and fault-free state. The vehicle performance parameters and the production of selected exhaust emission components were determined for selected vehicle operating characteristics. The results show that in the normal operation of vehicles, there are situations where a failure in the electronic system of the engine has a significant impact on its operating characteristics and, at the same time, some of these failures are not identifiable by the vehicle operator. The findings of the publication can be used in the drafting of legislation, in the field of production and operation of road vehicles, and also in the mathematical modeling of the production of gaseous emissions by road transport.
Highly Sensitive and Selective Detection of Hydrogen Using Pd-Coated SnO2 Nanorod Arrays for Breath-Analyzer Applications
Authors: Jung Hwaebong , Hwang Junho , Choe Yong-Sahm , Lee Hyun-Sook , Lee Wooyoung
Published: March 2022

We report a breath hydrogen analyzer based on Pd-coated SnO2 nanorods (Pd-SnO2 NRs) sensor integrated into a miniaturized gas chromatography (GC) column. The device can measure a wide range of hydrogen (1–100 ppm), within 100 s, using a small volume of human breath (1 mL) without pre-concentration. Especially, the mini-GC integrated with Pd-SnO2 NRs can detect 1 ppm of H2, as a lower detection limit, at a low operating temperature of 152 °C. Furthermore, when the breath hydrogen analyzer was exposed to a mixture of interfering gases, such as carbon dioxide, nitrogen, methane, and acetone, it was found to be capable of selectively detecting only H2. We found that the Pd-SnO2 NRs were superior to other semiconducting metal oxides that lack selectivity in H2 detection. Our study reveals that the Pd-SnO2 NRs integrated into the mini-GC device can be utilized in breath hydrogen analyzers to rapidly and accurately detect hydrogen due to its high selectivity and sensitivity.
Detection and Mosaicing Techniques for Low-Quality Retinal Videos
Authors: Camara José , Silva Bruno , Gouveia António , Pires Ivan Miguel , Coelho Paulo , Cunha António
Published: March 2022

Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images.
Hand Measurement System Based on Haptic and Vision Devices towards Post-Stroke Patients
Authors: Koter Katarzyna , Samowicz Martyna , Redlicka Justyna , Zubrycki Igor
Published: March 2022

Diagnostics of a hand requires measurements of kinematics and joint limits. The standard tools for this purpose are manual devices such as goniometers which allow measuring only one joint simultaneously, making the diagnostics time-consuming. The paper presents a system for automatic measurement and computer presentation of essential parameters of a hand. Constructed software uses an integrated vision system, a haptic device for measurement, and has a web-based user interface. The system provides a simplified way to obtain hand parameters, such as hand size, wrist, and finger range of motions, using the homogeneous-matrix-based notation. The haptic device allows for active measurement of the wrist’s range of motion and additional force measurement. A study was conducted to determine the accuracy and repeatability of measurements compared to the gold standard. The system functionality was confirmed on five healthy participants, with results showing comparable results to manual measurements regarding fingers’ lengths. The study showed that the finger’s basic kinematic structure could be measured by a vision system with a mean difference to caliper measurement of 4.5 mm and repeatability with the Standard Deviations up to 0.7 mm. Joint angle limits measurement achieved poorer results with a mean difference to goniometer of 23.6º. Force measurements taken by the haptic device showed the repeatability with a Standard Deviation of 0.7 N. The presented system allows for a unified measurement and a collection of important parameters of a human hand with therapist interface visualization and control with potential use for post-stroke patients’ precise rehabilitation.
Modular Single-Stage Three-Phase Flyback Differential Inverter for Medium/High-Power Grid Integrated Applications
Authors: Ali Ahmed Ismail M. , Tuan Cao Anh , Takeshita Takaharu , Sayed Mahmoud A. , Alaas Zuhair Muhammed
Published: March 2022

