bluuebunny/crossref_metadata_2025 huggingface dataset:
Atrial fibrillation detection using a automated electrocardiographic monitoring in a transient ischaemic attack service
Authors: D'Anna Lucio , Sikdar Oishi , Lim Suyin , Kalladka Dheeraj , Banerjee Soma
Published: February 2022

Background

The vast majority of the transient ischaemic attacks (TIA) services in UK reported significant delays in the initiation of the routine cardiac monitoring that may result in a significant number of missed atrial fibrillation (AF) paroxysms and increased long-term risk of recurrent stroke. Automated continuous ECG monitoring (ACEM) system has shown promising results in terms of AF detection but it is unclear if ACEM improves AF detection in a rapid outpatient TIA service.

Objectives

We assessed ACEM in patients with TIA with the aim to significantly reduce the delay to initiate the cardiac monitoring and to enhance the yield of AF detection in these patients. We also aimed to determine the impact of a more rapid initiation of ACEM on the 6-month risk of recurrent stroke/TIA.

Methods

This is an observational, prospective before (phase 1: 1 July to 31 December 2018) versus after (phase 2: 1 January to 30 June 2019) study of the effect of ACEM, compared with routine initiation of 24h-Holter ECG, in patients with TIA assessed in our service.

Results

The phase 1 (n=136) and phase 2 (n=105) cohorts did not differ with regards to age, risk factors, duration of cardiac monitoring. The rate of newly detected AF was significantly higher in phase 2 compared with phase 1 (9.52% vs 2.21%, p<0.001). The 6-month risk of recurrent stroke/TIA was significantly lower in phase 2 compared with phase 1 (7.4% vs 1%, p=0.018).

Conclusions

Early initiation of ACEM improves AF detection after TIA in a rapid TIA service and is associated with a reduced risk of recurrent TIA/stroke.
Oculo-auriculo-vertebral spectrum: new genes and literature review on a complex disease
Authors: Tingaud-Sequeira Angèle , Trimouille Aurélien , Sagardoy Thomas , Lacombe Didier , Rooryck Caroline
Published: February 2022

Oculo-auriculo-vertebral spectrum (OAVS) or Goldenhar syndrome is due to an abnormal development of first and second branchial arches derivatives during embryogenesis and is characterised by hemifacial microsomia associated with auricular, ocular and vertebral malformations. The clinical and genetic heterogeneity of this spectrum with incomplete penetrance and variable expressivity, render its molecular diagnosis difficult. Only a few recurrent CNVs and genes have been identified as causatives in this complex disorder so far. Prenatal environmental causal factors have also been hypothesised. However, most of the patients remain without aetiology. In this review, we aim at updating clinical diagnostic criteria and describing genetic and non-genetic aetiologies, animal models as well as novel diagnostic tools and surgical management, in order to help and improve clinical care and genetic counselling of these patients and their families.
In good hands: the phenomenological significance of human touch for nursing practices
Authors: Lemermeyer Gillian
Published: February 2022

Prevailing understandings of the nurse’s touch tend to be focused on its consoling, instrumental and communicative utility. What seems to be missing is an exploration of the ethical and existential significance of the nurse’s touch. As an aspect of nearly every human experience, touch has a depth and breadth of meanings that are hard to compass. We experience the world through our bodies, feeling our way through our lives. In the nurse’s world, touching contact with the person in care is often considered to be a fundamental gesture, inherent to nursing practices. Still, touch is often hidden, subsumed by the tasks of nursing themselves. In order to explore the meaningfulness of the nurse’s touch, I start with considering the sense of touch itself, exploring possibilities of the nurse’s touch. The experience of the nurse’s touch is investigated further through phenomenological reflection on descriptive accounts of the nurse’s touch from poetry, fictional prose, neonatal nurse interviews, as well as scholarly and personal accounts. These examples show insights into the nurse’s touch as a site for an ethical encounter.
Evaluating the use of reactivity levels to inform risk communication and improve service user experience in an HIV self-sampling service
Authors: Taylor Jennifer , O'Brien Sally , Fagg Jamie , Holdsworth Gillian , Amos-Gibbs Stuart , Ardines Elena , George Blake , Baraitser Paula
Published: February 2022

Objectives

This service improvement study scoped the value of using quantitative reactivity levels (RL) to introduce a two-tier counselling approach in HIV self-sampling services. The objectives were to (1) use self-reported confirmatory test results to estimate the positive predictive value (PPV) of the first-line assay and (2) to calculate the impact on the PPV and theoretical negative predictive value of a range of proposed RL thresholds, which could be used to differentiate between higher and lower reactive results.

