bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of an alternative biotreatment for the extraction of harmful iron and sulfur species from waterlogged wood
Authors: Monachon Mathilde , Albelda-Berenguer Magdalena , Lombardo Tiziana , Cornet Emilie , Moll-Dau Friederike , Schramm Janet , Schmidt-Ott Katharina , Joseph Edith
Published: September 2021

An innovative bioextraction method was tested and compared to common chemical extraction for the preservation of waterlogged archeological wood (WAW) artifacts. During burial, WAW artifacts accumulate iron and sulfur species forming iron sulfides. These compounds are harmless in the burial environment, where the oxygen content is low. But upon excavation, the WAW undergoes the oxidation of these compounds, and thus, irreversible physical and chemical damages occur. Fresh and archeological oak and pine samples were selected as representative species of WAW artifacts. Fresh samples were previously artificially contaminated to ascertain the presence of iron and sulfur. Thiobacillus denitrificans and natural iron chelators, called siderophores, were investigated to extract iron and sulfur as a 2-step biological treatment (BT) and compared to sodium persulfate–EDTA as chemical treatment (CT). Consolidation and freeze-drying were performed on the samples after BT and CT as traditional conservation protocols. BT and CT efficiency was evaluated through Raman, inductively coupled plasma–optical emission (ICP-OES), and Fourier transformed infrared (FTIR) spectroscopies. Raman and ICP showed that most of the iron and sulfur was extracted after BT, while some sulfur species remained present on CT samples. None of the extraction methods resulted in a degradation of the wood, as ascertained by FTIR analyses. Yet, all samples presented visual modifications after conservation. Pine samples treated with BT illustrated the oxidation of the species. Present principal component analysis (PCA) and analysis of variance (ANOVA) which were selected as statistical approaches and validated BT as a promising alternative extraction method, with encouraging extraction rates and less alteration of the sample appearance.
A Literature Survey on Algorithms and Hardware Architectures of Max-Log-MAP Demapping
Authors: Rizk Mostafa , Baghdadi Amer , Jézéquel Michel
Published: September 2021

Emergent wireless communication standards, which are employed in different transmission environments, support various modulation schemes. High-order constellations are targeted to achieve high bandwidth efficiency. However, the complexity of the symbol-by-symbol Maximum A Posteriori (MAP) algorithm increases dramatically for these high-order modulation schemes. In order to reduce the hardware complexity, the suboptimal Max-Log-MAP, which is the direct transformation of the MAP algorithm into logarithmic domain, is alternatively implemented. In the literature, a great deal of research effort has been invested into Max-Log-MAP demapping. Several simplifications are presented to meet with specific constellations. In addition, the hardware implementations dedicated for Max-Log-MAP demapping vary greatly in terms of design choices, supported flexibility and performance criteria, making them a challenge to compare. This paper explores the published Max-Log-MAP algorithm simplifications and existing hardware demapper designs and presents an extensive review of the current literature. In-depth comparisons are drawn amongst the designs and different key performance characteristics are described, namely, achieved throughput, hardware resource requirements and flexibility. This survey should facilitate fair comparisons of future designs, as well as opportunities for improving the design of Max-Log-MAP demappers.
Novel and Selective Photoelectrochemical Sensing of Levofloxacin Based on BiOI Microflower Spheres Modified with Bi-Metal and BiPO4
Authors: Chen Sheng , Tian Meiwen , Liu Shenwen
Published: September 2021

This paper reports the design of a photoelectrochemical (PEC) sensing method for detecting levofloxacin (LEVX) in tablets. In this paper, a three-dimensional plasma Bi/BiPO4/BiOI heterojunction was fabricated by a two-step method, in which Bi-metal and BiPO4 were fixed on the surface of BiOI with beads. The composites were characterized by a variety of analytical methods. Bi/BiPO4/BiOI-5% showed better photocatalytic activity than BiPO4/BiOI. Indium tin oxide (ITO) modified with the Bi/BiPO4/BiOI-5% composites was used to determine LEVX and achieved linear detection ranges of 0.2–2 [Formula: see text]M and 2–7 [Formula: see text]M. These composites present alternative materials for the analysis of antibiotics.
Gap probabilities in the bulk of the Airy process
Authors: Blackstone Elliot , Charlier Christophe , Lenells Jonatan
Published: September 2021

