bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Deep Learning Intelligent Laser Scanning Technology in Mural Digitization
Authors: Dong Zhiming
Published: August 2022

Ancient Chinese murals have a long history and a large number of types. They are witnesses to the development of ancient Chinese civilization. They still have important historical, artistic, and scientific values, as well as other values such as cultural relics, economy, and missionary education. Yet, the clear and smooth images of earlier have been damaged and covered by fog after the ancient murals were destroyed by human being and the nature. Therefore, the ancient murals that exist till now have been bothered by different damages and sickness. Protection and prevention are needed the most. In recent years, the application of scientific and technological means has played a role in the protection of ancient murals, making the work methods of cultural relics protection more scientific and diverse. In the context of the increasingly rich digital protection of cultural relics, the protection of murals requires more innovative work. However, at present, the resolution of ancient murals is low and the texture details are ambiguous, which leads to the problems of insufficient viewing and low research value. This paper focuses on ancient Chinese murals and conducts exploration on the phenomenon of mural damage and blurred colors. The deep learning intelligent laser scanning technology is used to extract the damaged ancient mural images. In this thesis, the images of murals have been restored using the new-super resolution technology to achieve the optimal mural images so that the mural images are more beautiful and artistic, which provides new ideas for the protection of murals.
Mechanical Behavior of Q460 High‐Strength Steel under Low‐Cycle Fatigue Loading
Authors: Han Xue , Chang Min , Li Haifeng , Cao Hualin , Cao Baoan
Published: August 2022

To investigate the mechanical properties of Q460 high‐strength steel under repeat tensile loading, 45 specimens were prepared for repeated tensile tests. The specimens are tested under 16 different loading regimes. The effects of welded joints, specimen shape and dimensions, and loading modes on the mechanical properties of the steel were analyzed. In addition, a finite element model of the specimen under cyclic load was established with ANSYS and the numerical results were compared with those of the experimental tests. This finite element model can accurately simulate the deformation characteristics of Q460 steel specimens. Experimental studies have found that the welded joints have adverse effects on the mechanical properties of this material. The cumulative effect of fatigue damage on the welded specimens was significant, and the ductility of welded specimens was poor under cyclic loading. The loading mode had a major impact on the results of the material tests. As the number of loading cycles increased, the cumulative effect of fatigue damage on the specimens was more evident, and the length, size, and cross‐sectional shape of the tensile zone all affected the stiffness of the specimen. Finally, based on the experimental research and numerical analysis results, a design formula for the tensile strength of Q460 steel under repeated loading was proposed. This formula can serve as a reference for the application of Q460 steel in seismic engineering.
A New Model for Predicting Nonsentinel Lymph Node Metastasis in Early-Stage Breast Cancer Using MMP15
Authors: Zeng Xue , Li Yubing , Sun Chaonan , Liu Zhuang , Zhao Jiaming , Ma Xinchi , Zhang Yanyu , Zhang Na
Published: August 2022

Background. In early-stage breast cancer (BC) patients, 40–70% of lymph node metastases are limited to the sentinel lymph nodes (SLNs). Patients at low risk for nonsentinel lymph node (NSLN) metastasis should be exempt from axillary lymph node dissection (ALND) or regional lymph node radiotherapy (RNI). Methods. The present study included 237 female early-stage BC patients with positive SLNs who received ALND. Based on the clinicopathological factors of the 158 patients in the training cohort, multivariate analysis was used to determine the independent risk factors for NSLN metastasis, which were used to establish the NSLN metastasis prediction model. The calibration and discrimination of this model were tested with the training and validation cohorts and compared to the Memorial Sloan Kettering Cancer Center (MSKCC) model. Results. Tumor size, neural invasion, lymphovascular invasion, expression of matrix metalloproteinase 15 (MMP15) in the cytoplasm, and the number of positive SLNs were statistically significant by multivariate analysis (

P
<
0.05

), which were used to establish the new model. The MSKCC model was verified by the training cohort, and the area under the receiver-operating characteristic (ROC) curve was 0.733 (95% CI: 0.650–0.816), which was less than that of the new model (0.824; 95% CI: 0.760–0.889). The area under the ROC curve in the validation cohort for the new model was 0.773 (95% CI: 0.669–0.877), and the calibration performed well. The false-negative rates were 3.2%, 6.5%, and 14.5% for the predicted probability cut-offs of 50%, 60%, and 70%, respectively. Conclusions. The new model included five variables: tumor size, neural invasion, lymphovascular invasion, cytoplasmic MMP15 expression, and the number of positive SLNs. The model with a cut-off of 60% could accurately identify low-risk patients with NSLN metastasis.
Driving Risk Analysis Based on Driving Experience at Hook-Turn Intersection Using the Emerging Virtual Reality Technology
Authors: Oh Taeho , Xu Yanping , Li Zhibin , Kim Inhi
Published: August 2022

