bluuebunny/crossref_metadata_2025 huggingface dataset:
Review on natural clay ceramic membrane: Fabrication and application in water and wastewater treatment
Authors: Azaman Fazureen , Muhamad Nor Mohd Al Amin , Wan Abdullah Wan Rafizah , Razali Mohd Hasmizam , Che Zulkifli Rumaizah , Ahmad Zaini Muhammad Abbas , Ali Asmadi
Published: February 2021

Membrane technology is important in industrial wastewater and water treatment. Recently, the polymeric membrane technology is widely chosen in these applications. However, they are lowtemperature ranges, low corrosion resistance, and low lifespan. Thus, researchers are actively trying to develop a better membrane technology such as natural clay ceramic membrane due to their excellent in chemical, mechanical and thermal resistance, high-pressure application and long lifespan. This detailed review compiles through the literature of current scientific research over the last ten years. Its highlights the key findings of factors in the fabrication of natural clay ceramic membrane that contributed to its properties. This review article presented an outline of the advantages, disadvantages, and how to overcome the disadvantages, structure, and preparation of ceramic membrane, including method, raw materials, drying and sintering temperature. The review confirmed that the sintering temperature, the composition of raw materials and pore-forming agent are significantly enhanced the mechanical strength and porosity of the natural clay ceramic membrane. However, further development and modification of the natural clay ceramic membrane technology and their applications to treat different environmental pollutants is still necessary.
Synergistic organic liquid formulation for succinic acid extraction from simulated aqueous solution
Authors: Nurulashikin Syahira , Othman Norasikin , Jusoh Norela
Published: February 2021

Bio-succinic acid (SA) is a new compound that will replace petroleum based succinic acid. The application of bio-based succinic acid is still restricted due to its excessive downstream processing cost. Liquid-liquid extraction (LLE) is one of the promising methods for the successful extraction of SA. Single extractant in the LLE formulation commonly had drawbacks of long extraction time and tiny loading capacity. The aim of this study is to create synergistic formulation to enhance the SA extraction performance. The diluent is fixed as palm oil as the characteristic shows that it is safer, renewable, and non-toxic to environment. Four different types of extractants including Aliquat 336, Trioctylamine (TOA), Tributylamine (TBA) and Tributyl-phosphate (TBP) was tested to find the best combination of synergist extractants. The result shows that almost 100 % of SA was extracted using synergistic mixture of TOA and Aliquat 336 at 0.2M with synergistic coefficient (SC) of 640. In conclusion, synergistic mixture has high potential to extract SA
Association of Myopia in Elementary School Students in Jiaojiang District, Taizhou City, China
Authors: Lu Xin , Guo Congcong , Xu Bin , Hou Chenwei , Huang Xiaoming , Xu Hui , Weng Zhichun
Published: February 2021

Background. The aim of our study was to evaluate the prevalence of myopia in elementary school students and to assess the risk factors for myopia. Methods. This school-based cross-sectional study was performed on students from two elementary schools in Jiaojiang, Taizhou City, China. A total of 556 students, whose age ranged from 9 to 12 years, were included. The uncorrected visual acuity and noncycloplegic refractive error tests were performed to determine the myopia. Each student was asked to fulfill the questionnaire about the possible factors associated with myopia. Multivariate logistic analyses of risk factors were conducted. Results. The overall prevalence of myopia among those students was 63.7%, ranged from 53.4% in grade 4 to 72.5% in grade 6. Multivariate logistic analysis showed that adjusting the height of desks and chairs according to the changing height and the presence of myopia in parents were significantly associated with myopia in these students, respectively. Conclusions. Our results showed that myopia among elementary school students was associated with environmental and hereditary factors.
Pathology, Risk Factors, and Oxidative Damage Related to Type 2 Diabetes‐Mediated Alzheimer’s Disease and the Rescuing Effects of the Potent Antioxidant Anthocyanin
Authors: Khan Muhammad Sohail , Ikram Muhammad , Park Tae Ju , Kim Myeong Ok
Published: February 2021

