bluuebunny/crossref_metadata_2025 huggingface dataset:
Need for improved diabetes support among people with psychiatric disorders and diabetes treated in psychiatric outpatient clinics: results from a Danish cross-sectional study
Authors: Knudsen Lenette , Hansen Dorte Lindqvist , Joensen Lene Eide , Wibaek Rasmus , Benros Michael Eriksen , Jørgensen Marit Eika , Andersen Gregers Stig
Published: January 2022

Introduction

People with psychiatric disorders have increased risk of premature death partly due to diabetes. This study aims to explore the quality of diabetes care, diabetes management, diabetes support and well-being of people with psychiatric disorders and diabetes.

Research design and methods

A total of 107 participants aged ≥18 years with diabetes and psychiatric disorders treated at psychiatric outpatient clinics in Denmark were recruited from August 2018 to June 2019. This descriptive cross-sectional study includes data from medical records on quality of diabetes care (eg, level and annual examination of hemoglobin A1c (HbA1c)) and questionnaires on diabetes management (measured on items from the Summary of Diabetes Self-Care Activities Scale and diabetes distress based on Problem Areas in Diabetes Scale (PAID-5)), diabetes support (no, some or high support from eight potential support persons and experience of care actions measured on items from Patient Assessment of Chronic Illness Care) and well-being (WHO 5-Item Scale and self-rated general health).

Results

The mean age was 52 years, 56% were men, the mean body mass index was 31.9 kg/m2, the median HbA1cwas 53 mmol/mol (7.0%) and the mean blood pressure was 131/83 mm Hg. The proportion with annual measurements of HbA1cwas 93%, blood pressure 80%, cholesterol 93%, foot examination 77% and eye examination 75%. Fifty-one per cent had high diabetes distress (PAID-5 score ≥8). Diabetologists and general practitioners (39% and 37%) were the health professionals most frequently reported to provide high diabetes support.

Conclusions

This study highlights a need for improved diabetes support in people with psychiatric disorders and diabetes. Although a high proportion received appropriate diabetes care, we found high levels of diabetes distress, moderate levels of optimal self-management behaviors, low well-being and low diabetes support from psychiatric health professionals, while one-third of the population found it relevant to receive diabetes support from psychiatric health professionals.
Impact of COVID-19 pandemic lockdown on movement behaviours of children and adolescents: a systematic review
Authors: Kharel Madhu , Sakamoto Jennifer Lisa , Carandang Rogie Royce , Ulambayar Shinejil , Shibanuma Akira , Yarotskaya Ekaterina , Basargina Milana , Jimba Masamine
Published: January 2022

Introduction

Several studies have examined how the lockdown restrictions enforced to halt the spread of COVID-19 have affected children and adolescents’ movement behaviours, but there is a need to synthesise these findings. Therefore, we conducted this systematic review to examine the impact of COVID-19 lockdown on children and adolescents’ movement behaviours.

Methods

We searched eight databases and grey literature for relevant studies of all study designs; and conducted a narrative analysis of the results following synthesis without meta-analysis guidelines. We used appropriate tools to assess the risk of bias in quantitative and qualitative studies. We compared changes in physical activity, screen time and sleep duration and quality from before to during the COVID-19 lockdown.

Results

This review included 71 studies reporting data from 35 countries and territories, mostly from high-income economies. A majority of the studies used a cross-sectional design and had fair to poor-quality ratings. Most studies reported reduced physical activity, increased screen time and longer sleep hours among children and adolescents. Children and adolescents facing strict lockdowns saw a larger decline in physical activity and a sharper increase in screen time than those under mild restrictions.

Conclusion

COVID-19-related lockdowns were detrimental to children and adolescents’ movement behaviours, with stricter lockdowns tending to have a bigger impact. Children and adolescents under COVID-19 restrictions are likely to be less active, spend more time on screen, and sleep longer hours than before the lockdown. More studies from low-income and middle-income countries could provide a clearer picture of the impact.

PROSPERO registration number

CRD42021245924.
Longitudinal survey of UK veterans with pre-existing mental health difficulties: mental health during the COVID-19 pandemic
Authors: Murphy Dominic , Hendrikx L J , Williamson C , Baumann J
Published: January 2022

Introduction

At the start of the COVID-19 pandemic, individuals with pre-existing mental health difficulties were thought to be vulnerable to mental health deterioration due to the emerging threat and the actions taken to control infection rates. Yet, there remained a paucity of research investigating changes in veteran well-being, a population facing higher rates of mental health difficulties compared with the general public. This longitudinal study aimed to investigate the mental health and well-being of UK veterans with pre-existing mental health difficulties at two time points during the COVID-19 pandemic.

Methods

UK treatment-seeking veterans (N=121) completed an online survey administered towards the end of the first UK lockdown in June 2020 and 1 year later. Data were gathered on sociodemographic characteristics as well as psychometric measures of post-traumatic stress disorder (PTSD), common mental disorders (CMDs), anger difficulties and alcohol misuse.

Results

The proportion of veterans meeting criteria of PTSD, anger and alcohol misuse remained similar across the two time points, while significantly fewer veterans met criteria for CMDs 1 year later. A notable proportion of the sample reported challenges in attending mental and physical health appointments, which was positively associated with not working and negatively associated with more COVID-19-related stressors.