This paper proposes a single-stage three-phase modular flyback differential inverter (MFBDI) for medium/high power solar PV grid-integrated applications. The proposed inverter structure consists of parallel modules of flyback DC-DC converters based on the required power level. The MFBDI offers many features for renewable energy applications, such as reduced components, single-stage power processing, high-power density, voltage-boosting property, improved footprint, flexibility with modular extension capability, and galvanic isolation. The proposed inverter has been modelled, designed, and scaled up to the required application rating. A new mathematical model of the proposed MFBDI is presented and analyzed with a time-varying duty-cycle, wide-range of frequency variation, and power balancing in order to display its grid current harmonic orders for grid-tied applications. In addition, an LPF-based harmonic compensation strategy is used for second-order harmonic component (SOHC) compensation. With the help of the compensation technique, the grid current THD is reduced from 36% to 4.6% by diminishing the SOHC from 51% to 0.8%. Moreover, the SOHC compensation technique eliminates third-order harmonic components from the DC input current. In addition, a 15% parameters mismatch has been applied between the flyback parallel modules to confirm the modular operation of the proposed MFBDI under modules divergence. In addition, SiC MOSFETs are used for inverter switches implementation, which decrease the inverter switching losses at high-switching frequency. The proposed MFBDI is verified by using three flyback parallel modules/phase using PSIM/Simulink software, with a rating of 5 kW, 200 V, and 50 kHz switching frequency, as well as experimental environments.
Single-Shot Intrinsic Calibration for Autonomous Driving Applications
Authors: Monrroy Cano Abraham , Lambert Jacob , Edahiro Masato , Kato Shinpei
Published: March 2022

In this paper, we present a first-of-its-kind method to determine clear and repeatable guidelines for single-shot camera intrinsic calibration using multiple checkerboards. With the help of a simulator, we found the position and rotation intervals that allow optimal corner detector performance. With these intervals defined, we generated thousands of multiple checkerboard poses and evaluated them using ground truth values, in order to obtain configurations that lead to accurate camera intrinsic parameters. We used these results to define guidelines to create multiple checkerboard setups. We tested and verified the robustness of the guidelines in the simulator, and additionally in the real world with cameras with different focal lengths and distortion profiles, which help generalize our findings. Finally, we used a 3D LiDAR (Light Detection and Ranging) to project and confirm the quality of the intrinsic parameters projection. We found it possible to obtain accurate intrinsic parameters for 3D applications, with at least seven checkerboard setups in a single image that follow our positioning guidelines.
A Review of Recent Developments in Driver Drowsiness Detection Systems
Authors: Albadawi Yaman , Takruri Maen , Awad Mohammed
Published: March 2022

Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.
On-Axis Optical Bench for Laser Ranging Instruments in Future Gravity Missions
Authors: Yang Yichao , Yamamoto Kohei , Dovale Álvarez Miguel , Wei Daikang , Esteban Delgado Juan José , Müller Vitali , Jia Jianjun , Heinzel Gerhard
Published: March 2022

The laser ranging interferometer onboard the Gravity Recovery and Climate Experiment Follow-On mission proved the feasibility of an interferometric sensor for inter-satellite length tracking with sub-nanometer precision, establishing an important milestone for space laser interferometry and the general expectation that future gravity missions will employ heterodyne laser interferometry for satellite-to-satellite ranging. In this paper, we present the design of an on-axis optical bench for next-generation laser ranging which enhances the received optical power and the transmit beam divergence, enabling longer interferometer arms and relaxing the optical power requirement of the laser assembly. All design functionalities and requirements are verified by means of computer simulations. A thermal analysis is carried out to investigate the robustness of the proposed optical bench to the temperature fluctuations found in orbit.
A Custom-Made Electronic Dynamometer for Evaluation of Peak Ankle Torque after COVID-19
Authors: Dragoi Iulia Iovanca , Popescu Florina Georgeta , Petrita Teodor , Alexa Florin , Tatu Romulus Fabian , Bondor Cosmina Ioana , Tatu Carmen , Bowling Frank L. , Reeves Neil D. , Ionac Mihai
Published: March 2022