The hook turn, which is rarely seen outside of Melbourne, Australia, reduces congestion in narrow road spaces shared with trams. Australia allows people from 44 nations to convert their home country driver’s license to an Australian driver’s license without a driving test. Visitors who have never heard of the hook-turn experience difficulty driving following the new traffic rule. From this aspect, investigating how inexperienced drivers encounter the hook-turn intersection is valuable for safety reasons. A driving simulator including virtual reality technology is developed to evaluate the level of safety of human driving behavior. The simulator in this research was developed by integrating Vissim and Unity3D embedded head-mounted display and driving devices to ensure a better driving experience. This research presented the development of a robust virtual reality driving simulator. It investigated how nonexperienced drivers respond to a completely new road condition. The results were compared with microsimulation outcomes (here, Vissim). The results showed that a human-driven car had a higher collision risk than a computer-driven car. The trajectories of the driver type were statistically different (t = 6.03, p 0.01, in the case of time-to collision ≤1.5 between experienced and computerized drivers). Participant responses to a postexperiment survey found that the simulator was realistic (4.31 out of 5.00), which could help beginner drivers (4.00 out of 5.00). Therefore, the simulator can be utilized for safety-related research as well as drivers’ training.
Rib Fracture Detection with Dual-Attention Enhanced U-Net
Authors: Zhou Zhengyin , Fu Zhihui , Jia Juncheng , Lv Jun
Published: August 2022

Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net’s skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.
Influence of Overweight and Obesity on Morbidity and Mortality among Hospitalized Patients in Sri Lanka: A Single-Center Analysis
Authors: Dilrukshi M. D. S. A. , Thotamuna V. , Senarath Yapa D. J. , De Silva L. , Ranasinghe P. , Katulanda P.
Published: August 2022

Background. Current evidence regarding the association between overweight and obesity and in-hospital morbidity and mortality is inconsistent and South Asian populations are underrepresented. Methods. Data relevant to anthropometry, hospital outcomes, complications, and medical diagnoses of all acute medical admissions to the National Hospital of Sri Lanka were collected over a period of 3 months. Analysis was performed with WHO international (ICs) and Asian obesity cut-offs (ACs). Results. Sample size was 2,128 (median age: 57 years [IQR: 42, 67], males: 49.7%). High prevalence of overweight (23.5%), generalized obesity (10.4%), central obesity (28.5%), and underweight (15.4%) was observed (ICs). Patients with either generalized or central obesity had significantly higher in-hospital mortality (4.8% versus 2.5%,

p
=
0.031

) and acute kidney injury (AKI) (3.9% versus 1.2%) (

p
=
0.001

) compared to normal weight. With ACs, overweight and obesity prevalence increased, without any significant increment in morbidity and mortality, but median length of hospital stay was significantly reduced in patients with generalized obesity compared to normal (3 [IQR: 2, 5] versus 4 [IQR: 2, 6],

p
=
0.014

). Infections (44.4%) and cardiovascular diseases (CVDs) (25.9%) were the most common causes of admission. Overweight and generalized obesity or central obesity were associated with increased prevalence of acute CVDs and CVD risk factors and lower prevalence of acute infections, whilst underweight showed an inverse association. Conclusion. A double burden of malnutrition and diseases were noted among hospital admissions, with obesity being a risk factor for in-hospital all-cause mortality and AKI. Overweight and obesity were associated with increased CVDs and reduced infections. Larger prospective studies are required to characterize these associations among South Asians.
Ordering Decisions of Supply Chain with Competition and Dual‐Fairness Concern
Authors: Liu Guangdong , Song Jingxiu , Chen Jinggui , Li Ziyang , Zhu Huagui
Published: August 2022

This study studies a supply chain consisting of one supplier and two retailers and analyzes the optimal decisions of ordering quantity in four types of no fairness concern, horizontal fairness concern, vertical fairness concern, and dual‐fairness concern and the impacts of fairness concern on supply chain. The results show that (1) vertical fairness concern can decrease the ordering quantity of the retailer with vertical fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern; (2) horizontal fairness concern can increase the ordering quantity of the retailer with horizontal fairness concern and improve the ordering quantity of the retailer without fairness concern, but does not influence the wholesale price; and (3) dual‐fairness concern can decrease the ordering quantity of the retailer with dual‐fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern as a whole. The numerical analysis also proves the findings.
Prediction of Exit Thickness and Its Compensation after Snake Rolling of Aluminum Alloy Thick Plate
Authors: Wang Xiuliang , Zhang Weidong , Ai Yibo
Published: August 2022

