bluuebunny/crossref_metadata_2025 huggingface dataset:
Seismic Performance of CFST Frame‐Steel Plate Shear Walls Connected to Beams Only
Authors: Rong Qin , Zhao Zhonghui , Guo Lanhui , Hou Xiaomeng , Lin Li , Bi Hongtao
Published: August 2021

The safety and cost of structures composed of concrete‐filled steel tube (CFST) frame‐steel plate shear walls (SPSWs) with two‐side connections are governed by the seismic performance. The response modification factor R and displacement amplification factor Cd are important seismic performance factors. In this paper, nonlinear seismic responses of 10‐story, 15‐story, and 20‐story CFST frame‐SPSWs (CFST‐SPSWs) are studied. A nonlinear finite element model which includes both material and geometric nonlinearities is developed using the finite element software OpenSees for this study. The accuracy of model was validated by comparing with experimental results. Nonlinear seismic analysis shows that CFST‐SPSWs, in high seismic region, behave in a stable and ductile manner. Also, R and Cd of CFST‐SPSWs were evaluated for the structure models using incremental dynamic analysis (IDA), and the average values of 3.17 and 3.05 are recommended, respectively. The recommended R value is greater than the value (2.8) in the “Chinese Code for seismic design of buildings” for composite structures, indicating the code is conservative. The structural periods provided by current code are generally lower than the periods calculated by finite element analysis. Research results show that R and Cd increase with increasing story number, span number, and structural period. Ductility reduction factor Rμ increases with increasing span number and decreasing story number. Overstrength factor Rs increases with increasing story number and decreasing span number.
Screening of Anti-Inflammatory Components of Qin Jin Hua Tan Tang by a Multivariate Statistical Analysis Approach for Spectrum-Effect Relationships
Authors: Duan Feipeng , Li Yisheng , Zhao Meizhen , Hu Tianyong , Pan Xinquan , Feng Yue , Ma Fang , Qiu Shuqi , Zheng Yiqing
Published: August 2021

Qing Jin Hua Tan Tang (QJHTT) exerts therapeutic effects in patients with chronic obstructive pulmonary disease (COPD) by alleviating inflammation. However, the anti-inflammatory components of QJHTT have not yet been reported. Our study aimed to screen the active anti-inflammatory components of QJHTT using a multivariate statistical analysis approach for spectrum-effect relationships. Different polar fractions of QJHTT were prepared using ethanol, ethyl acetate, and n-butanol to analyze the phytochemical components. Phytochemical fingerprints were generated using ultrahigh-performance liquid chromatography. In total, 24 peaks were observed in ten batches of QJHTT extracts. The anti-inflammatory activity was evaluated using a xylene-induced ear-swelling mouse model. Additionally, the spectrum-effect relationship between the relative areas of the 24 peaks and pharmacological activity was investigated using multivariate statistical analysis. The potential anti-inflammatory ingredients obtained from the screening (multivariate statistical analysis) will be validated for their anti-inflammatory effects and mechanisms utilizing a lipopolysaccharide-induced macrophage inflammation model. QJHTT ethanol extract 1 exhibited good anti-inflammatory activity. Peaks 11, 12, 13, 14, and 16, which were closely correlated with anti-inflammatory activity, were identified as meranzin, baicalin, baicalein, chrysin-7-O-β-D-glucuronide, and wogonoside, respectively. The anti-inflammatory activities of meranzin, baicalin, baicalein, and wogonoside were verified in vitro. These four bioactive components significantly inhibited the secretion of inflammatory factors in the lipopolysaccharide-stimulated macrophage cell line. This research successfully screened the QJHTT anti-inflammatory active ingredient group. Meranzin, baicalin, baicalein, chrysin-7-O-β-D-glucuronide, and wogonoside were predicted to be the anti-inflammatory active ingredient groups of QJHTT.
Low‐Cost Multisensor Integrated System for Online Walking Gait Detection
Authors: Yan Lingyun , Wei Guowu , Hu Zheqi , Xiu Haohua , Wei Yuyang , Ren Lei
Published: August 2021

A three‐dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low‐cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass‐produced low‐cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real‐time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule‐based calibration filter to classify kinematic data into five distinct gait events: heel‐strike, foot‐flat, heel‐off, toe‐off, and initial‐swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low‐cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.
Meditation-Induced Psychosis: Trigger and Recurrence
Authors: Joshi Sulochana , Manandhar Anusha , Sharma Pawan
Published: August 2021