The negative effects of SARS-CoV-2 infection on the musculoskeletal system include symptoms of fatigue and sarcopenia. The aim of this study is to assess the impact of COVID-19 on foot muscle strength and evaluate the reproducibility of peak ankle torque measurements in time by using a custom-made electronic dynamometer. In this observational cohort study, we compare two groups of four participants, one exposed to COVID-19 throughout measurements and one unexposed. Peak ankle torque was measured using a portable custom-made electronic dynamometer. Ankle plantar flexor and dorsiflexor muscle strength was captured for both feet at different ankle angles prior and post COVID-19. Average peak torque demonstrated no significant statistical differences between initial and final moment for both groups (p = 0.945). An increase of 4.8%, p = 0.746 was obtained in the group with COVID-19 and a decrease of 1.3%, p = 0.953 was obtained in the group without COVID-19. Multivariate analysis demonstrated no significant differences between the two groups (p = 0.797). There was a very good test–retest reproducibility between the measurements in initial and final moments (ICC = 0.78, p < 0.001). In conclusion, peak torque variability is similar in both COVID-19 and non-COVID-19 groups and the custom-made electronic dynamometer is a reproducible method for repetitive ankle peak torque measurements.
Sensing Control Parameters of Flute from Microphone Sound Based on Machine Learning from Robotic Performer
Authors: Kuroda Jin , Koutaki Gou
Published: March 2022

When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the musical instrument, and provide specific guidance. However, it is difficult to acquire the control parameters of wind instruments (e.g., saxophone or flute) such as flow and angle between the player and the musical instrument, since it is not possible to place sensors into the mouth. In this paper, we propose a sensorless control parameter estimation system based on the recorded sound of a wind instrument using only machine learning. In the machine learning framework, many training samples that have both sound and correct labels are required. Therefore, we generated training samples using a robotic performer. This has two advantages: (1) it is easy to obtain many training samples with exhaustive control parameters, and (2) we can use the correct labels as the given control parameters of the robot. In addition to the samples generated by the robot, some human performance data were also used for training to construct an estimation model that enhanced the feature differences between robot and human performance. Finally, a flute control parameter estimation system was developed, and its estimation accuracy for eight novice flute players was evaluated using the Spearman’s rank correlation coefficient. The experimental results showed that the proposed system was able to estimate human control parameters with high accuracy.
Enhanced Channel Calibration for the Image Sensor of the TuMag Instrument
Authors: Magdaleno Eduardo , Rodríguez Valido Manuel , Hernández David , Balaguer María , Ruiz Cobo Basilio , Orozco Suárez David , Álvarez García Daniel , González Argelio Mauro
Published: March 2022

The Sunrise missions consist of observing the magnetic field of the sun continuously for a few days from the stratosphere. In these missions, a balloon supporting a telescope and associated instrumentation, including a Tunable Magnetograph (TuMag), is lifted into the stratosphere. In the camera of this instrument, the image sensor sends its data to a Field Programmable Gate Array (FPGA) using eight transmission channels. These channels must be previously calibrated for a correct delivery of the image. For this mission, the FPGA has been exchanged for a newer and larger one, so the firmware has been adapted to the new device. In addition, the calibration algorithm has been parallelized as the main innovation of this work, taking advantage of the increase in logic resources of the new FPGA, in order to minimize the calibration time of the channels. The algorithm has been implemented specifically for this instrument without using the Input Serial Deserializer (ISERDES) Intellectual Property (IP), since this IP does not support the deserialization of the data sent by the image sensor to the FPGA.
Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning
Authors: Stankoski Simon , Kiprijanovska Ivana , Mavridou Ifigeneia , Nduka Charles , Gjoreski Hristijan , Gjoreski Martin
Published: March 2022

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson’s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device.
Image Synthesis Pipeline for CNN-Based Sensing Systems
Authors: Frolov Vladimir , Faizov Boris , Shakhuro Vlad , Sanzharov Vadim , Konushin Anton , Galaktionov Vladimir , Voloboy Alexey
Published: March 2022