Asymmetrical deformation zone of the snake rolling process results in complex metal flow and unknown edge of the deformation zone. In this study, three assumptions about the location of exit thickness appear and the exit thickness is calculated by those three assumptions. By comparing the exit thickness obtained by formulas and the finite element method, one formula with high accuracy is verified and the plastic deformation of the thick plate under several snake rolling conditions is obtained. The results show that through comparing the minimum thickness in different assumptions, the exit of the plastic deformation zone is located at the midpoint of the line between two work rolls’ axis and the exit thickness is approximately equal to the length of the line which includes that midpoint and locates between the two work rolls in the vertical direction. Then with the increasing offset distance, the exit thickness increases and that leads to less deformation inside the plate. For maintaining the exit thickness equal to the preset value, an exit thickness compensation model is established according to the geometry of the deformation zone and verified by the results of simulations.
Menstrual Factors, Reproductive History and Liver Cancer Risk: Findings from a Prospective Cohort Study in Chinese Women
Authors: Tuo Jia-Yi , Li Hong-Lan , Wang Jing , Fang Jie , Tan Yu-Ting , Xiang Yong-Bing
Published: August 2022

Background:

Many studies suggested that menstrual and reproductive factors affected the gender disparity in liver carcinogenesis, but the results were inconsistent. Moreover, there are few studies in Asian populations. Therefore, our study was to explore the association of menstrual and reproductive factors on liver cancer risk in Chinese women.

Methods:

72,807 women were recruited in 1996 to 2000 and followed until the end of 2016 in Shanghai, China. Cox regression models were used to estimate HRs and 95% confidence intervals (CIs) for the association of menstrual and reproductive factors with liver cancer.

Results:

258 liver cancer cases were identified during 1,269,531 person-years of follow-up. In premenopausal and postmenopausal women, hormone replacement therapy (HRT) and injective contraceptives were positively associated with liver cancer risk respectively (HR, 1.23, 95% CI, 1.15–1.30; HR, 1.23, 95% CI, 1.17–1.30; HR, 1.07, 95% CI, 1.05–1.10; HR, 1.08, 95% CI, 1.05–1.11), while older age at menopause, longer reproductive period and fewer live births were associated with reduced risk, especially among postmenopausal women (Ptrend < 0.05). In addition, liver cancer risk was elevated in postmenopausal women who received hysterectomy (HR, 1.07; 95% CI, 1.04–1.11), oophorectomy (HR, 1.05; 95% CI, 1.01–1.10) or oral contraceptives (HR, 1.06; 95% CI, 1.03–1.08). No association was found between age at menarche and liver cancer risk. Similar results were observed when excluding participants with less than 2 follow-up years.

Conclusions:

The findings suggested that female sex hormones could play significant roles in liver carcinogenesis.

Impact:

Our study was the first population-based cohort to provide epidemiology evidence of menstrual and reproductive factors on liver cancer risk in Chinese women.
The Timing, the Treatment, the Question: Comparison of Epidemiologic Approaches to Minimize Immortal Time Bias in Real-World Data Using a Surgical Oncology Example
Authors: Duchesneau Emilie D. , Jackson Bradford E. , Webster-Clark Michael , Lund Jennifer L. , Reeder-Hayes Katherine E. , Nápoles Anna M. , Strassle Paula D.
Published: August 2022

Background:

Studies evaluating the effects of cancer treatments are prone to immortal time bias that, if unaddressed, can lead to treatments appearing more beneficial than they are.

Methods:

To demonstrate the impact of immortal time bias, we compared results across several analytic approaches (dichotomous exposure, dichotomous exposure excluding immortal time, time-varying exposure, landmark analysis, clone-censor-weight method), using surgical resection among women with metastatic breast cancer as an example. All adult women diagnosed with incident metastatic breast cancer from 2013–2016 in the National Cancer Database were included. To quantify immortal time bias, we also conducted a simulation study where the “true” relationship between surgical resection and mortality was known.

Results:

24,329 women (median age 61, IQR 51–71) were included, and 24% underwent surgical resection. The largest association between resection and mortality was observed when using a dichotomized exposure [HR, 0.54; 95% confidence interval (CI), 0.51–0.57], followed by dichotomous with exclusion of immortal time (HR, 0.62; 95% CI, 0.59–0.65). Results from the time-varying exposure, landmark, and clone-censor-weight method analyses were closer to the null (HR, 0.67–0.84). Results from the plasmode simulation found that the time-varying exposure, landmark, and clone-censor-weight method models all produced unbiased HRs (bias −0.003 to 0.016). Both standard dichotomous exposure (HR, 0.84; bias, −0.177) and dichotomous with exclusion of immortal time (HR, 0.93; bias, −0.074) produced meaningfully biased estimates.

Conclusions:

Researchers should use time-varying exposures with a treatment assessment window or the clone-censor-weight method when immortal time is present.

