bluuebunny/crossref_metadata_2025 huggingface dataset:
A Study on Decision-Making of the Indian Railways Reservation System during COVID-19
Authors: Sharma Haresh Kumar , Majumder Saibal , Biswas Arindam , Prentkovskis Olegas , Kar Samarjit , Skačkauskas Paulius
Published: July 2022

The Indian Railways Reservation System (IRRS) is one of the world’s busiest reservation systems of railway tickets. Recently, the COVID-19 pandemic situation has severely impacted the Indian Railway’s (IR) transportation, which eventually has enforced the IR to alter the passenger reservation system. This research attempts to evaluate and analyse the factors that modify the IRRS. In this research, a rough set-based Data Mining Scaffolding (DMS) has been proposed. Here, the relevant preferential information related to the IRRS is managed by introducing a multi-criteria decision-making (MCDM), where a decision-maker (DM) can make a decision based on several decision rules. The effectiveness of the proposed DMS is explained by gathering realistic data of 26 trains, which run between railway stations of two metro cities of India during the COVID-19 pandemic period.
Effects of Knee Debridement with Flurbiprofen on Knee Function, Inflammatory Levels, and Bone Metabolism Activity in Patients with Knee Osteoarthritis
Authors: Lin Tao , Liu Zemiao , Ji Wei , Zhang Peng
Published: July 2022

Objective. The objective of this study is to explore the effects of knee debridement with flurbiprofen on the knee function, inflammatory levels, and bone metabolism activity in patients with knee osteoarthritis. Methods. 110 patients with knee osteoarthritis who underwent arthroscopic debridement in our hospital from 2020.01 to 2022.01 were selected for retrospective analysis. Based on whether or not flurbiprofen was used in combination during the perioperative phase, the patients were divided into the control group (only arthroscopic debridement of the knee) and the research group (flurbiprofen with arthroscopic debridement of the knee), with 55 cases in each group. The indexes such as knee function, inflammatory levels, and bone metabolism activity of the two groups were analyzed. Results. According to hospital for special surgery (HSS) evaluation for knee function, most patients in the control group were assessed as “moderate,” while patients in the research group were mainly focused on “excellent” and “good,” and their excellent and good rates were remarkably higher than those in the control group (

P
<
0.05

). There were no significant variations in bone metabolism indices such as osteoprotegerin levels (OPG), insulin-like growth factor-1 (IGF-1), β-isomerized C-terminal telopeptide (β-CTX), and receptor activator of nuclear factor-κB ligand (RANKL) before treatment between both groups (

P
>
0.05

), with higher OPG, IGF-1 levels, and remarkably lower β-CTX, RANKL levels in the research group than those in the control group after treatment (

P
<
0.05

). There were no remarkable differences in pain between both groups before treatment (

P
>
0.05

), while at 24 h and 48 h after surgery, the VAS scores in the research group were remarkably lower than those in the control group (

P
<
0.05

). In terms of inflammatory factors, the levels of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and cyclooxygenase-2 (COX-2) in the research group were remarkably lower than those in the control group after treatment (

P
<
0.05

). Conclusion. Arthroscopy coupled with flurbiprofen provides a good analgesic effect in the therapeutic treatment of patients with knee osteoarthritis, which contributes to the recovery of knee function with definite results. Its mechanism may be associated with the control of inflammatory response and the regulation of bone metabolism disorder.
Polymeric Droplets on SiO2 Nanoparticles through Wastewater Treatment of Carbon‐Based Contaminants in Photocatalytic Degradation
Authors: Saka Abel , Tesfaye Jule Leta , Gudata Lamessa , Karthi S. , Nagaprasad N. , Bhagat Suraj Kumar , Yaqub Muhammad , Ramaswamy Krishnaraj
Published: July 2022

The current work focus is on utilizing sunlight to catalyze the destruction of carbon‐based (organic) pollutants. To increase the morphological area and improve the proficiency of the photocatalytic technique, sodium alginate was used as a polymeric tool and arranged as drop practice. SiO2 nanoparticles were doped into sodium alginate droplets. The developed SiO2 nanomaterials were able to spread the wavelength diversity throughout a significant wavelength constituency. In the photocatalytic technique employing the lot photoreactor, MB was used as a sample of carbon‐based pollutants. The sunlight catalytic procedure was implemented from UV‐Vis or photo light droplets. The analysis of the synthesized droplets was tested using devices X‐ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence (PL) analysis. Correspondingly, the influences of different concentrations of SiO2 nanosolution (5 ml, 10 ml, 15 ml, and 20 ml) on the photocatalytic effectiveness of the deposited nanoparticles were studied. The output result revealed that sodium alginate beads doped with SiO2 at 20 ml were able to reduce (degrade) 98.9% underneath UV‐visible light. However, in the case of using other concentrations, SiO2 at 5, 10, and 15 ml were able to degrade 50%, 56.7%, and 67.9% under sunlight, respectively, after 2 h.
Mathematical Modelling and Hierarchical Encourage Particle Swarm Optimization Genetic Algorithm for Jet Pipe Servo Valve
Authors: Chen Jia , Li Fei , Yang Yi , Gao Yi
Published: July 2022