Meditation is regarded as a self-regulation approach to manage emotions. Meditation has a beneficial effect on mental health. Different kinds of meditation are practiced in many religions and cultures for the general wellbeing of an individual. However, meditation-related experiences and negative effects of meditation are not uncommon. Meditation-induced psychosis has been reported in the past. Here, we present a case of a 33-year-old male patient who developed acute and transient psychosis twice after meditation and discuss the role of meditation as a precipitating factor to psychosis.
Impaired Mitochondrial Bioenergetics Function in Pediatric Chronic Overlapping Pain Conditions with Functional Gastrointestinal Disorders
Authors: Chelimsky Gisela , Simpson Pippa , Zhang Liyun , Bierer Doug , Komas Steve , Kalyanaraman Balaraman , Chelimsky Thomas
Published: August 2021

Background. Fatigue is often the primary complaint of children with functional gastrointestinal disorders (FGDI) and other chronic overlapping pain disorders (COPC). The basis for this symptom remains unknown. We evaluated mitochondrial function in the white blood cells of these patients. Methods. This prospective Children’s Wisconsin IRB approved study recruited subjects aging 10–18 years from pediatric neurogastroenterology clinics and healthy comparison subjects (HC). Environmental and oxidative stressors can damage the mitochondrial respiratory chain. The known low-grade inflammation in COPC could, therefore, impact the respiratory chain and theoretically account for the disabling fatigue so often voiced by patients. Mitochondrial energy generation can be easily measured in peripheral mononuclear cells (PMC) as a general marker by the Seahorse XF96 Extracellular Flux Analyzer. We measured 5 parameters of oxygen consumption using this methodology: basal respiration (BR), ATP linked oxygen consumption (ATP-LC), maximal oxygen consumption rate (max R), spare respiratory capacity (SRC), and extracellular acidification rate (ECAR), which reflect non-electron chain energy generation through glycolysis. In health, we expect high ATP linked respiration, high reserve capacity, low proton leak, and low non-mitochondrial respiration. In disease, the proton leak typically increases, ATP demand increases, and there is decreased reserve capacity with increased non-mitochondrial respiration. Findings and clinical data were compared to healthy control subjects using a Mann–Whitney test for skewed variables, Fisher’s exact test for dichotomous variables, and regression tree for association with functional outcome (functional disability inventory, FDI). Results. 19 HC and 31 COPC showed no statistically significant difference in age. FGID, orthostatic intolerance, migraine, sleep disturbance, and chronic fatigue were present in the majority of COPC subjects. BR, ECAR, and ATP-LC rates were lower in the COPC group. The low BR and ATP-LC suggest that mitochondria are stressed with decreased ability to produce ATP. Tree analysis selected SRC as the best predictor of functional disability: patients with SRC >150 had a greater FDI (more disability) compared to patients with SRC <=150,

p

-value = 0.021. Conclusion. Subjects with COPC have reduced mitochondrial capacity to produce ATP. Predisposing genetic factors or reversible acquired changes may be responsible. A higher SRC best predicts disability. Since a higher SRC is typically associated with more mitochondrial reserve, the SRC may indicate an underutilized available energy supply related to inactivity, or a “brake” on mitochondrial function. Prospective longitudinal studies can likely discern whether these findings represent deconditioning, true mitochondrial dysfunction, or both.
Research on the Benefit-Sharing Model of Collaborative Innovation Mechanism in Power Innovation Park
Authors: Ji Liyan , Zhang Jingning , Pi Yichen , Li Cunbin
Published: August 2021

Currently, innovation is no longer limited to a single enterprise. The joint realization of collaborative innovation by multiple parties has become the main driving force for innovation and development. The Power Innovation Park is an emerging innovation and entrepreneurship model. It is guided by power grid companies, and the school, government, and enterprise are combined in the park. The Power Innovation Park urgently needs theoretical guidance in terms of the resource allocation mechanism, support incentive mode, and benefit-sharing mode, so this paper takes the park as an example to study the benefit-sharing mode of collaborative innovation. In this paper, the transformation methods of the Power Innovation Park are divided into two types: cooperation between members within the park and dominated by external enterprises in the park. This paper applies cooperative game theory and HJB equation to study the benefit-sharing model of each member in the Power Innovation Park under two different result transformation modes. Research has shown that an appropriate method of transformation of results can maximize the overall and individual interests of the park and that individual interests are affected by many factors. Finally, this paper puts forward policy recommendations to promote the collaborative innovation development of the innovation park through the research results.
Thermal and Environmental Analysis Solar Water Heater System for Residential Buildings
Authors: Alayi Reza , Khalilpoor Nima , Heshmati Saeid , Najafi Atabak , Issakhov Alibek
Published: August 2021