The pathology and neurodegeneration in type 2 diabetes‐ (T2D‐) mediated Alzheimer’s disease (AD) have been reported in several studies. Despite the lack of information regarding the basic underlying mechanisms involved in the development of T2D‐mediated AD, some common features of the two conditions have been reported, such as brain atrophy, reduced cerebral glucose metabolism, and insulin resistance. T2D phenotypes such as glucose dyshomeostasis, insulin resistance, impaired insulin signaling, and systemic inflammatory cytokines have been shown to be involved in the progression of AD pathology by increasing amyloid‐beta accumulation, tau hyperphosphorylation, and overall neuroinflammation. Similarly, oxidative stress, mitochondrial dysfunction, and the generation of advanced glycation end products (AGEs) and their receptor (RAGE) as a result of chronic hyperglycemia may serve as critical links between diabetes and AD. The natural dietary polyflavonoid anthocyanin enhances insulin sensitivity, attenuates insulin resistance at the level of the target tissues, inhibits free fatty acid oxidation, and abrogates the release of peripheral inflammatory cytokines in obese (prediabetic) individuals, which are responsible for insulin resistance, systemic hyperglycemia, systemic inflammation, brain metabolism dyshomeostasis, amyloid‐beta accumulation, and neuroinflammatory responses. In this review, we have shown that obesity may induce T2D‐mediated AD and assessed the recent therapeutic advances, especially the use of anthocyanin, against T2D‐mediated AD pathology. Taken together, the findings of current studies may help elucidate a new approach for the prevention and treatment of T2D‐mediated AD by using the polyflavonoid anthocyanin.
A Class of Variable-Order Fractional
p

·

-Kirchhoff-Type Systems
Authors: Wu Yong , Qiao Zhenhua , Hamdani Mohamed Karim , Kou Bingyu , Yang Libo
Published: February 2021

This paper is concerned with an elliptic system of Kirchhoff type, driven by the variable-order fractional

p

x

-operator. With the help of the direct variational method and Ekeland variational principle, we show the existence of a weak solution. This is our first attempt to study this kind of system, in the case of variable-order fractional variable exponents. Our main theorem extends in several directions previous results.
Research on WiFi Penetration Testing with Kali Linux
Authors: Lu He-Jun , Yu Yang
Published: February 2021

Aiming at the vulnerability of wireless network, this paper proposed a method of WiFi penetration testing based on Kali Linux which is divided into four stages: preparation, information collection, simulation attack, and reporting. By using the methods of monitoring, scanning, capturing, data analysis, password cracking, fake wireless access point spoofing, and other methods, the WiFi network penetration testing with Kali Linux is processed in the simulation environment. The experimental results show that the method of WiFi network penetration testing with Kali Linux has a good effect on improving the security evaluation of WiFi network.
Security Analysis of a Lightweight Identity-Based Two-Party Authenticated Key Agreement Protocol for IIoT Environments
Authors: Li Yuting , Cheng Qingfeng , Shi Wenbo
Published: February 2021

Internet of Things brings convenience to the social life, at the same time, putting forward higher requirements for the security of data transmission and storage. Security incidents based on industrial Internet of Things have occurred frequently recently, which should be given full consideration. The identity-based authenticated key agreement protocol can solve these security threats to a certain extent. Recently, a lightweight identity-based authenticated key agreement protocol for Industrial Internet of Things, called ID-2PAKA protocol, was claimed to achieve secure authentication and meet security properties. In this paper, we show that the ID-2PAKA protocol is insecure in identity authentication and cannot resisting ephemeral key compromise impersonation attack.
A Multilevel Single Stage Network for Face Detection
Authors: Hui Kanghua , Wang Jin , He Huaiqing , Ip W. H.
Published: February 2021

Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two‐stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real‐time performance.
Soil Organic Carbon Distribution in a Humid Tropical Plain of Cameroon: Interrelationships with Soil Properties
Authors: Kome Georges Kogge , Enang Roger Kogge , Yerima Bernard Palmer Kfuban
Published: February 2021