Methods

We studied HIV testing data from a UK-based online sexual health service from 1 December 2018 to 15 July 2020. Routinely collected, anonymised data on RL and self-reported confirmatory testing outcomes were used to calculate PPV at a range of RL thresholds.

Results

116 474 adequate HIV tests were reported during the study period, of which 889 were reactive (0.76%, 95% CI 0.71% to 0.82%). Confirmatory testing outcomes were self-reported by 572 (64.34%) reactive service users, of whom 43 (7.52%) had a new HIV diagnosis, 412 (72.03%) were negative and 117 (20.45%) already had a known HIV diagnosis. PPV was 9.45% at an RL threshold of >1. Quantitative RLs were reported for 30 281 results, of which 200 were reactive. PPV increased to 80% at an RL threshold of >30. No confirmed HIV diagnoses were reported among service users with RL <30.

Conclusion

RLs could be used to support a more nuanced approach to counselling, potentially reducing anxiety prior to confirmatory testing.
Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria
Authors: Kidd Andrew C , Anderson Owen , Cowell Gordon W , Weir Alexander J , Voisey Jeremy P , Evison Matthew , Tsim Selina , Goatman Keith A , Blyth Kevin G
Published: February 2022

Background

In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy.

Methods

In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen’s kappa. Survival was assessed using Kaplan-Meier methodology.

Results

Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3(p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival.

Conclusion

We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.
Guarding Machine Learning Hardware Against Physical Side-channel Attacks
Authors: Dubey Anuj , Cammarota Rosario , Suresh Vikram , Aysu Aydin
Published: February 2022

Machine learning (ML) models can be trade secrets due to their development cost. Hence, they need protection against malicious forms of reverse engineering (e.g., in IP piracy). With a growing shift of ML to the edge devices, in part for performance and in part for privacy benefits, the models have become susceptible to the so-called physical side-channel attacks.

ML being a relatively new target compared to cryptography poses the problem of side-channel analysis in a context that lacks published literature. The gap between the burgeoning edge-based ML devices and the research on adequate defenses to provide side-channel security for them thus motivates our study. Our work develops and combines different flavors of side-channel defenses for ML models in the hardware blocks. We propose and optimize the
first defense based on Boolean masking
. We first implement all the masked hardware blocks. We then present an adder optimization to reduce the area and latency overheads. Finally, we couple it with a shuffle-based defense.

We quantify that the area-delay overhead of masking ranges from 5.4× to 4.7× depending on the adder topology used and demonstrate a first-order side-channel security of millions of power traces. Additionally, the shuffle countermeasure impedes a straightforward second-order attack on our first-order masked implementation.
Diverse, Neural Trojan Resilient Ecosystem of Neural Network IP
Authors: Olney Brooks , Karam Robert
Published: February 2022

Adversarial machine learning is a prominent research area aimed towards exposing and mitigating security vulnerabilities in AI/ML algorithms and their implementations. Data poisoning and neural Trojans enable an attacker to drastically change the behavior and performance of a
Convolutional Neural Network (CNN)
merely by altering some of the input data during training. Such attacks can be catastrophic in the field, e.g. for self-driving vehicles. In this paper, we propose deploying a CNN as an
ecosystem of variants
, rather than a singular model. The ecosystem is derived from the original trained model, and though every derived model is structurally different, they are all functionally equivalent to the original and each other. We propose two complementary techniques:
stochastic parameter mutation
, where the weights θ of the original are shifted by a small, random amount, and a
delta-update
procedure which functions by XOR’ing all of the parameters with an update file containing the Δ θ values. This technique is effective against transferability of a neural Trojan to the greater ecosystem by amplifying the Trojan’s malicious impact to easily detectable levels; thus, deploying a model as an ecosystem can render the ecosystem more resilient against a neural Trojan attack.
Power-based Attacks on Spatial DNN Accelerators
Authors: Li Ge , Tiwari Mohit , Orshansky Michael
Published: February 2022

With proliferation of DNN-based applications, the confidentiality of DNN model is an important commercial goal. Spatial accelerators, which parallelize matrix/vector operations, are utilized for enhancing energy efficiency of DNN computation. Recently, model extraction attacks on simple accelerators, either with a single processing element or running a binarized network, were demonstrated using the methodology derived from
differential power analysis (DPA)
attack on cryptographic devices. This article investigates the vulnerability of realistic spatial accelerators using general, 8-bit, number representation.

We investigate two systolic array architectures with weight-stationary dataflow: (1) a 3 × 1 array for a dot-product operation and (2) a 3 × 3 array for matrix-vector multiplication. Both are implemented on the SAKURA-G FPGA board. We show that both architectures are ultimately vulnerable. A conventional DPA succeeds fully on the 1D array, requiring 20K power measurements. However, the 2D array exhibits higher security even with 460K traces. We show that this is because the 2D array intrinsically entails multiple MACs simultaneously dependent on the same input. However, we find that a novel template-based DPA with multiple profiling phases is able to fully break the 2D array with only 40K traces. Corresponding countermeasures need to be investigated for spatial DNN accelerators.
NN-Lock
: A Lightweight Authorization to Prevent IP Threats of Deep Learning Models
Authors: Alam Manaar , Saha Sayandeep , Mukhopadhyay Debdeep , Kundu Sandip
Published: February 2022

The prevalent usage and unparalleled recent success of Deep Neural Network (DNN) applications have raised the concern of protecting their Intellectual Property (IP) rights in different business models to prevent the theft of trade secrets. In this article, we propose a lightweight, generic, key-based DNN IP protection methodology,
NN-Lock
, to defend against unauthorized usage of stolen DNN models.
NN-Lock
utilizes SBox, a cryptographic primitive, with good security properties to encrypt each parameter of a trained DNN model with the secret keys derived from a master key through a key-scheduling algorithm. The method ensures that only an authorized user with a correct master key can accurately use the locked DNN model. Evaluation results of
NN-Lock
on a Google Coral edge device for various DNN architectures on several datasets show that for an incorrect master key, the accuracy of a locked model is that of a random classifier. The dense network of encrypted parameters makes the method robust against the
model fine-tuning attack
and a novel
approximation attack
using the Genetic Algorithm, which achieves reasonable success against another recent IP protection scheme called HPNN Chakraborty et al.
2020
. The security evaluation of
NN-Lock
against other families of attacks demonstrates its soundness in practical scenarios.
NN-Lock
does not modify any internal structure of a DNN model, making it scalable for all of the existing DNN implementations without adversely affecting their performance.
Detecting Dynamic Manipulation of the Space Charge Region in Doped Ceria with Scanning Thermo-Ionic Microscopy
Authors: Gerwe Brian S , Nasr Esfahani Ehsan , Li Jiangyu , Adler Stuart B.
Published: February 2022

A new generation of high performance energy storage and conversion devices is instrumental to commercial adoption of renewable, but intermittent, energy sources. Developing new materials for these devices often relies on bulk characterization techniques, such as electrochemical impedance spectroscopy, which are inherently unable to spatially resolve electrochemical phenomena and material properties. Conversely, scanning probe methods (SPM) have long been employed to probe local electrochemistry and material properties; however, relating these measurements to macroscopic performance can be challenging. We aim to bridge this gap by using SPM as a localized in operando witness during macroscopic measurements.

Workers have recently employed Scanning Thermo-Ionic Microscopy (STIM), a strain-based SPM that leverages mechano-thermo-chemical coupled effects to detect local ionic concentrations. Briefly, STIM operates by inducing thermal stresses in the material from a periodic temperature perturbation at the scanning probe tip. The strain related to these thermal stresses drives ionic motion and is detected by deflection of the probe tip at a higher order harmonic of the temperature perturbation. This allows the ionic response to be distinguished from electrostatic and electromechanical responses, which occur at the temperature perturbation frequency.

Previous ex-situ measurements demonstrated the potential of STIM to image grain boundary effects in doped ceria, possibly due to space charge regions. More recently, STIM was implemented point-wise to study manipulation of the space charge region under static polarization. Though promising, intensive hardware demands prevented imaging with STIM, thus ultimately reducing the impact of s-SPM implementation. Herein we demonstrate the use of data science enhanced SPM to overcome hardware limitations and extend our work to in operando, dynamic polarization studies. Furthermore, the effect of adsorbed surface species on STIM response is examined by controlling temperature, gas environment, and relative humidity.
The Coupling between Mechanics and Intercalation Chemistry in Layered Battery Materials
Authors: Radin Maxwell , Vinckeviciute Julija , Kaufman Jonas , Thomas John C. , Van der Ven Anton
Published: February 2022

In addition to a change in lattice parameters, many materials also experience complex structural transformations upon intercalation. For example, many layered lithium and sodium oxides transform between the O3/O1/P3 stacking sequences, and Ni and Mn oxides often undergo transformations associated with collective Jahn-Teller distortions. Many questions remain about how these structural transformations affect battery performance, and in particular, how they affect cycle life limitations arising from fracture and mechanical damage.

Here we present new models to capture the coupling between intercalation chemistry and mechanics arising from stacking-sequence changes and Jahn-Teller distortions. This includes first-principles simulations and mesoscale models to elucidate the thermodynamics and kinetics of stacking sequence changes, as well as analyses of how these transformations might result in irreversible changes to microstructure. Regarding the mechanics of Jahn-Teller distortions, we have developed anharmonic vibrational models to explore the transition from a collective distortion to a dynamic one in layered materials such as lithium nickel and manganese oxides (as well as their sodium analogues). Numerical simulations reveal how the interplay between the shape of the Jahn-Teller energy landscape and rigidity of the crystal impact the local structure and thermodynamic properties of the dynamically distorted phase.
Effect of Electrochemically Induced Fracture and Fatigue on Capacity and Kinetics of LiXMn2O4
Authors: McGrogan Frank P , Bishop Sean R. , Raja Shilpa , Chiang Yet-Ming , Van Vliet Krystyn J.
Published: February 2022

Li-ion battery (LIB) electrodes suffer from gradual performance loss during repeated electrochemical cycling. The battery community has identified a variety of mechanical and chemical degradation routes leading to capacity fade in LIBs, including fracture of the active electrode particles driven by cycling-induced chemomechanical stress. However, the detailed mechanisms by which this fracture due to electrochemical cycling – or electrochemomechanical fatigue – promotes capacity fade and impedance growth are not understood completely even in the most well studied positive electrode systems.

Here, we demonstrate this connection between fracture, capacity, and impedance growth in LiXMn2O4 cathodes via complex cycling schedules designed to produce discrete fracture events. We implement variations in current and potential to control fracture by electrochemical means, confirmed with in situ acoustic emissions monitoring and post-mortem electron microscopy. We use electrochemical impedance spectroscopy and distribution of relaxation times analysis to identify the physical contributions to electrochemical impedance, and find that fracture causes an immediate increase in electronic contact resistance by creating insulating interfaces in the bulk of the electrode. We also observe an onset of severe capacity fade concurrent with electrochemically induced fracture, indicating that fracture plays a role in accelerating dissolution reactions that compromise battery cathode performance. Thus, we identify two distinct degradation mechanisms acting separately on capacity and impedance over two different time scales. The separation of these performance-reducing effects in a well-studied cathode material is critical to understanding the coupling between electrochemical and mechanical degradation in LIBs.
Adaptive and maladaptive roles of lipid droplets in health and disease
Authors: Pressly Jeffrey D. , Gurumani Margaret Z. , Varona Santos Javier T. , Fornoni Alessia , Merscher Sandra , Al-Ali Hassan
Published: February 2022

Advances in the understanding of lipid droplet biology have revealed essential roles for these organelles in mediating proper cellular homeostasis and stress response. Lipid droplets were initially thought to play a passive role in energy storage. However, recent studies demonstrate that they have substantially broader functions, including protection from reactive oxygen species, endoplasmic reticulum stress, and lipotoxicity. Dysregulation of lipid droplet homeostasis is associated with various pathologies spanning neurological, metabolic, cardiovascular, oncological, and renal diseases. This review provides an overview of the current understanding of lipid droplet biology in both health and disease.
Targeting mineralocorticoid receptors in diet-induced hepatic steatosis and insulin resistance
Authors: Habibi Javad , Chen Dongqing , Hulse Jack L. , Whaley-Connell Adam , Sowers James R. , Jia Guanghong
Published: February 2022

Mineralocorticoid receptor (MR) activation plays an important role in hepatic insulin resistance. However, the precise mechanisms by which MR activation promotes hepatic insulin resistance remains unclear. Therefore, we sought to investigate the roles and mechanisms by which MR activation promotes Western diet (WD)-induced hepatic steatosis and insulin resistance. Six-week-old C57BL6J mice were fed either mouse chow or a WD, high in saturated fat and refined carbohydrates, with or without the MR antagonist spironolactone (1 mg/kg/day) for 16 wk. WD feeding resulted in systemic insulin resistance at 8 and 16 wk. WD also induced impaired hepatic insulin metabolic signaling via phosphoinositide 3-kinases/protein kinase B pathways, which was associated with increased hepatic CD36, fatty acid transport proteins, fatty acid-binding protein-1, and hepatic steatosis. Meanwhile, consumption of a WD-induced hepatic mitochondria dysfunction, oxidative stress, and inflammatory responses. These abnormalities occurring in response to WD feeding were blunted with spironolactone treatment. Moreover, spironolactone promoted white adipose tissue browning and hepatic glucose transporter type 4 expression. These data suggest that enhanced hepatic MR signaling mediates diet-induced hepatic steatosis and dysregulation of adipose tissue browning, and subsequent hepatic mitochondria dysfunction, oxidative stress, inflammation, as well as hepatic insulin resistance.
Presumed Glenoid SLAP Tear in an Adolescent Football Player Belied the Presence of a Coracoid Growth Plate Fracture: A Diagnostic Conundrum
Authors: Skedros John G. , Marshall J. Hunter , Cronin John T.
Published: February 2022

We report the case of a 13-year-old male who sustained a right shoulder injury while playing quarterback in an American football game. A noncontrast MRI scan showed evidence of a possible grade 1 SLAP tear (SLAP = superior labral anterior-posterior), and surgery was recommended. However, at the preoperative visit, the surgeon was suspicious that the main source of pain had been missed. Bilateral shoulder radiographs taken for comparative analysis revealed a Salter-Harris type I coracoid growth-plate fracture. Therefore, the surgeon planned to manage the patient nonoperatively and postponed the proposed SLAP tear surgery. The patient was instructed to cease participation in athletic events and undergo physical therapy. Hoping to expedite his healing with platelet or stem cell injections, the parents sought an opinion from another physician who obtained a right shoulder MRI with intra-articular contrast. This confirmed that there was no SLAP tear. We concluded that the patient initially had glenohumeral and coracoclavicular ligament strains and a coracoid growth-plate fracture. The SLAP tear suggested by the initial MRI proved to be a red herring, belying an accurate diagnosis. The patient ultimately recovered fully with physical therapy and without surgery or other interventions.
Distribution Characteristics of ST-Segment Elevation Myocardial Infarction and Non-ST-Segment Elevation Myocardial Infarction Culprit Lesion in Acute Myocardial Infarction Patients Based on Coronary Angiography Diagnosis
Authors: Cao Guanglin , Zhao Zheng , Xu Zesheng
Published: February 2022

This research was aimed at exploring the application value of coronary angiography (CAG) based on a convolutional neural network algorithm in analyzing the distribution characteristics of ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) culprit lesions in acute myocardial infarction (AMI) patients. Methods. Patients with AMI treated in hospital from June 2019 to December 2020 were selected as subjects. According to the results of an echocardiogram, the patients were divided into the STEMI group (44 cases) and the NSTEMI group (36 cases). All patients received CAG. All images were denoised and edge detected by a convolutional neural network algorithm. Then, the number of diseased vessels, the location of diseased vessels, and the degree of stenosis of diseased vessels in the two groups were compared and analyzed. Results. The number of patients with complete occlusion (3 cases vs. 12 cases) and collateral circulation (5 cases vs. 20 cases) in the NSTEMI group was significantly higher than that in the STEMI group, and the difference was statistically significant,

P
<
0.05

. There was a statistically significant difference in the number of lesions between the distal LAD (1 case vs. 10 cases) and the distal LCX (4 cases vs. 11 cases),

P
<
0.05

. There was a statistically significant difference in the number of patients with one lesion branch (1 vs. 18) and three lesion branches (25 vs. 12) between the two groups,

P
<
0.05

. The image quality after the convolution neural network algorithm is significantly improved, and the lesion is more prominent. Conclusion. The convolutional neural network algorithm has good performance in DSA image processing of AMI patients. STEMI and NSTEMI as the starting point of AMI disease analysis to determine the treatment plan have high clinical application value. This work provided reference and basis for the application of the convolutional neural network algorithm and CAG in the analysis of the distribution characteristics of STEMI and NSTEMI culprit lesions in AMI patients.
The Effect of Completion Risk and Project Profitability on the Investment Decisions of the Private Sector in PPP Projects
Authors: Geng Linna , Zheng Hezhong , Sun Yao
Published: February 2022

Public-private partnership (PPP) is becoming increasingly popular around the world for the development of infrastructure. However, it is vital that the private sector knows how to make its investment decisions, especially when it bears the burden of completion risk, and the cash flow of PPP projects is hard to predict. In previous studies, completion risk and project profitability have been recognized as critical factors that influence the involvement of the private sector in PPP projects. This study further investigates how these two factors affect private sector investment decisions, including its involvement, withdrawal, and capital structure decisions. First, a continuous real option method is built to explore the investment boundary and default boundary of the private sector. The results show that an increase in completion risk does not necessarily increase the investment boundary; rather, the relationship between them depends on the degree of private sector risk tolerance. The results also indicate that the investment boundary decreases with the expected rate of return and increases with the tax rate, risk-free rate, and volatility of cash flow. The default boundary decreases with the expected rate of return and volatility of cash flow and increases with the risk-free rate. Second, by comparing two different financial arrangements, the results suggest that using debt capital can help lower the private sector’s investment boundary. Third, the results reveal the optimal debt level of private sector investment in PPP projects by showing that the optimal debt level increases with the tax rate and decreases with the default loss rate. These results can provide some managerial insights for the private sector as it makes decisions on PPP project investments. They can also provide some policy insights for governments to better promote private sector investment in PPP projects.
Application of Artificial Intelligence in Radiotherapy of Nasopharyngeal Carcinoma with Magnetic Resonance Imaging
Authors: Zhao Wanlu , Zhang Desheng , Mao Xinjian
Published: February 2022

The value of automatic organ-at-risk outlining software for radiotherapy is based on artificial intelligence technology in clinical applications. The accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma was investigated. In the automatic segmentation model which is proposed in this paper, after CT scans and manual segmentation by physicians, CT images of 147 nasopharyngeal cancer patients and their corresponding outlined OARs structures were selected and grouped into a training set (115 cases), a validation set (12 cases), and a test set (20 cases) by complete randomization. Adaptive histogram equalization is used to preprocess the CT images. End-to-end training is utilized to improve modeling efficiency and an improved network based on 3D Unet (AUnet) is implemented to introduce organ size as prior knowledge into the convolutional kernel size design to enable the network to adaptively extract features from organs of different sizes, thus improving the performance of the model. The DSC (Dice Similarity Coefficient) coefficients and Hausdorff (HD) distances of automatic and manual segmentation are compared to verify the effectiveness of the AUnet network. The mean DSC and HD of the test set were 0.86 ± 0.02 and 4.0 ± 2.0 mm, respectively. Except for optic nerve and optic cross, there was no statistical difference between AUnet and manual segmentation results (

P

 > 0.05). With the introduction of the adaptive mechanism, AUnet can achieve automatic segmentation of the endangered organs of nasopharyngeal carcinoma based on CT images more accurately, which can substantially improve the efficiency and consistency of segmentation of doctors in clinical applications.
Mechanism and Kinematics for Flapping Wing Micro Air Vehicles Maneuvering Based on Bilateral Wings
Authors: Zhang Rui , Zhang Haiyang , Xu Lichao , Xie Peng , Wu Jing , Wang Chao
Published: February 2022

With the development and application of a bionic flapping micro aircraft vehicle (FMAV), further requirements for its high maneuvering flight characteristics have been put forward. A controlling tail cannot meet the requirements of the maneuvering flight of insect-sized FMAVs, and it has become necessary to find new mechanisms for high maneuvering flight. In this study, two flapping mechanisms with movable hinges based on a double crank rocker mechanism (DCRM) and a single crank slider mechanism (SCSM) are proposed. The instantaneous flapping angles, their extremum and means, and the flapping amplitudes of the two mechanisms are studied for various hinge offsets. A comparison between these two mechanisms indicates that the mechanism based on SCSM is more appropriate in providing adjustable flapping amplitudes for lateral wings. Finally, a dragonfly-like prototype assembled using a carbon fiber plate with adjustable wings is designed and manufactured based on the SCSM. Flight tests are conducted to show that the rack and pinion mechanism can accomplish the movement of hinges neatly, and the proposed flapping mechanism makes left turns and right turns effectively while continuing to fly forward. The prototype is good in terms of crashworthiness and the replacement of components, and it offers an easy and reliable technique for the maneuvering flight of FMAVs.
Coordinated Development of Logistics Development and Low-Carbon Environmental Economy Base on AHP-DEA Model
Authors: Wang Jinlan , Li Huiwen , Guo Huifang
Published: February 2022

With the intensification of global environmental pollution and the overexploitation of resources and energy, low-carbon environmental economy (LCEE) has become the important means for countries in the world to achieve sustainable economic, resource, and environmental development. Therefore, the research on the coordinated development of logistics development and LCEE is very necessary in the development of our country’s LCEE. Firstly, this paper discusses the development level of logistics industry in Hainan Province from five aspects: energy consumption, carbon emission, carbon productivity, economic benefit index, and carbon emission efficiency. Secondly, choose carbon emission intensity to represent the development level of low-carbon economy. Based on the efficiency coefficient method, coefficient of variation method, linear weighting method, spatial description method, and system evolution equation method, the coordination measurement method is adopted. Finally, the coordinated development of logistics development and low-carbon environmental economy in Hainan Province is analyzed. On this basis, this paper puts forward the related promotion strategies for the coordinated development of logistics development and logistics industry in Hainan. The research results show that the system coupling degree in Hainan Province is about 0.9, indicating that the logistics industry and the low-carbon economic subsystem have a relatively high degree of coupling development. Compared with other provinces, Hainan’s level of coupled and coordinated development is at the upstream level.
Quantitative Analysis of COVID‐19 Pandemic Responses Based on an Improved SEIR‐SD Model
Authors: Liu Yang , Liu Bingrui , Deng Yi , Liu Jia
Published: February 2022