Due to the reduction of fossil resources, the replacement of renewable energy sources such as solar energy has become mandatory. Solar energy does not contain pollution and widely available in all parts of the world, especially in warm regions. Our country (IRAN) is geographically located in a hot and dry region, and with more than 280 sunny days per year, one of the nonpower applications of solar energy is heating space and water consumption of the building using solar thermal energy. Solar water heaters can be used to heat the water used in buildings, which is the main purpose of this study. Water heating consumes an average of 20% to 30% of the total energy consumption in the residential building. Therefore, using solar water heaters annually can provide 70% of the energy needed for water heating. The system designed in this research is able to provide 75% of the hot water consumption needs. If an auxiliary heat source is used next to this system, all hot water needs of the building can be met throughout the year. In this case, as much as 237.3 kWh, energy will be saved from fossil energy sources.
Effects of Economic Policy Uncertainty on Manufacturing Structural Upgrading: Evidence from China
Authors: Hu Renjing , Yan Yanyang
Published: August 2021

Manufacturing is one of the drivers of China’s growth, realizing structural upgrading is vital to achieve high-quality economic development during periods of economic policy uncertainty. Based on firm-level and province-level panel data from 1997 to 2018, this paper used a fixed effect panel data model and panel quantile regression model to investigate the effect of economic policy uncertainty on structural upgrading in manufacturing. The findings indicate that the effect of economic policy uncertainty on structural upgrading in manufacturing is significantly positive and great in regions at advanced stages of manufacturing structure. The discussion about the results suggests that the mechanism of economic policy uncertainty affecting structural upgrading in manufacturing operates through pushing the manufacturing industry to implement service transformation strategies along with vertical integration.
Evaluation of Medical Care for Diabetic and Hypertensive Patients in Primary Care in Mexico: Observational Retrospective Study
Authors: Lara-Esqueda Agustin , Zaizar-Fregoso Sergio A. , Madrigal-Perez Violeta M. , Ramirez-Flores Mario , Montes-Galindo Daniel A. , Martinez-Fierro Margarita L. , Rodriguez-Sanchez Iram P. , Guzman-Esquivel José , Meza-Robles Carmen , Ceja-Espiritu Gabriel , Kuri-Morales Pablo A. , Delgado-Enciso Josuel , Barriguete-Melendez Armando , Galvan-Salazar Hector R. , Barajas-Saucedo Carlos E. , Guillermo-Espinosa Elvin , Lara-Basulto Agustin D. , Gonzalez-Roldan Jesus F. , Delgado-Enciso Ivan
Published: August 2021

Introduction. The present study evaluated the quality of medical care for patients diagnosed with diabetes mellitus (DM), hypertension (HBP), and both pathologies (DM+HBP) within a public health system in Mexico. Methods. 45,498 patients were included from 2012 to 2015. All information was taken from the electronic medical record database. Each patient record was compared against the standard to test the quality of medical care. Results. Glycemia with hypertension goals reached 29.6% in DM+HBP, 48.6% in DM, and 53.2% in HBP. The goals of serum lipids were reached by 3% in DM+HBP, 5% in DM, and 0.2% in HBP. Glycemia, hypertension, and LDL cholesterol reached 0.04%. 15% of patients had an undiagnosed disease. Clinical follow-up examinations reached 20% for foot examination and clinical eye examination. Specialty referrals reached 1% in angiology or cardiology. Conclusion. Goals for glycemic and hypertension reached 50% in the overall population, while serum lipids, clinical follow-up examinations, and referral to a specialist were deficient. Patients who had both diseases had more consultations, better control for hypertension and lipids, but inferior glycemic control. Overall, quality care for DM and/or HBP has not been met according to the standards.
On Fuzzy Fundamental Groups and Fuzzy Folding of Fuzzy Minkowski Space
Authors: Talafha Adeeb G. , Alqaraleh Sahar M. , El-Ahmady A. E. , Al Jaraden Jehad
Published: August 2021

In this paper, we studied the relations between new types of fuzzy retractions, fuzzy foldings, and fuzzy deformation retractions, on fuzzy fundamental groups of the fuzzy Minkowski space

M
˜

4

. These geometrical transformations are used to give a combinatorial characterization of the fundamental groups of fuzzy submanifolds on

M
˜

4

. Then, the fuzzy fundamental groups of the fuzzy geodesics and the limit fuzzy foldings of