Conclusions

These findings suggest that, to date, veterans with pre-existing mental health difficulties appear to demonstrate resilience as the COVID-19 pandemic progressed. However, as the pandemic continued, veterans faced significantly more COVID-19-related stressors, less social support, as well as difficulties attending health appointments.
Prehospital benzodiazepine use and need for respiratory support in paediatric seizures
Authors: Pfeiffer Christina K , Smith Karen , Bernard Stephen , Dalziel Stuart R , Hearps Stephen , Geis Tobias , Kabesch Michael , Babl Franz E
Published: January 2022

Background

Paramedics are frequently called to attend seizures in children. High-quality evidence on second-line treatment of benzodiazepine (BZD)-refractory convulsions with parenteral long-acting antiepileptic drugs in children has become available from the ED. In order to address the potential need for an alternative agent, we set out to determine the association of BZD use prehospital and the need for respiratory support.

Methods

We conducted a retrospective observational study of state-wide ambulance service data (Ambulance Victoria in Victoria, Australia, population: 6.5 million). Children aged 0–17 years assessed for seizures by paramedics were analysed for demographics, process factors, treatment and airway management. We calculated adjusted ORs (AOR) of the requirement for respiratory support in relation to the number of BZD doses administered.

Results

Paramedics attended 5112 children with suspected seizures over 1 year (1 July 2018 to 30 June 2019). Overall, need for respiratory support was low (n=166; 3.2%). Before ambulance arrival, 509 (10.0%) had already received a BZD and 420 (8.2%) were treated with midazolam by paramedics. Of the 846 (16.5%) patients treated with BZD, 597 (70.6%) received 1 BZD dose, 156 (18.4%) 2 doses and 93 (11.0%) >2 doses of BZD. Patients who were administered 1, 2 and >2 doses of BZD received respiratory support in 8.9%, 32.1% (AOR 4.6 vs 1 dose, 95% CI 2.9 to 7.4) and 49.5% (AOR 10.3 vs 1 dose, 95% CI 6.0 to 17.9), respectively.

Conclusions

Increasing administration of BZD doses was associated with higher use of respiratory support. Alternative prehospital antiepileptic drugs to minimise respiratory depression should be investigated in future research.
Cost-effectiveness of transcatheter edge-to-edge repair in secondary mitral regurgitation
Authors: Cohen David J , Wang Kaijun , Magnuson Elizabeth , Smith Robert , Petrie Mark C , Buch Mamta Heena , Abraham William , Lindenfeld Joann , Mack Michael J , Stone Gregg W , Cleland John G F
Published: January 2022

Background

Transcatheter edge-to-edge mitral valve repair (TMVr) improves symptoms and survival for patients with heart failure with reduced left ventricular ejection fraction (HFrEF) and severe secondary mitral regurgitation despite guideline-recommended medical therapy (GRMT). Whether TMVr is cost-effective from a UK National Health Service (NHS) perspective is unknown.

Methods

We used patient-level data from the Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients with Functional Mitral Regurgitation (COAPT) trial to perform a cost-effectiveness analysis of TMVr +GRMT versus GRMT alone from an NHS perspective. Costs for the TMVr procedure were based on standard English tariffs and device costs. Subsequent costs were estimated based on data acquired during the trial. Health utilities were estimated using the Short-Form 6-Dimension Health Survey.

Results

Costs for the index procedural hospitalisation were £18 781, of which £16 218 were for the TMVr device. Over 2-year follow-up, TMVr reduced subsequent costs compared with GRMT (£10 944 vs £14 932, p=0.006), driven mainly by reductions in heart failure hospitalisations; nonetheless, total 2-year costs remained higher with TMVr (£29 165 vs £14 932, p<0.001). When survival, health utilities and costs were projected over a lifetime, TMVr was projected to increase life expectancy by 1.57 years and quality-adjusted life expectancy by 1.12 quality-adjusted life-years (QALYs) at an incremental cost of £21 980, resulting in an incremental cost-effectiveness ratio (ICER) of £23 270 per QALY gained (after discounting). If the benefits of TMVr observed in the first 2 years were maintained without attenuation, the ICER improved to £12 494 per QALY.

Conclusions

For patients with HFrEF and severe secondary mitral regurgitation similar to those enrolled in COAPT, TMVr increases life expectancy and quality-adjusted life expectancy compared with GRMT at an ICER that represents good value from an NHS perspective.
Stem bark of Fraxinus rhynchophylla ameliorates the severity of pancreatic fibrosis by regulating the TGF-β/Smad signaling pathway
Authors: Choi Ji-Won , Shin Joon Yeon , Zhou Ziqi , Kim Dong-Uk , Kweon Bitna , Oh Hyuncheol , Kim Youn-Chul , Song Ho-Joon , Bae Gi-Sang , Park Sung-Joo
Published: January 2022

