bluuebunny/crossref_metadata_2025 huggingface dataset:
Pollution and Risk Assessment of Heavy Metals in the Sediments and Soils around Tiegelongnan Copper Deposit, Northern Tibet, China
Authors: Luo Yuhu , Jia Qinxian
Published: November 2021

The surface sediments of the Rongna River and the surface soils around the Tiegelongnan copper deposit were collected, and the heavy metals Cu, Zn, Pb, Cr, Cd, As, Hg, and Ni were measured for their concentrations and health risk assessment. When the Rongna River passed through the Cu deposit area, the concentrations of Cu, Zn, As, Cd, Ni, and Hg in the surface sediments increased significantly, and the concentrations of Cu, Zn, and As exceeded the corresponding Grade II environmental quality standard. The heavy metals in the soil of the mining area were greater than the background value of the soil in Tibet. The geoaccumulation index indicated that the sediments of the river entering the mining area were very highly polluted by Cu and moderately polluted by Cd and Zn, and the soils in the mining area were moderately polluted by Cu. The potential ecological risk (PER) indices revealed that the sediments of the river entering the mining area had significantly high ecological risks, while the PER of the sediments away from the river section of the mining area was low, and the PER of the soils around the Cu deposit was moderate. The results of the health risk assessment indicated that the noncarcinogenic risks of heavy metals in sediments and soil of the mining area were within the acceptable range for adults and children. However, the carcinogenic risk of As and Cd in the sediment and As in the soil exceeds the relevant national standards, which may pose a certain risk to human health.
Student Behavior Analysis and Research Model Based on Clustering Technology
Authors: Li Guozhang , Alfred Rayner , Wang Xue
Published: November 2021

Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire school education management system. This study mainly introduces the application of student behavior analysis and research models based on clustering technology. This paper uses the application research of student behavior analysis and research model based on clustering technology, uses clustering technology to analyze student behavior, and reasonably analyzes the feasibility of KMEANS algorithm and campus data mining. The cluster analysis algorithm is used to divide students into different groups according to the characteristics of the students, and then, data analysis and data association rules’ mining are performed on each group of students. At the same time, the decision tree algorithm is used to predict the future of students based on the historical data of the students and the current data of the students. The development status of the school helps the school to understand the situation of the students in real time, make predictions and warnings for possible situations, provide personalized applications for teachers and students, and provide decision-making support for the management. It can be seen from the experimental analysis that the application of student behavior analysis and research models based on clustering technology has increased the efficiency of student education by 17%. The limitations of student behavior analysis and research on clustering technology provide good applications for the KMEANS algorithm. Analysis, discussion, and summary of the methods and approaches are obtained to enrich the academic research results.
An Efficient Herbal Approach for Treating Fungal Infection in Cervical Cancer Patients by Developing and Optimizing a Vaginal Suppository
Authors: Ghazwani Mohammed , Hani Umme , Osmani Riyaz Ali M. , Rahamathulla Mohamed , Begum M. Yasmin , Wahab Shadma , Gowda D. V. , Ravikumar Akhila Akkihebbal , kumar Honnavalli Yogish , Ather Hissana , Moin Afrasim
Published: November 2021

Aim. The study is aimed at developing curcumin suppositories as a promising approach for natural antifungal management of vaginal candidiasis in cervical cancer patients to eradicate side effects produced by current antifungal drugs. The objective of the study was to optimize the suppositories using optimal (custom) design employing Design-Expert 13 software to recognize the concentration of polyethylene glycols (PEG) and Poloxamer 407 and obtain a stable suppository. Methodology. Combinations of PEG 1500 (10%–40%), PEG 6000 (40%–60%), and Poloxamer 407 (5%–30%) were entered as factors, and the responses evaluated were hardness, deformation time, and % drug release. In addition, the formulation was also evaluated for visual examination, weight variation, pH determination, drug content, hardness test, disintegration time, melting zone, deformation time, in vitro drug release, antifungal activity, and stability tests. Results. Suppositories were devoid of holes and cracks, with a characteristic odor and a dark yellowish-orange color. All formulations passed the weight variation test. Formulations exhibited pH ranging from 5.5 to 6.5. Drug content was observed to be

98.65
±
0.041
%

–

99.85
±
0.041
%

. The hardness of the formulation was between 2.9 and 4.2 kg/cm2. The disintegration time ranged from

11
±
0.052
 
min

to

20
±
0.011
 
min

. The melting point was between

41
±
0
.