Soil organic carbon (SOC) determination is very important in the assessment of agronomic potential of a soil. The objective of this study was to determine SOC contents and stock distribution with depth in relation to selected soil properties. Five types of soils, namely, Mollic Endoaquents, Oxyaquic Paleudalfs, Oxyaquic Udifluvents, and Mollic Udifluvents from a humid tropical plain and Typic Eutrudepts from an adjacent foot slope, were studied. The soils have all developed from fluvial sediments. Morphological and physicochemical characteristics of the soils were obtained using standard methods. Soil texture varied across the different sites and within soil profiles with textural classes of genetic horizons ranging from sandy loam to heavy clay. The soils are generally young soils under development as indicated by their high silt/clay ratios which ranged between 0.23 and 2.45. All the soils were generally acidic with pH-H2O values ranging from 4.5 to 6.2. Exchangeable H+ and Al3+ ranged from 0.5 to 2.3 and 0.2 to 3.3 cmolckg−1, respectively. SOC contents are generally higher in surface horizons and decrease with depth. In general, SOC correlated significantly with bulk density (BD) (r = −0.648,

p
<
0.01

), water holding capacity (r = 0.589,

p
<
0.01

), exchangeable Al3+ (r = 0.707,

p
<
0.01

), and exchangeable H+ (r = 0.456,

p
<
0.05

). The correlation between SOC and exchangeable Al3+ was strongest in the Mollic Endoaquents (r = 0.931,

p
<
0.01

). SOC contents correlated significantly with Munsell soil color attributes, explaining between 40 and 57% of SOC variation. Total SOC stocks at a depth of 100 cm varied between 260.1 and 363.5 t·ha−1, and the variation in SOC stocks across a profile appears to be controlled by genetic horizon depth, while land use type influences SOC stock variations across genetic surface horizons.
Game‐Based Trust in Complex Networks: Past, Present, and Future
Authors: Yi Li , Fang Weidong , Zhang Wuxiong , Gao Weiwei , Li Baoqing
Published: February 2021

As an efficient approach, the trust policy is implemented to defend against insider attacks in complex networks. However, the imperfection of trust relationships directly hinders the effort to quantitatively calculate trust value, especially in choosing a cooperative partner. Fortunately, the game theory is gradually concerned with addressing the above issue to further enhance security. In this paper, the game theory and the trust policy are reviewed briefly. Then, the research roadmap on game‐based trust in complex networks is discussed and analysed deeply. Furthermore, some research directions in the near future are given. It is worth mentioning that our contributions not only describe the evolution of the game‐based trust clearly but also suggest the trust mechanism based on the bounded rational game more suitable to uncertain information.
T Cell Exhaustion and CAR‐T Immunotherapy in Hematological Malignancies
Authors: Tang Lu , Zhang Yinqiang , Hu Yu , Mei Heng
Published: February 2021

T cell exhaustion has been recognized to play an immunosuppressive role in malignant diseases. Persistent tumor antigen stimulation, the presence of inhibitory immune cells and cytokines in tumor microenvironment (TME), upregulated expression of inhibitory receptors, changes in T cell‐related transcription factors, and metabolic factors can all result in T cell exhaustion. Strategies dedicated to preventing or reversing T cell exhaustion are required to reduce the morbidity from cancer and enhance the effectiveness of adoptive cellular immunotherapy. Here, we summarize the current findings of T cell exhaustion in hematological malignancies and chimeric antigen receptor T (CAR‐T) immunotherapy, as well as the value of novel technologies, to inverse such dysfunction. Our emerging understanding of T cell exhaustion may be utilized to develop personalized strategies to restore antitumor immunity.
An Improved Image Steganography Scheme Based on Partial Preservation Embedding Algorithm for Wireless Visual Sensor Networks
Authors: Shen Qian , Jiang Tao , Zhu Yongjun , Wu Yin
Published: February 2021

With the continuous improvement of encryption algorithms, some applications based on the architecture of wireless visual sensor networks have gradually shifted their attention to the imperceptibility and antijamming performance of secret images. To reduce the probability of secret images being detected, the current research focuses on hiding secret data in the least-significant bit of the cover image in the spatial domain or embedding data into the coefficients of the high-frequency band in the transformational domain, which usually leads to poor performance in a hostile environment. Therefore, some researchers proposed to substitute the coefficients of the medium-frequency band in the transformational domain with secret information to enhance the anti-interference performance. However, this idea would severely affect the imperceptibility of secret images. As a result, an improved version based on the partial preservation embedding algorithm was designed in this paper. Theory analysis and simulation results indicate that the proposed scheme performs better than the existing methods by directly substituting the coefficients of the medium-frequency band in the transformational domain, especially in the case of strong noise interference.
Cardiac Autonomic Nervous System Activity during Slow Breathing in Supine Position
Authors: Kai Satoru , Nagino Koji , Aoki Takuma , Imura Tatsuya , Kiyoshima Keita , Satake Yoshinobu , Matsuura Kaname , Mima Kota , Yasuoka Shoki , Yabuuchi Akinobu
Published: February 2021