Chronic pancreatitis (CP) is a pathological fibroinflammatory syndrome of the pancreas. Currently, there are no therapeutic agents available for treating CP-associated pancreatic fibrosis. Fraxinus rhynchophylla (FR) reportedly exhibits anti-inflammatory, antioxidative and antitumor activities. Although FR possesses numerous properties associated with the regulation of diverse diseases, the effects of FR on CP remain unknown. Herein, we examined the effects of FR on CP. For CP induction, mice were intraperitoneally administered cerulein (50 μg/kg) 6 times a day, 4 days per week for 3 weeks. FR extract (100 or 400 mg/kg) or saline (control group) was intraperitoneally injected 1 hour before the first cerulein injection. After 3 weeks, the pancreas was harvested for histological analysis. In addition, pancreatic stellate cells (PSCs) were isolated to examine the antifibrogenic effects and regulatory mechanisms of FR. Administration of FR significantly inhibited histological damage in the pancreas, increased pancreatic acinar cell survival, decreased PSC activation and collagen deposition, and decreased pro-inflammatory cytokines. Moreover, FR treatment inhibited the expression of fibrotic mediators, such as α-smooth muscle actin (α-SMA), collagen, fibronectin 1, and decreased pro-inflammatory cytokines in isolated PSCs stimulated with transforming growth factor (TGF)-β. Furthermore, FR treatment suppressed the phosphorylation of Smad 2/3 but not of Smad 1/5 in TGF-β-stimulated PSCs. Collectively, these results suggest that FR ameliorates pancreatic fibrosis by inhibiting PSC activation during CP.
PD-L1 CAR effector cells induce self-amplifying cytotoxic effects against target cells
Authors: Bajor Malgorzata , Graczyk-Jarzynka Agnieszka , Marhelava Katsiaryna , Burdzinska Anna , Muchowicz Angelika , Goral Agnieszka , Zhylko Andriy , Soroczynska Karolina , Retecki Kuba , Krawczyk Marta , Klopotowska Marta , Pilch Zofia , Paczek Leszek , Malmberg Karl-Johan , Wälchli Sébastien , Winiarska Magdalena , Zagozdzon Radoslaw
Published: January 2022

Background

Immune checkpoint inhibitors and chimeric antigen receptor (CAR)-based therapies have transformed cancer treatment. Recently, combining these approaches into a strategy of PD-L1-targeted CAR has been proposed to target PD-L1high tumors. Our study provides new information on the efficacy of such an approach against PD-L1low targets.

Methods

New atezolizumab-based PD-L1-targeted CAR was generated and introduced into T, NK, or NK-92 cells. Breast cancer MDA-MB-231 and MCF-7 cell lines or non-malignant cells (HEK293T, HMEC, MCF-10A, or BM-MSC) were used as targets to assess the reactivity or cytotoxic activity of the PD-L1–CAR-bearing immune effector cells. Stimulation with IFNγ or with supernatants from activated CAR T cells were used to induce upregulation of PD-L1 molecule expression on the target cells. HER2–CAR T cells were used for combination with PD-L1–CAR T cells against MCF-7 cells.

Results

PD-L1–CAR effector cells responded vigorously with degranulation and cytokine production to PD-L1high MDA-MB-231 cells, but not to PD-L1low MCF-7 cells. However, in long-term killing assays, both MDA-MB-231 and MCF-7 cells were eliminated by the PD-L1–CAR cells, although with a delay in the case of PD-L1low MCF-7 cells. Notably, the coculture of MCF-7 cells with activated PD-L1–CAR cells led to bystander induction of PD-L1 expression on MCF-7 cells and to the unique self-amplifying effect of the PD-L1–CAR cells. Accordingly, PD-L1–CAR T cells were active not only against MDA-MD-231 and MCF-7-PD-L1 but also against MCF-7-pLVX cells in tumor xenograft models. Importantly, we have also observed potent cytotoxic effects of PD-L1–CAR cells against non-malignant MCF-10A, HMEC, and BM-MSC cells, but not against HEK293T cells that initially did not express PD-L1 and were unresponsive to the stimulation . Finally, we have observed that HER-2–CAR T cells stimulate PD-L1 expression on MCF-7 cells and therefore accelerate the functionality of PD-L1–CAR T cells when used in combination.

Conclusions

In summary, our studies show that CAR-effector cells trigger the expression of PD-L1 on target cells, which in case of PD-L1–CAR results in the unique self-amplification phenomenon. This self-amplifying effect could be responsible for the enhanced cytotoxicity of PD-L1–CAR T cells against both malignant and non-malignant cells and implies extensive caution in introducing PD-L1–CAR strategy into clinical studies.
Defining unique clinical hallmarks for immune checkpoint inhibitor-based therapies
Authors: Michielin Olivier , Lalani Aly-Khan , Robert Caroline , Sharma Padmanee , Peters Solange
Published: January 2022