31

°

C

and

58
±
0
.

62

°

C

. Deformation time ranged from

10
±
0.45

to

35
±
0.52
 
min

. Most of the formulations resulted in 90% of drug release at 40 min, and the zone of inhibition noted was

19.6
±
0.4
 
mm

. All the selected factors have a significant effect on the response chosen for the study. Conclusion. The optimized curcumin vaginal suppository formulation can be an efficient herbal treatment devoid of side effects to treat vaginal candidiasis in cervical cancer patients.
SOCS1 Mediates Berberine‐Induced Amelioration of Microglial Activated States in N9 Microglia Exposed to β Amyloid
Authors: Guo Qi , Wang Chen , Xue Xiaorong , Hu Bin , Bao He
Published: November 2021

Attenuating β amyloid‐ (Aβ‐) induced microglial activation is considered to be effective in treating Alzheimer’s disease (AD). Berberine (BBR) can reduce microglial activation in Aβ‐treated microglial cells; the mechanism, however, is still illusive. Silencing of cytokine signaling factor 1 (SOCS1) is the primary regulator of many cytokines involved in immune reactions, whose upregulation can reverse the activation of microglial cells. Microglia could be activated into two different statuses, classic activated state (M1 state) and alternative activated state (M2 state), and M1 state is harmful, but M2 is beneficial. In the present study, N9 microglial cells were exposed to Aβ to imitate microglial activation in AD. And Western blot and immunocytochemistry were taken to observe inducible nitric oxide synthase (iNOS), Arginase‐1 (Arg‐1), and SOCS1 expressions, and the enzyme‐linked immunosorbent assay (ELISA) was used to measure inflammatory and neurotrophic factor release. Compared with the normal cultured control cells, Aβ exposure markedly increased the level of microglial M1 state markers (P < 0.05), including iNOS protein expression, tumor necrosis factor α (TNF‐α), interleukin 1β (IL‐1β), and IL‐6 releases, and BBR administration upregulated SOSC1 expression and the level of microglial M2 state markers (P < 0.05), such as Arg‐1 expression, brain‐derived neurotrophic factor (BDNF), and glial cell‐derived neurotrophic factor (GDNF) releases, downregulating the SOCS1 expression by using siRNA, however, significantly reversed the BBR‐induced effects on microglial M1 and M2 state markers and SOCS1 expression (P < 0.05). These findings indicated that BBR can inhibit Aβ‐induced microglial activation via modulating the microglial M1/M2 activated state, and SOCS1 mediates the process.
Development of a New Cold Metal Transfer Arc Additive Die Manufacturing Process
Authors: Zhang An , Xing Yanfeng , Yang Fuyong , Zhang Xiaobing , Wang Hongze , Yu Tiejun
Published: November 2021

Due to its high efficiency, cold metal transfer (CMT) arc additive manufacturing presents considerable potential in the aluminium alloy additive manufacturing industry. However, during CMT arc additive manufacturing, the surrounding air environment promotes the lateral flow of liquid aluminium and the instability of the molten pool, reduces the surface quality and material utilisation of deposition walls, and causes internal hydrogen pores and coarse columnar grains, which negatively affect the structure and mechanical properties of the deposition walls. This study developed a CMT arc additive die manufacturing process to control the substrate material and deposition path to improve the physical properties of the deposition wall. The experimental results indicated that the copper plates can affect molten pool flow and material formation in the additive process, minimise hydrogen pores, and refine columnar grains. The porosity dropped from 2.03% to 0.93%, and the average grain size decreased from 16.2 ± 1.4 to 13.6 ± 1.3 μm, thereby enhancing the structure and mechanical properties of the deposition wall to attain standard additive manufacturing products.
ANLN Regulated by miR-30a-5p Mediates Malignant Progression of Lung Adenocarcinoma
Authors: Deng Feng , Xu Zhili , Zhou Jun , Zhang Ruhu , Gong Xiaowei
Published: November 2021

