bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization and Analysis of Liquid Anaerobic Co-Digestion of Agro-Industrial Wastes via Mixture Design
Authors: Regalado Roberto Eloy Hernández , Weide Tobias , Baumkötter Daniel , Wettwer Lukas , Häner Jurek , Brügging Elmar , Tränckner Jens
Published: May 2021

Anaerobic co-digestion (AcoD) is a widely employed technique to produce biogas from simultaneous digestion of various biomasses. However, the selection of the optimal proportions of the substrates in the mixtures presents a challenge. This research used a mixture design to investigate the interactions between the liquid fraction of piglet manure (PM), cow manure (CWM), and starch wastewater (SWW). A modified Gompertz model was used to identify the statistically significant parameters of the methane production curves. The optimal compositions of the mixtures were identified based on multi-objective optimization of the maximal methane yield (YCH4) and maximal methane specific production rate (rCH4) parameters. The study was validated using a double mixture of PM and CWM and a triple mixture. The estimated degradation rates for both mixtures were faster than the predicted ones. The absolute relative errors of rCH4 were 27.41% for the double mixture and 5.59% for the triple mixture, while the relative errors of YCH4 were 4.64% for the double mixture and 10.05% for the triple mixture. These relative errors are within the normal limits of a process with high variability like AD. Thus, mixture design supported by the tested models is suitable for the definition of practically advisable mixtures of substrates.
Enhancement of Thermophilic Biogas Production from Palm Oil Mill Effluent by pH Adjustment and Effluent Recycling
Authors: Singkhala Apinya , Mamimin Chonticha , Reungsang Alissara , O-Thong Sompong
Published: May 2021

A sudden pH drops always inhibits the anaerobic digestion (AD) reactor for biogas production from palm oil mill effluent (POME). The pH adjustment of POME by oil palm ash addition and the biogas effluent recycling effect on the preventing of pH drop and change of the archaea community was investigated. The pH adjustment of POME to 7.5 increased the methane yield two times more than raw POME (pH 4.3). The optimal dose for pH adjustment by oil palm ash addition was 5% w/v with a methane yield of 440 mL-CH4/gVS. The optimal dose for pH adjustment by biogas effluent recycling was 20% v/v with a methane yield of 351 mL-CH4/gVS. Methane production from POME in a continuous reactor with pH adjustment by 5% w/v oil palm ash and 20% v/v biogas effluent recycling was 19.1 ± 0.25 and 13.8 ± 0.3 m3 CH4/m3-POME, respectively. The pH adjustment by oil palm ash enhanced methane production for the long-term operation with the stability of pH, alkalinity, and archaea community. Oil palm ash increased the number of Methanosarcina mazei and Methanothermobacter defluvii. Oil palm ash is a cost-effective alkali material as a source of buffer and trace metals for preventing the pH drop and the increased methanogen population in the AD process.
Hybrid Approach for Mixing Time Characterization and Scale-Up in Geometrical Nonsimilar Stirred Vessels Equipped with Eccentric Multi-Impeller Systems—An Industrial Perspective
Authors: Martinetz Michael C. , Kaiser Florian , Kellner Martin , Schlosser Dominik , Lange Andreas , Brueckner-Pichler Michaela , Brocard Cécile , Šoóš Miroslav
Published: May 2021

Multipurpose stirring and blending vessels equipped with various impeller systems are indispensable in the pharmaceutical industry because of the high flexibility necessary during multiproduct manufacturing. On the other hand, process scale-up and scale-down during process development and transfer from bench or pilot to manufacturing scale, or the design of so-called scale-down models (SDMs), is a difficult task due to the geometrical differences of used vessels. The present work comprises a hybrid approach to predict mixing times from pilot to manufacturing scale for geometrical nonsimilar vessels equipped with single top, bottom or multiple eccentrically located impellers. The developed hybrid approach is based on the experimental characterization of mixing time in the dedicated equipment and evaluation of the vessel-averaged energy dissipation rate employing computational fluid dynamics (CFD) using single-phase steady-state simulations. Obtained data are consequently used to develop a correlation of mixing time as a function of vessel filling volume and vessel-averaged energy dissipation rate, which enables the prediction of mixing times in specific vessels based on the process parameters. Predicted mixing times are in good agreement with those simulated using time-dependent CFD simulations for tested operating conditions.
The Material Culture of Buddhist Propagation: Reinstating Buddhism in Early Colonial Seoul
Authors: Lee Seunghye
Published: May 2021

