bluuebunny/crossref_metadata_2025 huggingface dataset:
RE-Based Inorganic-Crystal Nanofibers Produced by Electrospinning for Photonic Applications
Authors: Toncelli Alessandra
Published: May 2021

Electrospinning is an effective and inexpensive technique to grow polymer materials in nanofiber shape with exceptionally high surface-area-to-volume ratio. Although it has been known for about a century, it has gained much interest in the new millennium thanks to its low cost and versatility, which has permitted to obtain a large variety of multifunctional compositions with a rich collection of new possible applications. Rare-earth doped materials possess many remarkable features that have been exploited, for example, for diode pumped bulk solid-state lasers in the visible and near infrared regions, or for biomedical applications when grown in nanometric form. In the last few decades, electrospinning preparation of rare-earth-doped crystal nanofibers has been developed and many different materials have been successfully grown. Crystal host, crystal quality and nanosized shape can deeply influence the optical properties of embedded rare earth ions; therefore, a large number of papers has recently been devoted to the growth and characterization of rare earth doped nanofibers with the electrospinning technique and an up-to-date review of this rapidly developing topic is missing; This review paper is devoted to the presentation of the main results obtained in this field up to now with particular insight into the optical characterization of the various materials grown with this technique.
Recycling Blast Furnace Ferronickel Slag as a Replacement for Paste in Mortar: Formation of Carboaluminate, Reduction of White Portland Cement, and Increase in Strength
Authors: Guan Qingfeng , Xia Jingliang , Wang Jing , Leng Faguang , Zhou Yongxiang , Cao Changwei
Published: May 2021

Blast furnace ferronickel slag (BFFS) is generated in the production of ferronickel alloys and is used as cement replacement in concrete or mortar. The effectivity in reducing cement consumption and improving performance are limited. By referring to the paste replacement method, this work used BFFS to replace an equal volume of the white Portland cement paste to obtain greater performance enhancement. BFFS was used with five levels of replacement (0%, 5%, 10%, 15%, 20%) and four water-to-cement ratios (0.40, 0.45, 0.50, 0.55) were designed. Fluidity, mechanical strength, hydration products, and pore structure of every mixture were measured. The results showed that the workability of the mortars decreased due to the reduced volume of water, but the 28-day compressive strength of the mortars increased, and the cement content of the mortars was also reduced by 33 wt %. The X-ray diffraction (XRD) patterns revealed that there existed a carboaluminate phase, and the presence of the ettringite was stabilized, indicating that the accumulating amount of the hydration products of the mortar increased. Furthermore, the BFFS could consume the portlandite and free water to form a higher amount of chemically bound water due to its pozzolanic activity. A high degree of hydration and a large volume of the hydration products refined the porosity of the hardened mortars, which explained the enhancement of the strength of the mortars. Compared to the cement replacement method, the paste replacement method was more effective in preparing eco-friendly mortar or concrete by recycling BFFS for reducing the cement content of the mortar while improving its strength.
Architecture-Promoted Biomechanical Performance-Tuning of Tissue-Engineered Constructs for Biological Intervertebral Disc Replacement
Authors: Lang Gernot , Obri Katja , Saravi Babak , Boccaccini Aldo R. , Früh Anton , Seidenstücker Michael , Kurz Bodo , Schmal Hagen , Rolauffs Bernd
Published: May 2021