We consider the probability that no points lie on g large intervals in the bulk of the Airy point process. We make a conjecture for all the terms in the asymptotics up to and including the oscillations of order 1, and we prove this conjecture for g = 1.
Liquidity-free implied volatilities: An approach using conic finance
Authors: Michielon Matteo , Khedher Asma , Spreij Peter
Published: September 2021

In this paper, we consider the problem of calculating risk-neutral implied volatilities of European options without relying on option mid prices but solely on bid and ask prices. We provide an approach, based on the conic finance paradigm, that allows to uniquely strip risk-neutral implied volatilities from bid and ask quotes, and that does not require restrictive assumptions. Our methodology also allows to jointly calculate the implied liquidity of the market. The idea outlined in this paper can be applied to calculate other implied parameters from bid and ask security prices as soon as their theoretical risk-neutral counterparts are strictly increasing with respect to the former.
Tephra layers in the marine environment: a review of properties and emplacement processes
Authors: Freundt Armin , Schindlbeck-Belo Julie C. , Kutterolf Steffen , Hopkins Jenni L.
Published: September 2021

This review focuses on the recognition of volcanic ash occurrences in marine sediment cores and on using their appearance and properties to deduce their origin. Widespread marine tephra layers are important marker horizons for both volcanological as well as general geological investigations. We describe ash detection by visual inspection and logging of sediment cores. Ash layer structure and texture, particle morphologies and lithological compositions of primary volcanic deposits are summarized and processes modifying them are discussed, both natural processes acting on and in the seafloor, i.e. erosion and bioturbation, and anthropogenic modifications during drilling/coring and core preparation. We discuss primary emplacement processes of marine fall and flow tephra deposits derived from either subaerial or submarine sources in order to identify distinguishing properties. We also elaborate on processes generating secondary, resedimented volcaniclastic layers such as submarine landslides and shelf erosion as well as fluvial input and ice-rafting, and how they can be distinguished from primary volcaniclastic deposits, which is essential in tephrostratigraphy. Finally, methods of tephra correlation between cores and on-land deposits/volcanoes are illustrated because they allow us to extend the 1D information from single cores to 3D distribution and facies changes of tephras and to bridge the land–sea gap.
Layer-based Composite Reputation Bootstrapping
Authors: Mistry Sajib , Qu Lie , Bouguettaya Athman
Published: September 2021

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.
Automated Security Assessment Framework for Wearable BLE-enabled Health Monitoring Devices
Authors: Zendehdel Ghazale Amel , Kaur Ratinder , Chopra Inderpreet , Stakhanova Natalia , Scheme Erik
Published: September 2021

The growth of IoT technology, increasing prevalence of embedded devices, and advancements in biomedical technology have led to the emergence of numerous wearable health monitoring devices (WHMDs) in clinical settings and in the community. The majority of these devices are Bluetooth Low Energy (BLE) enabled. Though the advantages offered by BLE-enabled WHMDs in tracking, diagnosing, and intervening with patients are substantial, the risk of cyberattacks on these devices is likely to increase with device complexity and new communication protocols. Furthermore, vendors face risk and financial tradeoffs between speed to market and ensuring device security in all situations. Previous research has explored the security and privacy of such devices by manually testing popular BLE-enabled WHMDs in the market and generally discussed categories of possible attacks, while mostly focused on IP devices. In this work, we propose a new semi-automated framework that can be used to identify and discover both known and unknown vulnerabilities in WHMDs. To demonstrate its implementation, we validate it with a number of commercially available BLE-enabled enabled wearable devices. Our results show that the devices are vulnerable to a number of attacks, including eavesdropping, data manipulation, and denial of service attacks. The proposed framework could therefore be used to evaluate potential devices before adoption into a secure network or, ideally, during the design and implementation of new devices.
Cloud-based Network Virtualization in IoT with OpenStack
Authors: Benomar Zakaria , Longo Francesco , Merlino Giovanni , Puliafito Antonio
Published: September 2021

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.
Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics
Authors: Pagani Alessio , Wei Zhuangkun , Silva Ricardo , Guo Weisi
Published: September 2021

Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.
Optimal COVID-19 Vaccination Strategies with Limited Vaccine and Delivery Capabilities
Authors: Santini Simone
Published: September 2021

We develop a model of infection spread that takes into account the existence of a vulnerable group as well as the variability of the social relations of individuals. We develop a compartmentalized power-law model, with power-law connections between the vulnerable and the general population, considering these connections as well as the connections among the vulnerable as parameters that we vary in our tests.