The purpose of this study is to clarify cardiac autonomic nervous system activity during slow breathing exercises in a supine position. Eighteen healthy young males were participated. Heart rate variability was measured for 5 minutes at rest, 5 minutes at slow breathing, and then 5 minutes at rest. As a result, the LF/HF ratio increased with slow breathing, but HF value did not change. We suggest that the increased LF/HF ratio may be due to increased airway resistance. Cardiac autonomic nervous system activity during slow breathing in the supine position was revealed.
Research on Feature Extracted Method for Flutter Test Based on EMD and CNN
Authors: Zheng Hua , Wu Zhenglong , Duan Shiqiang , Zhou Jiangtao
Published: February 2021

Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.
Relationship between Cognitive Learning Psychological Classification and Neural Network Design Elements
Authors: Yang Xing , Yong Tingjun , Li Meihua , Wang Wenying , Xie Huichun , Du Jinping
Published: February 2021

This article first analyzes the research background of the design elements of cognitive psychology and neural networks at home and abroad, roughly understands the research status and research background of these two courses at home and abroad, and discusses the application of cognitive psychology to neural networks. The design method has not yet formed a systematic theoretical system. Then, a systematic theoretical analysis of the research in this article is carried out to analyze the relationship between the various characteristics of cognitive psychology and the design elements of the neural network, and it uses these relationships to guide the design practice. Second, it analyzes the relationship between the influence and interaction of cognitive psychology on neural network design and connects cognitive psychology with neural network design. Finally, according to the theoretical analysis and research of the system, the application of cognitive psychology in neural network design, design practice, and the relationship between the two are systematically reviewed. Through the exploratory research on cognitive psychology in neural network design, we can see that the combination of neural network design and psychology, art aesthetics, and other cross‐disciplinary and multidisciplinary research is necessary, which can promote the scientific and technological progress of neural network design in the context of the information age and the improvement of public mental health. Under the background of the era in which the neural network design becomes the link between people′s emotions and culture, we must fully understand the essential role of each element in neural network design and build a design concept based on cognitive psychology and emotional experience. It is hoped that the content of this topic can provide a certain reference value for the future development of neural network design and cognitive psychology and clarify the new development direction.
Improved Survival of Leukemic Mice Treated with Sodium Caseinate in Combination with Daunorubicin without Toxicity
Authors: Aguiñiga-Sánchez Itzen , Meléndez-Ibarra Frida Montserrat , Ledesma-Martínez Edgar , Weiss-Steider Benny , Fajardo-Orduña Guadalupe Rosario , Rangel-Corona Rosalva , García-Gervasio Sac-Nicte , Ramírez-Padilla María Guadalupe , Lara-Castañeda José Luis , Santiago-Osorio Edelmiro
Published: February 2021

In recent years, low doses of chemotherapy have been resumed and explored for the treatment of acute myeloid leukemia. Thus, CPX-351, a dual-drug liposomal encapsulation of cytarabine and daunorubicin, was approved by the US Food and Drug Administration, to deliver a synergistic 5 : 1 molar drug ratio into leukemia cells to a greater extent than normal bone marrow cells and significantly enhance survival compared with conventional treatment in older and newly diagnosed AML patients, but overall survival rate remains low; therefore, the need for new therapeutic options continues. Sodium caseinate (SC), a salt of casein, the main milk protein, has cytotoxic effect in leukemia cell lines, but promotes proliferation of hematopoietic normal cells, while its administration in leukemic mice promotes survival for more than 40 days, but bone marrow surviving mice still harbour leukemic cells, but it is not known whether the combination with cytarabine or daunorubicin can improve survival without damaging normal hematopoietic cells. Here, it is shown that, in vitro, the combination of the IC25 of SC-cytarabine or SC-daunorubicin synergizes in the elimination of leukemic cells, with evident induction of apoptosis, while the proliferation of mononuclear cells of bone marrow is not affected. In leukemic mice, the combined administration of SC-daunorubicin or SC-cytarabine promotes the highest survival rate at 40 days; in addition, no autoproliferating cells were detected in the bone marrow of survivors of more than 60 days, evidence of eradication of leukemic cells, but only the bone marrow of mice treated with the SC-daunorubicin combination proliferated in the presence of interleukin-3, which shows that this combination is not toxic to normal bone marrow cells, thus emerging as a possible antileukemic agent.
Evaluating Safety Issues for Taxi Transport Management
Authors: Aghabayk Kayvan , Rejali Sina , Samerei Seyed Alireza , Shiwakoti Nirajan
Published: February 2021