M
˜

4

are presented and obtained. Finally, we proved a sequence of theorems concerning the isomorphism between the fuzzy fundamental group and the fuzzy identity group.
Complex Dynamics Analysis of Closed-Loop Supply Chain considering CSR and Fairness Concerns
Authors: Zhang Fei , Hu Zhimin , Yang Yunzhong , Ma Xiaogang
Published: August 2021

This paper constructs a supply chain model composed of a manufacturer and a recycler. The manufacturer’s CSR and the recycler’s fairness concerns are introduced to the benchmark model in turn, and the optimal decision-making problems under different models are studied and compared. The findings show that the manufacturer’s utility will increase and the recycler’s utility will decrease when the manufacturer undertakes CSR within a reasonable range. The optimal utility of manufacturers does not change, and the utility of the recycler is affected by the proportion of CSR undertaken by the manufacturer when the recycler considers fairness concerns. Based on the CSR and fairness model, this paper constructs a dynamic decision system of production quantity and eco-innovation effort. We analyze the influence of adjustment speed on the dynamic decision system and obtain the conditions required to maintain system stability. The research conclusion indicates that with the increase of adjusting parameters, the system gradually appears chaotic state from a stable state and the chaotic state of the system has a negative impact on the utility of manufacturer and recycler. In order to avoid chaos in the system, this paper uses the delayed feedback method to control the system.
Establishment and Evaluation of Energy Consumption, Carbon Emission, and Economic Models of Retreaded Tires Based on Life Cycle Theory
Authors: Wang Qiang , Wang Shaojie , Jiang Li
Published: August 2021

This paper identifies the system composition of the life cycle of retreaded tires and constructs the energy consumption model, carbon emission model, and economic model of retreaded tires based on the life cycle theory. Moreover, the theoretical calculation model and method for the energy consumption, carbon emission, and economy at the production phase, transportation phase, usage phase, and reuse phase of retreaded tires are proposed. After that, this paper puts forward the energy substitution model, carbon reduction model, and cost profit model of five reuse methods of retreaded tires, namely, secondary retreading, mechanical pulverization, low-temperature pulverization, combustion decomposition, and combustion power generation. Finally, this paper proposes the evaluation index for the energy consumption, carbon emission, and economy in the life cycle of retreaded tires and quantitatively analyzes the energy consumption, carbon emission, and cost profit list in each phase of the life cycle of retreaded tires, obtaining the energy recovery rate, carbon reduction rate, and profit-to-cost ratio of the five reuse methods of retreaded tires. The main conclusions of this paper are as follows: the energy consumption and carbon emission of retreaded tires are the largest at the production phase, while the energy consumption and carbon emission are the lowest at the transportation phase. Among the five reuse methods, the energy recovery effect, carbon reduction rate, and economy of secondary retreading are the optimal ones, and the quantitative results show that retreading is the most effective way for the reuse of waste tires.
Observation of Intramural Fibroid Expulsion on MRI after Uterine Artery Embolization
Authors: Matsushima Hisakazu , Kageyama Ken , Yamamoto Akira , Jogo Atsushi , Sohgawa Etsuji , Nota Takehito , Murai Kazuki , Ogawa Satoyuki , Nakano Mariko , Shimono Taro , Miki Yukio
Published: August 2021

Uterine artery embolization (UAE) is a type of noninvasive treatment for symptomatic uterine fibroids. One of the complications of UAE is fibroid expulsion. Here, we report a case of a 45-year-old woman who underwent UAE for an intramural fibroid, which resulted in fibroid expulsion. To the best of our knowledge, there are only few reports of expulsion of intramural fibroids. The process of fibroid protrusion from the myometrium into the uterine cavity was depicted on magnetic resonance imaging (MRI) in this case. We discuss the risk factors and mechanisms of fibroid expulsion after UAE.
Fixed‐Time Disturbance Observer‐Based Adaptive Finite‐Time Guidance Law Design considering Impact Angle Constraint and Autopilot Dynamics
Authors: Wu Yunjie , Ma Fei , Yang Xiaofei , Wang Siqi , Liu Xiaodong
Published: August 2021