The restrictive measures against Buddhism under the Neo-Confucian Chosŏn dynasty resulted in the decline of Korean Buddhism at the start of the twentieth century. As the Chosŏn government started to make sweeping changes in the name of modernization, Korean Buddhist monks found an opportunity to revitalize their tradition through measures of reform. This paper examines one instance of attempts to bring Korean Buddhism back to the center of the country in the early twentieth century. The establishment of the Buddhist Central Propagation Space in 1920, examined thoroughly for the first time in this study, shows a meaningful yet ultimately unsuccessful attempt at modernizing Korean Buddhism in the dynamics of the colonial Buddhism. Moving beyond the nationalist critique of its founder Yi Hoegwang, who has been heavily criticized for his pro-colonialist undertakings in later historiography, I reconsider the significance of this propagation space in the history of Buddhist propagation and respatialization of Seoul during the early colonial period. My analysis of Three Gates in a Single Mind commissioned for this urban Buddhist temple in 1921 not only shows the diversity of modern Korean Buddhist paintings but also reveals a new role assigned to Buddhist icons in the changing context of Pure Land practice. I also discuss the seminal contribution of the court lady Ch’ŏn Ilch’ŏng to the founding of the propagation space, thereby restoring the voice of one important laywoman in the modernization of Korean Buddhism.
On the Possibilities of Critical Raw Materials Production from the EU’s Primary Sources
Authors: Lewicka Ewa , Guzik Katarzyna , Galos Krzysztof
Published: May 2021

Sufficient supplies of critical raw materials (CRMs) for rapidly developing technologies, e.g., Li-ion batteries, wind turbines, photovoltaics, digitization, etc., have become one of the main economic challenges for the EU. Due to growing import dependency and associated risk of supply disruptions of these raw materials from third countries, there is a need to encourage their domestic production. This is an important starting point for EU value chains crucial for the sustainable economic growth of the whole Union. This contribution has evaluated the possibilities of CRMs supply from the EU’s primary sources. A three-step approach, including an assessment of CRMs’ importance for the EU’s economic growth, their significance in at least two of the three strategic industrial sectors (i.e., renewable energy, e-mobility, defense and aerospace), and their potential availability from EU mineral deposits, has been applied. Results of the analysis have shown that, of 29 critical mineral raw materials (according to the 2020 EC list), the potential to develop manufacturing from the Union mineral deposits exists for 11 CRMs, i.e., cobalt, graphite (natural), HREE, LREE, lithium, magnesium, niobium, PGMs, silicon metal, titanium, and tungsten, while some other CRMs, namely gallium, germanium, indium, and vanadium can be recovered as by-products. Measures to mitigate EU import dependency have been also proposed.
Carbon Sequestration Potential of Forest Invasive Species: A Case Study with Acacia dealbata Link
Authors: Nunes Leonel J. R. , Raposo Mauro A. M. , Meireles Catarina I. R. , Pinto Gomes Carlos J. , Almeida Ribeiro Nuno M. C.
Published: May 2021