The rapid development of machine learning technologies in recent years has led to the emergence of CNN-based sensors or ML-enabled smart sensor systems, which are intensively used in medical analytics, unmanned driving of cars, Earth sensing, etc. In practice, the accuracy of CNN-based sensors is highly dependent on the quality of the training datasets. The preparation of such datasets faces two fundamental challenges: data quantity and data quality. In this paper, we propose an approach aimed to solve both of these problems and investigate its efficiency. Our solution improves training datasets and validates it in several different applications: object classification and detection, depth buffer reconstruction, panoptic segmentation. We present a pipeline for image dataset augmentation by synthesis with computer graphics and generative neural networks approaches. Our solution is well-controlled and allows us to generate datasets in a reproducible manner with the desired distribution of features which is essential to conduct specific experiments in computer vision. We developed a content creation pipeline targeted to create realistic image sequences with highly variable content. Our technique allows rendering of a single 3D object or 3D scene in a variety of ways, including changing of geometry, materials and lighting. By using synthetic data in training, we have improved the accuracy of CNN-based sensors compared to using only real-life data.
Dielectric Measurement of Agricultural Grain Moisture—Theory and Applications
Authors: Jones Scott B. , Sheng Wenyi , Or Dani
Published: March 2022

Moisture content is a critical variable for the harvesting, processing, storing and marketing of cereal grains, oilseeds and legumes. Efficient and accurate determination of grain moisture content even with advanced nondestructive techniques, remains a challenge due to complex water-retaining biological structures and hierarchical composition and geometry of grains that affect measurement interpretation and require specific grain-dependent calibration. We review (1) the primary factors affecting permittivity measurements used in practice for inferring moisture content in grains; (2) develop novel methods for estimating critical parameters for permittivity modeling including packing density, porosity, water binding surface area and water phase permittivity and (3) represent the permittivity of packs of grains using dielectric mixture theory as a function of moisture content applied to high moisture corn (as a model grain). Grain permittivity measurements are affected by their free and bound water contents, chemical composition, temperature, constituent shape, phase configuration and measurement frequency. A large fraction of grain water is bound exhibiting reduced permittivity compared to that of free water. The reduced mixture permittivity and attributed to hydrophilic surfaces in starches, proteins and other high surface area grain constituents. The hierarchal grain structure (i.e., kernel, starch grain, lamella, molecule) and the different constituents influence permittivity measurements due to their layering, geometry (i.e., kernel or starch grain), configuration and water-binding surface area. Dielectric mixture theory offers a physically-based approach for modeling permittivity of agricultural grains and similar granular media.
An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net)
Authors: Brahim Khawla , Arega Tewodros Weldebirhan , Boucher Arnaud , Bricq Stephanie , Sakly Anis , Meriaudeau Fabrice
Published: March 2022

Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classification of prior information U-Net (ICPIU-Net) to efficiently segment the left ventricle (LV) myocardium, myocardial infarction (MI), and microvascular-obstructed (MVO) tissues from late gadolinium enhancement magnetic resonance (LGE-MR) images. Our approach was developed using two subnets cascaded to first segment the LV cavity and myocardium. Then, we used inclusion and classification constraint networks to improve the resulting segmentation of the diseased regions within the pre-segmented LV myocardium. This network incorporates the inclusion and classification information of the LGE-MRI to maintain topological constraints of pathological areas. In the testing stage, the outputs of each segmentation network obtained with specific estimated parameters from training were fused using the majority voting technique for the final label prediction of each voxel in the LGE-MR image. The proposed method was validated by comparing its results to manual drawings by experts from 50 LGE-MR images. Importantly, compared to various deep learning-based methods participating in the EMIDEC challenge, the results of our approach have a more significant agreement with manual contouring in segmenting myocardial diseases.
Walk-IT: An Open-Source Modular Low-Cost Smart Rollator
Authors: Fernandez-Carmona Manuel , Ballesteros Joaquin , Díaz-Boladeras Marta , Parra-Llanas Xavier , Urdiales Cristina , Gómez-de-Gabriel Jesús Manuel
Published: March 2022

Rollators are widely used in clinical rehabilitation for gait assessment, but gait analysis usually requires a great deal of expertise and focus from medical staff. Smart rollators can capture gait parameters autonomously while avoiding complex setups. However, commercial smart rollators, as closed systems, can not be modified; plus, they are often expensive and not widely available. This work presents a low cost open-source modular rollator for monitorization of gait parameters and support. The whole system is based on commercial components and its software architecture runs over ROS2 to allow further customization and expansion. This paper describes the overall software and hardware architecture and, as an example of extended capabilities, modules for monitoring dynamic partial weight bearing and for estimation of spatiotemporal gait parameters of clinical interest. All presented tests are coherent from a clinical point of view and consistent with input data.
Probabilistic 3D Reconstruction Using Two Sonar Devices
Authors: Joe Hangil , Kim Jason , Yu Son-Cheol
Published: March 2022