The jet pipe servo (JPS) valve is one key component, whose dynamic performance directly influences the aircraft’s maneuverability. In this paper, a more accurate mathematical model and a novel multiobjective hierarchical encourage particle swarm optimization genetic algorithm (HEPGA) are proposed to improve the dynamic performance of the jet pipe servo valve. By optimizing the main structure parameters of the jet pipe servo valve, the adjustment and overshoot in the dynamic performance are reduced by 24.28% and 51.39%, respectively, compared with the prototype before optimization. To obtain a more accurate mathematical model, the computational fluid dynamics (CFD) is introduced to modify the analytical model considering the turbulent submerged free jet. Different from conventional numerical simulation, the dynamic mesh technique is used to analyze the flow field distribution by considering the force interaction of various parts of the jet pipe servo valve under actual working condition. Then, the HEPGA with better convergence is utilized because of the conflict of adjustment and overshoot. This proposed hybrid algorithm introduces the concept of staff welfare system to divide the population into elite individuals and excellent individuals of particle swarm optimization and general individuals of genetic algorithm. Meanwhile, the convergency performance of the HEPGA is evaluated through the Rosenbrock function by comparing with other particle swarm genetic hybrid methods. Subsequently, the experimental platform is constructed and the dynamic performance tests are conducted on the prototype after optimization. The experimental results verify the accuracy of the established mathematical model and the significant improvement of dynamic performance of the jet pipe servo valve.
Tobacco Smoking and Survival Following a Diagnosis with Ovarian Cancer
Authors: Wang Tianyi , Read Susan H. , Moino Daniela , Ayoubi Yasmin , Chern Jing-Yi , Tworoger Shelley S.
Published: July 2022

Background:

Little is known about the influence of smoking on ovarian cancer survival. We investigated this relationship in a hospital-based study.

Methods:

Analyses included 519 women with ovarian cancer. We used multivariable adjusted Cox proportional hazards regression models to estimate HRs and 95% confidence intervals (CI).

Results:

Risk of all-cause mortality was increased for current smokers (HR = 1.70; 95% CI: 1.09–2.63) versus never smokers, especially for those with ≥15 cigarettes per day (HR = 1.92; 95% CI: 1.15–3.20). Results were largely similar after additional adjustment for debulking status (current vs. never smokers, HR = 2.96; 95% CI: 1.07–8.21) or neoadjuvant chemotherapy (comparable HR = 2.87; 95% CI: 1.02–8.06). Compared with never smokers, smoking duration ≥20 years (HR = 1.38; 95% CI: 0.94–2.03) and ≥20 pack-years (HR = 1.35; 95% CI: 0.92–1.99) were suggestively associated with worse outcomes. Current smoking was also positively associated with the risk of mortality among patients with ovarian cancer recurrence (current vs. never/past smokers, HR = 2.79; 95% CI: 1.44–5.41), despite the null association between smoking and recurrence (HR = 1.46; 95% CI: 0.86–2.48). Furthermore, no association was observed for smoking initiation before age 18 (HR = 1.22; 95% CI: 0.80–1.85), or either environmental smoke exposure at home (HR = 1.16; 95% CI: 0.76–1.78) or at work (HR = 1.10; 95% CI: 0.75–1.60).

Conclusions:

Our results suggest active tobacco smoking is associated with worse ovarian cancer outcomes, particularly after a recurrence.

Impact:

Our findings support structured smoking cessation programs for patients with ovarian cancer, especially in recurrent settings. Further research to confirm these findings and examine the interplay between smoking and the tumor immune microenvironment may help provide insight into ovarian cancer etiology.
Catching Up with the World: Pepsinogen Screening for Gastric Cancer in the United States
Authors: Zhou Margaret J. , Huang Robert J.
Published: July 2022

Gastric cancer remains a deadly cancer with poor outcomes in the United States. There is a need for screening strategies for gastric cancer in the U.S. population. With progressive Helicobacter pylori–mediated inflammation of the gastric mucosa, pepsinogen I levels decrease and the pepsinogen I/II ratio decreases. Pepsinogen test positivity (PG+) has been evaluated as a promising screening test among Asian and European populations; however, its utility in multiethnic U.S. populations is poorly described. In this case-control study nested within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, In and colleagues evaluate the discrimination of PG+ in serum collected from individuals prior to the development of gastric cancer. The authors find that PG+ individuals were at nearly 10-fold increased risk for developing gastric cancer, and this effect remained robust after adjusting for Helicobacter pylori status, family history, education, smoking, and obesity. In subgroup analysis, the predictive ability of the test was particularly robust for noncardia gastric cancers, and nonpredictive of cardia gastric cancers. Serum pepsinogen testing holds promise as a noninvasive screening strategy to triage individuals at heightened risk for gastric cancer, and may help to improve early diagnosis in the United States.