Introduction

Immuno-oncology therapies, including immune checkpoint inhibitors (ICIs), have transformed cancer care and have brought into question whether classic oncology efficacy assessments adequately describe the distinctive responses observed with these agents. With more ICI-based therapies being approved across multiple tumor types, it is essential to define unique clinical hallmarks of these agents and their associated assessments to better reflect the therapeutic impact for both patients and physicians. Long-term survival and objective responses, such as depth and durability of responses, treatment-free survival, efficacy in brain metastases, improved health-related quality of life, and unique safety profiles, are among the hallmarks that have emerged for ICI therapies. An established clinical hallmark is a sustained long-term survival, as evidenced by a delayed separation of Kaplan-Meier survival curves, and a plateau at ~3 years. Combination ICI therapies provide the opportunity to raise this plateau, thereby affording durable survival benefits to more patients. Deepening of responses over time is a unique clinical ICI hallmark, with patients responding long term and with more durable complete responses. Depth of response has demonstrated prognostic value for long-term survival in some cancers, and several ICI studies have shown sustained responses even after discontinuing ICI therapy, offering the potential for treatment-free intervals. Although clinical evidence supporting efficacy in brain metastases is limited, favorable ICI intracranial responses have been seen that are largely concordant with extracranial responses. While patient outcomes can be significantly improved with ICIs, they are associated with unique immune-mediated adverse reactions (IMARs), including delayed ICI toxicities, and may require multidisciplinary management for optimal care. Interestingly, patients discontinuing ICIs for IMARs may maintain responses similar to patients who did not discontinue for an IMAR, whether they restarted ICI therapy or not.

Conclusion

Herein, we comprehensively review and refine the clinical hallmarks uniquely associated with ICI therapies, which not only will rejuvenate our assessment of ICI therapeutic outcomes but also will lead to a greater appreciation of the effectiveness of ICI therapies.
Price and income elasticity of cigarette demand in Bosnia and Herzegovina by different socioeconomic groups
Authors: Gligorić Dragan , Preradović Kulovac Dragana , Mićić Ljubiša , Pepić Anđela
Published: January 2022

Background

Tobacco tax policy in Bosnia and Herzegovina (B&H) assumes a gradual annual increase in specific excise taxes on cigarettes. However, it is insufficient to reduce significantly consumption. This paper examines effects of the increase in cigarette prices and disposable income on cigarette demand in B&H by different income consumer groups.

Methods

Based on the Household Budget Surveys and microdata from 2007, 2011 and 2015, we employed logit model to estimate prevalence and Deaton’s model to estimate intensity elasticity of cigarette demand for the sample of 21 424 households (9953 are smoking households) by different income groups. We used obtained elasticities and estimated the impact of tax increase on cigarette consumption and government revenue in three tax increase scenarios.

Results

Ten per cent price increase would reduce the consumption of low-income households by 14%, as opposed to 9.9% for middle-income and 7% for high-income households. Low-income households would significantly increase the demand for cigarettes compared with high-income households if income increased. Increase in the specific excise tax by 25% would reduce cigarette consumption and increase government revenue, while the low-income group would experience a reduction in tax burden.

Conclusions

Changes in prices have different impacts on tobacco prevalence and consumption of low-income compared with middle-income and high-income socioeconomic groups. Low-income households are most responsive to changes in prices and income. Thus, the poor in B&H would benefit from an increase in tobacco excise taxes and price.
Preparation of highly dispersed CuO-ZnO-ZrO2 catalysts and their improved catalytic performance for hydrogenation of CO2
Authors: Lu Jun , Zhang Yan , Wang Shuai , Li Zhen
Published: January 2022

Highly dispersed CuO-ZnO-ZrO2 (CZZ) catalysts were prepared by the citrate gel method. The structures of catalysts and precursors were characterized by XPS, BET, XRD, H2-TPR, H2-TPD, CO2-TPD, and TG-DTA. The effects of wet gel drying time and citric acid dosage on the catalyst structure were studied and compared with the catalysts prepared by the combustion method to investigate the performance of different catalysts for CO2 hydrogenation to methanol. Studies have shown that prolonging the drying time of wet gel can effectively prevent the splashing of the catalyst during calcination. The BET surface area of the catalyst dried at 120 °C for 48 h is 44.8 m2/g, which is higher than that of the combustion method. When the amount of citric acid is equal to the stoichiometric ratio, the catalyst has the best performance. Under the conditions of 240 °C, 2.6 MPa, space velocity of 3600 h–1, and H2/CO2 volume ratio of 3, the space–time yield of methanol reaches 110.3 g/(kg h). Excessive citric acid will affect the dispersion of catalyst components and result in decomposition residue covering catalyst surface active sites, which is not conducive to CO2 hydrogenation reaction.
Research on Obstacle Avoidance Method of Robot Based on Region Location
Authors: Gu Yuwan , Yang Qiuyuan , Zhu Zhitao , Xu Shoukun , Qian Hui
Published: January 2022

In order to solve the problem of environment generalization in continuous state space, an obstacle avoidance method based on region location is proposed. The method is divided into three steps: (1) Using Region Proposal Network (RPN) to localize the obstacle area; (2). The environment map is established by the regional position mapping relation; and (3). The Deep Q-Learning Network (DQN) is used to realize collision detection of the robot, then pixel collision detection module is introduced and finally the pixel collision simulation distance sensor is combined to obtain the distance between the robot and obstacle and whether the collision or not. In this paper, the experiments were carried out in static obstacle environment and in dynamic and static obstacle environment for robot obstacle avoidance tasks. Experimental results show that the problem of environment generalization can be effectively solved by introducing pixel collision detection in the process of robot obstacle avoidance, and the network model trained in a dynamic environment has some generalization ability.
BSR-TC: Adaptively Sampling for Accurate Triangle Counting over Evolving Graph Streams
Authors: Xuan Wei , Cao Huawei , Yan Mingyu , Tang Zhimin , Ye Xiaochun , Fan Dongrui
Published: January 2022