Three-dimensional reconstruction is a crucial technique for mapping and object-search tasks, but it is challenging in sonar imaging because of the nature of acoustics. In underwater sensing, many advanced studies have introduced approaches that have included feature-based methods and multiple imaging at different locations. However, most existing methods are prone to environmental conditions, and they are not adequate for continuous data acquisition on moving autonomous underwater vehicles (AUVs). This paper proposes a sensor fusion method for 3D reconstruction using acoustic sonar data with two sonar devices that provide complementary features. The forward-looking multibeam sonar (FLS) is an imaging sonar capable of short-range scanning with a high horizontal resolution, and the profiling sonar (PS) is capable of middle-range scanning with high reliability in vertical information. Using both sonars, which have different data acquisition planes and times, we propose a probabilistic sensor fusion method. First, we extract the region of interest from the background and develop a sonar measurement model. Thereafter, we utilize the likelihood field generated by the PS and estimate the elevation ambiguity using importance sampling. We also present the evaluation of our method in a ray-tracing-based sonar simulation environment and the generation of the pointclouds. The experimental results indicate that the proposed method can provide a better accuracy than that of the conventional method. Because of the improved accuracy of the generated pointclouds, this method can be expanded for pointcloud-based mapping and classification methods.
Efficient Graphical Algorithm of Sensor Distribution and Air Volume Reconstruction for a Smart Mine Ventilation Network
Authors: Liu Yujiao , Liu Zeyi , Gao Ke , Huang Yuhan , Zhu Chengyao
Published: March 2022

The accurate and reliable monitoring of ventilation parameters is key to intelligent ventilation systems. In order to realize the visualization of airflow, it is essential to solve the airflow reconstruction problem using few sensors. In this study, a new concept called independent cut set that depends on the structure of the underlying graph is presented to determine the minimum number and location of sensors. We evaluated its effectiveness in a coal mine owned by Jinmei Corporation Limited (Jinmei Co., Ltd., Shanghai, China). Our results indicated that fewer than 30% of tunnels needed to have wind speed sensors set up to reconstruct the well-posed airflow of all the tunnels (>200 in some mines). The results showed that the algorithm was feasible. The reconstructed air volume of the ventilation network using this algorithm was the same as the actual air volume. The algorithm provides theoretical support for flow reconstruction.
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach
Authors: Sahu Abhijeet , Davis Katherine
Published: March 2022

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.
Rapid Demagnetization of New Hybrid Core for Energy Harvesting
Authors: Mech Rafał , Wiewiórski Przemysław , Wachtarczyk Karol
Published: March 2022

This paper presents the results obtained using the rapid demagnetization method in the case of an NdFeB magnet and a new hybrid core. The developed core consists of three basic elements: an NdFeB magnet, Terfenol-D, and a specifically developed metallic alloy prepared by means of a suction casting method. The main goal of proposing a new type of core in the event of rapid demagnetization is to partially replace the permanent magnet with another material to reduce the rare-earth material while keeping the amount of generated electricity at a level that makes it possible to power low-power electrical devices. To “capture” the rapid change of magnetic flux, a small number of coils were made around the core. However, the very low voltage level at very high current required the use of specialized electronic transducers capable of delivering a voltage level appropriate for powering a microprocessor system. To overcome this problem, a circuit designed by the authors that enabled voltage processing from low impedance magnetic circuits was used. The obtained results demonstrated the usefulness of the system at resonant frequencies of up to 1 MHz.
Sci: An Inclusive, Multidisciplinary Scientific Journal
Authors: Abdin Ahmad Yaman , Jacob Claus
Published: March 2022