See related article by In et al., p. 1426
Paclitaxel Treatment Effects on Neurofilament Light Chain (NF-L), a Possible Biomarker of Chemotherapy-Induced Peripheral Neuropathy (CIPN)
Authors: Benashley LW , Bucy AM , Wertheim BC , Blew R , Chalasani P , Thomson C , Bea JW
Published: July 2022

Purpose: The purpose of the study was to determine if neurofilament light chain (NF-L), a biomarker of neurologic damage in disease states such as ALS, diabetes, and Parkinson's disease increases with neurotoxic chemotherapy. Methods: Female breast cancer patients (N=21) at two sites (Tucson and Flagstaff. AZ) receiving weekly or biweekly paclitaxel chemotherapy for 3-4 cycles (700-1500mg/m2 total dose) completed blood draws every two weeks. A single molecule array (SiMoA) was used to quantify NF-L levels in serum samples (Quanterix). Patients completed clinical neuropathy grading (CTCAE) and a symptom questionnaire with a neuropathy specific subscale (FACT GOG-Ntx) on day 1 of each paclitaxel cycle and within 30 days of the final treatment to characterize chemotherapy induced peripheral neuropathy (CIPN). The association between cumulative paclitaxel dose and NF-L (log-transformed) or Ntx symptom score (log of score plus one) were tested using linear mixed-effects models, adjusted for site, age, and BMI. The association between cumulative paclitaxel dose and CTCAE neuropathy grade was tested using a multilevel mixed-effects logistic regression model, adjusted for site, age, and BMI. Results: Participants were aged 55.7 ± 11.7 years and with early-stage breast cancer. Serum NF-L increased significantly during taxane chemotherapy: baseline of 38.8 ng/mL; end of study measurement 280.6 ng/mL; p = 0.001. The clinical CTCAE neuropathy grade and self-reported neuropathy symptoms worsened from baseline to end-of-study, with CTCAE of 0.2 ± 0.4 to 1.1 ± 0.6 and Ntx subscale (reversed-scored) of 38.4 ± 6.0 to 30.7 ± 7.6; both p < 0.003. Conclusions: NF-L and CIPN symptoms increased concurrently during taxane treatment. CIPN biomarker qualification is a critical area of survivorship research given the high prevalence of CIPN and the effects of CIPN on pain, quality of life, and dose reductions. Future research with a larger sample, longer follow-up, and objective neuropathic measures may establish whether NF-L can be used to distinguish women who will develop CIPN during taxane treatment versus those who will not.
Karyotyping of Lymphocytes and Epithelial Cells of Distinct Embryonic Origin Does Not Help to Predict the Turner Syndrome Features
Authors: Pavlicek Jan , Soucek Ondrej , Vrtel Radek , Klaskova Eva , Hana Vaclav , Stara Veronika , Adamova Katerina , Fürst Tomas , Hana Jr. Vaclav , Kapralova Sabina , Prochazka Martin , Snajderova Marta , Tomaskova Hana , Tüdös Zbynek , Vrbicka Dita , Vrtel Petr , Zapletalova Jirina , Tauber Zdenek , Lebl Jan
Published: July 2022

Background: In Turner syndrome (TS), fluorescent in situ hybridization (FISH) karyotyping offers an alternative to classical karyotyping. Objective: We tested the added value of FISH karyotyping from lymphocytes (mesodermal origin), buccal cells (ectodermal origin), and a rear-tongue smear (endodermal origin) to determine the 45,X cell line fraction and its impact on patient phenotype. Design and Patients: Classical karyotyping and three FISH assays were done in 153 girls and women previously diagnosed with TS in four university hospitals. The 45,X cell line fraction was determined for each method and correlated with the major phenotypic signs. Results: Classical karyotyping identified 45,X/46,XX mosaicism in 77/153 subjects (50%), 45,X monosomy in 52/153 (34%), and other karyotypes in 24/153 (16%). FISH from lymphocytes verified 45,X in 47/52 original cases, whereas 4/52 had 45,X/46,XX and 1/52 45,X/47,XYY mosaicism. The 45,X cell line fraction was higher in FISH from lymphocytes compared to classical karyotyping (median 86.4% vs. 70.0%; p < 0.001), while there was no difference for FISH from buccal or rear-tongue smear cells. The mean 45,X cell line fraction was more abundant in patients with several of the characteristic phenotypic signs compared to patients without them (p < 0.01), but the predictive power was insufficient. Conclusion: FISH analysis confirmed the findings of classical karyotyping; only a few 45,X monosomy cases were reclassified as mosaics. The 45,X cell line fraction did not show clinically meaningful prediction of the phenotype. FISH analysis of buccal or rear-tongue epithelial cells may be a non-inferior, less invasive alternative to classical karyotyping.
Different perspective on teaching socio-scientific subjects
Authors: Koçoğlu Erol , Kalin Özlem Ulu , Demir Fatıma Betül
Published: July 2022

