bluuebunny/crossref_metadata_2025 huggingface dataset:
Rotation forest of random subspace models
Authors: Alexandropoulos Stamatios-Aggelos N. , Aridas Christos K. , Kotsiantis Sotiris B. , Gravvanis George A. , Vrahatis Michael N.
Published: May 2022

During the last decade, a variety of ensembles methods has been developed. All known and widely used methods of this category produce and combine different learners utilizing the same algorithm as the basic classifiers. In the present study, we use two well-known approaches, namely, