bluuebunny/crossref_metadata_2025 huggingface dataset:
Risks and benefits of percutaneous coronary intervention in spontaneous coronary artery dissection
Authors: Kotecha Deevia , Garcia-Guimaraes Marcos , Premawardhana Diluka , Pellegrini Dario , Oliver-Williams Clare , Bountziouka Vasiliki , Wood Alice , Natarajan Nalin , Jackson Robert , Chan Nathan , Ziaullah Jan , Rakhit Roby D , Hoole Stephen P , Johnson Tom W , Kadziela Jacek , Ludman Peter , Samani Nilesh J , Maas Angela H E M , van Geuns Robert-Jan , Alfonso Fernando , Adlam David
Published: May 2021

Objective

To investigate percutaneous coronary intervention (PCI) practice in an international cohort of patients with spontaneous coronary artery dissection (SCAD). To explore factors associated with complications and study angiographic and longer term outcomes.

Methods

SCAD patients (n=215, 94% female) who underwent PCI from three national cohort studies were investigated and compared with a matched cohort of conservatively managed SCAD patients (n=221).

Results

SCAD-PCI patients were high risk at presentation with only 8.8% undergoing PCI outside the context of ST-elevation myocardial infarction/cardiac arrest, thrombolysis in myocardial infarction (TIMI) 0/1 flow or proximal dissections. PCI complications occurred in 38.6% (83/215), with 13.0% (28/215) serious complications. PCI-related complications were associated with more extensive dissections (multiple vs single American Heart Association coronary segments, OR 1.9 (95% CI: 1.06–3.39),p=0.030), more proximal dissections (proximal diameter per mm, OR 2.25 (1.38–3.67), p=0.001) and dissections with no contrast penetration of the false lumen (Yip-Saw 2 versus 1, OR 2.89 (1.12–7.43), p=0.028). SCAD-PCI involved long lengths of stent (median 46mm, IQR: 29–61mm). Despite these risks, SCAD-PCI led to angiographic improvements in those with reduced TIMI flow in 84.3% (118/140). Worsening TIMI flow was only seen in 7.0% (15/215) of SCAD-PCI patients. Post-PCI major adverse cardiovascular and cerebrovascular events (MACCE) and left ventricular function outcomes were favourable.

Conclusion

While a conservative approach to revascularisation is favoured, SCAD cases with higher risk presentations may require PCI. SCAD-PCI is associated with longer stent lengths and a higher risk of complications but leads to overall improvements in coronary flow and good medium-term outcomes in patients.
NKTR-255, a novel polymer-conjugated rhIL-15 with potent antitumor efficacy
Authors: Miyazaki Takahiro , Maiti Mekhala , Hennessy Marlene , Chang Thomas , Kuo Peiwen , Addepalli Murali , Obalapur Palakshi , Sheibani Sara , Wilczek Joanna , Pena Rhoneil , Quach Phi , Cetz Janet , Moffett Andrew , Tang Yinyan , Kirk Peter , Huang Jicai , Sheng Dawei , Zhang Ping , Rubas Werner , Madakamutil Loui , Kivimäe Saul , Zalevsky Jonathan
Published: May 2021

Background

NKTR-255 is a novel polyethylene glycol-conjugate of recombinant human interleukin-15 (rhIL-15), which was designed to retain all known receptor binding interactions of the IL-15 molecule. We explored the biologic and pharmacologic differences between endogenous IL-15 receptor α (IL-15Rα)-dependent (NKTR-255 and rhIL-15) and IL-15Rα-independent (precomplexed rhIL-15/IL-15Rα) cytokines.

Methods

In vitro pharmacological properties of rhIL-15, NKTR-255 and precomplex cytokines (rhIL-15/IL-15Rα and rhIL-15 N72D/IL-15Rα Fc) were investigated in receptor binding, signaling and cell function. In vivo pharmacokinetic (PK) and pharmacodynamic profile of the cytokines were evaluated in normal mice. Finally, immunomodulatory effect and antitumor activity were assessed in a Daudi lymphoma model.

Results

NKTR-255 and rhIL-15 exhibited similar in vitro properties in receptor affinity, signaling and leukocyte degranulation, which collectively differed from precomplexed cytokines. Notably, NKTR-255 and rhIL-15 stimulated greater granzyme B secretion in human peripheral blood mononuclear cells versus precomplexed cytokines. In vivo, NKTR-255 exhibited a PK profile with reduced clearance and a longer half-life relative to rhIL-15 and demonstrated prolonged IL-15R engagement in lymphocytes compared with only transient engagement observed for rhIL-15 and precomplexed rhIL-15 N72D/IL-15Rα Fc. As a consequent, NKTR-255 provided a durable and sustained proliferation and activation of natural killer (NK) and CD8+T cells. Importantly, NKTR-255 is more effective than the precomplexed cytokine at inducing functionally competent, cytotoxic NK cells in the tumor microenvironment and the properties of NKTR-255 translated into superior antitumor activity in a B-cell lymphoma model versus the precomplexed cytokine.

Conclusions

Our results show that the novel immunotherapeutic, NKTR-255, retains the full spectrum of IL-15 biology, but with improved PK properties, over rhIL-15. These findings support the ongoing phase 1 first-in-human trial (NCT04136756) of NKTR-255 in participants with relapsed or refractory hematologic malignancies, potentially advancing rhIL-15-based immunotherapies for the treatment of cancer.
FOXP3-based immune risk model for recurrence prediction in small-cell lung cancer at stages I–III
Authors: Jiang Minlin , Wu Chunyan , Zhang Liping , Sun Chenglong , Wang Hao , Xu Yi , Sun Hui , Zhu Jun , Zhao Wencheng , Fang Qiyu , Yu Jia , Chen Peixin , Wu Shengyu , Zheng Zixuan , He Yayi , Zhou Caicun
Published: May 2021

Background

Immunotherapies may prolong the survival of patients with small-cell lung cancer (SCLC) to some extent. The role of forkhead box protein P3 (FOXP3) in tumor microenvironment (TME) remains controversial. We aimed to examine FOXP3-related expression characteristics and prognostic values and to develop a clinically relevant predictive system for SCLC.

Methods

We enrolled 102 patients with histologically confirmed SCLC at stages I–III. Through immunohistochemistry, we determined the expression pattern of FOXP3 and its association with other immune biomarkers. By machine learning and statistical analysis, we constructed effective immune risk score models. Furthermore, we examined FOXP3-related enrichment pathways and TME traits in distinct cohorts.

Results

In SCLC, FOXP3 level was significantly associated with status of programmed death-ligand 1 (PD-L1), programmed cell death protein 1 (PD-1), CD4, CD8, and CD3 (p=0.002, p=0.001, p=0.002, p=0.030, and p<0.001). High FOXP3 expression showed longer relapse-free survival (RFS) than the low-level group (41.200 months, 95% CI 26.937 to 55.463, vs 14.000 months, 95% CI 8.133 to 19.867; p=0.008). For tumor-infiltrating lymphocytes (TILs), subgroup analysis demonstrated FOXP3 and PD-1, PD-L1, lymphocyte activation gene-3, CD3, CD4, or CD8 double positive were significantly correlated with longer RFS. We further performed importance evaluation for immune biomarkers, constructed an immune risk score incorporating the top three important biomarkers, FOXP3, TIL PD-L1, and CD8, and found their independently prognostic role to predict SCLC relapse. Better predictive performance was achieved in this immune risk model compared with single-indicator-based or two-indicator-based prediction systems (area under the curve 0.715 vs 0.312–0.711). Then, relapse prediction system integrating clinical staging and immune risk score was established, which performed well in different cohorts. High FOXP3-related genes were enriched in several immune-related pathways, and the close relationships of interleukin-2,CD28, basic excision repair genesMUTYH,POLD1,POLD2, and oxidative phosphorylation related gene cytochrome c oxidase subunit 8A with FOXP3 expression were revealed. Moreover, we found low-immune risk score group had statistically higher activated CD4+memory T cells (p=0.014) and plasma cells (p=0.049) than the high-risk group. The heterogeneity of tumor-infiltrating immune cells might represent a promising feature for risk prediction in SCLC.

Conclusion

FOXP3 interacts closely with immune biomarkers on tumor-infiltrating cells in TME. This study highlighted the crucial prognostic value and promising clinical applications of FOXP3 in SCLC.
Leveraging physician leadership in healthtech startups in India
Authors: Raj Ankit
Published: May 2021

Background

India has seen a rapid surge in health tech startups that have disrupted many aspects of the healthcare industry. While positive trends have emerged in online pharmacy, telemedicine, and medical education; we are still to see these health tech startups create similar dividends in other aspects of healthcare especially in direct care and treatment. While startups have grown leaps and bounds in multiple industries, health tech startups have not grown to the same extent. There are many existing factors for this supposedly lackluster performance. The author discusses one of these factors contributing to the slow growth of health tech startups in India.

Conclusion

The author highlights the lack of intent from the top leadership and executive bodies of these startups to involve physicians at all levels of decision making. A lack of insights from professionals directly involved with the functioning of healthcare and lack of inexperience from outsiders managing these startups may have contributed to this slow growth of health tech startups in India. The author further discusses possible solutions and key factors that can help contribute to better engagement of physicians in decision-making tables of the health tech startups.
Right atrium and cryptogenic ischaemic stroke in the young: a case–control study
Authors: Pöyhönen Pauli , Kuusisto Jouni , Pirinen Jani , Räty Heli , Lehmonen Lauri , Paakkanen Riitta , Martinez-Majander Nicolas , Gerdts Eva , Putaala Jukka , Sinisalo Juha , Järvinen Vesa
Published: May 2021

Background

Recent studies suggest left atrial (LA) dysfunction in cryptogenic stroke. We studied the dynamics of right atrium (RA) and right atrial appendage (RAA) in young adults with cryptogenic stroke. We hypothesised that bi-atrial dysfunction and blood stagnation might contribute to thrombosis formation in patients with patent foramen ovale (PFO), as deep venous thrombosis is detected only in the minority of patients.

Methods

Thirty patients (aged 18–49) with a first-ever cryptogenic stroke and 30 age-matched and sex-matched stroke-free controls underwent cardiac magnetic resonance (CMR) imaging. An approach to estimate the RAA volume was developed, using crista terminalis and pectinate muscles as anatomical landmarks. Atrial expansion indices were calculated as (maximal volume – minimal volume) ×100%/minimal volume. Total pulmonary to systemic blood flow ratio (Qp/Qs) was based on phase contrast CMR. Right-to-left shunt (RLS) was evaluated with transoesophageal echocardiography in 29 patients and transcranial Doppler in 30 controls, moderate-to-severe RLS considered as clinically significant.

Results

We found that RA and RAA volumes were similar between patients and controls. Also, RA expansion index was similar, but RAA (95.6%±21.6% vs 108.7%±25.8%, p=0.026) and LA (126.2%±28% vs 144.9%±36.3%, p=0.023) expansion indices were lower in patients compared with controls. Seven (24%) of 29 patients had an RLS compared with 1 (3%) of 30 controls (p=0.012). Among 59 study subjects, RLS was associated with lower RA (81.9%±15.9% vs 98.5%±29.5%, p=0.030), RAA (84.7%±18% vs 105.6%±24.1%, p=0.022), LA (109.8%±18.6% vs 140.1%±33.7%, p=0.017) and LAA (median 102.9% (IQR 65.6%–121.7%) vs 229.1% (151.8%–337.5%], p=0.002) expansion indices and lower Qp/Qs ratio (0.91±0.06 vs 0.98±0.07, p=0.027).

Conclusions

This study suggests bi-atrial dysfunction in young adults with cryptogenic stroke, associated with moderate-to-severe RLS. Dysfunction of the atria and atrial appendages may be an additional mechanism for PFO-related stroke.

Trial registration number

NCT01934725.
Soil-water retention behaviour of fine/coarse soil mixture with varying coarse grain contents and fine soil dry densities
Authors: Su Yu , Cui Yu-Jun , Dupla Jean-Claude , Canou Jean
Published: May 2021

The interlayer soil identified in conventional French rail track corresponds to a mixture of fine soil and coarse grains. To investigate the role of fines in the soil-water retention property of such a mixture, different coarse grain contents fv and dry densities of fine soil ρd–f were considered. The filter paper method was applied to measure matric suction. Mercury intrusion porosimetry tests were performed to observe the microstructure of the fine soil. In terms of the gravimetric water content of fine soil wf with matric suction ψ, the soil-water retention curve (SWRC) was significantly affected by ρd–f for ψ < 715 kPa, while independent of ρd–f for ψ > 715 kPa. Interestingly, this threshold ψ corresponded to a delimiting pore diameter of the bi-modal microstructure of fine soil, which separated micropores from macropores. In terms of degree of saturation Sr with ψ, the SWRC was significantly affected by ρd–f in the full suction range, while it was independent of fv. These findings help to better understand the results for samples where dry density of mixture ρd is kept constant: an increase in fv resulted in a decrease in ρd–f and the suction changed accordingly. In this case, both fv and ψ affected the mechanical behavior.
Preliminary observations on the flower visitors ofNasa colanii(Loasaceae), a poorly known species endemic to northern Peru
Authors: Wong Sato Akira A. , Villanueva-Espinoza Rosa , Revilla Italo , Fernandez-Hilario Robin
Published: May 2021

Nasa is the most diverse genus among the family Loasaceae and it is mainly distributed in Peru. Most species of this genus are microendemics, with distributions restricted to very specific locations; such is the case of Nasa colanii Dostert & Weigend. This species — previously only known from the type collected in 1978 from the Amazonas region of Peru — lacks information about its biology and ecology. To improve our understanding of the reproductive biology and pollination ecology of N. colanii, we observed flower development and insect floral visits in a cloud forest in the Amazonas region. The flowers of N. colanii are nectariferous and protandrous. The typical stamen movement to the flower center of the subfamily Loasoideae appears to be autonomous. Only well-developed (elongated) pistils showed positive stigmatic receptivity to 3% hydrogen peroxide. Syrphid flies (Baccha sp.) and small beetles (Baridinae spp. and Alticini sp.) were the only observed flower visitors. The syrphid flies visited the flower and foraged for pollen, whereas the small beetles were found already in the flowers and seemingly used it as a shelter. Results suggest that N. colanii might be adapted to fly pollination.
Evaluation of otolith microstructures for ageing of larval white sturgeon
Authors: Frommel Andrea Y. , Pourfaraj Vahab , Brauner Colin J. , McAdam D. Steven O.
Published: May 2021

Sturgeon populations are endangered worldwide, mainly due to habitat degradation and overexploitation causing recruitment failure. Understanding of early development, survival and growth in sturgeon is limited by a lack of a validated method to directly estimate larval age. In a laboratory calibration study, we reared white sturgeon (Acipenser transmontanus) larvae from hatch for 3 weeks at 12 and 16 °C and two feeding regimes (fed and unfed) to determine the usefulness of their vateritic microotoconia for larval ageing and the influence of environmental factors on ring structure and size. By marking the otoliths twice at known ages with Alizarin Red S, we were able to confirm the presence of daily rings in the largest microotoconia and the feasibility of ageing larval sturgeon using otoliths. Three observers blind to age and treatment assessed larval age from daily rings with an overall precision of 67%–82% and 30%–70% accuracy, dependent on larval age. Neither temperature nor feeding had a significant effect on ring width or readability. Thus, ageing sturgeon larvae using otolith microstructures is a promising tool for sturgeon conservation efforts.
Pedal equation and Kepler kinematics
Authors: Nathan Joseph Amal
Published: May 2021

Kepler’s laws are an appropriate topic to highlight the significance of the pedal equation in physics. There are several articles that obtain Kepler’s laws through conservation and gravitation laws. This can be shown more easily and ingeniously if one uses the pedal equation of an ellipse. In fact, the complete kinematics of a particle in an attractive central force field can be derived from one single pedal form. Though many articles use the pedal equation, the classical procedure (without proof) for obtaining the pedal equation is mentioned only in a few because the classical derivations can sometimes be lengthier and are not simple. In this paper, using elementary physics, we derive the pedal equation for all conic sections in a unique, short, and pedagogical way. Later, from the dynamics of a particle in the attractive central force field, we deduce the single pedal form, which elegantly describes all the possible trajectories. Also, for the purpose of completeness, we derive Kepler’s laws.
On nuclear coalescence in small interacting systems
Authors: Kachelrieß M. , Ostapchenko S. , Tjemsland J.
Published: May 2021

The formation of light nuclei can be described as the coalescence of clusters of nucleons into nuclei. In the case of small interacting systems, such as dark matter and $$e^+e^-$$

e
+

e
-

annihilations or pp collisions, the coalescence condition is often imposed only in momentum space and hence the size of the interaction region is neglected. On the other hand, in most coalescence models used for heavy ion collisions, the coalescence probability is controlled mainly by the size of the interaction region, while two-nucleon momentum correlations are either neglected or treated as collective flow. Recent experimental data from pp collisions at LHC have been interpreted as evidence for such collective behaviour, even in small interacting systems. We argue that these data are naturally explained in the framework of conventional QCD inspired event generators when both two-nucleon momentum correlations and the size of the hadronic emission volume are taken into account. To include both effects, we employ a per-event coalescence model based on the Wigner function representation of the produced nuclei states. This model reproduces well the source size for baryon emission and the coalescence factor $$B_2$$

B
2

measured recently by the ALICE collaboration in pp collisions.
No-go theorem for inflation in Ricci-inverse gravity
Authors: Do Tuan Q.
Published: May 2021

In this paper, we study the so-called Ricci-inverse gravity, which is a very novel type of fourth-order gravity proposed recently. In particular, we are able to figure out both isotropically and anisotropically inflating universes to this model. More interestingly, these solutions are shown to be free from a singularity problem. However, stability analysis based on the dynamical system method shows that both isotropic and anisotropic inflation of this model turn out to be unstable against field perturbations. This result implies a no-go theorem for both isotropic and anisotropic inflation in the Ricci-inverse gravity.
No light at the end of the tunnel
Authors: Reiss H. R.
Published: May 2021

The tunneling model for laser-induced processes implies the replacement of the propagating field of a laser by an oscillatory electric field. The view of the electric field as the primary influence in charged particle interactions fails for laser processes where the propagation property is important. Electric fields lack several quintessential laser-field properties that become dominant at high intensities and/or low frequencies. Quantum tunneling is not a concept generally suited to laser light. Conversely, laser criteria do not apply to electric-field phenomena like Sauter–Schwinger pair production in the vacuum, contrary to a widespread assumption.

Graphic abstract
An Effective Method of Evaluating Pension Service Quality Using Multi-Dimension Attention Convolutional Neural Networks
Authors: Li Chunshan , Wang Yuanyuan , Li Dongmei , Chu Dianhui , Ma Mingxiao
Published: May 2021

How to make an accurate evaluation of the quality of pension service has become the most important task. However, in the real world, many customs always forget to rate pension service. They only leave a few short, less semantic, and discontinuous review words below the service. This paper will propose an effective multi-dimension attention convolutional neural networks (MACNNs) model to analyze customer review texts and predict the pension service quality. In MACNN, the emoticon feature, sentiment feature, and word feature can be extracted together to construct feature space. And then attention layer and convolution layer work together to predict the service quality. Compared with the traditional machine learning methods and neural network methods, this method is more objective and accurate to reflect consumers’ real evaluation of pension service.
Using API Call Sequences for IoT Malware Classification Based on Convolutional Neural Networks
Authors: Lin Qianguang , Li Ni , Qi Qi , Hu Jiabin
Published: May 2021

Internet of Things (IoT) devices built on different processor architectures have increasingly become targets of adversarial attacks. In this paper, we propose an algorithm for the malware classification problem of the IoT domain to deal with the increasingly severe IoT security threats. Application executions are represented by sequences of consecutive API calls. The time series of data is analyzed and filtered based on the improved information gains. It performs more effectively than chi-square statistics, in reducing the sequence lengths of input data meanwhile keeping the important information, according to the experimental results. We use a multi-layer convolutional neural network to classify various types of malwares, which is suitable for processing time series data. When the convolution window slides down the time sequence, it can obtain higher-level positions by collecting different sequence features, thereby understanding the characteristics of the corresponding sequence position. By comparing the iterative efficiency of different optimization algorithms in the model, we select an algorithm that can approximate the optimal solution to a small number of iterations to speed up the convergence of the model training. The experimental results from real world IoT malware sample show that the classification accuracy of this approach can reach more than 98%. Overall, our method has demonstrated practical suitability for IoT malware classification with high accuracies and low computational overheads by undergoing a comprehensive evaluation.
New results on nuclear magicity and possible extension of the nuclear landscape
Authors: Ismail M. , Ellithi A. Y. , Khaled Alaa , Anwer Hisham
Published: May 2021

In nuclear theory, there is always a quest for possible extensions of the nuclear landscape and extending our knowledge to the limits of nuclear existence. In this study, we examine the stability and structural properties of a wide range of nuclei in super- and ultra-heavy region in a phenomenological semi-microscopic approach. we calculated the shell correlation energy, residual pairing correction energy, two-nucleon separation energy and two-nucleon energy gap for 3670 even–even nuclei along [Formula: see text]-stability line and two-neutron driplines in the ranges [Formula: see text] with [Formula: see text] and [Formula: see text] with [Formula: see text], respectively. To assure reliability and confidence of the new results in the ultra-heavy region, we extended the search space to include heavy and super-heavy nuclei. We report 83 double magic nuclei and address the predominance of proton and neutron magic numbers. Our calculations reproduced known results on nuclear magicity and present strong evidences on islands of stability and magic numbers in super- and ultra-heavy regions. We also address shifts in nuclear magicity along the nuclear landscape close to the [Formula: see text]-stability line and close to the neutron rich regions.
QUANTUM TRANSPORT AND THERMOELECTRICITY IN COMPLEX MAGNETIC GRAPHENE STRUCTURES
Authors: RODRÍGUEZ-GONZÁLEZ R. , RODRÍGUEZ-VARGAS I. , GAGGERO-SAGER L. M.
Published: May 2021

Magnetic field effects have been instrumental to unveil several exotic phenomena in two-dimensional (2D) materials. Here, we show that graphene exhibits self-similar transport once the material is nanostructured with a magnetic field in complex fashion. In specific, when magnetic barriers are arranged according to the Cantor set rules. In this study, the charge carriers are described as quantum relativistic particles through an effective low-energy Hamiltonian. The transmission, transport and thermoelectric properties are computed with the transfer matrix method, the Landauer–Büttiker formalism and the Cutler–Mott formula, respectively. Self-similarity is reflected in the conductance spectra and Seebeck coefficient for different structural parameters such as generation number, the intensity of the magnetic field, the height of the barrier and the total length of the system. Moreover, well-defined scaling rules, which described fairly good the scalability between self-similar patterns, are obtained. We also compare the self-similar patterns of magnetic complex structures with the corresponding ones to magnetic-electric complex structures, finding better scalability for the former. It is worth mentioning that as far as we have corroborated the breaking symmetry associated to the magnetic field is paramount for the self-similar transport. So, magnetic complex structures constitute an excellent option to corroborate the peculiar phenomenon of self-similar transport.
Fuzzy Graph Cellular Automaton and Its Applications in Parking Recommendations
Authors: Praba B. , Saranya R.
Published: May 2021

The scope of this paper is to make the best use of cellular automaton. It is important that they can simulate not just a discrete model but also used to solve practical problems. To stimulate the research in this field, we define Fuzzy Graph Cellular Automaton (FGCA) and classify the fuzzy rule matrix according to the rules of the cellular automaton. We also provide the details of the generations of FGCA. To cover the defined concept, the parking recommendations have been figured out to show the effective performance of the research. In this proposed model, the fuzzy neighbourhood function represents the possible cell to which the vehicle can moved so that an efficient parking management can be maintained. By using fuzzy graph cellular automaton in parking recommendations, the results are more accurate than the other models. A comparative analysis is also done. In parking recommendations, the possibility of the available parking space can be predicted appropriately using the defined concepts. The results are simulated with [Formula: see text] coding in MATlab.
Complex Fuzzy Krasner Hyperrings
Authors: Al Tahan M. , Davvaz B.
Published: May 2021

There exist different kinds of hyperrings. If the addition [Formula: see text] is a hyperoperation and the multiplication ⋅ is an ordinary operation, then we can consider an additive hyperring. A special case of this type is the Krasner hyperring. In this paper, we apply the concept of complex fuzzy sets to Krasner hyperrings, define their complex fuzzy hyperideals and identify their properties.
Mid-Infrared Detector Array Technologies for SOFIA and Sub-Orbital Observatory Instruments
Authors: Pipher Judith L. , McMurtry Craig W. , Cabrera Mario S. , Forrest William J.
Published: May 2021

The status of various photovoltaic, photoconductive, BIB/IBC, superlattice, TES and KID technologies to produce arrays sensitive from 15 to [Formula: see text]m (Mid-infrared) will be reviewed to assess where reasonable investments should be made for SOFIA and other sub-orbital observatory instruments. These include HgCdTe, Si:As IBC, Si:Sb BIB, Ge:Ga, type 2 superlattice arrays of both III–V and II–VI materials and both TES and KID arrays. KID technologies for this wavelength region show promise, although to date there are no published experimental data on KID arrays for the mid-infrared.
A Survey on Healthcare Data: A Security Perspective
Authors: Singh A. K. , Anand A. , Lv Z. , Ko H. , Mohan A.
Published: May 2021

With the remarkable development of internet technologies, the popularity of smart healthcare has regularly come to the fore. Smart healthcare uses advanced technologies to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. Unfortunately, medical data security is a serious issue in the smart healthcare systems. It becomes a fundamental challenge that requires the development of efficient innovative strategies towards fulfilling the healthcare needs and supporting secure healthcare transfer and delivery. This article provides a comprehensive survey on state-of-the-art techniques for health data security and their new trends for solving challenges in real-world applications. We survey the various notable cryptography, biometrics, watermarking, and blockchain-based security techniques for healthcare applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their objective, methodology, type of medical data, important features, and limitations. At the end, we discuss the open issues and research directions to explore the promising areas for future research.
A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis
Authors: Mohammed Mazin Abed , Elhoseny Mohamed , Abdulkareem Karrar Hameed , Mostafa Salama A. , Maashi Mashael S.
Published: May 2021

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Authors: Jia Xiaowei , Willard Jared , Karpatne Anuj , Read Jordan S. , Zwart Jacob A. , Steinbach Michael , Kumar Vipin
Published: May 2021

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions.
General Lake Model (GLM)
is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a
physics-guided recurrent neural network model (PGRNN)
that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.
X-ray Excited Luminescence from Cr Doped YAG Crystals
Authors: Katsumata Toru , Ogawa Misa , Takahagi Kotomi , Isobe Ayame , Aizawa Hiroaki
Published: May 2021

X-ray excited visible light luminescence were evaluated from Cr doped Y3Al5O12 (YAG) crystals using an X-ray powder diffractometer adapting a fiber spectrometer, a digital camera, and a LED illumination. Red colored luminescence peaking at λ = 678, 690, 708, and 726 nm were observed from Cr doped YAG under irradiation by both CuKα X-ray and a violet LED (λ = 405 nm). Peak wavelengths of X-ray excited luminescence are the same as those of photoluminescence (PL). The peak intensities of X-ray excited luminescence, which increases linearly with X-ray strength, increases up to Cr concentration at around 0.5 mol%, then decreases gradually with Cr concentration due to a concentration quenching. It was found that the concentration quenching of X-ray excited luminescence occurs at a lower concentration at around 0.5 mol% than that of PL at around 3.5 mol%.
A Low Program Voltage Enabled Flash like AlGaN/GaN Stack Layered MIS-HEMTs Using Trap Assisted Technique
Authors: Mazumder Soumen , Pal Parthasarathi , Tsai Ting-Jia , Lin Pu-Chou , Wang Yeong-Her
Published: May 2021

In this paper, a flash like Al2O3/SiO2stacked layer AlGaN/GaN-based metal insulator semiconductor high electron mobility transistor (MIS-HEMT) was fabricated using trap assisted technique. The MIS-HEMT showed a large positive shifting of threshold voltage (∆VTH) of 4.6 V after applying a low program voltage (VP) of 3 V, resulting in a very low threshold voltage of −0.3 V with a decent maximum drain current (IDMAX) of 575 mA mm−1. A ultraviolet-ozone (UV/O3) surface treatment was done prior to gate dielectric deposition to produce a thin gallium oxynitride (GaOXNY) layer at GaN/oxide interface, which correspondingly acts as a charge trapping layer, resulting in the reduction in VP. The capacitance-voltage (C–V) measurements revealed that the traps contributing to the significant positive shifting of VTHhad a density of 5.7 × 1012cm−2. These traps were attributed to the border or oxide defects. A significant reduction in gate leakage current (IG) of more than three orders of magnitude was found in MIS-HEMT, due to the high quality Al2O3/SiO2stack gate dielectric layer compared to conventional HEMT. The flash like stack layered programmed MIS-HEMT exhibited a GMMAXof 123 mS mm−1, on-off ratio of 1.7 × 107, subthreshold slope of 121 mV dec−1with a reduced gate leakage current of 7.5 × 10−9A mm−1.
Impact of presymptomatic COVID-19 on vascular and skeletal muscle function: a case study
Authors: Trinity Joel D. , Craig Jesse C. , Fermoyle Caitlin C. , McKenzie Alec I. , Lewis Matthew T. , Park Soung Hun , Rondina Matthew T. , Richardson Russell S.
Published: May 2021

This unique case study details the impact of SARS-CoV-2 infection on vascular and skeletal muscle function in a young predominantly presymptomatic woman. Prior to COVID-19 diagnosis, substantial reductions in vascular, skeletal muscle, and mitochondrial function were observed along with an elevation in IL-10. This integrative case study indicates that the presymptomatic impact of COVID-19 is widespread and may help elucidate the acute and long-term sequelae of this disease.
Social Network Big Data Hierarchical High‐Quality Node Mining
Authors: Jia Dongning , Yin Bo , Huang Xianqing
Published: May 2021

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self‐organizing self‐learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e‐commerce platform, and cultivating their own customer base.
Redefined “Maclaurin Symmetric Mean Aggregation Operators Based on Cubic Pythagorean Linguistic Fuzzy Numbers”
Authors: Naeem Muhammad , Ashraf Shahzaib , Abdullah Saleem , AL‐Harbi F. M.
Published: May 2021

In this study, we highlight the errors in Sections 2.3, 2.4, 3, and 4 in the article by Fahmi et al. (J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02272-9) by counter definitions and theorems. We find that the definition of cubic Pythagorean fuzzy set (CPFS) (Definition 2.3.1) and operational laws (Definition 2.3.2) violates the rules to consider the membership and nonmembership functions, and then, we redefined the corrected definition and their operations for CPFS. Furthermore, we redefine the concept of cubic Pythagorean linguistic fuzzy set (CPLFS) and their basic operational laws. In addition, we find that Sections 3 and 4 (consist of a list of Maclaurin symmetric mean (MSM) and dual MSM aggregation operators) are invalid, and then, we redefined the list of updated MSM and dual MSM aggregation operators in correct way. Finally, we established the numerical application of the proposed improved algorithm using cubic Pythagorean linguistic fuzzy information to show the applicability and effectiveness of the proposed technique.
Energy Audit and Feasibility of Solar PV Energy System: Case of a Commercial Building
Authors: Chisale Sylvester William , Mangani Patrick
Published: May 2021