In late 2019, the COVID‐19 pandemic began to spread over the world, causing millions of deaths. In the first few months of the pandemic, several countries (such as China) prevented the spread of the pandemic successfully. By contrast, the pandemic in many other countries was not controlled well. For example, India encountered a second serious outbreak of COVID‐19 from April 2021 due to the poor resistance measures implemented by the government. To figure out the effective countermeasures to the pandemic, this research proposes a COVID‐19 pandemic and its response system, which consists of the infection subsystem, the quarantine subsystem, and the medical subsystem. On this basis, an improved SEIR‐SD model is established which is utilized to analyze the response measures to the pandemic quantitatively. This model successfully simulates the actual epidemic scenarios in Wuhan, which verifies its effectiveness. Afterward, the impact of hospital administration rate, quarantine rate, average contact number, and contact infection rate on the cumulative number of infections and deaths are analyzed by simulation. The results show that both the medical and administrative efforts, especially in the early stage of the epidemic, are significant in reducing the number of infections and shortening the epidemic period. In the medical aspect, the more stringent quarantine brings the earlier inflection point of the epidemic; more importantly, improving the treatment rate significantly reduces the scale of the epidemic. In the administrative aspect, enforcing individual protection and strict community closure can effectively cut off the transmission of the virus and curb the spread of the epidemic. Finally, this research proposes several practical suggestions in response to the COVID‐19 pandemic. The main contribution of this research is that the effects of different response measures on the number of new infections daily and the cumulative number of deaths of a country or region in the COVID‐19 pandemic are estimated quantitatively based on modeling and simulation.
Anomaly Detection of System Call Sequence Based on Dynamic Features and Relaxed-SVM
Authors: Liao Xiaoyao , Wang Changzhi , Chen Wen
Published: February 2022

The system call sequences of processes are important for host-based anomaly detection. However, the detection accuracy can be seriously degenerated by the subsequences which simultaneously appeared in the call sequences of both normal and abnormal processes. Furthermore, the detection may be obstructed especially when the normal/abnormal distributions of subsequences are extremely imbalanced along with many ambiguous samples. In the paper, the system call sequences are divided into weighted subsequences with fixed-length. Secondly, a suffix tree of each system call sequence is constructed to automatically extract the variable-length subsequence from the longest repeated substring of the tree. The frequencies of the fixed-and variable-length subsequences that appeared in each system call sequence constitute its feature vector. Finally, vectors are input into a cost-sensitive and relaxed support vector machine, in which the penalty-free slack of the relaxed SVM is split independently between the two classes with different weights. The experimental results on two public datasets ADFA-LD and UNM showed that the AUC of the proposed method can reach 99%, while the false alarm rate is only 2.4%.
Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction
Authors: Bao Yin-Xin , Cao Yang , Shen Qin-Qin , Shi Quan
Published: February 2022

The recent proposed Spatial-Temporal Residual Network (ST-ResNet) model is an effective tool to extract both spatial and temporal characteristics and has been successfully applied to urban traffic status prediction. However, the ST-ResNet model only extracts the local spatial characteristics and ignores the very important global spatial characteristics. In this paper, a novel Global-Local Spatial-Temporal Residual Correlation Network (GL-STRCN) model is proposed for urban traffic status prediction to further improve the prediction accuracy of the existing ST-ResNet model. The GL-STRCN model firstly applies Pearson’s correlation coefficient method to extract high correlation series. Then, considering both global and local spatial properties, two components consisting of 2D convolution and residual operation are used to capture spatial features. After that, based on Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), a novel long-term temporal feature extraction component is proposed to capture temporal features. Finally, the spatial and temporal features are aggregated together in a weighted way for final prediction. Experiments have also been performed using two datasets from TaxiCD and PEMS-BAY. The results indicated that the proposed model produces a better prediction performance compared with the results based on other baseline solutions, e.g., CNN, ST-ResNet, GL-TCN, and DGLSTNet.
Location of Emergency Escape Ramps on Two-Lane Rural Highways Based on the Fuzzy Multicriteria Decision-Making Method
Authors: Heidarian Mohammad , Abdi Kordani Ali , Sarkar Alireza , Hosseinian Seyed Mohsen
Published: February 2022