This paper aims to establish an effective guidance law to accomplish the interception guidance mission for a missile intercepting a target with impact angle constraint and autopilot dynamics. To achieve this purpose, a fixed‐time disturbance observer‐based adaptive finite‐time guidance law is presented. First, a fixed‐time disturbance observer (FTDO) is designed to guarantee the fast estimation of the lumped disturbance caused by the target maneuver. Then, the FTDO‐based adaptive integral sliding mode backstepping (AISMB) guidance law is constructed for the interception guidance problem. Besides, several adaptive laws are established to estimate the derivative of virtual control inputs, making the “differential explosion problem” of conventional backstepping get avoided. The finite‐time convergence characteristic of the closed‐loop system is analyzed by utilizing the Lyapunov stability theory. Finally, the simulation examples are conducted to demonstrate the effectiveness of the proposed composite guidance law.
Bibliometrics Analysis of Diagnostic Test Accuracy Studies of Bladder Cancer
Authors: Zhang Yuan , Xu Yao , Li Shuo
Published: August 2021

Background. Bladder cancer is amongst the top 10 most common neoplasms worldwide. It is responsible for over 150,000 deaths per annum. It is of great importance to study its clinical diagnosis and management. As a literature synthesis technique, bibliometrics analysis helps us to take our knowledge of bladder cancer diagnosis research, topics, and trends a step further. It is critical to elucidate the literature landscape and its pertinent impact on this field, in order to have a better understanding and improved management of bladder cancer. Materials and Methods. Search terms related to bladder cancer diagnosis were used to retrieval publications which met the predefined criteria in the Scopus database developed by Elsevier. Included articles were further evaluated by year of publication, country, language, institution, article type, source journal, coauthorship networks, and text mining of titles. The R software, as well as the tm, ggplot2 packages, and the VOSviewer (version 1.6.15) were used as software tools. Results. A comprehensive literature search in the Scopus database returned a total of 824 publication items. Among them, 601 (72.94%) were published as journal articles, and 117 (14.20%) were published as reviews. The number of annual publications in this field has been increasing in recent years. English represents the language used in the majority of the articles (634, 77.13%). The highest number of citations received for a single article is as high as 628 times. We also clustered and visualized a network of 701 authors with no less than 3 publications. The term cloud and hierarchical clustering dendrogram which revealed with text mining were used to discover the hot research topics in this field. We also statistically analyzed evolution of the major journal impact indices, including Impact Factor, EigenFactor Score, and CiteScore of 11 journals with a period of 9 years. Conclusion. Through bibliometrics analysis, we found hot topics and related research focuses and trends in bladder cancer diagnosis. This bibliometrics analysis has identified influential articles in the field and provides a useful guide to researchers as to what type of article constitutes a highly citable publication in this subject. In addition, a coauthorship network helps researchers find out which team may be a potential partner and where their studies focus on.
Effect of a Drug Delivery System Made of Quercetin Formulated into PEGylation Liposomes on Cervical Carcinoma In Vitro and In Vivo
Authors: Li Jian , Li Zhen , Gao Yanting , Liu Shihe , Li Kun , Wang Shuai , Gao Liming , Shi Ming , Liu Zhiwei , Han Zengsheng , Qiu Yan
Published: August 2021

Quercetin is a well-known flavonoid for its potent antitumor and antiproliferative effects on a wide range of human cancer cell lines. However, the delivery of quercetin is challenging due to its extreme insolubility in water. The intention of this study was to evaluate the antitumor effect of quercetin-loaded PEGylated liposomes (PEG-Que-NLs) in vitro and in vivo. We first prepared PEG-Que-NLs by method of thin film hydration; further determined, the optimum ratios of quercetin to Soybean phosphatidylcholine (SPC), to cholesterol (CHL), and to PEG-4000 were 1 : 8, 1 : 2, and 1 : 2 (

w
/
w

), respectively, and the optimal hydration temperature was 55°C when the mean vesicle diameter and apparent Zeta potential of PEG-Que-NLs were found to be

171.3
±
10.4

 nm and

−
13.1
±
2.1

 mV, respectively; the encapsulation efficiency and the drug loading of PEG-Que-NLs were

81.25
±
3.12
%

and

8.5
±
0.77
%

, respectively. Drug release study in vitro showed that PEG-Que-NLs exhibited a slow-release effect without significant burst effect. Furthermore, the inhibition effect of PEG-Que-NLs on HeLa cells was considerably higher than free quercetin (free-Que) and quercetin liposomes (Que-NLs). Intravenous injection of PEG-Que-NLs into U14 bearing mouse models inhibited the cervical carcinoma growth significantly, and the tumor inhibition rate was much higher than free-Que and Que-NLs. These results of this study indicated that PEG-Que-NLs exhibited potential application prospects in the treatment of malignant tumors because of its tumor targeting, slow-release properties, and the solubility improvement of quercetin.
Research on Channel Optimization of Ads‐B Aviation Target Surveillance Radar Based on Improved Filtering Algorithm
Authors: Zheng Xiaoxia , Tang Bin , Pu Hongping
Published: August 2021