Background: Biological approaches to intervertebral disc (IVD) restoration and/or regeneration have become of increasing interest. However, the IVD comprises a viscoelastic system whose biological replacement remains challenging. The present study sought to design load-sharing two-component model systems of circular, nested, concentric elements reflecting the nucleus pulposus and annulus fibrosus. Specifically, we wanted to investigate the effect of architectural design variations on (1) model system failure loads when testing the individual materials either separately or homogeneously mixed, and (2) also evaluate the potential of modulating other mechanical properties of the model systems. Methods: Two sets of softer and harder biomaterials, 0.5% and 5% agarose vs. 0.5% agarose and gelatin, were used for fabrication. Architectural design variations were realized by varying ring geometries and amounts while keeping the material composition across designs comparable. Results: Variations in the architectural design, such as lamellar width, number, and order, combined with choosing specific biomaterial properties, strongly influenced the biomechanical performance of IVD constructs. Biomechanical characterization revealed that the single most important parameter, in which the model systems vastly exceeded those of the individual materials, was failure load. The model system failure loads were 32.21- and 84.11-fold higher than those of the agarose materials and 55.03- and 2.14-fold higher than those of the agarose and gelatin materials used for system fabrication. The compressive strength, dynamic stiffness, and viscoelasticity of the model systems were always in the range of the individual materials. Conclusions: Relevant architecture-promoted biomechanical performance-tuning of tissue-engineered constructs for biological IVD replacement can be realized by slight modifications in the design of constructs while preserving the materials’ compositions. Minimal variations in the architectural design can be used to precisely control structure–function relations for IVD constructs rather than choosing different materials. These fundamental findings have important implications for efficient tissue-engineering of IVDs and other load-bearing tissues, as potential implants need to withstand high in situ loads.
Mechanical Behavior Evaluation of Tempcore and Hybrid Reinforcing Steel Bars via a Proposed Fatigue Damage Index in Long Terms
Authors: Basdeki Maria , Apostolopoulos Charis
Published: May 2021

As it is widely known, corrosion constitutes a major deterioration factor for reinforced concrete structures which are located in coastal areas. This phenomenon, combined with repeated loads and, especially, intense seismic events, negatively affect their useful service life. It is well known that the microstructure of steel reinforcing bars has a significant impact either on their corrosion resistance or on their fatigue life. In the present manuscript, an effort has been made to study the effect of corrosive factors on fatigue response for two types of steel reinforcement: Tempcore steel B reinforcing bars and a new-generation, dual-phase (DP) steel F reinforcement. The findings of this experimental study showed that DP steel reinforcement’s rate of degradation due to corrosion seemed apparently lighter than Tempcore B with respect to its capacity to bear repeated loads to a satisfactory degree after corrosion. For this purpose, based on a quality material index that characterizes the mechanical performance of materials, an extended damage material indicator for fatigue conditions is similarly proposed for evaluating and classifying these two types of rebars in terms of material quality and durability. The outcomes of this investigation demonstrated the feasibility of fatigue damage indicators in the production cycle as well as at different exposure times, once corrosion phenomena had left their mark in steel reinforcement.
Cytotoxic Effect of Graphene Oxide Nanoribbons on Escherichia coli
Authors: Qiang Shirong , Li Zhengbin , Zhang Li , Luo Dongxia , Geng Rongyue , Zeng Xueli , Liang Jianjun , Li Ping , Fan Qiaohui
Published: May 2021

The biological and environmental toxicity of graphene and graphene derivatives have attracted great research interest due to their increasing applications. However, the cytotoxic mechanism is poorly understood. Here, we investigated the cytotoxic effect of graphene oxide nanoribbons (GORs) on Escherichia coli (E. coli) in an in vitro method. The fabricated GORs formed long ribbons, 200 nm wide. Based on the results of the MTT assay and plate-culture experiments, GORs significantly inhibited the growth and reproduction of E. coli in a concentration-dependent manner. We found that GORs stimulated E. coli to secrete reactive oxygen species, which then oxidized and damaged the bacterial cell membrane. Moreover, interaction between GORs and E. coli cytomembrane resulted in polysaccharide adsorption by GORs and the release of lactic dehydrogenase. Furthermore, GORs effectively depleted the metal ions as nutrients in the culture medium by adsorption. Notably, mechanical cutting by GORs was not obvious, which is quite different from the case of graphene oxide sheets to E. coli.
Recent Progress in the Design and Medical Application of In Situ Self-Assembled Polypeptide Materials
Authors: Wang Tian-Tian , Xia Yi-Yi , Gao Jian-Qing , Xu Dong-Hang , Han Min
Published: May 2021