Biological invasions are of complex solution, consuming resources for their control and eradication. However, in many of the documented processes that are available, this is an attempt with no solution in sight. The possibility of increasing the pressure over these species while creating value chains has been presented as a method for ensuring the sustainability of their control and eradication processes. In the case of invasive forest species in Portugal, such as Acacia dealbata Link, this control is becoming increasingly important. In addition to the negative impacts on biodiversity, the proliferation of this species has economic implications due to its competition with forest production species such as Pinus pinaster Aiton and Eucalyptus globulus Labill. Another critical aspect to be considered is the increase of the risk of rural fires, which is enhanced by the accumulation of low-value biomass around production forests. In this work, the possibility of using this species as a vehicle for the capture and sequestration of carbon in the medium and long-term was evaluated from a perspective of providing ecosystem services as a measure to mitigate climate change. However, due to its highly heliophilous character, it was found that the growth capacity of this species is rapidly conditioned by the position of each tree within a stand, not being able to maintain that capacity in the medium and long term.
The Impact of Weather and Seasons on Falls and Physical Activity among Older Adults with Glaucoma: A Longitudinal Prospective Cohort Study
Authors: Vongsachang Hursuong , Mihailovic Aleksandra , E Jian-Yu , Friedman David S. , West Sheila K. , Gitlin Laura N. , Ramulu Pradeep Y.
Published: May 2021

Understanding periods of the year associated with higher risk for falling and less physical activity may guide fall prevention and activity promotion for older adults. We examined the relationship between weather and seasons on falls and physical activity in a three-year cohort of older adults with glaucoma. Participants recorded falls information via monthly calendars and participated in four one-week accelerometer trials (baseline and per study year). Across 240 participants, there were 406 falls recorded over 7569 person-months, of which 163 were injurious (40%). In separate multivariable regression models incorporating generalized estimating equations, temperature, precipitation, and seasons were not significantly associated with the odds of falling, average daily steps, or average daily active minutes. However, every 10 °C increase in average daily temperature was associated with 24% higher odds of a fall being injurious, as opposed to non-injurious (p = 0.04). The odds of an injurious fall occurring outdoors, as opposed to indoors, were greater with higher average temperatures (OR per 10 °C = 1.46, p = 0.03) and with the summer season (OR = 2.69 vs. winter, p = 0.03). Falls and physical activity should be understood as year-round issues for older adults, although the likelihood of injury and the location of fall-related injuries may change with warmer season and temperatures.
Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture
Authors: Lee Ju Hyun , Ostwald Michael J. , Kim Mi Jeong
Published: May 2021

Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces.
Monitoring Hardening Behavior of Cementitious Materials Using Contactless Ultrasonic Method
Authors: Hong Jinyoung , Choi Hajin
Published: May 2021

We propose a novel contactless ultrasonic method for monitoring the hardening behavior of cementitious materials. The goal of this method is to obtain high-quality data to compare the unique hardening process between rapid setting cement (RSC) and ordinary Portland cement (OPC) mortars without physical coupling to the surface of the specimens. To monitor the hardening behavior of cementitious materials, conventional approaches use contact or embedded-type sensors, which limit field application. Our solution is to measure leaky Rayleigh waves at the interface between air and cementitious materials, which allows for the estimation of the physical state of the medium in real time. The modulus development was back-calculated based on the increment of wave velocity using the developed sensor array and transform-based signal processing. We experimentally demonstrated that the proposed method possibly exhibits unique hardening information about flash setting, effects of a retarder, and modulus increments from RSC specimens.
Ankle Muscle Activations during Different Foot-Strike Patterns in Running
Authors: Lin Jian-Zhi , Chiu Wen-Yu , Tai Wei-Hsun , Hong Yu-Xiang , Chen Chung-Yu
Published: May 2021

This study analysed the landing performance and muscle activity of athletes in forefoot strike (FFS) and rearfoot strike (RFS) patterns. Ten male college participants were asked to perform two foot strikes patterns, each at a running speed of 6 km/h. Three inertial sensors and five EMG sensors as well as one 24 G accelerometer were synchronised to acquire joint kinematics parameters as well as muscle activation, respectively. In both the FFS and RFS patterns, according to the intraclass correlation coefficient, excellent reliability was found for landing performance and muscle activation. Paired t tests indicated significantly higher ankle plantar flexion in the FFS pattern. Moreover, biceps femoris (BF) and gastrocnemius medialis (GM) activation increased in the pre-stance phase of the FFS compared with that of RFS. The FFS pattern had significantly decreased tibialis anterior (TA) muscle activity compared with the RFS pattern during the pre-stance phase. The results demonstrated that the ankle strategy focused on controlling the foot strike pattern. The influence of the FFS pattern on muscle activity likely indicates that an athlete can increase both BF and GM muscles activity. Altered landing strategy in cases of FFS pattern may contribute both to the running efficiency and muscle activation of the lower extremity. Therefore, neuromuscular training and education are required to enable activation in dynamic running tasks.
Mass Tracking in Cellular Networks for the COVID-19 Pandemic Monitoring
Authors: Khatib Emil J. , Perles Roselló María Jesús , Miranda-Páez Jesús , Giralt Victoriano , Barco Raquel
Published: May 2021