In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS‐B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one‐step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS‐B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs.
CY-09 Inhibits NLRP3 Inflammasome Activation to Relieve Pain via TRPA1
Authors: Fan Youjia , Xue Gaici , Chen Qianbo , Lu Ye , Dong Rong , Yuan Hongbin
Published: August 2021

Peripheral tissue damage leads to inflammatory pain, and inflammatory cytokine releasing is the key factor for inducing the sensitization of nociceptors. As a calcium ion channel, TRPA1 plays an important role in pain and inflammation, thus becoming a new type of anti-inflammatory and analgesic target. However, there is no consensus on the role of this channel in mechanical hyperalgesia caused by inflammation. Here, we aim to explore the role and underlying mechanism of the inflammasome inhibitor CY-09 in two classic inflammatory pain models. We evaluated pain behavior on animal models, cytokine levels, intracellular Ca2+ levels, transient TRPA1 expression, NF-κB transcription, and NLPR3 inflammasome activation. Consistently, CY-09 reduced the production of inflammatory cytokines, intracellular Ca2+ levels, and the activation of TRPA1 by inhibiting the activation of inflammasomes, thereby reducing the proinflammatory polarization of macrophages and alleviating animal pain and injury. Importantly, AITC (TRPA1 agonist) significantly reversed the analgesic effect of CY-09, indicating that TRPA1 was involved in the analgesic effect of CY-09. Our findings indicate that CY-09 relieves inflammation and pain via inhibiting TRPA1-mediated activation of NLRP3 inflammasomes. Thus, NLRP3 inflammasome may be a potential therapeutic target for pain treatment and CY-09 may be a pharmacological agent to relieve inflammatory pain, which needs further research.
The Use of Screening Algorithm to Defer Blood Donors with Subclinical Malaria
Authors: Acheampong Desmond Omane , Aninagyei Enoch
Published: August 2021

Plasmodium falciparum infection in blood donors is common in malaria endemic countries, including Ghana. To date, there are no established exclusion criteria to defer a donor carrying malaria parasites. Therefore, based on significant independent variables identified in this study, donor malaria screening algorithm was developed to be used by blood banks to screen blood donors for subclinical malaria. Each significant variable was weighted one (1) point and its alternative response was weighted negative one (−1) point. Accumulation of the points determines the risk level of the donor. These weighted points were used to categorize infected donors as having negligible (<2 points), tolerable (3-4 points), undesirable (5–8 points), or intolerable (>9 points) risk. Based on accumulated weight of ≥5 points, the algorithm was 94.7% (54/57) sensitive but 82% (298/364) specific. With this level of specificity, 18% of the blood donors without malaria would have been deferred. Therefore, it is imperative that all donors with accumulated risk ≥5 be screened for malaria using either malaria rapid test kit or microscopy.
Prediction of Frequency Response Function for Cylindrical Thin-Walled Workpiece with Fixture Support Constraints
Authors: Jia Jinjie , Sun Yuwen , Niu Jinbo
Published: August 2021

Auxiliary fixtures are widely used to enhance the rigidity of cylindrical thin-walled workpieces (CTWWs) in the machining process. Nevertheless, the accurate and efficient prediction of frequency response function (FRF) for the workpiece-fixture system remains challenging due to the complicated contact constraints between workpiece and fixture. This paper proposes an analytical solution for the comprehensive FRF analysis of the CTWW-fixture system. Firstly, based on the vector mechanics, the mode shape functions of the workpiece are presented using the classical theory of thin shell. The variable separation method is utilized to deal with the inter-mode coupling of the workpiece. Secondly, the motion equation of the CTWW with fixture constraints is established using analytical mechanics from the viewpoint of energy balance. Finally, the FRFs of the CTWW-fixture system are derived by means of modal superposition. Experimental modal tests verify that the predicted FRFs are in good agreement with the measured curves.
Hermite–Hadamard-Type Inequalities for the Generalized Geometrically Strongly Modified
h
-Convex Functions
Authors: Yu Xishan , Saleem Muhammad Shoaib , Waheed Shumaila , Khan Ilyas
Published: August 2021