We use the model to study a number of vaccination strategies under two hypotheses: first, we assume a limited availability of vaccine but an infinite vaccination capacity, so all the available doses can be administered in a short time (negligible with respect to the evolution of the epidemic). Then, we assume a limited vaccination capacity, so the doses are administered in a time non-negligible with respect to the evolution of the epidemic.

We develop optimal strategies for the various social parameters, where a strategy consists of (1) the fraction of vaccine that is administered to the vulnerable population and (2) the criterion that is used to administer it to the general population. In the case of a limited vaccination capacity, the fraction (1) is a function of time, and we study how to optimize it to obtain a maximal reduction in the number of fatalities.
CGRA-EAM—Rapid Energy and Area Estimation for Coarse-grained Reconfigurable Architectures
Authors: Wijtvliet Mark , Corporaal Henk , Kumar Akash
Published: September 2021

Reconfigurable architectures are quickly gaining in popularity due to their flexibility and ability to provide high energy efficiency. However, reconfigurable systems allow for a huge design space. Iterative design space exploration (DSE) is often required to achieve good Pareto points with respect to some combination of performance, area, and/or energy. DSE tools depend on information about hardware characteristics in these aspects. These characteristics can be obtained from hardware synthesis and net-list simulation, but this is very time-consuming. Therefore, architecture models are common. This work introduces CGRA-EAM (Coarse-Grained Reconfigurable Architecture - Energy & Area Model), a model for energy and area estimation framework for coarse-grained reconfigurable architectures. The model is evaluated for the Blocks CGRA. The results demonstrate that the mean absolute percentage error is 15.5% and 2.1% for energy and area, respectively, while the model achieves a speedup of close to three orders of magnitude compared to synthesis.
FG-LiquID
Authors: Liang Yumeng , Zhou Anfu , Zhang Huanhuan , Wen Xinzhe , Ma Huadong
Published: September 2021

Contact-less liquid identification via wireless sensing has diverse potential applications in our daily life, such as identifying alcohol content in liquids, distinguishing spoiled and fresh milk, and even detecting water contamination. Recent works have verified the feasibility of utilizing mmWave radar to perform coarse-grained material identification, e.g., discriminating liquid and carpet. However, they do not fully exploit the sensing limits of mmWave in terms of fine-grained material classification. In this paper, we propose FG-LiquID, an accurate and robust system for fine-grained liquid identification. To achieve the desired fine granularity, FG-LiquID first focuses on the small but informative region of the mmWave spectrum, so as to extract the most discriminative features of liquids. Then we design a novel neural network, which uncovers and leverages the hidden signal patterns across multiple antennas on mmWave sensors. In this way, FG-LiquID learns to calibrate signals and finally eliminate the adverse effect of location interference caused by minor displacement/rotation of the liquid container, which ensures robust identification towards daily usage scenarios. Extensive experimental results using a custom-build prototype demonstrate that FG-LiquID can accurately distinguish 30 different liquids with an average accuracy of 97%, under 5 different scenarios. More importantly, it can discriminate quite similar liquids, such as liquors with the difference of only 1% alcohol concentration by volume.
MetaTP
Authors: Zhong Weida , Suo Qiuling , Gupta Abhishek , Jia Xiaowei , Qiao Chunming , Su Lu
Published: September 2021

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.
Self-supervised Learning for Reading Activity Classification
Authors: Islam Md. Rabiul , Sakamoto Shuji , Yamada Yoshihiro , Vargo Andrew W. , Iwata Motoi , Iwamura Masakazu , Kise Koichi
Published: September 2021