The year 2020 was marked by the emergence of the COVID-19 pandemic. After months of uncontrolled spread worldwide, a clear conclusion is that controlling the mobility of the general population can slow down the propagation of the pandemic. Tracking the location of the population enables better use of mobility limitation policies and the prediction of potential hotspots, as well as improved alert services to individuals that may have been exposed to the virus. With mobility in their core functionality and a high degree of penetration of mobile devices within the general population, cellular networks are an invaluable asset for this purpose. This paper shows an overview of the possibilities offered by cellular networks for the massive tacking of the population at different levels. The major privacy concerns are also reviewed and a specific use case is shown, correlating mobility and number of cases in the province of Málaga (Spain).
Automated Quality Assessment of Interferometric Ring Laser Data
Authors: Brotzer Andreas , Bernauer Felix , Schreiber Karl Ulrich , Wassermann Joachim , Igel Heiner
Published: May 2021

In seismology, an increased effort to observe all 12 degrees of freedom of seismic ground motion by complementing translational ground motion observations with measurements of strain and rotational motions could be witnessed in recent decades, aiming at an enhanced probing and understanding of Earth and other planetary bodies. The evolution of optical instrumentation, in particular large-scale ring laser installations, such as G-ring and ROMY (ROtational Motion in seismologY), and their geoscientific application have contributed significantly to the emergence of this scientific field. The currently most advanced, large-scale ring laser array is ROMY, which is unprecedented in scale and design. As a heterolithic structure, ROMY’s ring laser components are subject to optical frequency drifts. Such Sagnac interferometers require new considerations and approaches concerning data acquisition, processing and quality assessment, compared to conventional, mechanical instrumentation. We present an automated approach to assess the data quality and the performance of a ring laser, based on characteristics of the interferometric Sagnac signal. The developed scheme is applied to ROMY data to detect compromised operation states and assign quality flags. When ROMY’s database becomes publicly accessible, this assessment will be employed to provide a quality control feature for data requests.
Construction of Stretching-Bending Sequential Pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences
Authors: Wu Yiguang , Wang Meizhen , Liu Xuejun , Wang Ziran , Ma Tianwu , Xie Yujia , Li Xiuquan , Wang Xing
Published: May 2021

Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods based on computer vision (CV) find it difficult to recognize the work cycles of earthmoving excavators effectively in long video sequences. Even the most advanced sequential pattern-based approach finds recognition difficult because it has to discern many atomic actions with a similar visual appearance. In this paper, we combine atomic actions with a similar visual appearance to build a stretching–bending sequential pattern (SBSP) containing only “Stretching” and “Bending” atomic actions. These two atomic actions are recognized using a deep learning-based single-shot detector (SSD). The intersection over union (IOU) is used to associate atomic actions to recognize the work cycle. In addition, we consider the impact of reality factors (such as driver misoperation) on work cycle recognition, which has been neglected in existing studies. We propose to use the time required to transform “Stretching” to “Bending” in the work cycle to filter out abnormal work cycles caused by driver misoperation. A case study is used to evaluate the proposed method. The results show that SBSP can effectively recognize the work cycles of earthmoving excavators in real time in long video sequences and has the ability to calculate the productivity of earthmoving excavators accurately.
Inorganic Thermoelectric Fibers: A Review of Materials, Fabrication Methods, and Applications
Authors: Xin Jiwu , Basit Abdul , Li Sihui , Danto Sylvain , Tjin Swee Chuan , Wei Lei
Published: May 2021