Triangle counting is a fundamental graph mining problem, widely employed in various real-world application scenarios. Given the large scale of graph streams and limited memory space, it is feasible to achieve the estimation of global and local triangles by sampling. Existing streaming algorithms for triangle counting can be generalized into two categories: Reservoir-based methods and Bernoulli-based methods. The former use a fixed memory budget, whose size is difficult to set for accurate estimation without any prior knowledge about graph streams. The latter sample edges by a specified probability, but memory budget is uncontrollable for following a binomial distribution. In this work, we propose a novel and bounded-sampling-ratio algorithm for both global and local triangle counting, called BSR-TC, by adaptively resizing memory budget upwards over evolving graph streams. Specifically, our proposed single-pass BSR-TC can gain more advantage than the state-of-the-art algorithms over the continuous growth of graph streams. Experimental results show that BSR-TC achieves accuracy of at least 99.8% for global triangles, when the ratio of initial memory budget against whole graph streams [Formula: see text] and given [Formula: see text], respectively.
GLOBAL ANALYSIS OF DIFFERENT COMPARTMENTS IN A GIVING-UP SMOKING MODEL
Authors: ALZAID SARA SALEM , ALKAHTANI BADR SAAD T.
Published: January 2022

Smoking can cause numerous diseases like lung cancer, mouth diseases, bronchitis, stroke, heart diseases, etc. To overcome such and other related diseases, we need to avoid the smoking habit from the population at large scale. The key purpose of this work is to discuss and analyze different kinds of smoking phases which are very common in our society. The model is formulated in the form of a system of ordinary differential equations keeping in view the existing literature. The model is investigated for possible equilibria besides positivity and boundedness of solution. The model exhibits six equilibrium points and the stability analysis of each fixed point is investigated. The local stability analysis of each equilibrium point is carried out with the help of linearization approach and it is shown that these equilibria are locally asymptotically stable under certain conditions. We also checked the fixed points for global analysis with the help of Lyapunov stability theory and stability criteria were obtain therein. We also perform the local sensitivity analysis. To verify the theoretical results, a scheme is developed using non-standard finite difference method. Simulations suggest that the obtained graphical results support our analytical findings with a high degree of accuracy.
DYNAMICAL BEHAVIOR OF THE LONG WAVES IN THE NONLINEAR DISPERSIVE MEDIA THROUGH ANALYTICAL AND NUMERICAL INVESTIGATION
Authors: HOU ENRAN , WANG FUZHANG , SALAMA SAMIR A. , KHATER MOSTAFA M. A.
Published: January 2022

This paper studies the well-known mathematical model’s analytical wave solutions (modified Benjamin–Bona–Mahony (BBM) equation), which demonstrates the propagation of long waves in the nonlinear dispersive media in a visual illusion. Six recent analytical and semi-analytical schemes (extended simplest equation (ESE) method, modified Kudryashov (MKud) method, sech–tanh expansion method, Adomian decomposition (ADD) method, El Kalla (EK) expansion method, variational iteration (VI) method) are applied to the considered model for constructing abundant analytical and semi-analytical novel solutions. This variety of solutions aims to investigate the analytical techniques’ accuracy by calculating the absolute error between analytical and semi-analytical solutions that shows the matching between them. The analytical results are sketched through two-dimensional (2D), three-dimensional (3D), contour plot, spherical plot and polar plot. The stability characterization of the analytical solutions is investigated through the Hamiltonian system’s features. The originality and novelty of this paper are discussed, along with previously published papers.
CAPUTO-BASED MODEL FOR INCREASING STRAINS OF CORONAVIRUS: THEORETICAL ANALYSIS AND EXPERIMENTAL DESIGN
Authors: BALEANU DUMITRU , ALSHOMRANI ALI S.
Published: January 2022

One of the most severe and troubling diseases these days is COVID-[Formula: see text] pandemic. The COVID-[Formula: see text] pandemic’s dangerous effects are extremely rapid, and infection normally results in death within a few weeks. As a consequence, it is important to delve deeper into the complexities of this elusive virus. In this study, we propose a Caputo-based model for increasing COVID-19 strains. The memory effect and hereditary properties of the fractional variant for the model enable us to fully comprehend the dynamics of the model’s features. The existence of unique solution using the fixed-point theorem and Arzelá–Ascoli principle as well as the stability analysis of the model by means of Ulam–Hyer stability (UHS) and generalized Ulam–Hyer stability (GUHS) have been discussed. Furthermore, the parameters of the model are estimated using [Formula: see text] months data points chosen from Nigeria using the nonlinear least-squares technique. The best-suited parameters and the optimized Caputo fractional-order parameter [Formula: see text] are obtained by running simulations for both models. The proposed model is shown to comprehend the dynamical behavior of the virus better than the integer-order version. In addition, to shed more light on the model’s characteristics, various numerical simulations are performed using an efficient numerical scheme.
EXACT SOLITON SOLUTIONS FOR CONFORMABLE FRACTIONAL SIX WAVE INTERACTION EQUATIONS BY THE ANSATZ METHOD
Authors: ALQARALEH SAHAR M. , TALAFHA ADEEB G. , MOMANI SHAHER , AL-OMARI SHRIDEH , AL-SMADI MOHAMMED
Published: January 2022