In the ever-changing world based on the needs, curiosity and entrepreneurial drive of the humankind, interest in socio-scientific issues has been increasing every day with the discussion and different approaches in society. It could be suggested that the increase in this interest has been due to both the contribution and damages of these issues to the environment. The present study aimed to analyze the socio-scientific issues instructed with various strategies, methods and techniques in different learning environments in educational institutions as a result of the developments in science and technology due to social needs based on teacher perceptions. The study group included 50 teachers in different fields and employed in various schools in Turkey. The study sample was assigned with criterion sampling technique and the admission criterion was employment in Turkish public schools. In the study, the data were collected with a semi-structured interview form developed by the authors, and the findings were analyzed with content analysis. The study findings demonstrated that the teachers in the study group had different and significant perceptions on the instruction of socio-scientific topics in learning environments.
The effect of Kahoot! learning media on learning outcomes of German language students
Authors: Litualy Samuel Jusuf , Serpara Henderika , Wenno Eldaa Crystle
Published: July 2022

This study aimed to determine the effect of the Kahoot! educational game learning media on German language students' learning outcomes in the Sprechfertigkeit 2 course. The sample of this study consisted of 37 students. The test instruments were given during the pretest and posttest. The data were processed and analyzed by the SPSS program. The Cronbach Alpha validity test showed that the study had high validity (0.764). The instrument normality test shows the value of p more than alpha (0.05). The statistical test resulted that the value of t-test=631 (more than t-table=398). The study revealed a significant effect of the Kahoot! educational game learning media on learning outcomes of German students in Sprechfertigkeit 2.
Power quality assessment of novel multilevel and multistring inverters for electric vehicle applications
Authors: Sampathkumar Sri Krishna Kumar , Pradyumna Kumar Dhal
Published: July 2022

The requirement and demand of electric vehicles is extravagantly increasing in the current era for avoiding pollutions and as well to overcome the consumptions of liquid fuels. Under this scenario, for the production and applicability of electric vehicles, inverters play a major role to render appropriate battery power supply for the perfect operation of the electric vehicle. This research study devises a novel modified multilevel inverter and a new multistring inverter designed by reducing the number of switches and enabling the effective operation of the electric vehicle. Both the developed multilevel and multistring inverter is analyzed with respect to their switching states, their output voltage and the evaluated harmonic distortion. For comparison, a sinusoidal alternating current voltage is considered as reference and all the newly modeled configurations are compared with that of the existing inverter models to prove their superiority. The switched states of the designed inverters as applicable for electric vehicles are examined to attain a logical analysis with respect to the duty cycle of the individual switches. The power quality assessment of the designed inverter models are guaranteed based on the minimized total harmonic distortion and on comparison with the other inverter models from the earlier state-of-the-art techniques.
Controlling matrix converter in flywheel energy storage system using AFPM by processor-in-the-loop method
Authors: Do Nguyen Hung , Ho Sy Manh , Le Quoc Tuan , Hoang Tung , Tran Trong Minh , Vu Phuong
Published: July 2022

Flywheel energy storage systems are considered as the grid integration of renewable energy sources due to their inherent advantages such as fast response, long cycle life and flexibility in providing auxiliary services to the grid, such as frequency regulation, and voltage support. The article focuses on the design of the controller, then conducts processor-in-the-loop (PIL) simulation of the dynamics part using the axial flux permanent magnet (AFPM) motor with the matrix converter on MATLAB/Simulink combined with the controller on the TMS32F28377S microcontroller card of Texas Instruments to evaluate the efficiency of the energy storage system.
Solid waste recycling and management cost optimization algorithm
Authors: Noori Mustafa Abdulsatar , Al-Janabi Thair Abed , Hussien Sura Abed Sarab
Published: July 2022

Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, to find the optimal solution. This method also included multiple adaptive and random variables to guarantee that the solution space was explored and used in various optimization tasks. When all criteria are considered, the results of this study show that the SSA is efficient for least-distance path allocation. The simulation findings reveal a significant improvement over the well-known particle swarm optimization (PSO) algorithm, with recycling and disposal costs decreasing by 10% to 30%.
The feature extraction for classifying words on social media with the Naïve Bayes algorithm
Authors: Lubis Arif Ridho , Nasution Mahyuddin Khairuddin Matyuso , Sitompul Opim Salim , Zamzami Elviawaty Muisa
Published: July 2022

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.
A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms
Authors: Pandi Vigneshwaran , Nithiyanandam Prasath , Manickavasagam Sindhuja , Meerasha Islabudeen Mohamed , Jaganathan Ragaventhiran , Balasubramanian Muthu Kumar
Published: July 2022

An exponential growth posting on the web about the product reviews on social media, there has been a great deal of examination being done on sorting out the purchasing behaviors of the client. This paper depends on utilizing twitter for sentiment analysis to comprehend the customer purchasing behavior. There has been a significant increase in e-commerce, particularly in persons purchasing products on the internet. As a result, it becomes a fertile hotspot for opinion analysis and belief mining. In this investigation, we look at the problem of recognizing and anticipating a client's purchase goal for an item. The sentiment analysis helps to arrive at a more indisputable outcome. In this study, the support vector machine, naive Bayes, and logistic regression methods are investigated for understanding the customer's sentiment or opinion on a specific product. These strategies have been demonstrated to be genuinely for making predictions using the analysis models which examine the client's conclusion/sentiment the most precisely. The exactness for each machine learning algorithm will be analyzed and the calculation which is the most precise would be viewed as ideal.
A machine learning approach for Bengali handwritten vowel character recognition
Authors: Ahsan Shahrukh , Nawaz Shah Tarik , Sarwar Talha Bin , Ullah Miah M. Saef , Bhowmik Abhijit
Published: July 2022

Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spoken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in distinguishing different characters. The real highlight is that the recognition results are more efficient than expected with a simple machine learning technique. The proposed method uses the Python library Scikit-Learn, including NumPy, Pandas, Matplotlib, and support vector machine (SVM) classifier. The proposed model uses a dataset derived from the BanglaLekha isolated dataset for the training and testing part. The new approach shows positive results and looks promising. It showed accuracy up to 94% for a particular character and 91% on average for all characters.
A smart traffic light using a microcontroller based on the fuzzy logic
Authors: Desmira Desmira , Hamid Mustofa Abi , Bakar Norazhar Abu , Nurtanto Muhammad , Sunardi Sunardi
Published: July 2022

Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fuzzy-logic-based microcontroller for a greater adaptability of the traffic light to the dynamics of the vehicles that are to cross the intersection. The ATMega2560 microcontroller-based smart traffic light is designed to create a breakthrough in the breakdown of congestions at road junctions, thereby optimizing the real-time happenings in the road. Ultrasonic, infrared, and light sensors are used in this smart traffic light, resulting in the smart traffic light’s effectiveness in parsing jams. The four sets of sensors that are placed in four sections determine the traffic light timing process. When the length of vehicle queue reaches the sensor, a signal is sent as the microcontroller’s digital input. Ultrasonic and infrared sensors can reduce congestions at traffic lights by giving a green light time when one or all of the sensors are active so that the vehicle congestions can be relieved.
A novel evolutionary optimization algorithm based solution approach for portfolio selection problem
Authors: Shahid Mohammad , Shamim Mohd , Ashraf Zubair , Ansari Mohd Shamim
Published: July 2022

The portfolio selection problem is one of the most common problems which drawn the attention of experts of the field in recent decades. The mean variance portfolio optimization aims to minimize variance (risk) and maximize the expected return. In case of linear constraints, the problem can be solved by variants of Markowitz. But many constraints such as cardinality, and transaction cost, make the problem so vital that conventional techniques are not good enough in giving efficient solutions. Stochastic fractal search (SFS) is a strong population based meta-heuristic approach that has derived from evolutionary computation (EC). In this paper, a novel portfolio selection model using SFS based optimization approach has been proposed to maximize Sharpe ratio. SFS is an evolutionary approach. This algorithm models the natural growth process using fractal theory. Performance evaluation has been conducted to determine the effectiveness of the model by making comparison with other state of art models such as genetic algorithm (GA) and simulated annealing (SA) on same objective and environment. The real datasets of the Bombay stock exchange (BSE) Sensex of Indian stock exchange have been taken in the study. Study reveals the superior performance of the SFS than GA and SA.
Bio-inspired and deep learning approach for cerebral aneurysms prediction in healthcare environment
Authors: Raghavan Srividhya Srinivasa , Arunachalam Arunachalam
Published: July 2022