Sci (ISSN 2413-4155) is an international, open-access journal that covers most fields of scientific research. It has set out to challenge the conventional single- and double-blind peer review processes by adopting a post-publication public peer review (P4R) model. The model faced some difficulties with indexing and archiving services, prolongated the peer review process and its transparency received some opposition. It was therefore necessary to revisit the P4R model and modify it, resulting in the hybrid model (P4R hybrid) which is implemented in Sci today. Sci remains open to the whole scientific community as an inclusive and multidisciplinary scientific journal. In this context, we present you with six valuable contributions to the first Special Issue of Feature Papers Editors Collection 2020. The topics of the contributions address relevant and compelling issues ranging from data protection, material science, COVID-19 to the environment and climate change.
Semi-Quantitative and Qualitative Distinction of Aromatic and Flavour Compounds in Charcoal Grilled, Electric Barbecue Grilled, Infrared Grilled and Superheated-Steam Roasted Lamb Meat Patties Using GC/MC, E-nose and E-tongue
Authors: Suleman Raheel , Hui Teng , Wang Zhenyu , Alarcon-Rojo Alma Delia , Liu Huan , Zhang Dequan
Published: March 2022

This study investigated the influence of four different methods of cooking (charcoal grilling, electric barbecue grilling, superheated-steam roasting and infrared grilling) on the volatile profile of lamb meat patties. The study included the patties of the oyster cut muscle of lambs cooked using charcoal grilling, electric barbecue grilling, infrared grilling and superheated-steam roasting methods. The principal component analysis (PCA) of electric nose data showed a total variance of 73.71%. The e-nose values showed differentiation of the volatiles released from the lamb meat patties. Data of PCA of e-nose and GC-MS revealed good separation between groups. Significance (p < 0.05) was found for the flavour profile values of charcoal-grilled and superheated-steam-roasted samples while the lowest significance was observed between infrared- and electric-barbecue-grilled samples. Saltiness and sourness were higher in superheated-steam-roasted patties than charcoal-grilled samples through e-tongue. The main volatile compound found in all the lamb patties was 2,3-octanedione with a content of 20.43 µg/g–27.83 µg/g. 1-hexanol was highest at 34.74 µg/g in the charcoal-grilled samples while 2,3-octanedione was highest at 35.83 µg/g in superheated-steam-roasted patties.
Sports Activity Levels of Sexual Minority Groups in Germany
Authors: Müller Johannes , Delto Hannes , Böhlke Nicola , Mutz Michael
Published: March 2022

It is widely assumed that LGBT+ people may feel insecure and unwelcome in sports settings, which are often characterized by a binary gender order and a culture of heteronormativity. Previous research also suggests that LGBT+ individuals experience homophobia in the context of sport. Despite these findings, reliable quantitative data on the sports participation levels of sexual minority groups are scarce. The paper addresses this academic void by analyzing sports activity data of sexual minority groups. The 2019 wave of the German Socio-Economic Panel (SOEP) study is analyzed, which includes a novel LGBT+ boost sample of respondents who self-identify as lesbian, gay, bisexual, transgender, or non-binary with regard to gender. The analysis of this sample shows that sports activity levels (with regard to frequency and duration) of homo- and bisexual individuals are comparable to the heterosexual majority. Although findings show that a high share of homo- and bisexual individuals experience sexual discrimination, discrimination is not associated with lower participation rates in sports. We thus conclude that the domain of sport—although by no means free of discrimination—offers sufficient participation opportunities for LGBT+ people.
Post-Separation Physical Custody Arrangements in Germany: Examining Sociodemographic Correlates, Parental Coparenting, and Child Adjustment
Authors: Langmeyer Alexandra N. , Recksiedler Claudia , Entleitner-Phleps Christine , Walper Sabine
Published: March 2022