Taxi drivers face many problems every day including safety issues. The tendency to quickly transport passengers to their destinations for more income has resulted in dangerous driving behaviors leading to traffic violations. So, taxi drivers need appropriate support and training programs to improve safety and reduce the risk of crashes. Implementing different support and safety training programs requires an effective management system. There is a dearth of research on the safety issues of taxis from the perspective of taxi organization managers. This study aims to evaluate the safety issues of taxi transport management through a case study of the Tehran Taxi Organization. A questionnaire survey was conducted with 22 regional managers and 20 transportation specialists of the Tehran Taxi Organization. Issues related to taxi drivers, roads and road users, vehicles, and management systems were evaluated in the questionnaire. Participants determined the relevance level and priority ranking of each question. The level of agreement was then tested using the Kendall concordance test. According to the results, the use of GPS was selected as the best in-vehicle monitoring system that can be used to evaluate drivers in the fleet. Participants believed that passengers’ loading and unloading had the most risk for taxi users. The start-inhibit technology to detect open doors was unanimously evaluated as an efficient technology for taxi safety. With respect to educating taxi users, starting education in schools had the most relevance and priority. Recommendations for increasing the safety of taxis include the use of GPS in taxis to monitor and evaluate drivers, receiving crash reports from police and submitting monthly safety assessment reports, flexibility in drivers’ working hours’ schedule, providing training on drivers fatigue management, and evaluating drivers’ health.
An Algorithm for Motion Estimation Based on the Interframe Difference Detection Function Model
Authors: Zhang Tengfei , Kang Huijuan
Published: February 2021

In this paper, we simulate the estimation of motion through an interframe difference detection function model and investigate the spatial‐temporal context information correlation filtering target tracking algorithm, which is complex and computationally intensive. The basic theory of spatiotemporal context information and correlation filtering is studied to construct a fast target tracking method. The different computational schemes are designed for the flow of multiframe target detection from background removal to noise reduction, to single‐frame detection, and finally to multiframe detection, respectively. This enables the ground‐based telescope to effectively detect spatial targets in dense stellar backgrounds in both modes. The method is validated by simulations and experiments and can meet the requirements of real projects. The interframe bit attitude estimation is optimized by using the beam‐parity method to reduce the interframe estimation noise; a global optimization strategy based on the bit attitude map is used in the back end to reduce the system computation amount and make the global bit attitude estimation more accurate; a loop detection based on the word pocket model is added to the system to reduce the cumulative error.
Gravitational Collapse and Singularity Removal in Rastall Theory
Authors: Dorrani Ehsan
Published: February 2021

In the present work, we study spherically symmetric gravitational collapse of a homogeneous fluid in the framework of Rastall gravity. Considering a nonlinear equation of state (EoS) for the fluid profiles, we search for a class of nonsingular collapse solutions and the possibility of singularity removal. We find that depending on the model parameters, the collapse scenario halts at a minimum value of the scale factor at which a bounce occurs. The collapse process then enters an expanding phase in the postbounce regime, and consequently the formation of a spacetime singularity is prevented. We also find that, in comparison to the singular case where the apparent horizon forms to cover the singularity, the formation of apparent horizon can be delayed allowing thus the bounce to be causally connected to the external universe. The nonsingular solutions we obtain satisfy the weak energy condition (WEC) which is crucial for physical validity of the model.
Stability Analysis of a Weathered‐Basalt Soil Slope Using the Double Strength Reduction Method
Authors: Liu Xiaoliang , Chen Xin , Su Mei , Zhang Shilin , Lu Dengfeng
Published: February 2021