Thermoelectric technology can directly harvest the waste heat into electricity, which is a promising field of green and sustainable energy. In this aspect, flexible thermoelectrics (FTE) such as wearable fabrics, smart biosensing, and biomedical electronics offer a variety of applications. Since the nanofibers are one of the important constructions of FTE, inorganic thermoelectric fibers are focused on here due to their excellent thermoelectric performance and acceptable flexibility. Additionally, measurement and microstructure characterizations for various thermoelectric fibers (Bi-Sb-Te, Ag2Te, PbTe, SnSe and NaCo2O4) made by different fabrication methods, such as electrospinning, two-step anodization process, solution-phase deposition method, focused ion beam, and self-heated 3ω method, are detailed. This review further illustrates that some techniques, such as thermal drawing method, result in high performance of fiber-based thermoelectric properties, which can emerge in wearable devices and smart electronics in the near future.
A Systematic Review for Cognitive State-Based QoE/UX Evaluation
Authors: Bañuelos-Lozoya Edgar , González-Serna Gabriel , González-Franco Nimrod , Fragoso-Diaz Olivia , Castro-Sánchez Noé
Published: May 2021

Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.
Universal Programmable Portable Measurement Device for Diagnostics and Monitoring of Industrial Fluid Power Systems
Authors: Dindorf Ryszard , Wos Piotr
Published: May 2021

This paper presents a new universal programmable portable measuring device (PMD) as a complete, accurate, and efficient solution for monitoring and technical diagnostics of industrial fluid power systems. PMD has programmable functions designed for recording, processing, and graphical visualization of measurement results at the test stand or the place of operation of fluid power systems. PMD has a built-in WiFi communication module for transferring measurement data via Industrial Internet of Things (IIoT) technology for online remote monitoring of fluid power systems. PMD can be programmed for a variety of measuring tasks in servicing, repairing, diagnosing, and monitoring fluid power systems. For this purpose, the fluid dynamic quantity, mechanical quantity, and electrical quantity can be measured. The adjustment of the PMD to the indirect measurement of leakage flow rate in a compressed air system (CAS) is presented in detail. Measuring instruments and PMDs were connected to a branch of the pipeline. The tests used the measurement system to estimate the leakage flow rate through air small nozzles, as well as other CAS indicators.
Dual-Retarder Mueller Polarimetry System for Extraction of Optical Properties of Serum Albumin Protein Media
Authors: Phan Quoc-Hung , Han Chien-Yuan , Lien Chi-Hsiang , Pham Thi-Thu-Hien
Published: May 2021

A dual liquid-crystal variable retarder Mueller polarimetry system incorporating a gold-based surface plasmon resonance prism coupler was proposed for extracting the optical properties of serum albumin protein media in the reflectance configuration. The feasibility of the proposed system was demonstrated by measuring the circular dichroism and circular birefringence properties of glucose tissue phantom solutions with different albumin concentrations. The results showed that the circular dichroism increased with albumin concentration, while the optical rotation angle increased with glucose concentration. Both properties reduced over time as a result of the protein glycation effect, which led to a gradual reduction in the glucose content of the sample.
A Heterogeneous Layer-Based Trustworthiness Model for Long Backhaul NVIS Challenging Networks and an IoT Telemetry Service for Antarctica
Authors: Mallorquí Adrià , Zaballos Agustín
Published: May 2021

Antarctica is a key location for many research fields. The lack of telecommunication systems that interconnect remote base camps hardens the possibility of building synergies among different polar research studies. This paper defines a network architecture to deploy a group of interconnected remote Antarctic wireless sensor networks providing an IoT telemetry service. Long backhaul NVIS links were used to interconnect remote networks. This architecture presents some properties from challenging networks that require evaluating the viability of the solution. A heterogeneous layer-based model to measure and improve the trustworthiness of the service was defined and presented. The model was validated and the trustworthiness of the system was measured using the Riverbed Model simulator.
Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson’s Disease
Authors: Castiglia Stefano Filippo , Tatarelli Antonella , Trabassi Dante , De Icco Roberto , Grillo Valentina , Ranavolo Alberto , Varrecchia Tiwana , Magnifica Fabrizio , Di Lenola Davide , Coppola Gianluca , Ferrari Donatella , Denaro Alessandro , Tassorelli Cristina , Serrao Mariano
Published: May 2021