Convexity theory becomes a hot area of research due to its applications in pure and applied mathematics, especially in optimization theory. The aim of this paper is to introduce a broader class of convex functions by unifying geometrically strong convex function with

h

convex functions. This new class of functions is called as generalized geometrically strongly modified

h

-convex functions. We established Hermite–Hadamard-type inequalities for the generalized geometrically strongly modified

h

-convex functions. Our results can be considered as generalization and extension of literature.
NIR Irradiation Based on Low‐Power LED Drive Module Design for Fat Reduction
Authors: Yoon Ki-Cheol , Kim Kwang Gi
Published: August 2021

In this study, we designed a low‐power visible ray (V) drive module based on a light‐emitting diode (LED) to initiate fat reduction using light source irradiation. A chemical phantom of muscle and fat was fabricated, and the performance of the proposed LED drive module was tested using this chemical phantom. The LED light source could reduce fat by irradiating the skin 4–5 cm deep. The device exhibits a negative feedback and parallel amplification to maintain a stable circuit based on low‐power consumption. Muscles have a high‐water content and low impedance, whereas fats have a low water content and significant salt content. Therefore, fat exhibits high impedance. Chemical phantoms were fabricated according to these impedance values, and the fat reduction effect using the LED circuits was analyzed. When the fat phantom was irradiated by the light source, the fat impedance lowered, and we confirmed that fat reduction could be obtained. This study is expected to be applicable to family medicine and weight management health care.
Internet of things and fuzzy logic for smart street lighting prototypes
Authors: Eriyadi Mindit , Abdullah Ade Gafar , Hasbullah Hasbullah , Mulia Sandy Bhawana
Published: August 2021

Internet of things (IoT) and fuzzy logic are very useful in increasing the efficiency and effectiveness of a system; this study applies both to the street lighting systems. The prototype of a street lighting control and monitoring system has been completed. The status of lights that are on or off and the value of the light intensity can be monitored by using IoT. The intensity of the light is fuzzy controlled by utilizing the presence of vehicles and pedestrians around the lights. The prototype is made with a scale against real conditions. Data is processed and transmitted using a microcontroller and Wi-Fi on the IoT module. Mobile applications have been used on smartphone interfaces to monitor and control lamps wherever they are connected to the Internet. Changes in the status of lights to turn on or off are done by the relay module. The fuzzy light intensity control system uses sensors and microcontrollers by utilizing the presence of vehicles and pedestrians around the lights. Performance evaluation has been carried out on a miniature street lighting with the results of monitoring and control following its function. An analysis of the resulting energy savings has been demonstrated.
Applying fuzzy proportional integral derivative on Internet of things for figs greenhouse
Authors: Riansyah Andi , Mulyono Sri , Roichani M.
Published: August 2021

Indonesia is an agrarian country where most of population work as farmers. Various planting media have been developed in Indonesia such as using greenhouses. Greenhouse is one of very promising planting media for plant cultivators, because it can be a solution to challenges of extreme climate change. In a greenhouse, the state of the room can be easily controlled using technologies such as automatic watering systems, air temperature control, air humidity and soil moisture. This research focuses on figs by applying fuzzy proportional integral derivative (FPID) as artificial intelligence on the Internet of things (IoT) for greenhouses. It uses Tsukamoto method serves to monitor air conditions and soil conditions and then it is coupled with proportional integral derivative (PID) control to control air temperature, air humidity, and soil moisture so that it is always in the ideal condition of figs in greenhouse. By implementing FPID on IoT for greenhouse, the development of figs in greenhouse can be optimized because air and soil conditions can be maintained in ideal conditions.
Segmentation atrioventricular septal defect by using convolutional neural networks based on U-NET architecture
Authors: Sapitri Ade Iriani , Nurmaini Siti , Sukemi Sukemi , Rachmatullah M. Naufal , Darmawahyuni Annisa
Published: August 2021

Congenital heart disease often occurs, especially in infants and fetuses. Fetal image is one of the issues that can be related to the segmentation process. The fetal heart is an important indicator in the process of structural segmentation and functional assessment of congenital heart disease. This study is very challenging due to the fetal heart has a relatively unclear structural anatomical appearance, especially in the artifacts in ultrasound images. There are several types of congenital heart disease that often occurs namely in septal defects it consists of the atrial septal defect, ventricular septal defect, and atrioventricular septal defect. The process of identifying the standard of the heart, especially the fetus, can be identified with a 2D ultrasound video in the initial steps to diagnose congenital heart disease. The process of diagnosis of fetal heart standards can be seen from a variety of spaces, i.e., 4 chamber views. In this study, the standard semantic segmentation process of the fetal heart is abnormal and normal in terms of the perspective of 4 chamber views. The validation evaluation results obtained in this study amounted to 99.79% pixel accuracy, mean iou 96.10%, mean accuracy 97.82%, precision 96.41% recall 95.72% and F1 score 96.02%.
Deep ensemble learning for skin lesions classification with convolutional neural network
Authors: Pratiwi Renny Amalia , Nurmaini Siti , Rini Dian Palupi , Rachmatullah Muhammad Naufal , Darmawahyuni Annisa
Published: August 2021