Reading analysis can relay information about user's confidence and habits and can be used to construct useful feedback. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. We propose a Self-supervised Learning (SSL) method for reading analysis. Previously, SSL has been effective in physical human activity recognition (HAR) tasks, but it has not been applied to cognitive HAR tasks like reading. We first evaluate the proposed method on a four-class classification task on reading detection using electrooculography datasets, followed by an evaluation of a two-class classification task of confidence estimation on multiple-choice questions using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used as comparisons for the proposed SSL method. The results show that the proposed SSL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates the proposed method is the superior choice for reading analysis tasks. These results are important for informing the design of automatic reading analysis platforms.
EchoSpot
Authors: Lian Jie , Lou Jiadong , Chen Li , Yuan Xu
Published: September 2021

Indoor localization has played a significant role in facilitating a collection of emerging applications in the past decade. This paper presents a novel indoor localization solution via inaudible acoustic sensing, called EchoSpot, which relies on only one speaker and one microphone that are readily available on audio devices at households. We program the speaker to periodically send FMCW chirps at 18kHz-23kHz and leverage the co-located microphone to capture the reflected signals from the body and the wall for analysis. By applying the normalized cross-correlation on the transmitted and received signals, we can estimate and profile their time-of-flights (ToFs). We then eliminate the interference from device imperfection and environmental static objects, able to identify the ToFs corresponding to the direct reflection from human body. In addition, a new solution to estimate the ToF from wall reflection is designed, assisting us in spotting a human location in the two-dimensional space. We implement EchoSpot on three different types of speakers, e.g., Amazon Echo, Edifier R1280DB, and Logitech z200, and deploy them in real home environments for evaluation. Experimental results exhibit that EchoSpot achieves the mean localization errors of 4.1cm, 9.2cm, 13.1cm, 17.9cm, 22.2cm, respectively, at 1m, 2m, 3m, 4m, and 5m, comparable to results from the state-of-the-arts while maintaining favorable advantages.
Write, Attend and Spell
Authors: Zhang Qian , Wang Dong , Zhao Run , Yu Yinggang , Jing JiaZhen
Published: September 2021

Text entry on a smartwatch is challenging due to its small form factor. Handwriting recognition using the built-in sensors of the watch (motion sensors, microphones, etc.) provides an efficient and natural solution to deal with this issue. However, prior works mainly focus on individual letter recognition rather than word recognition. Therefore, they need users to pause between adjacent letters for segmentation, which is counter-intuitive and significantly decreases the input speed. In this paper, we present 'Write, Attend and Spell' (WriteAS), a word-level text-entry system which enables free-style handwriting recognition using the motion signals of the smartwatch. First, we design a multimodal convolutional neural network (CNN) to abstract motion features across modalities. After that, a stacked dilated convolutional network with an encoder-decoder network is applied to get around letter segmentation and output words in an end-to-end way. More importantly, we leverage a multi-task sequence learning method to enable handwriting recognition in a streaming way. We construct the first sequence-to-sequence handwriting dataset using smartwatch. WriteAS can yield 9.3% character error rate (CER) on 250 words for new users and 3.8% CER for words unseen in the training set. In addition, WriteAS can handle various writing conditions very well. Given the promising performance, we envision that WriteAS can be a fast and accurate input tool for smartwatch.
Inverse Foraging
Authors: Freire Maria L. Montoya , Oulasvirta Antti , Di Francesco Mario
Published: September 2021

Users' engagement with pervasive displays has been extensively studied, however, determining how their content is interesting remains an open problem. Tracking of body postures and gaze has been explored as an indication of attention; still, existing works have not been able to estimate the interest of passers-by from readily available data, such as the display viewing time. This article presents a simple yet accurate method of estimating users' interest in multiple content items shown at the same time on displays. The proposed approach builds on the information foraging theory, which assumes that users optimally decide on the content they consume. Through inverse foraging, the parameters of a foraging model are fitted to the values of viewing times observed in practice, to yield estimates of user interest. Different foraging models are evaluated by using synthetic data and with a controlled user study. The results demonstrate that inverse foraging accurately estimates interest, achieving an R2 above 70% in comparison to self-reported interest. As a consequence, the proposed solution allows to dynamically adapt the content shown on pervasive displays, based on viewing data that can be easily obtained in field deployments.
OpiTrack
Authors: Gullapalli Bhanu Teja , Carreiro Stephanie , Chapman Brittany P. , Ganesan Deepak , Sjoquist Jan , Rahman Tauhidur
Published: September 2021

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.
WiStress
Authors: Ha Unsoo , Madani Sohrab , Adib Fadel
Published: September 2021

Stress plays a critical role in our lives, impacting our productivity and our long-term physiological and psychological well-being. This has motivated the development of stress monitoring solutions to better understand stress, its impact on productivity and teamwork, and help users adapt their habits toward more sustainable stress levels. However, today's stress monitoring solutions remain obtrusive, requiring active user participation (e.g., self-reporting), interfering with people's daily activities, and often adding more burden to users looking to reduce their stress.