In this paper, a conformable fractional time derivative of order [Formula: see text] is considered in view of the Lax-pair of nonlinear operators to derive a fractional nonlinear evolution system of partial differential equations, called the Fractional-Six-Wave-Interaction-Equations, which is derived in terms of one temporal plus one and two spatial dimensions. Further, an ansatz consisting of linear combinations of hyperbolic functions with complex coefficients is utilized to obtain an infinite set of exact soliton solutions for this system. Certain numerical examples are introduced to show the effectiveness of the ansatz method in obtaining exact solutions for similar systems of nonlinear evolution equations.
MODELING THE TRANSMISSION PHENOMENA OF COVID-19 INFECTION WITH THE EFFECT OF VACCINATION VIA NONINTEGER DERIVATIVE UNDER REAL STATISTIC
Authors: CUI TING , LIU PEIJIANG
Published: January 2022

The infection of coronavirus (COVID-19) is a dangerous and life-threatening disease which spread to almost all parts of the globe. We present a mathematical model for the transmission of COVID-19 with vaccination effects. The basic properties of fractional calculus are presented for the inspection of the model. We calculate the equilibria of the model and determined the reproduction number [Formula: see text]. Local asymptotic stability conditions for the disease-free are obtained which determines the conditions to stabilize the exponential spread of the disease. The nonlinear least-square procedure is utilized to parameterize the model from actual cases reported in Pakistan. By fixed point theory, we prove the existence of a unique solution. We also present numerical results to simulate virus transmission and compare the results with those of the Caputo derivative. We study the solution pathways of the COVID-19 system to provide effective control policies for the infection. Significant changes have been noticed by lowering the order of fractional derivative.
Torsional Vibration of Functionally Porous Nanotube Based on Nonlocal Couple Stress Theory
Authors: Mahmoudi Rouzbeh , Barati Abbas , Hosseini Mohammad , Hadi Amin
Published: January 2022

Presented in this paper is a numerical solution for torsional vibration analysis of a functionally porous nanotube under a magnetic field. The size effect in microscale can be captured using the nonlocal couple stress theory that predicts softening and hardening in micro-size. The torsional vibration of functionally porous nanotube with magnetic field based on nonlocal couple stress theory is examined. The governing equation is derived using Hamilton’s principle and the generalized differential quadrature method (GDQM) was employed to solve it. A comparison between the results of this work with the other paper reveals the accuracy of this study. The effects of some parameters such as porosity, magnetic field, and small-scale parameters were investigated. The results show that different materials have different behavior in micro-size that can be covered softening and hardening.
Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare
Authors: Lv Zhihan , Yu Zengchen , Xie Shuxuan , Alamri Atif
Published: January 2022

Two-dimensional

1

arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements.

This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the
convolutional neural network (CNN)
is modified to build an interactive
smart healthcare prediction and evaluation model (SHPE model)
based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.
A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy
Authors: Masud Mehedi , Alhamid Mohammed F. , Zhang Yin
Published: January 2022

Nowadays, artificial intelligence (AI) provides tremendous prospects for driving future healthcare while empowering patients and service providers. The extensive use of digital healthcare produces a massive amount of multimedia healthcare data continuously (e.g., MRI, X-Ray, ultrasound images, etc.). Hence, it needs special data analytics techniques to provide a smart diagnosis to the patients. Recent advancements in artificial intelligence and machine learning techniques, particularly Deep learning (DL) methods, have demonstrated tremendous medical diagnosis progress and achievements. Diabetic Retinopathy (DR), cataract, macular degeneration, and glaucoma are the most common eye problems due to diabetes. Numerous models have been proposed using deep learning models to diagnose diabetic retinopathy, but no model is perfect for detecting DR diseases. This article presents a deep learning model to analyze diabetic retinopathy images to classify DR patients’ severity levels. The model applies a custom-weighted loss function in the model’s training and achieves 92.49% accuracy and a 0.945 Cohen Kappa score on test data. The model’s weighted average precision was 93%, recall 92%, and f1 score 93%. The model is compared with several state-of-the-art pre-trained models. We observe that the proposed model performs better in accuracy results and Cohen Kappa score.
Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention
Authors: Yan Chenggang , Meng Lixuan , Li Liang , Zhang Jiehua , Wang Zhan , Yin Jian , Zhang Jiyong , Sun Yaoqi , Zheng Bolun
Published: January 2022

Different from general face recognition, age-invariant face recognition (AIFR) aims at matching faces with a big age gap. Previous discriminative methods usually focus on decomposing facial feature into age-related and age-invariant components, which suffer from the loss of facial identity information. In this article, we propose a novel Multi-feature Fusion and Decomposition (MFD) framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants. Specifically, we first sample multiple face images of different ages with the same identity as a face time sequence. Then, the multi-head attention is employed to capture contextual information from facial feature series, extracted by the backbone network. Next, we combine feature decomposition with fusion based on the face time sequence to ensure that the final age-independent features effectively represent the identity information of the face and have stronger robustness against the aging process. Besides, we also mitigate imbalanced age distribution in the training data by a re-weighted age loss. We experimented with the proposed MFD over the popular CACD and CACD-VS datasets, where we show that our approach improves the AIFR performance than previous state-of-the-art methods. We simultaneously show the performance of MFD on LFW dataset.
Fine-grained Human Analysis under Occlusions and Perspective Constraints in Multimedia Surveillance
Authors: Cucchiara Rita , Fabbri Matteo
Published: January 2022

Human detection in the wild is a research topic of paramount importance in computer vision, and it is the starting step for designing intelligent systems oriented to human interaction that work in complete autonomy. To achieve this goal, computer vision and machine learning should aim at superhuman capabilities. In this work, we address the problem of fine-grained human analysis under occlusions and perspective constraints. More specifically, we discuss some issues and some possible solutions to effectively detect people using pose estimation methods and to detect humans under occlusions both in the two-dimensional (2D) image plane and in the 3D space exploiting single monocular cameras. Dealing with occlusion can be done at the joint level or pixel level: We discuss two different solutions, the former based on a supervised neural network architecture for detecting occluded joints and the latter based on a semi-supervised specialized GAN that exploits both appearance and human shape attributes to determine the missing parts of the visible shape. To deal with perspective constraints, we further discuss a neural approach based on a double architecture that learns to create an optimal neural representation, which is useful to reconstruct the 3D position of human keypoints starting with simple RGB images. All these approaches have a critical point in common: the need for large annotated datasets. To have large, fair, consistent, transparent, and ethical datasets, we propose the adoption of synthetic datasets as, for example, JTA and MOTSynth. In this article, we discuss the pros and cons of using synthetic datasets while tackling several human-centered AI issues with respect to European GDPR rules for privacy. We further explore and discuss an application in the field of risk assessment by space occupancy estimation during the COVID-19 pandemic called Inter-Homines.
From Coarse to Fine: Hierarchical Structure-aware Video Summarization
Authors: Li Wenxu , Pan Gang , Wang Chen , Xing Zhen , Han Zhenjun
Published: January 2022

Hierarchical structure is a common characteristic for some kinds of videos (e.g., sports videos, game videos): The videos are composed of several actions hierarchically and there exist temporal dependencies among segments with different scales, where action labels can be enumerated. Our ideas are based on two observations: First, the actions are the fundamental units for people to understand these videos. Second, the humans summarize a video by iteratively observing and refining, i.e., observing segments in video and hierarchically refining the boundaries of important actions. Based on the above insights, we generate action proposals to construct the structure of the video and formulate the summarization process as a hierarchical refining process. We also train a hierarchical summarization network with deep Q-learning (HQSN) to achieve the refining process and explore temporal dependency. Besides, we collect a new dataset that consists of structured game videos with fine-grain actions and importance annotations. The experimental results demonstrate the effectiveness of the proposed method.
Instance Correlation Graph for Unsupervised Domain Adaptation
Authors: Wu Lei , Ling Hefei , Shi Yuxuan , Zhang Baiyan
Published: January 2022

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application fields. Due to the expensive cost of manual labeling efforts, it is important to transfer knowledge from a label-rich source domain to an unlabeled target domain. The core problem is how to learn a domain-invariant representation to address the domain shift challenge, in which the training and test samples come from different distributions. First, considering the geometry of space probability distributions, we introduce an effective Hellinger Distance to match the source and target distributions on statistical manifold. Second, the data samples are not isolated individuals, and they are interrelated. The correlation information of data samples should not be neglected for domain adaptation. Distinguished from previous works, we pay attention to the correlation distributions over data samples. We design elaborately a Residual Graph Convolutional Network to construct the Instance Correlation Graph (ICG). The correlation information of data samples is exploited to reduce the domain shift. Therefore, a novel Instance Correlation Graph for Unsupervised Domain Adaptation is proposed, which is trained end-to-end by jointly optimizing three types of losses, i.e., Supervised Classification loss for source domain, Centroid Alignment loss to measure the centroid difference between source and target domain, ICG Alignment loss to match Instance Correlation Graph over two related domains. Extensive experiments are conducted on several hard transfer tasks to learn domain-invariant representations on three benchmarks: Office-31, Office-Home, and VisDA2017. Compared with other state-of-the-art techniques, our method achieves superior performance.
Are We Doing Enough?
Authors: Joshi K. D.
Published: January 2022

The obligation to tackle biases and injustices rooted in the publishing ecosystem has reached the academic communities' consciousness (Royal Society, 2022; APA, 2021; Nature, 2022). Similarly, the IS community is institutionalizing governance mechanisms to demonstrate its commitment to Diversity, Equity and Inclusion (DEI) (Burton-Jones & Sarker, 2021; Association for Information Systems, 2021), in hopes of reversing the unwanted effects of explicit and implicit biases in communicating and publishing IS scholarship.
Hydrogen Sensing Properties of a Tin Dioxide Thin Film Incorporated with Evaporated Palladium Nanoparticles
Authors: Hsu Chih-Chia , Niu Jing-Shiuan , Liu Wen-Chau
Published: January 2022