The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQAdet in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability.
Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness
Authors: Marotta Luca , Buurke Jaap H. , van Beijnum Bert-Jan F. , Reenalda Jasper
Published: May 2021

Physical fatigue is a recurrent problem in running that negatively affects performance and leads to an increased risk of being injured. Identification and management of fatigue helps reducing such negative effects, but is presently commonly based on subjective fatigue measurements. Inertial sensors can record movement data continuously, allowing recording for long durations and extensive amounts of data. Here we aimed to assess if inertial measurement units (IMUs) can be used to distinguish between fatigue levels during an outdoor run with a machine learning classification algorithm trained on IMU-derived biomechanical features, and what is the optimal configuration to do so. Eight runners ran 13 laps of 400 m on an athletic track at a constant speed with 8 IMUs attached to their body (feet, tibias, thighs, pelvis, and sternum). Three segments were extracted from the run: laps 2–4 (no fatigue condition, Rating of Perceived Exertion (RPE) = 6.0 ± 0.0); laps 8–10 (mild fatigue condition, RPE = 11.7 ± 2.0); laps 11–13 (heavy fatigue condition, RPE = 14.2 ± 3.0), run directly after a fatiguing protocol (progressive increase of speed until RPE ≥ 16) that followed lap 10. A random forest classification algorithm was trained with selected features from the 400 m moving average of the IMU-derived accelerations, angular velocities, and joint angles. A leave-one-subject-out cross validation was performed to assess the optimal combination of IMU locations to detect fatigue and selected sensor configurations were considered. The left tibia was the most recurrent sensor location, resulting in accuracies ranging between 0.761 (single left tibia location) and 0.905 (all IMU locations). These findings contribute toward a balanced choice between higher accuracy and lower intrusiveness in the development of IMU-based fatigue detection devices in running.
Phase Stability under Thermal Drifts in Photodiode-Conditioning Transimpedance Amplifiers for Distance Metrology
Authors: Meca Meca Francisco Javier , Martín-Gorostiza Ernesto , García-Garrido Miguel Ángel , Salido-Monzú David
Published: May 2021

Transimpedance amplifiers (TIA) are widely used for front-end signal conditioning in many optical distance measuring applications in which high accuracy is often required. Small effects due to the real characteristics of the components and the parasitic elements in the circuit board may cause the error to rise to unacceptable levels. In this work we study these effects on the TIA delay time error and deduce analytic expressions, taking into account the trade-off between the uncertainties caused by the delay time instability and by the signal-to-noise ratio. A specific continuous-wave phase-shift case study is shown to illustrate the analysis, and further compared with real measurements. General strategies and conclusions, useful for designers of this kind of system, are extracted too. The study and results show that the delay time thermal stability is a key determinant factor in the measured distance accuracy and, without an adequate design, moderate temperature variations of the TIA can cause extremely high measurement errors.
Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review
Authors: Hickey Blake Anthony , Chalmers Taryn , Newton Phillip , Lin Chin-Teng , Sibbritt David , McLachlan Craig S. , Clifton-Bligh Roderick , Morley John , Lal Sara
Published: May 2021

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
Wear Diagnostics of the Thrust Bearing of NK-33 Turbo-Pump Unit on the Basis of Single-Coil Eddy Current Sensors
Authors: Belosludtsev Viktor , Borovik Sergey , Danilchenko Valeriy , Sekisov Yuriy
Published: May 2021

The problem of early wear diagnostics of the combined journal-and-thrust bearing of the turbo-pump unit (TPU) of the liquid-propellant rocket engine NK-33 is considered. A feature of the problem is the significant restriction on modifications of the power plant’s design. The original solution based on replacing the standard induction sensors of the turbo-pump rotational speed currently used in TPU by single-coil eddy current sensors (SCECS) with sensitive elements in the form of a segment of a linear conductor is proposed. The SCECS provide the monitoring of the axial displacement of the shaft in the thrust bearing, which characterizes the state of the unit and increases with the bearing wear. The function of the TPU shaft’s rotational speed measuring also remains. The article describes the proposed approach as well as a laboratory prototype of the system for early detection of the TPU thrust bearing’s wear. The results of the prototype research that confirm the feasibility of the proposed approach are analyzed.
A Double Siamese Framework for Differential Morphing Attack Detection
Authors: Borghi Guido , Pancisi Emanuele , Ferrara Matteo , Maltoni Davide
Published: May 2021

Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results.
Discovery of Orphan Olfactory Receptor 6M1 as a New Anticancer Target in MCF-7 Cells by a Combination of Surface Plasmon Resonance-Based and Cell-Based Systems
Authors: Choi Yae Rim , Shim Jaewon , Park Jae-Ho , Kim Young-Suk , Kim Min Jung
Published: May 2021

Olfactory receptors (ORs) account for 49% of all G protein-coupled receptors (GPCRs), which are important targets for drug discovery, and hence ORs may also be potential drug targets. Various ORs are expressed in breast cancer cells; however, most of them are orphan receptors, and thus, their functions are unknown. Herein, we present an experimental strategy using a surface plasmon resonance (SPR) system and a cell-based assay that allowed the identification of orphan OR6M1 as a new anticancer target in the MCF-7 breast cancer cell line. After the construction of stable OR6M1-expressing cells, the SPR-based screening of 108 chemicals for ligand activity was performed against OR6M1-expressing whole cells (primary screening) or membrane fragments (secondary screening). As a result, anthraquinone (AQ) and rutin were discovered to be new OR6M1 ligands. Based on calcium imaging in OR6M1-expressing Hana3A cells, AQ and rutin were classified as an OR6M1 agonist and antagonist, respectively. Cell viability and live/dead assays showed that AQ induced the death of MCF-7 cells, which was inhibited by rutin. Therefore, OR6M1 may be considered an anticancer target, and AQ may be considered a chemotherapeutic agent. This combined method can be widely used to discover the ligands and functions of other orphan GPCRs.
Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
Authors: Kim Taehoon , Kim Wonbin , Seo Daehee , Lee Imyeong
Published: May 2021

Recently, as Internet of Things systems have been introduced to facilitate diagnosis and treatment in healthcare and medical environments, there are many issues concerning threats to these systems’ security. For instance, if a key used for encryption is lost or corrupted, then ciphertexts produced with this key cannot be decrypted any more. Hence, this paper presents two schemes for key recovery systems that can recover the lost or the corrupted keys of an Internet of Medical Things. In our proposal, when the key used for the ciphertext is needed, this key is obtained from a Key Recovery Field present in the cyphertext. Thus, the recovered key will allow decrypting the ciphertext. However, there are threats to this proposal, including the case of the Key Recovery Field being forged or altered by a malicious user and the possibility of collusion among participating entities (Medical Institution, Key Recovery Auditor, and Key Recovery Center) which can interpret the Key Recovery Field and abuse their authority to gain access to the data. To prevent these threats, two schemes are proposed. The first one enhances the security of a multi-agent key recovery system by providing the Key Recovery Field with efficient integrity and non-repudiation functions, and the second one provides a proxy re-encryption function resistant to collusion attacks against the key recovery system.
Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches
Authors: Wang Zijian , Wu Yimin , Yang Lichao , Thirunavukarasu Arjun , Evison Colin , Zhao Yifan
Published: May 2021

The existing deep learning-based Personal Protective Equipment (PPE) detectors can only detect limited types of PPE and their performance needs to be improved, particularly for their deployment on real construction sites. This paper introduces an approach to train and evaluate eight deep learning detectors, for real application purposes, based on You Only Look Once (YOLO) architectures for six classes, including helmets with four colours, person, and vest. Meanwhile, a dedicated high-quality dataset, CHV, consisting of 1330 images, is constructed by considering real construction site background, different gestures, varied angles and distances, and multi PPE classes. The comparison result among the eight models shows that YOLO v5x has the best mAP (86.55%), and YOLO v5s has the fastest speed (52 FPS) on GPU. The detection accuracy of helmet classes on blurred faces decreases by 7%, while there is no effect on other person and vest classes. And the proposed detectors trained on the CHV dataset have a superior performance compared to other deep learning approaches on the same datasets. The novel multiclass CHV dataset is open for public use.
Requirements for Supporting Diagnostic Equipment of Respiration Process in Humans
Authors: Nitkiewicz Szymon , Barański Robert , Galewski Marek , Zajączkiewicz Hanna , Kukwa Andrzej , Zając Andrzej , Ejdys Stanisław , Artiemjew Piotr
Published: May 2021