Energy situation in Malawi is continuing facing critical challenges to satisfy the existing demand. However, energy consumption and energy conservation studies have been neglected to help overcome this problem. In this paper, electric energy audit was conducted for a commercial building in Mchinji, Malawi, in order to identify energy-saving opportunities. The study employed a mixed method research which involved a series of surveys, observation, data collection, and analysis. The current energy consumption was determined and compared with the proposed energy consumption after replacing some equipment. The proposed system saved up to 33.46% of energy. The study also suggested behavior change towards energy saving. Additionally, an alternative energy system was also suggested. Thus, the HOMER software was employed to design, optimize, and analyze a solar-battery-grid-connected energy system. The proposed system has a simple payback period of 9.8 years. The system’s cost of energy was estimated as 0.0372 $/kWh, and the capital cost was $ 150,887.
An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection
Authors: Wang Xin , Niu Xinzheng , Zhu Jiahui , Liu Zuoyan
Published: May 2021

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.
Quantum Clone Elite Genetic Algorithm‐Based Evaluation Mechanism for Maximizing Network Efficiency in Soil Moisture Wireless Sensor Networks
Authors: Xiao Jing , Liu Yang , Zhou Jie
Published: May 2021

In agriculture, soil moisture wireless sensor networks (SMWSNs) are used to monitor the growth of crops for obtaining higher yields. The purpose of this paper is to improve the network efficiency of SMWSNs. Therefore, we propose a novel network efficiency evaluation mechanism which is suitable for soil moisture sensors and design a sensor target allocation model (STAM) for the actual agricultural situation. After that, a quantum clone elite genetic algorithm (QCEGA) is proposed; then, QCEGA is applied to optimize the STAM for obtaining optimal results. QCEGA uses the parallel mechanism of quantum computing to encode individuals, integrates the quantum revolving gate in quantum computing and the concept of cloning in biology to avoid the algorithm from falling into local optimum, and applies the elite strategy to speed up the convergence of the algorithm. Subsequently, the proposed algorithm is compared with simulated annealing (SA) and particle swarm optimization (PSO). Under the novel network efficiency evaluation mechanism, the simulation results demonstrate that the network efficiency based on QCEGA is higher than that of SA and PSO; what is more, QCEGA has better convergence performance. In comparison with traditional wireless sensor network efficiency evaluation approaches, our methods are more in line with the development of modern agriculture and can effectively improve the efficiency of SMWSNs, thus ensuring that crops can have a better growth condition.
An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification
Authors: Reshi Aijaz Ahmad , Rustam Furqan , Mehmood Arif , Alhossan Abdulaziz , Alrabiah Ziyad , Ahmad Ajaz , Alsuwailem Hessa , Choi Gyu Sang
Published: May 2021

Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID‐19 based on the chest X‐ray image classification. Due to the nonavailability of sufficient‐size and good‐quality chest X‐ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very‐small‐sized and imbalanced dataset with image‐quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X‐ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID‐19 X‐ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.
A Folded Concave Penalty Regularized Subspace Clustering Method to Integrate Affinity and Clustering
Authors: Zhang Wenjuan , Feng Xiangchu , Xiao Feng , Chen Yunmei
Published: May 2021

Most sparse or low-rank-based subspace clustering methods divide the processes of getting the affinity matrix and the final clustering result into two independent steps. We propose to integrate the affinity matrix and the data labels into a minimization model. Thus, they can interact and promote each other and finally improve clustering performance. Furthermore, the block diagonal structure of the representation matrix is most preferred for subspace clustering. We define a folded concave penalty (FCP) based norm to approximate rank function and apply it to the combination of label matrix and representation vector. This FCP-based regularization term can enforce the block diagonal structure of the representation matrix effectively. We minimize the difference of l1 norm and l2 norm of the label vector to make it have only one nonzero element since one data only belong to one subspace. The index of that nonzero element is associated with the subspace from which the data come and can be determined by a variant of graph Laplacian regularization. We conduct experiments on several popular datasets. The results show our method has better clustering results than several state-of-the-art methods.