In this paper, we introduce WiStress, the first system that can passively monitor a user's stress levels by relying on wireless signals. WiStress does not require users to actively provide input or to wear any devices on their bodies. It operates by transmitting ultra-low-power wireless signals and measuring their reflections off the user's body. WiStress introduces two key innovations. First, it presents the first machine learning network that can accurately and robustly extract heartbeat intervals (IBI's) from wireless reflections without constraints on a user's daily activities. Second, it introduces a stress classification framework that combines the extracted heartbeats with other wirelessly captured stress-related features in order to infer a subject's stress level. We built a prototype of WiStress and tested it on 22 different subjects across different environments in both stress-induced and free-living conditions. Our results demonstrate that WiStress has high accuracy (84%-95%) in inferring a person's stress level in a fully-automated way, paving the way for ubiquitous sensing systems that can monitor stress and provide feedback to improve productivity, health, and well-being.
Learning When Agents Can Talk to Drivers Using the INAGT Dataset and Multisensor Fusion
Authors: Wu Tong , Martelaro Nikolas , Stent Simon , Ortiz Jorge , Ju Wendy
Published: September 2021

This paper examines sensor fusion techniques for modeling opportunities for proactive speech-based in-car interfaces. We leverage the Is Now a Good Time (INAGT) dataset, which consists of automotive, physiological, and visual data collected from drivers who self-annotated responses to the question "Is now a good time?," indicating the opportunity to receive non-driving information during a 50-minute drive. We augment this original driver-annotated data with third-party annotations of perceived safety, in order to explore potential driver overconfidence. We show that fusing automotive, physiological, and visual data allows us to predict driver labels of availability, achieving an 0.874 F1-score by extracting statistically relevant features and training with our proposed deep neural network, PazNet. Using the same data and network, we achieve an 0.891 F1-score for predicting third-party labeled safe moments. We train these models to avoid false positives---determinations that it is a good time to interrupt when it is not---since false positives may cause driver distraction or service deactivation by the driver. Our analyses show that conservative models still leave many moments for interaction and show that most inopportune moments are short. This work lays a foundation for using sensor fusion models to predict when proactive speech systems should engage with drivers.
CardiacWave
Authors: Xu Chenhan , Li Huining , Li Zhengxiong , Zhang Hanbin , Rathore Aditya Singh , Chen Xingyu , Wang Kun , Huang Ming-chun , Xu Wenyao
Published: September 2021

Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).
Reaction Products Affecting the PEFC Catalyst Ink Property
Authors: Uemura Suguru , Sasabe Takashi , Sakai Katsunori , Matsumoto Hidetoshi , Sugimori Hidekazu , Shinohara Kazuhiko , Hirai Shuichiro
Published: September 2021

Catalyst ink is a key material of the catalyst layer in a proton exchange membrane fuel cell, but can be degraded as the reactions of alcohols are promoted by Pt in the ink. The present study quantitatively determined the concentrations of reaction products in an ethanol/water-based catalyst ink, and the effects of these substances on the ink and catalyst layer were investigated. The acetaldehyde concentration was found to be the key factor, as acetaldehyde caused particle agglomeration in the catalyst ink and micro-scale crack formation in the catalyst layer.
Fitting of Low–Pt PEM Fuel Cell Polarization Curves by Means of a Single–Pore Catalyst Layer Model
Authors: Kulikovsky Andrei
Published: September 2021