Impact:

Using methods that appropriately account for immortal time will improve evidence and decision-making from research using real-world data.
A Phase I Trial of CT900, a Novel α-Folate Receptor–Mediated Thymidylate Synthase Inhibitor, in Patients with Solid Tumors with Expansion Cohorts in Patients with High-Grade Serous Ovarian Cancer
Authors: Banerjee Susana , Michalarea Vasiliki , Ang Joo Ern , Ingles Garces Alvaro , Biondo Andrea , Funingana Ionut-Gabriel , Little Martin , Ruddle Ruth , Raynaud Florence , Riisnaes Ruth , Gurel Bora , Chua Sue , Tunariu Nina , Porter Joanna C. , Prout Toby , Parmar Mona , Zachariou Anna , Turner Alison , Jenkins Ben , McIntosh Stuart , Ainscow Ed , Minchom Anna , Lopez Juanita , de Bono Johann , Jones Robert , Hall Emma , Cook Natalie , Basu Bristi , Banerji Udai
Published: August 2022

Purpose:

CT900 is a novel small molecule thymidylate synthase inhibitor that binds to α-folate receptor (α-FR) and thus is selectively taken up by α-FR–overexpressing tumors.

Patients and Methods:

A 3+3 dose escalation design was used. During dose escalation, CT900 doses of 1–6 mg/m2 weekly and 2–12 mg/m2 every 2 weeks (q2Wk) intravenously were evaluated. Patients with high-grade serous ovarian cancer were enrolled in the expansion cohorts.

Results:

109 patients were enrolled: 42 patients in the dose escalation and 67 patients in the expansion cohorts. At the dose/schedule of 12 mg/m2/q2Wk (with and without dexamethasone, n = 40), the most common treatment-related adverse events were fatigue, nausea, diarrhea, cough, anemia, and pneumonitis, which were predominantly grade 1 and grade 2. Levels of CT900 more than 600 nmol/L needed for growth inhibition in preclinical models were achieved for >65 hours at a dose of 12 mg/m2. In the expansion cohorts, the overall response rate (ORR), was 14/64 (21.9%). Thirty-eight response-evaluable patients in the expansion cohorts receiving 12 mg/m2/q2Wk had tumor evaluable for quantification of α-FR. Patients with high or medium expression had an objective response rate of 9/25 (36%) compared with 1/13 (7.7%) in patients with negative/very low or low expression of α-FR.

Conclusions:

The dose of 12 mg/m2/q2Wk was declared the recommended phase II dose/schedule. At this dose/schedule, CT900 exhibited an acceptable side effect profile with clinical benefit in patients with high/medium α-FR expression and warrants further investigation.
Targeted Therapies and Utility of the Lung-molGPA in Non-Small-Cell Lung Cancer Patients with Brain Metastases
Authors: Cacho-Díaz Bernardo , Cuapantécatl Laura D. , Garcilazo-Reyes Ytel J. , Cabrera-Miranda Luis , Hernandez-Martinez Juan-Manuel , Arrieta Oscar
Published: August 2022

Introduction: Therapeutic advances have increased the survival of non-small-cell lung cancer (NSCLC) patients as well as the lifetime risk of being diagnosed with brain metastases (BM). Although BM have historically been associated with poor prognosis, it is unclear whether they remain a strong predictor of reduced survival. This study aimed to evaluate the prognostic value of BM and the utility of the Lung-molGPA. Methods: This single-center retrospective database analysis included 1,393 NSCLC patients with newly diagnosed BM who registered at the Instituto Nacional de Cancerología of Mexico (INCan) from 2010 to 2020. The Kaplan-Meier method and log-rank test were used for the survival analysis. Survival times were calculated from the date of NSCLC diagnosis (OS), or BM diagnosis, to the date of death or last follow-up. Cox proportional-hazards models were used to calculate the hazard ratio (HR) for death and the significance of the parameters evaluated. Results: Out of 1,058 patients who underwent genetic testing for epidermal growth factor receptor (EGFR) mutations and/or anaplastic lymphoma kinase (ALK) rearrangements, 650 had a positive tumor mutational/rearrangement status (543 had EGFR mutations, 104 had ALK rearrangements, and 3 had both EGFR and ALK alterations). Median OS did not differ between patients with BM and without BM (17.7 months [95% CI, 15.4–19.0] vs. 16.6 months [95% CI, 14.3–19.0]; p = 0.362). In contrast, the presence of BM was associated with worse OS in patients with a negative tumor mutational status (HR: 1.225 [95% CI, 1.041–1.443]; p = 0.015), who did not receive TKI therapy (HR: 1.269 [95% CI, 1.082–1.488]; p = 0.003), or with non-adenocarcinoma histology (HR: 1.582 [95% CI, 1.118–2.238]; p = 0.01). The median survival after BM diagnosis was 4.27, 6.96, 14.68, and 18.89 months for adenocarcinoma patients with Lung-molGPA scores 0–1, 1.5–2, 2.5–3, and 3.5–4, respectively (p < 0.0001). For non-adenocarcinoma patients with Lung-molGPA scores 0–1, 1.5–2, and 2.5–3, the corresponding estimates were 0.95, 2.89, and 9.39 months, respectively (p < 0.0001). Conclusions: These results show that the prognosis of NSCLC patients with BM is no longer uniformly poor and should be individually assessed. Furthermore, the validity of the Lung-molGPA was confirmed in an independent population from a different geographical region.
Four Cases of Serum Copper Excess in Patients with Renal Anemia Receiving a Hypoxia-Inducible Factor-Prolyl Hydroxylase Inhibitor: A Possible Safety Concern
Authors: Nakamura Hironori , Kurihara Shigekazu , Anayama Mariko , Makino Yasushi , Nagasawa Masaki
Published: August 2022