One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.
Automatic cerebrovascular segmentation methods-a review
Authors: Taher Fatma , Prakash Neema
Published: August 2021

Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.
A compact deep learning model for Khmer handwritten text recognition
Authors: Annanurov Bayram , Noor Norliza
Published: August 2021

The motivation of this study is to develop a compact offline recognition model for Khmer handwritten text that would be successfully applied under limited access to high-performance computational hardware. Such a task aims to ease the ad-hoc digitization of vast handwritten archives in many spheres. Data collected for previous experiments were used in this work. The oneagainst-all classification was completed with state-of-the-art techniques. A compact deep learning model (2+1CNN), with two convolutional layers and one fully connected layer, was proposed. The recognition rate came out to be within 93-98%. The compact model is performed on par with the state-of-theart models. It was discovered that computational capacity requirements usually associated with deep learning can be alleviated, therefore allowing applications under limited computational power.
A neural network combined with sliding mode controller for the two-wheel self-balancing robot
Authors: Nguyen Duc-Minh , Nguyen Van-Tiem , Nguyen Trong-Thang
Published: August 2021

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.
Proposing WPOD-NET combining SVM system for detecting car number plate
Authors: Huu Phat Nguyen , Quoc Cuong Vu
Published: August 2021

Nowadays, there are many smart parking lots using plate detection system to control in/out vehicles. However, the disadvantages of systems are a fixed environment and necessity of manual labor and requirement of checkpoints in entrances. To solve the problems, a novel algorithm for wide-angle detecting car number plate using warped planar object detection (WPOD-NET) and a modified support vector machine (SVM) system is proposed. Comparing to other models, the proposal improves not only the range of detection angle but also the accuracy of detecting in shady conditions. The results show that the accuracy of proposal model is up to 95.1% with 1000 testing images in various scenarios.
Spatial decision tree model for garlic land suitability evaluation
Authors: Nurkholis Andi , Sitanggang Imas Sukaesih , Annisa Annisa , Sobir Sobir
Published: August 2021

Predicting land and weather characteristics as indicators of land suitability is very important in increasing effectiveness in food production. This study aims to evaluate the suitability of garlic land using spatial decision tree algorithm. The algorithm is the improvement of the conventional decision tree algorithm in which spatial join relation is included to grow up spatial decision tree. The spatial dataset consists of a target layer that represents garlic land suitability and ten explanatory layers that represent land and weather characteristics in the study areas of Magetan and Solok district, Indonesia. This study generated the best spatial decision trees for each study area. On Magetan dataset, the best model has 33 rules with 94.34% accuracy and relief variable as the root node, whereas on Solok dataset, the best model has 66 rules with 60.29% accuracy and soil texture variable as the root node.
Design of grip strength measuring system using FSR and flex sensors using SVM algorithm
Authors: Biju Soly Mathew , Sheikh Hashir Zahid , Malek Mohamed Fareq , Oroumchian Farhad , Bell Alison
Published: August 2021

This paper proposes a design of a complete system to identify weak grip strength that is caused by multiple factors like ageing, diseases, or accidents. This paper presents a grip measurement system that comprises of force sensing resistor and flex sensor to evaluate the condition of the hand. The system is tested by gripping a pencil and a cylindrical object using the glove, to determine the condition of the hand. Force sensitive resistor (FSR) evaluates the force applied by the different parts of the palm on the object being grasped. Flex sensor evaluates the bending of the fingers and thumb. The data from the sensors is then compared with existing data to evaluate the state of the hand. The data from the sensors is stored on the personal computer (PC) through serial communication. A model is trained using the data from the sensors, which determine if the grip strength of the user is weak or strong. The model is also trained to differentiate between two modes that are pen mode and object mode. The model achieved an accuracy of 90.8 percent using support vector machine (SVM) algorithm. This glove can be deployed in medical centers to assist in grip strength measurement.
An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking
Authors: Nakrani Naitik , Joshi Maulin M.
Published: August 2021

In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.
Human behavior scoring in credit card fraud detection
Authors: Sadgali Imane , Sael Nawal , Benabbou Faouzia
Published: August 2021

Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.