Background. ANLN and miR-30a-5p may be involved in the progression of lung adenocarcinoma (LUAD). However, their underlying mechanism in LUAD has not been completely comprehended. Methods. Differential expression analysis, binding site prediction, and survival analysis were conducted by bioinformatics approaches. ANLN mRNA and miR-30a-5p expression were detected by qRT-PCR. ANLN protein expression was detected by western blot. Cell behaviors in LUAD were examined by functional experiments. Results. ANLN was activated in LUAD cells in terms of mRNA and protein. High ANLN level was positively correlated with poor prognosis. Enforced ANLN stimulated protumorigenesis LUAD cell behaviors. miR-30a-5p could target ANLN mRNA, as revealed and verified through assays. Remarkably low miR-30a-5p expression was observed in LUAD cells, and it could repress ANLN expression. The accelerated cell behaviors by overexpression of ANLN were counteracted by upregulating miR-30a-5p. Conclusion. Overall, miR-30a-5p remarkably restrained the malignant progression of LUAD cells by constraining ANLN expression. Thus, ANLN and miR-30a-5p could be novel therapeutic targets of LUAD.
Match of the Bimaxillary Basal Bone Arches and Its Variations among Individuals
Authors: Chen Wenqian , Zeng Hao , Sun Luna , Xu Qiuping , Chen Zhenxue , Sun Yunhan , Jia Qi , Liu Chengyun , Guo Jing
Published: November 2021

Introduction. This study is aimed at illustrating the bimaxillary basal bone contours, to clarify the match of the basal bone arches of the upper and lower, especially the posterior segments, including the second molar and retromolar region. Methods. Based on 100 cone-beam computed tomography (CBCT) images (50 males and 50 females), we obtained 100 pairs of basal bone arches, which were the horizontal inner cortex contours passing the furcation of the first molar paralleled to the lower occlusal plane. The Generalized Procrustes Analysis (GPA) was applied to depict average contours and calculate the ratio and difference width of both upper and lower dental arches in different positions. Variations of the basal bone morphology among individuals were revealed using Principal Component Analysis (PCA). Results. The width discrepancy occurred at 7-7 segment (male: upper 65.62 mm and lower 68.81 mm and female: upper 62.98 mm and lower 68.38 mm) and the retromolar region (male: upper 64.67 mm and lower 71.96 mm and female: upper 62.34 mm and lower 71.44 mm). The ratio (p=0.006) and difference value (p=0.009) of 7-7 segment and the ratio of retromolar region (p=0.044) differed in genders. Setting 2 mm overjet, the upper basal bone arch was wider than the lower by approximate 2 mm on both sides, except the second molar and retromolar region. According to PCA, the variation of basal bone arches appeared mainly at terminal segments. Conclusions. For both male and female, the bimaxillary basal bone matched except terminal segments. Mismatch of female bimaxillary posterior basal bone was more pronounced than male. The basal bone arches of male were wider and longer than that of female.
Adding Base-Excision Repair Inhibitor TRC102 to Standard Pemetrexed–Platinum–Radiation in Patients with Advanced Nonsquamous Non–Small Cell Lung Cancer: Results of a Phase I Trial
Authors: Biswas Tithi , Dowlati Afshin , Kunos Charles A. , Pink John J. , Oleinick Nancy L. , Malik Shakun , Fu Pingfu , Cao Shufen , Bruno Debora S. , Bajor David L. , Patel Monaliben , Gerson Stanton L. , Machtay Mitchell
Published: November 2021

Purpose:

TRC102, a small-molecule base-excision repair inhibitor, potentiates the cytotoxicity of pemetrexed and reverses resistance by binding to chemotherapy-induced abasic sites in DNA. We conducted a phase I clinical trial combining pemetrexed and TRC102 with cisplatin–radiation in stage III nonsquamous non–small cell lung cancer (NS-NSCLC).