Copper is an indispensable trace metal element and is mainly absorbed in the stomach and small intestine and excreted into the bile. Hypoxia-inducible factor-prolyl hydroxylase inhibitors (HIF-PHIs) have emerged as a novel approach for renal anemia management. Many intestinal genes, including divalent metal transporter 1, duodenal cytochrome B, and copper transporter ATPase7A, related to iron absorption are transactivated by HlF-α, during iron deficiency. We first report 4 cases of patients with renal anemia who showed excess in serum copper level during roxadustat or daprodustat treatment, which were decreased to the normal level after discontinuing HIF-PHIs and changing the drug to darbepoetin alfa, suggesting that HIF-PHI is associated with serum copper excess. HIF-PHI modulates iron metabolism, such as iron absorption, sequestration, and mobilization, and may increase serum copper levels by increasing copper absorption and/or redistribution of copper in tissues. Therefore, it is urgent to examine the correlation between HIF-PHI use and serum copper levels because copper excess might be involved in several acute or chronic adverse events.
Erythrodermic Psoriasis Managed with Risankizumab
Authors: Alajlan Abdulmajeed , Madani Abdulaziz , Qadoumi Tala Ammar , Aljaloud Alhanouf , Alessa Mohammed
Published: August 2022

Erythrodermic psoriasis (EP) is a severe, often refractory, variant of psoriasis. Due to the high morbidity and mortality rate associated with EP and other causes of erythroderma, they are often classified as dermatologic emergencies. EP is usually a therapeutic challenge, where topical and conventional systemic therapies have yielded a less than satisfactory result in several patients. Furthermore, there are a limited number of studies evaluating other therapeutic modalities, such as biologic agents, with no clear treatment guidelines. In this case report, we present a patient who was diagnosed as a case of EP and showed an impressive response to risankizumab.
Keloid-like Pilomatricoma of the Auricle: A Case Report and Review of Literature
Authors: Dewi Shinta Trilaksmi , Rinonce Hanggoro Tri , Etnawati Kristiana , Wirohadidjojo Yohanes Widodo
Published: August 2022

Pilomatricoma is a rare benign skin adnexal tumor arising from hair matrix cells. It usually manifests as a firm-to-hard, well-circumscribed, nontender nodule often mistaken as a dermoid cyst. However, its clinical presentation has been reported to mimic keloid. In addition, although it occurs most frequently in the head and neck area, pilomatricoma is rarely reported on the auricle. Herein, we reported a case of pilomatricoma occurring on the posterior part of a 9-year-old girl’s auricle. Unlike classic pilomatricoma, the tumor closely resembled a keloid. Nevertheless, histological examination following excision of the tumor revealed a dermal tumor arranged in solid nests consisting of basaloid and ghost cells along with foreign body reaction, calcification, and ossification, confirming the diagnosis of pilomatricoma. The rarity, atypical clinical presentation, and unique location of this tumor contributed to the misdiagnosis of this case. Therefore, we reviewed previous cases of pilomatricoma of the auricle reported in the literature to provide a comprehensive understanding of this rare entity.
Current Research Status of Metronomic Chemotherapy in Combination Treatment of Breast Cancer
Authors: Liu Jiaxuan , He Maiyue , Wang Zijing , Li Qiao , Xu Binghe
Published: August 2022

Background: Metronomic chemotherapy (MCT), termed sustained low-dose administration with minimal toxicity, is a new modality of conventional chemotherapy, a verified therapy alternative, and has acquired significant recognition and interest in oncology. Numerous clinical trials of MCT in combination with other treatments, including targeted therapies, biologics, and endocrine therapy, are in progress to obtain better results. Summary: We comprehensively described the clinical benefits of MCT in combination with other treatments in different molecular subtypes of breast cancer and assessed the feasibility of its adoption in varying phases of treatment. Due to the promising preclinical and clinical investigations, it is expected that MCT in combination with other treatments will enhance the advantages of this strategy and apply it to clinical practice. Key Message: MCT, in combination with other therapeutic interventions, will fully exploit the benefits of this strategy, ushering in a new paradigm in oncology treatment and driving the transformation of cancer into a more manageable chronic disease using newly developed treatment approaches.
On picture fuzzy ideals on commutative rings
Authors: Panpho Phakakorn , Yiarayong Pairote
Published: August 2022

In this paper, we focus on combining the theories of picture fuzzy sets on rings and establishing a new framework for picture fuzzy sets on commutative rings. The aim of this manuscript is to apply picture fuzzy set for dealing with several kinds of theories in commutative rings. Moreover, we introduce the notions of picture fuzzy ideals on commutative rings and some properties of them are obtained. Finally, we give suitable definitions of the operations of picture fuzzy ideals over a commutative ring, as composition, product and intersection.
A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model
Authors: Solikhin Solikhin , Lutfi Septia , Purnomo Purnomo , Hardiwinoto Hardiwinoto
Published: August 2022

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.
Hybrid security in AOMDV routing protocol with improved salp swarm algorithm in wireless sensor network
Authors: Sulaiman Yousif Hardan , Rashid Sami Abduljabbar , Hamdi Mustafa Maad , Abdulrahman Faiyadh Zaid Omar , Jaafar Sadiq Abdulrahman Sabah , Ahmed Ahmed Jamal
Published: August 2022

During these years the current trends shows a fast expansion in the field of wireless sensor network (WSN) based applications. Due to this much vulnerability are created and also coverage optimization becomes essential to improve overall performance. However, maximum of the model concentrates only on security or efficiency. In order to create a highly efficient protocol both concepts need to get concerted. So, we developed a protocol namely hybrid security in ad-hoc on-demand multipath distance vector (AOMDV) routing protocol with improved salp swarm algorithm (HSA-ISSA). This model is sub-divided into three sections. They are, wormhole attack and gray hole attack construction AOMDV protocol, improved salp swarm algorithm (SSA) model is used for weighted distance position updates which leads to improve the efficiency. And to secure the network from attacks we use hybrid security with the help of Diffie-Hellman key interchange algorithm and elliptic-curve cryptography (ECC) algorithm. During performance evaluation the proposed HS-ISSA protocol provide stable results in terms of message success rate (MSR), end to end delay (E2E_Delay), network throughput (NT), and average energy efficiency (AEE). Our HAS-ISSA protocol outperformed all the other earlier works by providing hybrid security, optimized coverage as well as energy efficiency to the wireless sensor networks.
The impact of training data selection on the software defect prediction performance and data complexity
Authors: Langgu Sinaga Benyamin , Ahmad Sabrina , Abal Abas Zuraida , Wahyu Anggarajati Antasena
Published: August 2022

Directly learning a defect prediction model from cross-project datasets results in a model with poor performance. Hence, training data selection becomes a feasible solution to this problem. Limited comparative studies investigating the effect of training data selection on the prediction performance have presented contradictory results. Those studies also did not analyze why a training data selection method underperforms. This study aims to investigate the impact of training data selection on the defect prediction model and data complexity measures. The method is based on an empirical comparison between prediction performance and data complexity measure before and after selection. This study compared 13 training data selection methods on 61 projects using six classification algorithms and measured the data complexity using six complexity measures focusing on overlap class, noise level, and class imbalanced ratio. Experimental results indicate that the best method for each dataset varies depending on the dataset and classifiers. The training data selection most affects noise rate and class imbalance. We concluded that critically selecting the training data method could improve the performance of the prediction model. We recommend dealing with noise and unbalanced classes when designing training data methods.
Robust sliding mode controller for buck DC converter in off-grid applications
Authors: Awada Emad , Radwan Eyad , Nour Mutasim
Published: August 2022

This paper presents a robust sliding mode controller of DC-DC buck converter for renewable energy applications, such as photovoltaic systems in off-grid configurations. Photovoltaic systems in off-grid configuration are exposed to significant variations in input voltage and power loads. The proposed sliding mode controller presents a simple and efficient method of continuously updating the duty cycle of a pulse width modulation unit (PWM) of a buck converter. The PWM unit is operated at constant switching frequency of 10 kHz carrier signal and varying duty cycle. The differences in input voltage and power load are treated as two bounded uncertainties, thus eliminating the need for input voltage sensor and output current sensors leaving the system with a single sensor required to measure the converter output voltage. That is, measured output voltage is compared with the reference voltage to continuously update the average duty cycle value of PWM unit. Adjustment of PWM duty cycle is performed while maintaining the sliding condition always fulfilled. The simulation results of the proposed controller showed robustness and accuracy against power load fluctuation, changes in desired output voltage, and variations in the input supply voltage that may result from the varying level of irradiance and temperature.
Neural network based seizure detection system using statistical package analysis
Authors: Rajendran Priyanka , Ganapathy Kirupa
Published: August 2022

Due to the unpredictable interruptions within the functions of the human brain, disturbance occurs and it affects the behavior of the human and is equally laid low with the frequent occurrence termed as seizures. Therefore, the proposed system detects the seizure using machine learning algorithms. The electroencephalogram (EEG) contains information of the brain to detect the seizure. The objective is to evaluate the performance of machine learning classifiers K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) by comparing the accuracy of the classifier. This work uses total of 11,500 EEG samples from the UCI machine learning repository. The seizure detection was done in two ways. First method, features extracted from the EEG signal and classification techniques are done to classify the seizure. The second method uses the principal component analysis algorithm to improve the significant selections of features from the dataset. The outcomes are analyzed using the statistical package for the social science (SPSS) tools. ANN with extracted functions achieved 96% of accuracy and significant efficiency of (p less than 0.05) in comparison with different machine learning classifiers. It would be prudent to conclude that the ANN demonstrated the best accuracy, sensitivity, and specificity.
Radiation effect of M-slot patch antenna for wireless application
Authors: Allbadi Yousif , Ibrahim Hamd Huda , H. Qaddoori Ilham
Published: August 2022

Today, the specific absorption rate has become an important and necessary measurement when designing and implementing any type of antenna. In recent years, various devices have appeared that use different frequencies for wireless communication systems, which are a source of electromagnetic radiation. The M-slot antenna is designed in this paper to operate in multi-band frequencies for wireless communications using computer simulation technology (CST) software 2020. The radiation effect for this antenna is calculated for tissue mass of the human fingertips, which consists of three layers (skin, meat, and bone), over a mass of 1 g and 10 g according to the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP) organization. The results are shown three applications in the communication system, which are Wi-Fi, worldwide interoperability for microwave access (Wi-Max) and, satellite X-band and, the value of specific absorption rate (SAR) increase with increased frequency.
Deep learning algorithms to improve COVID-19 classification based on CT images
Authors: Abu Owida Hamza , S. Migdadi Hassan , Mohamed Hemied Omar Salah , Fankur Alshdaifat Nawaf Farhan , Ahmad Abuowaida Suhaila Farhan , S. Alkhawaldeh Rami
Published: August 2022

In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized; however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%.
MyHalal: an interactive application
Authors: Xin Ying Wong , Fitri Zaaba Zarul
Published: August 2022

According to the Halal industry development corporation (HDC). Malaysia’s Halal industry market value is expected to achieve approximately RM614.36 billion by 2025. It indicates that the Halal industry has great potential in covering local and international markets. In Malaysia, the department of islamic development malaysia (JAKIM) is the official agency responsible for Islamic affairs. In most circumstances, the public can check the Halal status of products by accessing the official website. The searching via the website is quite tedious and complex. Therefore, this work bridges the gap by proposing a mobile application namely MyHalal (MH) as a proof of concept which significantly improved the current method. This application will assist users in verifying the Halal status of food products and premises. In this context, MH also introduces a barcode scanner to check the product listing in JAKIM with camera access. Besides that, users also can report an issue when they found any misconduct regarding Halal certification. This mobile application is developed together with the web-based application as the backbone for the administrator to manage technical matters. It is expected to be a user-friendly platform for the users and a channel for JAKIM to manage information and spread awareness about Halal.
Comparison of photovoltaic nanogrid implementation: a simple reliability model for frontier, remote, and disadvantaged areas
Authors: Ryandi Ryandi , Banjar-Nahor Kevin Marojahan , Hariyanto Nanang
Published: August 2022

This paper aims to evaluate the photovoltaic (PV) nanogrid implementation and its risk for a project at a remote location. Unlike the normal approach, this approach is based on a discontinuity project. The main advantage of the model is the simplicity to compute the expected lifetime and display the reliability curve. The reliability is calculated using state space equation and a quantitative approach evaluates the risk of PV nanogrid scenarios based on costs. The MATLAB/Simulink simulates the scenarios including the basic scenario of a PV system that is a PV nanogrid consisting of a PV array and inverter, scenario 1 that is a PV nanogrid with a battery system, scenario 2 is an extension of scenario 1 with a battery repair, and scenario 3 is a PV nanogrid connected to the grid. The result indicates that adding a battery system increases the reliability and the expected lifetime of the system, and the battery system’s maintenance makes it higher. Scenario 3 shows high reliability and a longer expected lifetime. The risk matrix shows the position of reliability and its impact on each scenario. This work can be used in practice as an objective assessment to electrify frontier, remote, and disadvantaged (3T) areas beside levelized cost of energy (LCOE).
Optimization, design and control of a photovoltaic/wind turbine/battery system in Mediterranean climate conditions
Authors: Mezzai Nabil , Belaid Saloua , Rekioua Djamila , Rekioua Toufik
Published: August 2022