A recent single–pore model for performance of cathode catalyst layer (CCL) in a low–Pt PEM fuel cell is further developed and used to fit experimental polarization curves. Analytical formula for the limiting current density is derived, which contains oxygen diffusivities in the void pore and ionomer film covering Pt/C agglomerates. Least–squares fitting of model to experimental polarization curves returns CCL proton conductivity and parameters of oxygen transport in the pore and film.
Mst2 Overexpression Inhibits Thyroid Carcinoma Growth and Metastasis by Disrupting Mitochondrial Fitness and Endoplasmic Reticulum Homeostasis
Authors: Zhang Haichao , Qu Xin , Han Lu , Di Xu
Published: September 2021

Although the incidence of thyroid carcinoma has increased over the past several decades, it has an excellent prognosis and overall 5-year survival, with a stable mortality rate, except in cases with advanced stages or rare malignant tumor types. Biomarkers have emerged as effective targets of molecular therapy against thyroid carcinoma due to their rapid and convenient detection; however, there has been little clinical application. Macrophage stimulating 2 (Mst2) is a proapoptotic protein with implications in carcinogenesis and metastasis. We found that Mst2 overexpression-induced endoplasmic reticulum (ER) stress in MDA-T32 thyroid carcinoma cells, accompanied by elevated caspase-12 activity, increased apoptotic rate, and reduced cell viability. In addition, Mst2 overexpression contributed to mitochondrial damage, as evidenced by increased mitochondrial oxidative stress and activated the mitochondrial apoptotic pathway. Inhibition of the JNK pathway abolished these effects. These results show Mst2 to be a novel tumor suppressor that induces mitochondrial dysfunction and ER stress via the JNK pathway. Thus, Mst2 could potentially serve as a biomarker for developing targeted therapy against thyroid carcinoma.
Identification of the Novel Methylated Genes’ Signature to Predict Prognosis in INRG High-Risk Neuroblastomas
Authors: Liu Zhichao , Li Changchun
Published: September 2021

Background. Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients. Methods. Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA). Results. The methylated genes’ signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test,

P
<
0.001

) in the training group, and the validation of the signature’s risk stratification ability was carried out in the test group (log-rank test,

P
<
0.01

, median survival time: 30.48 vs. >120.36 months). The methylated genes’ signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas. Conclusions. The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival.
Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm
Authors: Bai Yanyang , Zhang Xuesheng
Published: September 2021

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.
Asymmetric Risk Spillover Networks and Risk Contagion Driver in Chinese Financial Markets: The Perspective of Economic Policy Uncertainty
Authors: Zhang Zongxin , Chen Ying , Hou Weijie
Published: September 2021

The global financial market shocks have intensified due to the COVID‐19 epidemic and other impacts, and the impacts of economic policy uncertainty on the financial system cannot be ignored. In this paper, we construct asymmetric risk spillover networks of Chinese financial markets based on five sectors: bank, securities, insurance, diversified finance, and real estate. We investigate the complexity of the risk spillover effect of Chinese financial markets and the impact of economic policy uncertainty on the level of network contagion of financial risk. The study yields three findings. First, the cross‐sectoral risk spillover effects of Chinese financial markets are asymmetric in intensity. The bank sector is systemically important in the risk spillover network. Second, the level of risk stress in the real estate sector has increased in recent years, and it plays an important role in the path of financial risk contagion. Third, Economic policy uncertainty has a significant positive impact on the level of network contagion of financial risk of Chinese financial markets.
[Retracted] Research on Association between Levels of Serum Adiponectin, Hs‐CRP, and sICAM‐1 and Hypertensive Cerebrovascular Complications
Authors: Niu Haijun , Jiang Rongyan , Dong Songwu , Xia Linhu , Fang Hongyi
Published: September 2021