Most children continue to live with their mother after a divorce or separation, yet paternal involvement in post-separation families has increased substantially in many Western nations. This shift has contributed to a growing share and more diverse set of post-separation parents opting for shared physical custody (SPC), which typically means that children alternate between the parental residences for substantive amounts of time. Profiling the case of Germany, where no legal regulations facilitating SPC are implemented to date, we examine the prevalence of SPC families, sociodemographic correlates of SPC, and its associations with parental coparenting and child adjustment. Using representative survey data sampled in 2019 (N = 800 minors of 509 separated parents), results revealed that only 6–8% of children practiced SPC. SPC parents were more likely to hold tertiary levels of schooling and to report a better coparenting relationship with the other parent. There was no link between SPC and child adjustment, yet conflictual coparenting was linked to higher levels of hyperactivity among SPC children. We conclude that the social selection into SPC and linkages between conflictual coparenting and hyperactivity among SPC children likely stem from the higher costs and the constant level of communication between the ex-partners that SPC requires.
Resampling under Complex Sampling Designs: Roots, Development and the Way Forward
Authors: Conti Pier Luigi , Mecatti Fulvia
Published: March 2022

In the present paper, resampling for finite populations under an iid sampling design is reviewed. Our attention is mainly focused on pseudo-population-based resampling due to its properties. A principled appraisal of the main theoretical foundations and results is given and discussed, together with important computational aspects. Finally, a discussion on open problems and research perspectives is provided.
The Effects of Subsidies on MSW Treatment Companies: Financial Performance and Policy Implications
Authors: Santos Eleonora , Lisboa Inês
Published: March 2022

Companies that provide public services usually receive subsidies from municipalities as compensation for replacing the latter in their functions as public service providers. However, the managers of those companies are not always interested in maintaining an economically sustainable company, as this implies turning off the tap on exploration subsidies. This article investigates the effect of subsidies on the financial performance of municipal solid waste collection and treatment companies for 2016–2020 and across NUTS II regions. The accounting data of 680 companies retrieved from the SABI database, make it possible to assess the economic sustainability of the companies through financial ratios and operational data. The results show that the financial structure and management of companies in the Centro, Lisbon, and the Azores allowed to take advantage of exploration subsidies to achieve economic sustainability. In the remaining regions these subsidies proved to be ineffective. These results have implications for managers and policy makers insofar as it sheds a light on the conditions under which subsidies contribute to the economical sustainability of companies in the sector.
Thermal Performance and Optimizing of Composite Trombe Wall with Temperature-Controlled DC Fan in Winter
Authors: Zhu Yuewei , Zhang Tao , Ma Qingsong , Fukuda Hiroatsu
Published: March 2022

This paper discusses an improved approach to the Trombe wall: an insulated panel is installed on the inner side, and vents are installed at the top and bottom to connect the outer and inner air layer with the interior. Direct current (DC) fans are installed in the upper vents for stable control of the air circulation. The study first analyzed the thermal performance of this composite Trombe wall, for which the heat load was 27.3% less compared to the classic Trombe wall and 32.1% less compared to the case without the Trombe wall. However, its efficiency for heating the room temperature was not high without heating. Then, we optimized the ventilation efficiency, the proportion of the Trombe wall in the room, and the type of glazing. The highest heat load savings could be achieved when the ventilation openings used high ventilation with temperature-controlled fans and the Trombe wall about 3% of the house floor area. With the use of Low-e double-glazing, we were able to save nearly 41.3% of the heat load than that with the regular single-glazing. For the composite Trombe wall, after taking into account the optimization factors, the room temperature was significantly higher, and could save nearly 52.3% of energy compared to the pre-optimization period.
An Analytical Study of the Latest Trends of Free-Form Molds
Authors: Youn Jongyoung , Yun Jiyoung , Kim Sungjin , Han Bumjin , Do Sunglok , Lee Donghoon
Published: March 2022

With the development of technology, the number of free-form structures—as well as their value—is increasing. In order to construct such free-form structures, a number of studies are being conducted on free-form molds from multifaceted perspectives. However, it is difficult to identify the progress of studies related to free-form molds, as the scope of the studies is redundant or similar in many cases. Therefore, the current study focused on the identification of the trends of preceding studies on free-form molds using the PRISMA technique. The study classified the studies into three topics in order to identify the trends: ‘free-form curve fabrication technology’, ‘free-form mold fabrication technology’, and the ‘analysis of free-form panel forms.’ Each topic was further categorized into two tiers for more in-depth analysis. The whole process was adopted in order to suggest the trends of studies on free-form molds. The findings are expected to be used to provide fundamental data for future studies on free-form molds, and to set the directions for new studies.
The American–China Trade War and Spillover Effects on Value-Added Exports from Indonesia
Authors: Purwono Rudi , Heriqbaldi Unggul , Esquivias Miguel Angel , Mubin M. Khoerul
Published: March 2022