This work demonstrates a new hydrogen gas sensor prepared by a radio-frequency (RF) sputtered tin dioxide (SnO2) thin film and palladium (Pd) nanoparticles (NPs). The related hydrogen gas sensing properties are comprehensively studied in this work. Materials characterizations were conducted by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDS), high-resolution scanning electron microscopy (HRSEM), and transmission electron microscopy (TEM). The employed Pd NPs greatly enlarge the surface area/volume (SA/V) ratio and enhance the catalytic activity of Pd metal. A significantly high sensing response of 1.31 × 105 under introduced 1000 ppm H2/air gas with a response (recovery) time of 81 s (11 s) and a lower detecting concentration of 10 ppb H2/air at 125 °C were acquired for the studied Pd NP/SnO2 device; as well, the studied sensor device could be operated at a lower temperature (25 °C). In addition, the studied device shows excellent selectivity and long-term stability in regard to hydrogen gas. On the basis of advantages above-mentioned and benefits of a relatively simple structure, ease of fabrication, low cost, and lower power consumption, the proposed device propitious for hydrogen gas detection.
Review—Biowaste as a Source of Conductive Carbon
Authors: Zia Umer , Iram Hira , Haider Hafiz Zeshan , Ameen Faiza , Abrar Maria , Atif Muhammad
Published: January 2022

Recycling trend has transformed the prerequisites of our cravings. Makeover of waste material into useful goods has become inevitable. Bio waste shares a major portion of waste materials, hence pursued for recycling. Activated carbon from bio waste has been reported remarkable in electrochemical performance (capacitance, charge density, charge-discharge ratio etc.). This survey of data has been compiled for different bio wastes as a source of activated carbons with conductive behavior.
(Invited) Electrocatalytic Carbon Dioxide Conversion on Cu Catalyst
Authors: Kwon Youngkook , Im Mintaek , Lim Jung-Ae , Kim DaJeong , Kim Dongyeon , Jung Hyunchul , Kim Soo Min , Kim Beom-Sik
Published: January 2022

The efficient electrochemical conversion of CO2 to fuels or stock chemicals with high-energy density would be a major step forward in the introduction of a carbon neutral energy cycle, as it would allow for the direct low-temperature conversion of (photo)-generated electrical current to stored chemical energy, in a manner very similar to the way nature stores solar energy. The high activity and selectivity towards electrocatalytic conversion of CO2 can be achieved with careful selection of catalyst and electrolyte.

Major progress in electrochemical CO2 reduction has been limited to two-electron involved products such as CO and formate. It has been known that Cu is prone to produce hydrocarbons, mainly C1 product such as methane and the formation of multi-carbon products via C-C coupling remains as one of the biggest scientific challenges to be resolved. The product selectivity of such conversion can be affected by the electrolyte composition and catalyst structure. Anion such as halides in the electrolyte nanostructures copper surface via electrochemical cycling which allows for a suppression of methane and an enhancement of ethylene and ethanol formation. On the other hands, cations have direct influence on CO2RR in which increasing the size of mono-valent cations can increase the selectivity and activity to C-C coupled products by lowering the pK
a of the hydrated cations at the cathode. In addition, a new principle of catalyst design which controls an atomic-level catalyst structure to accelerate electrocatalytic activity and selectivity toward the formation of C-C bonds will be discussed.
(Invited) Maximizing Efficiencies of Photocatalytic Water Splitting By Engineering Interfaces in Multi-Component Photocatalysts
Authors: Linic Suljo
Published: January 2022

Solar splitting of water to hydrogen and oxygen is a critical chemical transformation for which no commercially viable photocatalytic systems exist. Developing materials that could execute this reaction with high efficiencies would fundamentally change the environmental footprint of many segments of our economy, including chemical industry, manufacturing and energy sectors. The materials that have received the most attention for this application are hybrids that contain a semiconductor light absorber and an attached metal electrocatalyst that performs chemical transformations. In these systems, the semiconductor serves to provide the electromotive force (voltage) that is used by the electrocatalysts to drive the reaction. The main problems in the development of these hybrid photocatalysts are the chemical instability of the desired semiconductors (with the appropriate, relatively low band gap) under the relevant water splitting conditions, and the losses associated with the presence of the semiconductor/electrocatalyst junction in these photo-catalytic system.

Recently, it has been demonstrated that these low band gap semiconductor light absorbers can in some cases be stabilized by the use of protective insulator layers by forming metal/insulator/semiconductor (MIS) photocatalysts. This strategy incorporates a stable insulator layer placed between a metal electrocatalyst and semiconductor forming a metal-insulator-semiconductor junction. So far the central focus of this area of research has been on experimentally demonstrating the enhanced stability of the semiconductors covered by these insulating layers under the reaction conditions. In this contribution, we will discuss the realistic targets for solar water splitting for these MIS photocatalyst. We will also analyze the critical problems associated with these multi-component photocatalysts. We will discuss the impact of various components on the photo-catalysts performance. We will also show how by controlling the geometries of these systems at atomistic level we can optimize their solar to hydrogen efficiencies.