Inspired by molecular self-assembly, which is ubiquitous in natural environments and biological systems, self-assembled peptides have become a research hotspot in the biomedical field due to their inherent biocompatibility and biodegradability, properties that are afforded by the amide linkages forming the peptide backbone. This review summarizes the biological advantages, principles, and design strategies of self-assembled polypeptide systems. We then focus on the latest advances in in situ self-assembly of polypeptides in medical applications, such as oncotherapy, materials science, regenerative medicine, and drug delivery, and then briefly discuss their potential challenges in clinical treatment.
How Political Repression Stifled the Nascent Foundations of Heredity Research before Mendel in Central European Sheep Breeding Societies
Authors: Poczai Péter , Santiago-Blay Jorge A. , Sekerák Jiří , Szabó Attila T.
Published: May 2021

The nineteenth century was a time of great economic, social, and political change. The population of a modernizing Europe began demanding more freedom, which in turn propelled the ongoing discussion on the philosophy of nature. This spurred on Central European sheep breeders to debate the deepest secrets of nature: the transmission of traits from one generation to another. Scholarly questions of heredity were profoundly entwined with philosophy and politics when particular awareness of “the genetic laws of nature” claimed natural equality. The realization that the same rules of inheritance may apply to all living beings frightened both the absolutist political power and the divided society of the day. Many were not prepared to separate religious questions from novel natural phenomena. Open-minded breeders put their knowledge into practice right away to create sheep with better wool traits through inbreeding and artificial selection. This was viewed, however, as the artificial modification of nature operating against the cultural and religious norms of the day. Liberal attempts caught the attention of the secret police and, consequently, the aspirations of scholars were suppressed by political will during approximately 1820–1850.
Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists
Authors: Garcia A. Daniel E. , Sierra M. Sergio D. , Gomez-Vargas Daniel , Jiménez Mario F. , Múnera Marcela , Cifuentes Carlos A.
Published: May 2021

The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.
Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach
Authors: Abbas Ziaul Haq , Ali Zaiwar , Abbas Ghulam , Jiao Lei , Bilal Muhammad , Suh Doug-Young , Piran Md. Jalil
Published: May 2021

In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.
Cuffless Blood Pressure Measurement Using a Smartphone-Case Based ECG Monitor with Photoplethysmography in Hypertensive Patients
Authors: Sagirova Zhanna , Kuznetsova Natalia , Gogiberidze Nana , Gognieva Daria , Suvorov Aleksandr , Chomakhidze Petr , Omboni Stefano , Saner Hugo , Kopylov Philippe
Published: May 2021

The availability of simple, accurate, and affordable cuffless blood pressure (BP) devices has the potential to greatly increase the compliance with measurement recommendations and the utilization of BP measurements for BP telemonitoring. The aim of this study is to evaluate the correlation between findings from routine BP measurements using a conventional sphygmomanometer with the results from a portable ECG monitor combined with photoplethysmography (PPG) for pulse wave registration in patients with arterial hypertension. Methods: The study included 500 patients aged 32–88 years (mean 64 ± 7.9 years). Mean values from three routine BP measurements by a sphygmomanometer with cuff were selected for comparison; within one minute after the last measurement, an electrocardiogram (ECG) was recorded for 3 min in the standard lead I using a smartphone-case based single-channel ECG monitor (CardioQVARK®-limited responsibility company “L-CARD”, Moscow, Russia) simultaneously with a PPG pulse wave recording. Using a combination of the heart signal with the PPG, levels of systolic and diastolic BP were determined based on machine learning using a previously developed and validated algorithm and were compared with sphygmomanometer results. Results: According to the Bland–Altman analysis, SD for systolic BP was 3.63, and bias was 0.32 for systolic BP. SD was 2.95 and bias was 0.61 for diastolic BP. The correlation between the results from the sphygmomanometer and the cuffless method was 0.89 (p = 0.001) for systolic and 0.87 (p = 0.002) for diastolic BP. Conclusion: Blood pressure measurements on a smartphone-case without a cuff are encouraging. However, further research is needed to improve the accuracy and reliability of clinical use in the majority of patients.
Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis
Authors: Vezočnik Melanija , Kamnik Roman , Juric Matjaz B.
Published: May 2021