This paper examines the impact of special tariffs between China and the United States (US) on their indirect trade partners via spillover effects. We applied a Value-Added Real Effective Exchange Rate (VA-REER) index to simulate how an increase in tariffs induces changes in demand for goods from Indonesia and selected Asian partners. We used the Input–Output Database (WIOD) to simulate the spillover effects across partners via the Global Value Chain (GVC) using data from 2000 to 2014. The results suggest that demand is doubly more responsive to prices (tariffs) when value-added (VA-REER) index is used instead of the conventional REER index (gross trade). We found that US tariffs on Chinese goods have a negative spillover impact on Indonesia’s exports. Meanwhile, the Chinese tariffs on American goods lead to small increased demand for Indonesian exports. We also found that US and China become equally crucial for Indonesia under the Value-Added REER scheme, concluding that the conventional REER approach may have underestimated the impact of US tariffs on Chinese goods. Finally, we found that Indonesia would be at risk to trade shocks if the US applies tariffs on China, Asian partners (Japan and South Korea), and the European Union (EU).
Innovation Research on Symbiotic Relationship of Organization’s Tacit Knowledge Transfer Network
Authors: Xu Jiang , Wu Huihui , Zhang Jianhua
Published: March 2022

The sustainable development of organizations is inseparable from innovation, and tacit knowledge is the core resource used to achieve organizational innovation. Due to the implicitness of tacit knowledge and the complexity of members’ relationships, symbiotic relationships between members have dramatically affected the transfer effect of tacit knowledge. However, previous studies on tacit knowledge transfer only focus on the characteristics of the subject or object; fewer consider the role of symbiotic relationships between knowledge subjects. An organization’s tacit knowledge transfer network (OTKTN) is a dynamic knowledge transfer network established among multiple members. Tacit knowledge transfer and sharing among network members conform to the symbiotic feature. To examine various relationships between members, and to investigate the mechanisms that impact tacit knowledge transfer, this article aims to analyze the symbiotic relationships in OTKTN based on the symbiotic perspective. The Lotka–Volterra model was used to construct symbiotic evolution model, and symbiotic coefficients were constructed from the four levels: knowledge-based psychological personal ownership (KPPO) of the knowledge provider, media richness, trust of the knowledge receiver, and organizational rewards matching, to discuss symbiotic modes. Finally, numerical simulation software was applied to simulate the evolution of knowledge levels in members. The results show that the four kinds of symbiotic modes between members include independence, commensalism, asymmetric mutualism, and symmetric mutualism. Symmetric mutualism is the best mode. In this mode, maximum level in independence mode affects the final stable knowledge level; the initial knowledge amount and natural growth rate both affect knowledge growth rate. Media richness, receiver’s trust, and organizational rewards matching can increase members’ tacit knowledge, but the knowledge provider’s KPPO inhibits members’ tacit knowledge growth. This article provides guidance to form a healthy symbiotic relationship and help organizations increase tacit knowledge.
Disasters as Enablers of Negotiation for Sustainability Transition: A Case from Odaka, Fukushima
Authors: Matsuura Masahiro
Published: March 2022

Disaster risk at the community level is likely to increase as climate change worsens. In this study, the author investigated the impact of disasters on negotiation for community development, particularly as a promoter of sustainability negotiation. Studies on agenda setting in policy making and critical moments in negotiation were thoroughly reviewed. Based on the review, the author presents an extraordinary case of the Odaka community from Fukushima Prefecture, Japan. This community experienced a critical moment in the negotiation of its development after the Great East Japan Earthquake of 2011 and a subsequent nuclear disaster. The community also experienced a 5-year-long forced evacuation due to nuclear contamination from the Fukushima Dai-ichi nuclear power station. This case reveals a major shift in four aspects of negotiation—parties, interests, relationship, and legitimacy—and a transition to more sustainable developments. It also demonstrates the possibility that disasters can enable negotiation for more sustainable development patterns by transforming the associated settings.