Slope stability analysis of the mountain landforms in southwestern China has always been an important problem in the field of geotechnical engineering. The large landslide occurs in Jichang Town, Shuicheng County, Guizhou Province, China, on July 23, 2019, as the engineering background. Based on the nonlinear relationship between the soil water content, cohesion, and friction angle measured in laboratory tests, the finite element reduction problem of the double‐strength parameters is only transformed into a reduction problem of water content. Then, based on the redevelopment platform in the ABAQUS finite element software, a user subroutine to specify predefined field variables (UFIELD) was written to numerically simulate the stability of the Jichang slope before the landslide. The results show that the Jichang slope is mainly composed of basalt‐weathered red clay mixed with gravel of various particle sizes. The underlying bedrock is primarily the Permian Emeishan basalt with strong‐to‐weak weathering and a small amount of argillaceous siltstone. Due to the increase in water content caused by heavy rainfall, the strength of the soil decreased continuously. Once the critical stress state of the slope was exceeded, the plastic sliding block slipped at high speed over a long distance along the rock‐soil layer interface, and along the way, it scraped out and carried away the original loose topsoil and gravel blocks, which finally piled up in the form of a debris flow. In addition, the attenuations of the cohesion and friction angle are different. When the water content is less than 25%, the reduction coefficient of the friction angle is greater than the cohesion, which shows that the attenuation of the friction angle is stronger than that of the cohesion. The opposite is true when the water content is greater than 25%. The new method of double‐strength finite element reduction presented in this paper is reasonable and feasible and is more in line with the actual situation of weathered‐basalt soil slope instability in heavy rainfall areas.
Analysis on the Countermeasures of Optimizing Urban Tourism Public Service System under the Background of Smart City
Authors: Mo Jianghong , Ren Fei
Published: February 2021

At present, there are problems in the construction of the tourism public service system such as poor coordination and coordination among various departments, lack of resource integration mechanism, and low level of information of tourism public services. Therefore, by analysing the problems existing in the tourism public service system in the context of smart cities, this paper puts forward strategies to optimize the tourism public service system. First, we build a smart tourism public service system platform for overall development and optimize the tourism public service platform. Secondly, by focusing on the behaviour of tourists, we construct a big data analysis and management system for the behaviour of smart tourism tourists. Finally, for the traffic situation in the process of tourism, a smart traffic line network based on pheromone ant colony is constructed. The results of the questionnaire survey on tourists show that the optimized public service system platform has achieved great satisfaction.
Dual Generative Network with Discriminative Information for Generalized Zero‐Shot Learning
Authors: Xu Tingting , Zhao Ye , Liu Xueliang
Published: February 2021

Zero‐shot learning is dedicated to solving the classification problem of unseen categories, while generalized zero‐shot learning aims to classify the samples selected from both seen classes and unseen classes, in which “seen” and “unseen” classes indicate whether they can be used in the training process, and if so, they indicate seen classes, and vice versa. Nowadays, with the promotion of deep learning technology, the performance of zero‐shot learning has been greatly improved. Generalized zero‐shot learning is a challenging topic that has promising prospects in many realistic scenarios. Although the zero‐shot learning task has made gratifying progress, there is still a strong deviation between seen classes and unseen classes in the existing methods. Recent methods focus on learning a unified semantic‐aligned visual representation to transfer knowledge between two domains, while ignoring the intrinsic characteristics of visual features which are discriminative enough to be classified by itself. To solve the above problems, we propose a novel model that uses the discriminative information of visual features to optimize the generative module, in which the generative module is a dual generation network framework composed of conditional VAE and improved WGAN. Specifically, the model uses the discrimination information of visual features, according to the relevant semantic embedding, synthesizes the visual features of unseen categories by using the learned generator, and then trains the final softmax classifier by using the generated visual features, thus realizing the recognition of unseen categories. In addition, this paper also analyzes the effect of the additional classifiers with different structures on the transmission of discriminative information. We have conducted a lot of experiments on six commonly used benchmark datasets (AWA1, AWA2, APY, FLO, SUN, and CUB). The experimental results show that our model outperforms several state‐of‐the‐art methods for both traditional as well as generalized zero‐shot learning.
Increased Treatment Engagement and Adherence: Flexible Management with Prolonged-Release Buprenorphine in Treatment of Opioid Dependence
Authors: Hard Bernadette
Published: February 2021