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.
Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques
Authors: Blevins Morgan , Allen Harry , Colson Beckett , Cook Anna-Marie , Greenbaum Alexandra , Hemami Sheila , Hollmann Joseph , Kim Ernest , LaRocca Ava , Markoski Kenneth , Miraglia Peter , Mott Vienna , Robberson William , Santos Jose , Sprachman Melissa , Swierk Patricia , Tate Steven , Witinski Mark , Kratchman Louis , Michel Anna
Published: May 2021

Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis.
Model Calibration for a Rigid Hexapod-Based End-Effector with Integrated Force Sensors
Authors: Friedrich Christian , Ihlenfeldt Steffen
Published: May 2021

Integrated single-axis force sensors allow an accurate and cost-efficient force measurement in 6 degrees of freedom for hexapod structures and kinematics. Depending on the sensor placement, the measurement is affected by internal forces that need to be compensated for by a measurement model. Since the parameters of the model can change during machine usage, a fast and easy calibration procedure is requested. This work studies parameter identification procedures for force measurement models on the example of a rigid hexapod-based end-effector. First, measurement and identification models are presented and parameter sensitivities are analysed. Next, two excitation strategies are applied and discussed: identification from quasi-static poses and identification from accelerated continuous trajectories. Both poses and trajectories are optimized by different criteria and evaluated in comparison. Finally, the procedures are validated by experimental studies with reference payloads. In conclusion, both strategies allow accurate parameter identification within a fast procedure in an operational machine state.
Rain Area Detection in South-Western Kenya by Using Multispectral Satellite Data from Meteosat Second Generation
Authors: Kingsley Kumah K. , Maathuis Ben H. P. , Hoedjes Joost C. B. , Rwasoka Donald T. , Retsios Bas V. , Su Bob Z.
Published: May 2021

This study presents a rain area detection scheme that uses a gradient based adaptive technique for daytime and nighttime rain area detection and correction from reflectance and infrared (IR) brightness temperatures data of the Meteosat Second Generation (MSG) satellite. First, multiple parametric rain detection models developed from MSG’s reflectance and IR data were calibrated and validated with rainfall data from a dense network of rain gauge stations and investigated to determine the best model parameters. The models were based on a conceptual assumption that clouds characterised by the top properties, e.g., high optical thickness and effective radius, have high rain probabilities and intensities. Next, a gradient based adaptive correction technique that relies on rain area-specific parameters was developed to reduce the number and sizes of the detected rain areas. The daytime detection with optical (VIS0.6) and near IR (NIR1.6) reflectance data achieved the best detection skill. For nighttime, detection with thermal IR brightness temperature differences of IR3.9-IR10.8, IR3.9-WV73 and IR108-WV62 showed the best detection skill based on general categorical statistics. Compared to the Global Precipitation Measurement (GPM) Integrated Mult-isatellitE Retrievals for GPM (IMERG) and the gauge station data from the southwest of Kenya, the model showed good agreement in the spatial dynamics of the detected rain area and rain rate.
A New Challenge: Detection of Small-Scale Falling Rocks on Transportation Roads in Open-Pit Mines
Authors: Shi Tiandong , Zhong Deyun , Bi Lin
Published: May 2021

In transportation at open-pit mines, rocks dropped as a mining truck is driven will wear out the tires of the vehicle, thus increasing the mining cost. In the case of autonomous vehicles, the vehicle must automatically detect rocks on the transportation roads during the driving process. This will be a new challenge: rough road, rocks of small size and irregular shape, long detection distance, etc. This paper presents a detection method based on light detection and ranging (lidar). It includes two stages: (1) using the modified cloth simulation method to filter out the ground points; (2) using the regional growth method based on grid division to cluster non-ground points. Experimental results show that the method can detect rocks with a size of 20–30 cm at a distance of 40 m in front of the vehicle, and it takes only 0.3 s on an ordinary personal computer (PC). This method is easy to understand, and it has fewer parameters to be adjusted. Therefore, it is a better method for detecting small, irregular obstacles on a low-speed, unstructured and rough road.
Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types
Authors: Rombach Katharina , Michau Gabriel , Fink Olga
Published: May 2021

Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors and operating conditions cause variations in the condition monitoring (CM) data resulting in false alarms. Furthermore, novel types of faults can also cause variations in CM data. Since faults occur rarely in complex safety critical systems, a training dataset typically does not cover all possible fault types. To enable the detection of novel fault types, the models need to be sensitive to novel variations. Simultaneously, to decrease the false alarm rate, invariance to variations in CM data caused by changing operating conditions is required. We propose contrastive learning for the task of fault detection and diagnostics in the context of changing operating conditions and novel fault types. In particular, we evaluate how a feature representation trained by the triplet loss is suited to fault detection and diagnostics under the aforementioned conditions. We showcase that classification and clustering based on the learned feature representations are (1) invariant to changing operating conditions while also being (2) suited to the detection of novel fault types. Our evaluation is conducted on the bearing benchmark dataset provided by the Case Western Reserve University (CWRU).
Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network
Authors: Yin Cunyi , Chen Jing , Miao Xiren , Jiang Hao , Chen Deying
Published: May 2021

Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users’ privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.
Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology
Authors: Watts Jeremy , Khojandi Anahita , Vasudevan Rama , Nahab Fatta B. , Ramdhani Ritesh A.
Published: May 2021

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.
Upconversion Spectral Rulers for Transcutaneous Displacement Measurements
Authors: Suckey Melissa M. , Benza Donald W. , DesJardins John D. , Anker Jeffrey N.
Published: May 2021

We describe a method to measure micron to millimeter displacement through tissue using an upconversion spectral ruler. Measuring stiffness (displacement under load) in muscles, bones, ligaments, and tendons is important for studying and monitoring healing of injuries. Optical displacement measurements are useful because they are sensitive and noninvasive. Optical measurements through tissue must use spectral rather than imaging approaches because optical scattering in the tissue blurs the image with a point spread function typically around the depth of the tissue. Additionally, the optical measurement should have low background and minimal intensity dependence. Previously, we demonstrated a spectral encoder using either X-ray luminescence or fluorescence, but the X-ray luminescence required an expensive X-ray source and used ionizing radiation, while the fluorescence sensor suffered from interference from autofluorescence. Here, we used upconversion, which can be provided with a simple fiber-coupled spectrometer with essentially autofluorescence-free signals. The upconversion phosphors provide a low background signal, and the use of closely spaced spectral peaks minimizes spectral distortion from the tissue. The small displacement noise level (precision) through tissue was 2 µm when using a microscope-coupled spectrometer to collect light. We also showed proof of principle for measuring strain on a tendon mimic. The approach provides a simple method to study biomechanics using implantable sensors.
A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters
Authors: Dogariu Laura-Maria , Paleologu Constantin , Benesty Jacob , Stanciu Cristian-Lucian , Oprea Claudia-Cristina , Ciochină Silviu
Published: May 2021

The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.
Granular Data Access Control with a Patient-Centric Policy Update for Healthcare
Authors: Khan Fawad , Khan Saad , Tahir Shahzaib , Ahmad Jawad , Tahir Hasan , Shah Syed Aziz
Published: May 2021

Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute’s policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.
Current Trends and Challenges in Pediatric Access to Sensorless and Sensor-Based Upper Limb Exoskeletons
Authors: Gaudet Guillaume , Raison Maxime , Achiche Sofiane
Published: May 2021