The study is aimed at studying the association between the levels of serum adiponectin (ADPN), high‐sensitivity C‐reactive protein (hs‐CRP), and soluble intercellular adhesion molecule‐1 (sICAM‐1) and hypertensive cerebrovascular complications. 50 patients with hypertensive cerebrovascular disease treated in Gansu Provincial Hospital from December 2016 to December 2018 were selected as the experimental group, and 50 normal people who underwent physical examination were selected as the control group. The blood pressure, heart rate, and the complications were recorded, and the serum blood lipid indexes were detected. Moreover, the content of serum ADPN, hs‐CRP, and sICAM‐1; the neurological indexes; brain‐derived neurotrophic factor (BNDF); and neurone‐specific enolase (NSE) were also determined using ELISA. The content of aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), and serum creatinine (SCR) in the experimental group was significantly higher than that in control group (p < 0.05); the incidence of cerebrovascular complications, systolic blood pressure, diastolic blood pressure, and heart rate increased (p < 0.05); the content of total cholesterol (TC), triglyceride (TG), low‐density lipoprotein (LDL), high‐density lipoprotein (HDL), hs‐CRP, and sICAM‐1 obviously rose (p < 0.05); and the content of ADPN and HDL obviously declined (p < 0.05). Besides, the experimental group had evidently lower systolic blood flow velocity (Vs), diastolic blood flow velocity (Vd), and mean blood flow velocity (Vm) and evidently higher pulsatility index (PI) (p < 0.05). The levels of S100 and NSE in the experimental group increased significantly, and the level of BNDF decreased significantly (p < 0.05). In patients with hypertensive cerebrovascular disease, the level of ADPN declines; the levels of hs‐CRP and sICAM‐1 rise; the incidence rate of cerebrovascular complications is elevated; and there are changes in the blood lipid, cerebrovascular hemodynamic, and neurological indexes, thereby further promoting the occurrence and development of hypertensive cerebrovascular disease.
Media Influence and the Willingness to Buy Intangible Cultural Heritage Products: A Moderated Mediator Model
Authors: Huang Yijun , Lv Qinghua , Lin Jian
Published: September 2021

In the information society, the media exert a key influence on the consumer selection of intangible cultural heritage (ICH) products. Drawing on the literature of cultural identity, product involvement, and willingness to buy, this paper constructs and verifies the theoretical model of media influence-cultural identity-willingness to buy ICH products, with ICH product consumers as the objects. The results show that the media have a significantly positive effect on cultural identity, which in turn significantly promotes the willingness to buy, but the media do not significantly affect the willingness to buy. Cultural identity fully mediates the media influence on the willingness to buy. Referring to relevant studies on product involvement, the samples were divided into a set of high involvement and a set of low involvement. The moderating effect analysis reveals that the higher the product involvement, the more significant the effect of media on cultural identity and the more prominent the influence of cultural identity on the willingness to buy. Finally, the authors discussed the managerial implications of the research results for ICH inheritors and enterprises.
Evaluation of Cold Chain Management Performance for Temperature-Sensitive Pharmaceuticals at Public Health Facilities Supplied by the Jimma Pharmaceuticals Supply Agency Hub, Southwest Ethiopia: Pharmaceuticals Logistic Management Perspective Using a Multicentered, Mixed-Method Approach
Authors: Feyisa Diriba , Jemal Awol , Aferu Temesgen , Ejeta Fikadu , Endeshaw Alem
Published: September 2021

Background. Effective and efficient cold chain management maximizes utilization of healthcare resources, reduces cold chain products wastage, and improves the quality of health services. It eventually guarantees that clients receive cold chain products they need at service delivery points. The objective of this study was to evaluate cold chain management performance for temperature-sensitive medicines at public health facilities in Southwest Ethiopia supplied by the Jimma Pharmaceuticals Fund and Supply Agency hub. Method and Materials. The study used an institution-based cross-sectional study design. Forty-seven (47) public health facilities in Southwest Ethiopia were evaluated using checklists adopted from the Logistic Indicators Assessment Tool, Vaccine Management Assessment Tool, and Logistic System Assessment Tool. Results. The study revealed that the mean availability of essential cold chain products was 72.1 ± 14.8% while the average stock-out rate was 26.2 ± 8.6%. The median stock-out duration was 23 ± 21 days for all visited public health facilities. Two hundred and sixty-three (43.06 ± 15.3%) of the public health facilities’ stock records were found accurate, and the wastage rate due to expiration was 9.2 ± 7.8% for all visited health facilities. Thirty public health facilities (63.8 ± 36.2%) had acceptable storage conditions. Conclusions and Recommendations. Supply chain performance at the study facilities was not adequate overall, and focused efforts need to be directed at managing the availability of critical cold chain medicines. Some cold chain management challenges demand the attention of the top management, while the rest can be addressed by operational management at the facilities through provision of appropriate training and supervision of the cold chain pharmaceutical handlers.