Opioid dependence (OD) is effectively treated with well-evidenced regimens including psychosocial and opioid agonist pharmacotherapy. Many do not engage with treatment services; reasons include the burden of mandatory supervision and stigma. Injectable prolonged-release buprenorphine (PRB) offers choice and flexibility in treatment. Experience reported here demonstrates the potential for PRB to enable wider engagement with treatment services. Treatment was successful in patients unable to attend daily observed therapy due to work commitments, unable to use services for fear of stigma, or having not achieved goals on previous attempts with conventional approaches. PRB therapy was clinically successful without withdrawal signs or evidence of use of other drugs. Patient-reported outcomes were positive including maintained ability to work, manageable detoxification experience, and stigma-free treatment. This work provides evidence of PRB benefit in expanding treatment engagement.
Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research
Authors: Wei Wei , Yang Xu
Published: February 2021

Introduction. A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods. We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results. Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion. The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.
Behaviour Factor of Ductile Code‐Designed Reinforced Concrete Frames
Authors: Ferraioli Massimiliano
Published: February 2021

The current generation of seismic design codes is based on a linear elastic force‐based approach that includes the nonlinear response of the structure implicitly through a response modification factor (named reduction factor R in American codes or behaviour factor q in European codes). However, the use of a prescribed behaviour factor that is constant for a given structural system may fail in providing structures with the same risk level. In this paper, the behaviour factor of reinforced concrete frame structures is estimated by means of nonlinear static (pushover) and nonlinear incremental dynamic analyses. For this purpose, regular reinforced concrete frames of three, five, seven, and nine storeys designed for high ductility class according to the European and Italian seismic codes are investigated, and realistic input ground motions are selected based on the design spectra. Verified analysis tools and refined structural models are used for nonlinear analysis. Overstrength, redundancy, and ductility response modification factors are estimated, and the effects of some parameters influencing the behaviour factor, including the number of bays and the number of storeys, are evaluated. The results are finally compared with those obtained from a previous paper for steel moment‐resisting frames with the same geometry. According to the analysis results, the behaviour factors in the case of pushover analysis are significantly higher than those obtained in the case of nonlinear response history analysis. Thus, according to the pushover analysis, the behaviour factor provided by European and Italian standards seems highly conservative. On the contrary, the more refined nonlinear dynamic analysis shows that the code‐prescribed value may be slightly nonconservative for middle‐high‐rise frame structures due to unfavourable premature collapse mechanisms based on column plastic hinging at the first storey. Thus, some modifications are desirable in local ductility criteria and/or structural detailing of high ductility columns to implicitly ensure that the recommended value of the behaviour factor is conservative.
Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining
Authors: Cui Yimin
Published: February 2021

With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.
Oxyberberine Prevented Lipopolysaccharide‐Induced Acute Lung Injury through Inhibition of Mitophagy
Authors: Zhao Runmin , Wang Bingxia , Wang Dasheng , Wu Benhe , Ji Peiyu , Tan Dingyu
Published: February 2021

Acute lung injury (ALI) is a serious respiratory syndrome characterized with uncontrolled inflammatory response. Oxyberberine has strong potential for clinical usage since it showed strong anti‐inflammatory, antifungal, and antiarrhythmic effects in various diseases. In the present study, we evaluated whether oxyberberine can inhibit lipopolysaccharide‐ (LPS‐) induced ALI in vivo and further evaluated the possible involvement of mitophagy in vitro by using A549 cells, a human lung epithelial cell line. Our in vivo study shows that oxyberberine significantly inhibited LPS‐induced lung pathological injury and lung edema, as indicated by the changes in lung wet/dry ratio and total protein levels in the BALF in mice. Moreover, oxyberberine inhibited inflammation, as indicated by the changes of neutrophil accumulation and production of proinflammatory cytokines including tumor necrosis factor α (TNF‐α), interleukin 1β (IL‐1β), and IL‐6 in both the lung and bronchoalveolar lavage fluid (BALF) in ALI mice. Our in vitro study shows that LPS significantly decreased the protein level of mitochondrial proteins, including cytochrome c oxidase subunit IV (COX IV), p62, and mitofusin‐2 (Mfn2) in A549 cells. In addition, LPS induced significant Parkin1 translocation from cytoplasm to mitochondria. These changes were significantly inhibited by oxyberberine. Notably, the inhibitory effect of oxyberberine was almost totally lost in the presence of lysosome fusion inhibitor bafilomycin A1 (Baf), a mitophagy inhibitor. In conclusion, the present study demonstrated that oxyberberine alleviated LPS‐induced inflammation in ALI via inhibition of Parkin‐mediated mitophagy.
Predictors of Instrumental Activities of Daily Living Performance in Patients with Stroke
Authors: Ghaffari Amin , Rostami Hamid Reza , Akbarfahimi Malahat
Published: February 2021