Sensorless and sensor-based upper limb exoskeletons that enhance or support daily motor function are limited for children. This review presents the different needs in pediatrics and the latest trends when developing an upper limb exoskeleton and discusses future prospects to improve accessibility. First, the principal diagnoses in pediatrics and their respective challenge are presented. A total of 14 upper limb exoskeletons aimed for pediatric use were identified in the literature. The exoskeletons were then classified as sensorless or sensor-based, and categorized with respect to the application domain, the motorization solution, the targeted population(s), and the supported movement(s). The relative absence of upper limb exoskeleton in pediatrics is mainly due to the additional complexity required in order to adapt to children’s growth and answer their specific needs and usage. This review highlights that research should focus on sensor-based exoskeletons, which would benefit the majority of children by allowing easier adjustment to the children’s needs. Sensor-based exoskeletons are often the best solution for children to improve their participation in activities of daily living and limit cognitive, social, and motor impairments during their development.
Critical Image Identification via Incident-Type Definition Using Smartphone Data during an Emergency: A Case Study of the 2020 Heavy Rainfall Event in Korea
Authors: Choi Yoonjo , Kim Namhun , Hong Seunghwan , Bae Junsu , Park Ilsuk , Sohn Hong-Gyoo
Published: May 2021

In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.
SQM-LRU: A Harmony Dual-Queue Management Algorithm to Control Non-Responsive LTF Flow and Achieve Service Differentiation
Authors: Li Penghui , Jiang Xianliang , Zhu Jiahua , Jin Guang
Published: May 2021

The increase in network applications diversity and different service quality requirements lead to service differentiation, making it more important than ever. In Wide Area Network (WAN), the non-responsive Long-Term Fast (LTF) flows are the main contributors to network congestion. Therefore, detecting and suppressing non-responsive LTF flows represent one of the key points for providing data transmission with controllable delay and service differentiation. However, the existing single-queue management algorithms are designed to serve only a small number of applications with similar requirements (low latency, high throughput, etc.). The lack of mechanisms to distinguish different traffic makes it difficult to implement differentiated services. This paper proposes an active queue management scheme, namely, SQM-LRU, which realizes service differentiation based on Shadow Queue (SQ) and improved Least-Recently-Used (LRU) strategy. The algorithm consists of three essential components: First, the flow detection module is based on the SQ and improved LRU. This module is used to detect non-responsive LTF flows. Second, different flows will be put into corresponding high or low priority sub-queues depending on the flow detection results. Third, the dual-queue adopts CoDel and RED, respectively, to manage packets. SQM-LRU intends to satisfy the stringent delay requirements of responsive flow while maximizing the throughput of non-responsive LTF flow. Our simulation results show that SQM-LRU outperforms traditional solutions with significant improvement in flow detection and reduces the delay, jitter, and Flow Completion Time (FCT) of responsive flow. As a result, it reduced the FCT by up to 50% and attained 95% of the link utilization. Additionally, the low overhead and the operations incur O(1) cost per packet, making it practical for the real network.
A Mobile Device for Monitoring the Biological Purity of Air and Liquid Samples
Authors: Sikora Tomasz , Morawska Karolina , Lisowski Wiesław
Published: May 2021

A detector for identifying potential bacterial hazards in the air was designed and created in the Military Institute of Chemistry and Radiometry in the framework of the project FLORABO. The presence of fungi and bacteria in the air can affect the health of people in a given room. The need to control the amount of microorganisms, both in terms of quantity and quality, applies to both hospitals and offices. The device is based on the fluorescence spectroscopy analysis of the sample and then these results were compared to the resulting spectrogram database, which includes the standard curves obtained in the laboratory for selected bacteria. The measurements provide information about the presence, the type, and the approximate concentration of bacteria in the sample. The spectra were collected at different excitation wavelengths, and the waveforms are specific for each of the strains. It also takes under analysis the signal intensities of the different spectra (not only shape a maximum of the peak) so that the concentration of bacteria in the sample being tested can be determined. The device was tested in the laboratory with concentrations ranging from 10 to 108 cells/mL. Additionally, the detector can distinguish between the vegetative forms of spores of the bacteria.
Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP
Authors: Bellenger Clint , Miller Dean , Halson Shona , Roach Gregory , Sargent Charli
Published: May 2021