Diagnosis is being used in a variety of fields, including treatments, scientific knowledge, technology, industry, and many deals. A diagnosis begins with the person’s complaints and understanding something about the condition of the patient dynamically while in a question-and-answer session, as well as by taking measurements, like blood pressure or skin temperature, among other things. The prognosis is then calculated by considering the obtainable patient information. The adequate intervention is then prescribed, and the method may be repeated. In the medical field, humans, sometimes, have constraints when diagnosing diagnosis, primarily because this procedure is arbitrary and heavily relies on the assessor’s memories and perception of patient transmissions. The work is primarily concerned with the investigation of cerebellar aneurysm diagnosing. In the meantime, it’s become evident even during literature reviews research that a much more basis of theoretical research of a number of existing learning methods was required. As a result, this paper is to provide a comparison of classification techniques like tree structure, random trees, and regression. At about the same time, another important goal is to have a decision-making framework based on biomimetic elephant-whale enhancement for a great deal of consideration of cerebral aneurysm variables, providing a quick, accurate, and dependable clinical medicine remedy.
A new approach to solve the of maximum constraint satisfaction problem
Authors: Alaoui Mohammed El , Ettaouil Mohamed
Published: July 2022

The premature convergence of the simulated annealing algorithm, to solve many complex problems of artificial intelligence, refers to a failure mode where the process stops at a stable point that does not represent to an overall solution. Accelerating the speed of convergence and avoiding local solutions is the concern of this work. To overcome this weakness in order to improve the performance of the solution, a new hybrid approach is proposed. The new approach is able to take into consideration the state of the system during convergence via the use of Hopfield neural networks. To implement the proposed approach, the problem of maximum constraint satisfaction is modeled as a quadratic programming. This problem is solved via the use of the new approach. The approach is compared with other methods to show the effectiveness of the proposed approach.
The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest
Authors: Effendy Nazrul , Kurniawan Eko David , Dwiantoro Kenny , Arif Agus , Muddin Nidlom
Published: July 2022

The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.
Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising
Authors: Aneja Sandhya , Aneja Nagender , Abas Pg Emeroylariffion , Naim Abdul Ghani
Published: July 2022

Despite substantial advances in network architecture performance, the susceptibility of adversarial attacks makes deep learning challenging to implement in safety-critical applications. This paper proposes a data-centric approach to addressing this problem. A nonlocal denoising method with different luminance values has been used to generate adversarial examples from the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research (CIFAR-10) data sets. Under perturbation, the method provided absolute accuracy improvements of up to 9.3% in the MNIST data set and 13% in the CIFAR-10 data set. Training using transformed images with higher luminance values increases the robustness of the classifier. We have shown that transfer learning is disadvantageous for adversarial machine learning. The results indicate that simple adversarial examples can improve resilience and make deep learning easier to apply in various applications.
Graduate school students’ self-efficacy toward online learning in the midst of the COVID-19 pandemic
Authors: Cadapan Rommel Royce , Tindowen Darin Jan , Mendezabal Marie Jean , Quilang Pyrene
Published: July 2022

The COVID-19 pandemic brought drastic changes in graduate education. One of the most pressing concerns that graduate education students experience is their adjustment to the online learning modality. This study was conducted to determine their self-efficacy in online learning. A descriptive method of research was employed by 147 graduate school students in the Northern Philippines. The findings showed that students have a high level of self-efficacy when it comes to online learning. Despite their struggles and challenges in the online classroom, particularly in social interactions and communication with their classmates and teachers, they are eager to complete their respective degrees since they are confident in their learning management system's use. In addition, their current level of self-efficacy in online learning varies according to their age, occupation, and online courses they were previously enrolled in.
Feedback of students on internship program in an agricultural course
Authors: Nicolas Honeylet J. , Manalastas Helen G. , Capalad Robert A.
Published: July 2022

Internship is the essential part of curriculum where students get hands-on experience in training establishments that complement their formal learning in school. Studies on institutional internship programs are done to identify concerns that may hinder its successful implementation. Through the lens of student-interns, this study aimed to assess industry partners and the internship program for undergraduate Animal Science majors, in order to propose strategies toward enhanced internship implementation specifically for the institution’s new curriculum for agriculture students. The study utilized the descriptive-evaluative survey method using an adapted and modified questionnaire. Based on the results, the 217 surveyed student-interns assessed their host training establishments to be very satisfactory in terms of facilities, communication, regard for people, empowerment, and work ethics. They rated the internship program as excellent in overall effectiveness, and very satisfactory in policies. The interns seldom encountered problems while training. The proposed action plan gives strategic options in the areas of linkages and policies, to enhance the overall implementation of the internship program, toward optimized benefits for students, industry partners, and the college. This research can contribute to the improvement of internship in agriculture-related courses in higher education institutions.
Main individual factors influencing the learning approaches: The first-year students’ perspective
Authors: Jiyed Omar , Aydi Ouafae Idrissi , Alami Anouar , Benjelloun Nadia , Zaki Moncef , Lachkar Mohammed
Published: July 2022