Patients and Methods:

Fifteen patients were enrolled from 2015 to 2019. The primary objective was to determine the dose-limiting toxicity and maximum tolerated dose of TRC102 in combination with pemetrexed, cisplatin, and radiotherapy. Secondary objectives were to assess toxicity, tumor response, and progression-free survival at 6 months. Based on our preclinical experiments, pemetrexed–TRC102 was given on day 1, and cisplatin/radiotherapy was initiated on day 3. This schedule was duplicated in the second cycle. After completion, two additional cycles of pemetrexed–cisplatin were given. Toxicities were assessed using NCI CTACAE versions 4/5.

Results:

The median age was 69 years (45–79) with the median follow-up of 25.7 months (range, 7.9–47.4). No dose-limiting toxicities and no grade 5 toxicity were seen. Hematologic and gastrointestinal toxicities were the most common side effects. No clinical radiation pneumonitis was seen. Of 15 evaluable patients, three had complete response (20%), and 12 had partial response (80%). The 6-month progression-free survival was 80%, and the 2-year overall survival was 83%.

Conclusions:

Pemetrexed–TRC102 combined with cisplatin/radiotherapy in NS-NSCLC is safe and well tolerated. The recommended phase II dose is 200 mg TRC102 along with cisplatin–pemetrexed. No additional safety signal was seen beyond the expected CRT risks. A phase II trial, integrating post-CRT immunotherapy with this aggressive DNA-damaging regimen, is warranted.
Safety, Efficacy, and Biomarker Analysis of Toripalimab in Patients with Previously Treated Advanced Urothelial Carcinoma: Results from a Multicenter Phase II Trial POLARIS-03
Authors: Sheng Xinan , Chen Haige , Hu Bin , Yao Xudong , Liu Ziling , Yao Xin , Guo Hongqian , Hu Yi , Ji Zhigang , Luo Hong , Shi Benkang , Liu Jiyan , Wu Jin , Zhou FangJian , He Zhisong , Fan Jinhai , Wang Weifeng , Feng Hui , Yao Sheng , Keegan Patricia , Huang Yiran , Guo Jun
Published: November 2021

Purpose:

Immunotherapy offers a second-line option for patients with metastatic urothelial carcinoma (mUC) who failed standard therapy, but the biomarkers for predicting response remain to be explored. This study aims to evaluate the safety, efficacy, and correlative biomarker of toripalimab in patients with previously treated mUC.

Patients and Methods:

Patients with mUC received toripalimab 3 mg/kg Q2W. Clinical response was assessed every 8 weeks by an independent review committee per RECIST v1.1. Tumor PD-L1 expression, tumor mutational burden (TMB), and other biomarkers were evaluated.

Results:

Among the intention-to-treat population (n = 151), 85% of the patients experienced treatment-related adverse event (TRAE) and 20% experienced grade 3 and above TRAE. The objective response rate (ORR) was 26% with a disease control rate (DCR) of 45%. The median duration of response, progression-free survival (PFS), and overall survival (OS) were 19.7 months [95% confidence interval (CI): 13.9–not estimable], 2.3 months (95% CI, 1.8–3.6), and 14.4 months (95% CI, 9.3–23.1), respectively. Both PD-L1+ and TMB-high (10 mutations/Mb as the cutoff) patients had better ORR than PD-L1− patients (42% vs. 17%, P = 0.002) and TMB-low patients (48% vs. 22%, P = 0.014), respectively. The TMB-high group also showed better PFS (12.9 vs. 1.8 months, P < 0.001) and OS (not reached versus 10.0 months, P = 0.018) than the TMB-low group.

Conclusions:

Toripalimab has demonstrated encouraging clinical activity in the second-line treatment of mUC with a manageable safety profile. PD-L1 expression and TMB were two independent biomarkers in the study.
A Novel Third-generation EGFR Tyrosine Kinase Inhibitor Abivertinib for EGFR T790M-mutant Non–Small Cell Lung Cancer: a Multicenter Phase I/II Study
Authors: Zhou Qing , Wu Lin , Hu Pei , An Tongtong , Zhou Jianying , Zhang Li , Liu Xiao-Qing , Luo Feng , Zheng Xin , Cheng Ying , Yang Nong , Li Junling , Feng Jifeng , Han Baohui , Song Yong , Wang Kai , Zhang Li , Fang Jian , Zhao Hong , Shu Yongqian , Lin Xiao-Yan , Chen Zhihong , Gan Bin , Xu Wan-Hong , Tang Wei , Zhang Xiaoying , Yang Jin-Ji , Xu Xiao , Wu Yi-Long
Published: November 2021

Purpose:

To establish recommended phase II dose (RP2D) in phase I and evaluate safety and efficacy of abivertinib in patients with EGFR Thr790Met point mutation (T790M)-positive(+) non–small cell lung cancer (NSCLC) with disease progression from prior EGFR inhibitors in phase II.

Patients and Methods:

This multicenter, open-label study included 367 adult Chinese patients. Abivertinib at doses of 50 mg twice a day to 350 mg twice a day was evaluated in phase I in continual 28-day cycles, and the RP2D of 300 mg twice a day was used in phase II in continual 21-day cycles. Primary endpoints include RP2D in phase I and objective response rate (ORR) at RP2D in phase II.

Results:

The RP2D of 300 mg twice a day for abivertinib was established based on pharmacokinetics, efficacy, and safety profiles across doses in phase I. In phase II, 227 patients received RP2D for a median treatment duration of 24.6 weeks (0.43–129). Among 209 response–evaluable patients, confirmed ORR was 52.2% [109/209; 95% confidence interval (CI): 45.2–59.1]. Disease control rate (DCR) was 88.0% (184/209; 95% CI: 82.9–92.1). The median duration of response (DoR) and progression-free survival (PFS) was 8.5 months (95% CI: 6.1–9.2) and 7.5 months (95% CI: 6.0–8.8), respectively. The median overall survival (OS) was 24.9 months [95% CI: 22.4–not reachable (NR)]. All (227/227) patients reported at least 1 adverse event (AE), with 96.9% (220/227) of treatment-related AEs. Treatment-related serious AEs were reported in 13.7% (31/227) of patients. Death was reported in 4.4% (10/227) of patients, and none was deemed as treatment-related.

Conclusions:

Abivertinib of 300 mg twice a day demonstrated favorable clinical efficacy with manageable side effects in patients with EGFR T790M+ NSCLC.
Outstanding Response to Sorafenib in a Patient with Metastatic Gastrointestinal Stromal Tumour
Authors: Brinch Charlotte , Dehnfeld Marie , Hogdall Estrid , Poulsen Tim Svenstrup , Toxvaerd Anders , Al-Farra Gina , Bergenfeldt Magnus , Krarup-Hansen Anders
Published: November 2021

Gastrointestinal stromal tumour (GIST) is the most common sarcoma and can be seen in any part of the gastrointestinal tract. The effect of tyrosine kinase inhibitors varies with mutation status in receptor tyrosine kinase KIT and in platelet-derived growth factor receptor A (PDGFRA). This case presents a 61-year-old man, diagnosed with an 11-cm GIST located at the stomach with a high risk of recurrence. The patient showed intolerance to imatinib shortly after introduction and subsequently progressed on sunitinib and nilotinib. The patient started fourth-line treatment with sorafenib with an impressive response to a point at which metastases intra-abdominally and in the liver could be resected. After surgery, sorafenib was restarted. Due to toxicity, sorafenib dose was reduced over time. The dose was insufficient to control the disease since a new recurrence was detected. Mutation analyses revealed a GIST harbouring a deletion of codon p.I843_D846del, located at PDGFRA exon 18, right next to the codon D842 where mutations are known leading to imatinib resistance. In this case, the GIST was highly sensitive to sorafenib, and the response was dose related. It is mandatory to perform mutation analyses on primary tumour and at recurrence in the decision-making of the correct treatment for the patient. In March 2021, the patient had been in treatment with sorafenib for 12.5 years and was still without signs of recurrence. A multidisciplinary approach was essential for the long-term survival of the patient in this case.
A Seldom Cause of Exercise-Related Leg Pain in a Young Soldier-Athlete: A Case Report
Authors: Cherrad Taoufik , Belabbes Soufiane , Sinaa Mohamed , Zejjari Hassan , Louaste Jamal , Amhajji Larbi
Published: November 2021

Chronic leg pain is a common problem for young soldier-athletes. Differential diagnosis of this issue is extensive and includes more common entities such as medial tibial stress syndrome and tibial stress fracture and other scarce causes. Therefore, making a correct diagnosis proves to be vital for appropriate care. This topic discusses the case of a 36-year-old soldier who was diagnosed with a schwannoma of the left tibial nerve as a rare cause of leg pain. Its literature is scarce and intends to add further data about recommendations for investigation and management for this kind of lesion.
Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques
Authors: Abdulhussein Kareem Ghazi , Yasin Naseer Majeed , Hasan Ihsan Jabbar
Published: November 2021

In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.
The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer
Authors: Abbas Ali Iqbal , Anwer Afaneen
Published: November 2021

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
An optimum location of on-grid bifacial based photovoltaic system in Iraq
Authors: Shakir Amina Mahmoud , Yousif Siba Monther , Mahmood Anas Lateef
Published: November 2021

Bifacial photovoltaic (PV) module can gain 30% more energy compared to monofacial if a suitable location were chosen. Iraq (a Middle East country) has a variable irradiation level according to its geographic coordinates, thus, the performance of PV systems differs. This paper an array (17 series, 13 parallel) was chosen to produce 100 kWp for an on-grid PV system. It investigates the PV system in three cities in Iraq (Mosul, Baghdad, and Basrah). Effect of albedo factor, high and pitch of the bifacial module on energy yield have been studied using PVsyst (software). It has been found that the effect is less for a pitch greater than 6 m. The energy gained from bifacial and monofacial PV system module in these cities shows that Mosul is the most suitable for installing both PV systems followed by Baghdad and lastly Basrah. However, in Basrah, the bifacial gain is 12% higher in the energy than monofacial as irradiation there is higher than the other locations, especially for elevation above 1.5 m. Moreover, the cost of bifacial array is 7.23% higher than monofacial, but this additional cost is acceptable since the bifacial gain is about 11.3% higher energy compared to the monofacial.
Robust recognition technique for handwritten Kannada character recognition using capsule networks
Authors: Rani N. Shobha , N. Manohar , M. Hariprasad , B. R. Pushpa
Published: November 2021

Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize  Kannada handwritten characters.  Capsule network architecture is built of an input layer,  two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer.  For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.
Increasing radiation power in half width microstrip leaky wave antenna by using slots technique
Authors: Abdulhameed Muhannad Kaml , Hashim Sarah Rafil , Abdalhameed Noor Kamil , Al-Gburi Ahmed Jamal Abdullah
Published: November 2021

The radiation power in the endfire is decreased while the main beam of half substrate integrated waveguide scan from broadside to endfire in a forward. The design of half-width microstrip leaky-wave antenna (HW-MLWA) has been presented in this work to increase the power radiation near endfire by using the slots technique in the radiation element. This slot leads to a decrease the cross-polarization. The proposed design comprises one element of HW-MLWA with repeated meandered square slots in the radiation element. One aspect of this antenna is generated by using a half substrate integrated waveguide with a full tapered feed line. The proposed antenna was terminated by load of 50 Ω, and feed on the other end of the antenna. Finally, the suggested design is simulated and acceptable results were found. The released gain is increased from 10.6 dBi to 12 dBi at 4.3 GHz. This design is suitable for unmanned aerial vehicle UAVs at C band application.
Thriving information system through business intelligence knowledge management excellence framework
Authors: Mohamad Abdul Karim , Jayakrishnan Mailasan , Mohd Yusof Mokhtar
Published: November 2021