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP 2.0 and ECG over 15 opportunities during October–December 2018. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.
Characterization and Separation of Platinum-Based Antineoplastic Drugs by Zwitterionic Hydrophilic Interaction Liquid Chromatography (HILIC)–Tandem Mass Spectrometry, and Its Application in Surface Wipe Sampling
Authors: Dugheri Stefano , Mucci Nicola , Mini Enrico , Squillaci Donato , Marrubini Giorgio , Bartolucci Gianluca , Bucaletti Elisabetta , Cappelli Giovanni , Trevisani Lucia , Arcangeli Giulio
Published: May 2021

Platinum-based antineoplastic drugs (PtADs) are among the most important and used families of chemotherapy drugs, which, even showing severe side effects and being hindered by drug resistance, are not likely to be replaced clinically any time soon. The growing interest in the occupational health community in antineoplastic drug (AD) surface contamination requires the development of increasingly fast and easy high-throughput monitoring methods, even considering the lack of harmonized legally binding regulation criteria. Thus, a wipe sampling method together with zwitterionic hydrophilic interaction liquid chromatography (HILIC-Z)–tandem mass spectrometry (MS/MS) analysis was developed for the simultaneous evaluation of oxaliplatin, cisplatin, and carboplatin surface contaminations. A design of experiments approach was used to optimize the chromatographic conditions. Limits of quantification ranging from 2 to 5 ng/mL were obtained from interday and intraday repetitions for oxaliplatin and carboplatin, and between 170 and 240 ng/mL for cisplatin. The wipe desorption procedure is equivalent to other AD sampling methods, enabling a fast sample preparation, with an LC-MS/MS analysis time of less than 7 min.
Radar Target Simulation for Vehicle-in-the-Loop Testing
Authors: Diewald Axel , Kurz Clemens , Kannan Prasanna Venkatesan , Gießler Martin , Pauli Mario , Göttel Benjamin , Kayser Thorsten , Gauterin Frank , Zwick Thomas
Published: May 2021

Automotive radar sensors play a vital role in the current development of autonomous driving. Their ability to detect objects even under adverse conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. As their functional operation must be validated in-place, a fully integrated test system is required. Radar Target Simulators (RTS) are capable of executing end-of-line, over-the-air validation tests by looping back a received and afterward modified radar signal and have been incorporated into existing Vehicle-in-the-Loop (ViL) test beds before. However, the currently available ViL test beds and the RTS systems that they consist of lack the ability to generate authentic radar echoes with respect to their complexity. The paper at hand reviews the current development stage of the research as well as commercial ViL and RTS systems. Furthermore, the concept and implementation of a new test setup for the rapid prototyping and validation of ADAS functions is presented. This represents the first-ever integrated radar validation test system to comprise multiple angle-resolved radar target channels, each capable of generating multiple radar echoes. A measurement campaign that supports this claim has been conducted.
Detection of Clostridium perfringens and determination of enterotoxin genes (cpa and cpe) in traditional turkish chicken doner kebab
Authors: KEVENK Tahsin Onur , UYANIK Tolga , KANAT Sibel , ÇADIRCI Özgür , GÜCÜKOĞLU Ali
Published: May 2021

The demand for the fast-food industry in the world is increasing day by day. In this sense, chicken doner kebab becomes frequently preferred food source in daily life. At the same time, chicken doner kebab is both a good animal origin protein source and a cheaper option. Therefore, it is prepared and consumed in high amounts in Turkey. In this study, it was aimed to determine the presence of Clostridium perfringens and its toxin genes in traditional Turkish chicken doner kebabs purchased from restaurants and modified atmosphere packaged (MAP) samples collected from markets. For this purpose, 100 ready-to-cook and 100 ready-to-eat, totally 200 doner samples have been used as material. As a result, the prevalence of C. perfringens has been found 29%, 6% in ready-to-cook and ready-to-eat samples, respectively. The cpa gene was detected in all isolates. However, both cpa and cpe gene was found only in 4% of isolates.