Currently, the quality of student learning i.e. in-depth learning is essential in any planned reform. The massification phenomenon is one of the challenges facing this quality. This study explored the student characteristics influencing the learning process in an open access faculty, namely, the Dhar El Mahraz Faculty of Science in Fez, Morocco. Semi-structured interviews were conducted on 15 freshmen who enrolled in Earth and Universe Sciences and Life Sciences (EUS/LIS) program and presented a dominance of a surface learning approach. The main factors encouraging surface learning emerged were the learning habits and strategies, language competencies, motivational aspects and gender. The findings serve as an input for the design and implementation of actions to enhance deep learning.
The influence of fiqh insights and science literacy on student ability in developing Quran-based science
Authors: Umar Muhammad , Sukarno Sukarno
Published: July 2022

Islamic education institutions are responsible for the process of integration of knowledge which aims to encourage the mastery and improvement of general science and religious knowledge in harmony. This study aimed to see the extent of the influence of the ability of fiqh (Islamic jurisprudence) and scientific literacy on the ability of physics students in developing integrated learning Quran-based science. Through a quantitative approach with a survey model of physics education students involving 150 students, it was found that the correlation value Rcal (0.943)>Rtable (0.1339) with a sig. value (2-tailed) 0.000<0.05. Furthermore, it is also known that the correlation between scientific literacy and integration ability is Rcal (0.512)>Rtable (0.1339) with a sig. (2-tailed) 0.000<0.05. ANOVA test results (F test) showed the value of Fcal (632.126)>F table 3.91 with a probability of 0.05. Thus, it can be understood that simultaneously the independent variables (fiqh insights and scientific literacy) affect the dependent variable (the ability to develop integrated learning Quran-based science as tools of learning). These results are also supported by a sig. 0.00<0.05, which means that the independent variables simultaneously influence the dependent variable significantly.
The impact of ICT utilization to improve the learning outcome: A meta-analysis
Authors: Arlinwibowo Janu , Retnawati Heri , Kartowagiran Badrun
Published: July 2022

This study aimed to describe the impact of the utilization of technology on learning outcomes through a meta-analysis. Data was collected by documenting research that is resulted from various sources collected through internet. These articles were published from 2006 to 2019, in United Nations Language. The article discusses the influence of information and communication technology (ICT) on learning achievement, analyzed qualitatively, describing the data in the form of sample size, standard deviation, and average, and published in journals indexed in Google Scholar. The analysis design employed a contrast group with the random effect model that the effect size is corrected. The analysis was used JASP software for calculating the average aggregate difference, drawing a forest plot, and publication bias. The results of the analysis show that there is a significant difference between groups using ICT and non-ICT in the learning process (SE=1.13), groups of students who utilizing ICT in the learning have better learning outcomes than those who do not use ICT. Based on the results of the analysis, it is very clear the urgency of implementing ICT in supporting the learning process. Schools should conduct an assessment of the condition of the school and students and then choose the appropriate ICT implementation. Thus, the quality of the learning process and students' technological literacy improve.
Teacher-student interactions for enhanced learning in upper secondary mathematics classroom
Authors: Dorimana Aline , Uworwabayeho Alphonse , Nizeyimana Gabriel
Published: July 2022

Interactive teaching practices in mathematics classrooms are encouraged to meet the learning potential. However, how it is applied in the classroom is still challenging. This paper uses evidence gathered from a large research project to contribute to the understanding of teacher-student interactions. It followed an interventional approach with two teachers and 82 students of two grade 11 classes from one school of Kayonza District in Rwanda. Data were collected through observations, video recordings, and field notes. Results revealed that there is a variety of ways to facilitate and keep interactive learning. These include asking purposeful questions and giving feedback. The quality of teacher facilitation (52%) and students' engagement (60%) were moderately high. However, there was little guidance from teachers in the problem-solving process and the classroom discourse. It is argued that the role of instructional language in communication hinders the quality of teacher-student interactions. This leads us to advise an exploration of language supportive pedagogy in the Rwandan context.
The impact of Google Classroom to increase students’ information literacy
Authors: Dewi Citra Ayu , Muhali Muhali , Kurniasih Yeti , Lukitasari Diah , Sakban Abdul
Published: July 2022