Objective. Instrumental activities of daily livings are important for independent living and active participation in the community. The present study is aimed at determining factors predicting instrumental activities of daily living performance in patients with stroke. Methods. In this cross-sectional study, a convenient sample of 90 patients with stroke entered from five occupational therapy centers, which were selected based on the cluster randomization method. Lawton IADL scale, Barthel Index, Trail Making Test (A and B), Digit span subtest of Wechsler memory scale, Motorcity index, and Beck Depression Inventory-II were used to investigate the study’s aim. Statistical analyses were performed using independent sample

t

-test, one-way ANOVA, Pearson correlation, and multiple linear regression analysis. Results. Age (

r
=
−
0.384

,

p
<
0.001

), memory (

r
=
0.565

,

p
<
0.001

), basic activities of daily living (

r
=
0.818

,

p
<
0.001

), depression (

r
=
−
0.758

,

p
<
0.001

), Trial Making Test (B-A) (

r
=
−
0.614

,

p
<
0.001

), and motoricity index (

r
=
0.670

,

p
<
0.001

) were significantly associated with instrumental activities of daily living performance. Conclusions. Basic activities of daily living were the strongest predictor of IADL’s performance. Age, TMT (B-A), and depression were orderly the next strongest predictors. Stroke patients with more dependency in basic activities of daily living, older age, cognitive impairment, and depression are more opted to be dependent in instrumental activities of daily living and as a result, less participation in home and community affairs.
Injury Related to Fall and Its Predictors among Medically Diagnosed Adults with Visual Impairment in Ethiopia: An Observational Cross-Sectional Study
Authors: Gashaw Moges , Adie Admass Biruk
Published: February 2021

Background. Fall-related injury is the common cause of unintentional injury and premature death among people with visual impairment. So far, the knowledge about fall-related injuries among medically diagnosed visual impairment people living in low- and middle-income countries is scarce. Hence, this study is a preliminary attempt to assess the fall-related injury and its determinants among adult people with medically diagnosed visual impairment individuals. Methods. An institutional-based cross-sectional study was conducted from March to July 2018 with a total sample size of 337 study participants. The study participants were recruited by using a systematic random sampling method. Univariable and multivariable binary logistic regression model analysis was used to identify predictors of fall-related injuries with IBM Statistical Package for Social Sciences version 23. Results. A total of 320 adults with visual impairments participants have participated in this study. The finding of this study was reported as follows: 24.7% of (95% CI: 20.0–29.4%) adults with visual impairments experienced one or more fall-related injuries. The main predictors of fall-related injuries identified by multivariate analysis were severity of visual impairment: moderate (AOR, 2.91; 95% CI: 1.23 – 6.87), severe (AOR, 3.58; 95% CI: 1.26–10.17), cause of visual impairment: cataract (AOR, 10.63; 95% CI: 2.49 – 45.26), diabetic retinopathy (AOR, 15.35; 95% CI: 2.51–93.96), taking medication (AOR, 6.35; 95% CI: 2.93–13.75), having family support (AOR, 2.13; 95% CI: 1.08 – 4.19), and depression (AOR 3.82, 95% CI: 1.27–11.45). Conclusion. Soft tissue injuries were the most common fall-related injuries reported by the study participants. The severity of visual impairment, the cause of visual impairment, taking one or more medication, having family support, and having depression were significant predictors of fall-related injuries.
A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring
Authors: Gunasekara Shanaka Ramesh , Kaldera H. N. T. K. , Dissanayake Maheshi B.
Published: February 2021

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.