Today, the lack of practical research is one of the most important concerns worldwide, and location, which is an important issue in most projects, has received less attention from experts. In this study, the location criteria of emergency escape ramps were examined, and according to the Myers formula, the temperature-distance diagram along the route was obtained. Also, the speed of vehicles entering the escape ramp was examined according to the American Association of State Highway and Transportation Officials (AASHTO) in the proposed options that were obtained from the experts. Then, 3 general criteria and 16 subcriteria were classified, and finally, the importance of criteria was examined by the fuzzy analytic hierarchy process (F-AHP), and the high-priority options were illustrated using the fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS). The proposed options were evaluated and prioritized for an emergency escape ramp on the two-lane rural highways of Salavat Abad village pass of Sanandaj city, Kurdistan Province, Iran. The results showed that the technical criteria with more than 69% among the general criteria and subcriteria of the percentage of longitudinal slope, longitudinal slope length, and vehicle design speed were the most important factors influencing the location of an emergency escape ramp. Moreover, among the proposed options, the four options 3, 4, 5, and 6 had the highest score, and option 6 (Kurkur turn (2)) was identified as the most priority location for implementing the emergency escape ramp.
Deploying Machine Learning Techniques for Human Emotion Detection
Authors: Siam Ali I. , Soliman Naglaa F. , Algarni Abeer D. , Abd El-Samie Fathi E. , Sedik Ahmed
Published: February 2022

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.
Joint Scheduling and Power Optimization for Complex Status Updates in Multiantenna UAV-Assisted Multiaccess Edge Computing
Authors: Diao Xianbang , Yang Wendong , Zheng Jianchao , Cai Yueming
Published: February 2022

The status information of complex status update applications (e.g., real-time environmental monitoring in disaster areas) can only be obtained after data processing. Therefore, the impact of data processing on age of information (AoI) cannot be ignored in complex status update applications. In this paper, complex status updates in emergency scenario are investigated, where some multiantenna unmanned aerial vehicles (UAVs) help resource-limited user devices (UDs) complete complex status updates which involve computing processes. To reduce average AoI, UDs’ energy consumptions, and UAVs’ energy consumptions in a balanced manner, the weighted sum of the three metrics is aimed to be minimized. Scheduling strategies (i.e., data transmission and computing start moments, service period, and flight radius) and power (i.e., data and pilot transmit power) allocation strategies are jointly optimized to reduce the weighted sum. The original problem is NP-hard. Thus, it is decoupled into some subproblems. Then, the near-optimal solution or stationary point of each subproblem is obtained by analyzing the characteristics of the subproblems. Finally, an iterative algorithm is proposed to alternately optimize the scheduling and power allocation strategies to obtain the suboptimal solution of the original problem. Simulation results show that the proposed algorithm can effectively reduce the average AoI and the energy consumptions of UDs and UAVs in a balanced way.
Environmental Applications of Sorbents, High-Flux Membranes of Carbon-Based Nanomaterials
Authors: Babu J. Suresh , Prasanna H. Bhavani Naga , Babu J. Satish , Rao Yamarthi Narasimha , Beyan Surafel Mustefa
Published: February 2022

Carbon-based nanomaterials have unique and controllable properties, making it possible to find and treat environmental challenges. There are several environmental applications for carbon-based nanoparticles: sorbents, membranes, antimicrobial agents, and sensors. According to this review, carbon-based nanomaterials have a variety of environmental benefits. This article also looks at prospective uses of nanomaterials in environmental systems, utilizing carbonaceous nanoparticles as a guide for their physical, chemical, and electrical properties.
Virtual Reconstruction of Visually Conveyed Images under Multimedia Intelligent Sensor Network Node Layout
Authors: Yang Qi , Yang Jong Hoon
Published: February 2022

In this paper, multimedia intelligent sensing technology is applied to the virtual reconstruction of images to construct or restore images to the communication media for visual communication. This paper proposes image virtual reconstruction theory based on visual communication research, treats image virtual reconstruction content as open data links and customized domain ontology, establishes an interdisciplinary interactive research framework through the technical means of visual communication, solves the problem of data heterogeneity brought by image virtual reconstruction, and finally establishes a three-dimensional visualization research method and principle of visual communication. The research firstly visual communication cuts into the existing conservation principles and proposes the necessity of image virtual reconstruction from the perspective of visual communication; secondly, the thinking mode of digital technology is different from human thinking mode, and the process of calculation ignores the emotional and spiritual values, but the realization of value rationality must be premised on instrumental rationality. This requires a content judgment and self-examination of the technical dimensional model of image virtual reconstruction on top of comprehensive literature and empirical evidence. In response to the research difficulties such as the constructivity of visual communication, the solution of image virtual reconstruction of visual communication is proposed based on the data collection method and literature characteristics. The process of introducing the tools of computer science into humanity research needs to be placed in a continuous critical theory system due to the uncontrollable and subjective nature of visual content, and finally, based on the construction of information models for image virtual reconstruction, the ontology and semantics of information modeling are thoroughly investigated, and the problems related to them, such as interpretation, wholeness, and interactivity, are analyzed and solved one by one. The transparency of image virtual reconstruction is enhanced through the introduction of interactive metadata, and this theoretical system of virtual restoration is put into practice in the Dunhuang digital display design project.