The goal of this study is to design and optimize a photovoltaic/wind turbine/batteries system. The application is made in the area of Bejaia (Algeria), a Mediterranean region where the solar and wind energy are extremely exploitable in this area due to its geographical location. The total incident energy approach was used to develop the device under consideration. To optimize power, the fuzzy logic control (FLC) is applied and to highlight the benefits of this maximum power point tracking (MPPT) strategy, it was compared to perturb and observe (P and O) method. A power management control has been applied. The findings from the three different days are displayed and analyzed to demonstrate the applicability of the suggested system. The examined system is assessed using the Homer software to demonstrate the best feasible integration of the several sources at the Bejaia location. There has been an increase in renewable power, which is one of the study's key novelty and objectives, so less stress on batteries in a PV/wind system. This is owing to the suggested accurate sizing procedure and to the FLC algorithm. The findings of the suggested study under various solar irradiation and wind speed velocity profiles are presented to demonstrate its applicability.
Optimal backstepping controller for controlling DC motor speed
Authors: H. Abood Layla , Kadhim Oleiwi Bashra , Hussain Ali Ekbal
Published: August 2022

In this paper, squirrel search algorithm (SSA) is adapted to tune the optimal parameters of a backstepping controller for controlling speed in DC motor; Lyapunov stability theorem is applied to derive the control low to achieve system stability analysis. M-file and Simulink platform is used to simulate the response of the system, a comparison with conventional controllers is utilized, all gains are tuned using SSA method and integral time absolute error (ITAE) fitness function to test the efficient performance of the proposed controller also a comparison with other tuned different controllers is done. Transient response analysis is used to validating the proposed controller performance. The simulation results showed a stable response and efficint performance for the proposed controller when compared with the two conventional controllers used by 41.6% for PID controller and by 32% for PI controller in rise time while in settling time is superior by 36.97% for PID controller and by 41.82% for PI controlle these values led for achieving the desired speed in fast and stable response.
The color-adjustable phosphor Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ of WLEDs: luminescence and transfer of energy characteristics
Authors: Huu Phuc Dang , Xuan Le Phan
Published: August 2022

A high heat solid-state technique was used to produce a set of phosphor Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ (Sr:Mn). In Sr4La(PO4)3O, the luminescence characteristics, heat resistance, and energy conversion between Ce3+ and Tb3+ as well as Mn2+ were thoroughly researched. Introducing the activator Ce3+ particles can significantly increase the dim green-emitting from Tb3+ and the red-emitting from Mn via energy conversion. The color of the emissions might be changed by adjusting the Ce3+/Tb3+ and Ce3+/Mn2+ ion ratios. In the Sr4La(PO4)3O: 0.12 Ce3+,0.3 Mnz2+ specimen, we achieved white-emitting illumination, yielding hue coefficients (0.3326, 0.3298), showing that the phosphor Sr:Mn could have promising implications for white light-emitting diodes (WLEDs).
Embedded control unit design for energy management in smart homes
Authors: Mazen Abusharia Rawan , Mousa Al-Aubidy Kasim
Published: August 2022

This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.
A novel classification and clustering algorithms for intrusion detection system on convolutional neural network
Authors: Ramasamy Mathiyalagan , Vinitha Eric Pamela
Published: August 2022

At present data transmission widely uses wireless network framework for transmitting large volume of data. It generates numerous security problems and privacy issues which laid a way for developing IDS. IDS act as preventive technique in securing computer networks. Previously there are numerous metaheuristic and deep learning algorithms used in IDS for detecting threats. Some are affected by dynamic growth of feature spaces and others are degraded in performance during detection of threats. One fine-grained model for intrusion detection can be developed by selecting accurate features and testing them with the intelligent algorithms. Based on these explorations, in this research IDS is implemented with intelligence from preprocessing to feature classification. At first stage, data preprocessing is done using binning concept to reduce noise. Secondly feature selection is done dynamically using dynamic tree growth algorithm with fire fly optimization techniques. Finally, these features are processed using DTB-FFNN for detecting anomalies perfectly. This DTB-FFNN is evaluated with popular KDD dataset. Our proposed model cable news network (CNN)-classification is compared with existing intelligent techniques: feed forward deep neural network, support vectors machines, decision tree, and CNN-clustering is compared with k-means, density-based spatial clustering of applications with noise (DBSCAN). The experimental outcome proves that dynamic tree based FFNN and CNN-clustering produce higher accuracy than the existing models.