Information literacy at the higher education level is very important because it tends to be problematic in fostering students' critical thinking skills, including awareness, collection, understanding, analysis, synthesis, and the use of information and their attitudes to the treatment of information at the university level. This study examined the effects of the online learning Google Classroom in enhancing information literacy among students. This study observed a one-group pretest-posttest experimental design. This research involved 65 students from the Chemistry Course under the Biochemistry Program. The instrument used to assess students’ information literacy consist of fair set of multiple-choice questions. The test was found to be very valid at 4.22% and highly reliable at an alpha level of 0.86. The findings of this research showed that the information literacy of students improved before and after their learning exposure using Google Classroom based on the descriptive and inferential statistics. Moreover, the students' attitude towards the online platform shows that they are more willing to learn using Google Classroom than the non-online or conventional learning platforms. Therefore, applying Google Classroom in online learning is effectively boost students’ information literacy.
Students’ learning experiences in the Communication Program from a university in the Philippines
Authors: Gador Sunliegh Camay , Mangompit Rowanne Marie Maxilom , Magno Joseleanor Melliza
Published: July 2022

This study analyzed the students’ learning experiences in the Communication Program from a university in the Philippines. A descriptive method was used in analyzing the data gathered from the researcher-made questionnaires and exit interviews of the perceived fourth year student-respondents. Using categories of students’ learning experiences, findings revealed that experiential learning and social climate contribute mostly to their best and worst experiences in the university. Furthermore, aside from problem solving and application of theories into practice, quality of lectures, and discussion, three themes surfaced particularly related to the self, faculty, and university. As learners are encouraged to actively participate in the teaching-learning process and continue to make sense of their identity in a learning environment, the higher education institutions also have equal responsibilities of providing conditions that are conducive to achieving target learning outcomes. Such conditions comprise the schools’ facilities and information technology infrastructure, and carefully designed instructional packages including quality instructional materials and competent teachers. This study provides recommendations in redesigning the Communication Program based on the students' learning experiences.
Instrument evaluation of conspiracy theory about COVID-19: Exploratory factor analysis and confirmatory factor analysis
Authors: Fauzi Ahmad , Saefi Muhammad , Adi Widi Cahya , Kristiana Evi , Lestariani Nurlita
Published: July 2022

Students’ beliefs toward conspiracy theory (CT) of COVID-19 can be the indication of science education quality. However, a validated instrument to measure the level of students’ acceptance on the CT about COVID-19 still remains unexplored. This study aimed to evaluate the questionnaire of CT about COVID-19. The questionnaire consisted of 14 items and delivered online to the 400 students of Biology education in Indonesia. The data was analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). All items have standard deviation between 1.60 and 2.63, but one item was revealed to have insignificant correlation. The EFA result indicated that the other 13 items had loading factor 0.5 and were categorized into three dimensions. The third dimension was eliminated after the administration of CFA due to only one item in the dimension (λ>0.5). There were three items in the first dimension and two items in the second dimension resulted in λ<0.5. The correlated model from CFA disclosed good values of goodness of fit (GOF), χ2/df=2.527, root mean square error of approximation (RMSEA) standardized root mean square residual (SRMR)=0.014, comparative fit index (CFI)=0.964, goodness-of-fit index (GFI)=0.969, adjusted goodness of fit (AGFI)=0.940, Tucker-Lewis index (TLI)=0.947, and normed fit index (NFI)=0.943. Therefore, the questionnaire is strongly recommended to measure the level of students’ beliefs toward the CT of COVID-19. 
National identity: Prediction model of relationship between language decency and language character
Authors: Jamian Abdul Rasid , Omar Rosmaria , Idris Mohd Rashid Md , Wahid Azhar , Mohamad Ariff , Jobar Norfaizah Abdul , Ansary Khairil
Published: July 2022

The expression of decency in communication triggers individual character in formation of national identity. Language decency always applies in Malay language education context during learning session. But, how far it can shape the generation’s identity is still vague. Aim of this research was to thorough the relationship of language education decency with language character among students in public and private universities. A survey was conducted on 380 students of several universities in Malaysia. The data were collected by using Google Form with cooperation from university representatives. The quantitative data was analyzed by using SPSS and AMOS software through correlation and regression analysis by structural equation modelling (SEM). Findings showed that the most decency form used by students was words with value β=.67, p=.000. Language decency education had contributed 80% of R2 value for language character among students. The prediction model of relationship between decency language and character language had achieved equivalence value which RMSEA=.067, GFI=.977, CFI=.995, TLI=.991 and Chisq/df=2.682. Therefore, factor of decency language that applies among students in Malay language course can contribute on generation with nice language character and strong national identity.