In the current digitalization dilemma of an organization, there is a need for the business intelligence and knowledge management element for enhancing a perspective of learning and strategic management. These elements will comprise a significant evolution of learning, insight gained, experiences and knowledge through compelling theoretical impact for practitioners, academicians, and scholars in the pertinent field of interest. This phenomenon occurs due to digitalization transformation towards industry revolution 5.0 and organizational excellence in the information system area. This research focuses on the characteristic of a comprehensive performance measure perspective in an organization that conceives information assessment and key challenges of Business Intelligence and Knowledge Management in perceiving a relevant organizational excellence framework. The dynamic research focusing on the decision-making process and leveraging better knowledge creation. The future of organization excellence seemed to be convergent in determining the holistic performance measure perspective and its factors towards industry revolution 5.0. The research ends up with a typical basic excellence framework that will mash up some characteristics in designing an organizational strategic performance framework. The output is a conceptual performance measure framework for a typical decision-making application for organizational strategic performance management dashboarding.
Status index of optimal water variables for biodiversity conservation in the Lagoon of Sonso in Colombia
Authors: Florez-Yepes Gloria Yaneth , Rincón Alejandro , Henao Céspedes Vladimir , Granobles Tores Juan Carlos , Hoyos Velasco Fredy Edimer
Published: November 2021

In order to determine an index of the status of optimal water variables for biodiversity conservation in the Sonso Lagoon, the data obtained from 2004 to 2018 were used. To determine the index, a methodology based on a multivariate analysis of the physical-chemical water variables was used, as well as a correlation analysis for their delimitation. Subsequently, the definition of weights and the parameterization of the variables for the final construction of the index were made. As a result, it was found that the lagoon is in an adequate state with a value index of 0.65, with a highly vulnerable tendency to be in an acceptable state and go to a critical state, depending on the anthropic pressure it has. As a conclusion, it was obtained that variables such as dissolved oxygen, total phosphorus and electrical conductivity are determining factors in establishing the index.
Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method
Authors: Thongsri Nattaporn , Warintarawej Pattaraporn , Chotkaew Santi , Saetang Wanida
Published: November 2021

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.
Text pre-processing of multilingual for sentiment analysis based on social network data
Authors: Garg Neha , Sharma Kamlesh
Published: November 2021

Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.
MRI image segmentation using machine learning networks and level set approaches
Authors: Almajmaie Layth Kamil Adday , Raheem Ahmed Raad , Mahmood Wisam Ali , Albawi Saad
Published: November 2021

The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.
Deep learning-based decision support system for weeds detection in wheat fields
Authors: Jabir Brahim , Falih Noureddine
Published: November 2021

In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.
Parametric estimation in photovoltaic modules using the crow search algorithm
Authors: Montoya Oscar Danilo , Ramírez-Vanegas Carlos Alberto , Grisales-Noreña Luis Fernando
Published: November 2021

The problem of parametric estimation in photovoltaic (PV) modules considering manufacturer information is addressed in this research from the perspective of combinatorial optimization. With the data sheet provided by the PV manufacturer, a non-linear non-convex optimization problem is formulated that contains information regarding maximum power, open-circuit, and short-circuit points. To estimate the three parameters of the PV model (i.e., the ideality diode factor (a) and the parallel and series resistances (Rp and Rs)), the crow search algorithm (CSA) is employed, which is a metaheuristic optimization technique inspired by the behavior of the crows searching food deposits. The CSA allows the exploration and exploitation of the solution space through a simple evolution rule derived from the classical PSO method. Numerical simulations reveal the effectiveness and robustness of the CSA to estimate these parameters with objective function values lower than 1 × 10−28 and processing times less than 2 s. All the numerical simulations were developed in MATLAB 2020a and compared with the sine-cosine and vortex search algorithms recently reported in the literature.
Smart system for maintaining aquascape environment using internet of things based light and temperature controller
Authors: Hutabarat Daniel Patricko , Susanto Rudy , Prasetya Bryan , Linando Barry , Senanayake Senanayake Mudiyanselage Namal
Published: November 2021

The purpose of this research is to create a smart system based on internet of things (IoT) application for a plant aquarium. This smart system helps users to maintain the environment's parameters of the plant aquarium. In this study, the parameters to be controlled by the system are light intensity and temperature. The hardware used to develop this system is the ESP32 as the microcontroller, BH1750FVI as the light sensor, high power led (HPL) light-emitting diodes (LED) lamp as the light source, DS18B20 as temperature sensor, the heater, and the 220 VAC fan that is used to raise and lower the temperature. In this study also developed an application that is used by the user to provide input to the system. The developed application is then installed on the user's smartphone and used to connect the user to the system via the internet. The ease of adding and removing devices used on the system is a capability that is also being developed in this smart system. The developed system can produce light intensity with accuracy rate of 96% and always manage to keep the temperature within the predetermined range.
Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data
Authors: Krishna Sujatha , Vinayaka Murthy Udayarani
Published: November 2021

Big data has remodeled the way organizations supervise, examine and leverage data in any industry. To safeguard sensitive data from public contraventions, several countries investigated this issue and carried out privacy protection mechanism. With the aid of quasi-identifiers privacy is not said to be preserved to a greater extent. This paper proposes a method called evolutionary tree-based quasi-identifier and federated gradient (ETQI-FD) for privacy preservations over big healthcare data. The first step involved in the ETQI-FD is learning quasi-identifiers. Learning quasi-identifiers by employing information loss function separately for categorical and numerical attributes accomplishes both the largest dissimilarities and partition without a comprehensive exploration between tuples of features or attributes. Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. Then injecting Adaptive Lorentz noise to data attributes our ETQI-FD significantly minimizes the influence of noise on the final results and therefore contributing to privacy and accuracy. An experimental evaluation of ETQI-FD method achieves better accuracy and privacy than the existing methods.
Efficient energy for one node and multi-nodes of wireless body area network
Authors: Wali Sondous Sulaiman , Abdullah Mohammed Najm
Published: November 2021

Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput.
Classification of three pathological voices based on specific features groups using support vector machine
Authors: Altayeb Muneera , Al-Ghraibah Amani
Published: November 2021

Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.
Review on path planning algorithm for unmanned aerial vehicles
Authors: Ibrahim Nurul Saliha Amani , Saparudin Faiz Asraf
Published: November 2021

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.
Evaluation of one of leading Indonesia’s digital wallet using the unified theory of acceptance and use of technology
Authors: Limantara Natalia , Jovandy Jevan , Wardhana Anindya Khansalihara , Steven Steven , Jingga Fredy
Published: November 2021

The goal of this research is to assess the utilization of one of Indonesia's most popular digital wallets. Respondents in this study range in age from 15 to 44 years old and live on Java Island. The author employs the unified theory of acceptance and utilization of technology (UTAUT) paradigm to assess the use of this digital wallet. This UTAUT model comprises four latent variables that affect behavioral intention (BI) and use behavior (UB): performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). In this study, the UTAUT model is combined with two additional variables: perceived risk (PR) and promotional benefits (PB). According to the findings, performance expectancy, social influence, and perceived risk all have an impact on behavioral intention, whereas effort expectancy has a less impact. Furthermore, the factors facilitating conditions and promotional benefits have a minor impact on use behavior, whereas behavioral intention factors have a considerable impact on use behavior.
IPOC: an efficient approach for dynamic association rule generation using incremental data with updating supports
Authors: Naresh P. , Suguna R.
Published: November 2021

According to recent statistics, there was drastic growth in online business sector where more number of customers intends to purchase items. Due to these retailers accumulates huge volumes of data from day to day operations and engrossed in analyzing the data to watch the behavior of customers at items which strengthen the business promotions and catalog management. It reveals the customer interestingness and frequent items from large data. To carry out this there was known algorithms present which deals with static and dynamic data. Some of them are lag time and memory consuming and involves unnecessary process. This paper intents to implement an efficient incremental pre ordered coded tree (IPOC) generation for data updates and applies frequent item set generation algorithm on the tree. While incremental generation of tree, new data items will link to previous nodes in tree by increasing its support count. This removes the lagging issues in existing algorithms and does not need to mine from scratch and also reduces the time, memory consumption by the use of nodeset data structure. The results of proposed method was observed and analyzed with existing methods. The anticipated method shows improved results by means of generated items, time and memory.