There is abundant worldwide research conducted on the subject of the methods of human respiration process examination. However, many of these studies describe methods and present the results while often lacking insight into the hardware and software aspects of the devices used during the research. This paper’s goal is to present new equipment for assessing the parameters of human respiration, which can be easily adopted for daily diagnosis. This work deals with the issue of developing the correct method of obtaining measurement data. The requirements of the acquisition parameters are clearly pointed out and examples of the medical applications of the described device are shown. Statistical analysis of acquired signals proving its usability is also presented. In the examples of selected diseases of the Upper Respiratory Tract (URT), the advantages of the developed apparatus for supporting the diagnosis of URT patency have been proven.
Are UX Evaluation Methods Providing the Same Big Picture?
Authors: Nakamura Walter Takashi , Ahmed Iftekhar , Redmiles David , Oliveira Edson , Fernandes David , de Oliveira Elaine H. T. , Conte Tayana
Published: May 2021

The success of a software application is related to users’ willingness to keep using it. In this sense, evaluating User eXperience (UX) became an important part of the software development process. Researchers have been carrying out studies by employing various methods to evaluate the UX of software products. Some studies reported varied and even contradictory results when applying different UX evaluation methods, making it difficult for practitioners to identify which results to rely upon. However, these works did not evaluate the developers’ perspectives and their impacts on the decision process. Moreover, such studies focused on one-shot evaluations, which cannot assess whether the methods provide the same big picture of the experience (i.e., deteriorating, improving, or stable). This paper presents a longitudinal study in which 68 students evaluated the UX of an online judge system by employing AttrakDiff, UEQ, and Sentence Completion methods at three moments along a semester. This study reveals contrasting results between the methods, which affected developers’ decisions and interpretations. With this work, we intend to draw the HCI community’s attention to the contrast between different UX evaluation methods and the impact of their outcomes in the software development process.
HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting
Authors: Ilyas Naveed , Lee Boreom , Kim Kiseon
Published: May 2021

Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques.
A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles
Authors: Wang Mingyang , Chen Xinbo , Jin Baobao , Lv Pengyuan , Wang Wei , Shen Yong
Published: May 2021

The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.
A Novel Multi-Axial Pressure Sensor Probe for Measuring Triaxial Stress States Inside Soft Materials
Authors: Zullo Giuseppe , Silvestroni Anna Leidy , Candiotto Gianluca , Koptyug Andrey , Petrone Nicola
Published: May 2021

This paper presents the concept, design, construction, and validation of a novel probe based on the hexadic disposition of six pressure sensors suitable for measuring triaxial stress states inside bulky soft materials. The measurement of triaxial stress states inside bulk materials such as brain tissue surrogates is a challenging task needed to investigate internal organs’ stress states and validate FE models. The purpose of the work was the development and validation of a 17 × 17 × 17 mm probe containing six pressure sensors. To do so, six piezoresistive pressure sensors of 6 mm diameter were arranged into an hexad at three cartesian axes and bisecting angles, based on the analytical solution of the stress tensor. The resulting probe was embedded in a soft silicone rubber of known characteristics, calibrated under cyclic compression and shear in three orientations, and statically validated with combined loads. A calibration matrix was computed, and validation tests allowed us to estimate Von Mises stress under combined stress with an error below 6%. Hence, the proposed probe design and method can give indications about the complex stress state developing internally to soft materials under triaxial high-strain fields, opening applications in the analysis of biological models or physical surrogates involving parenchyma organs.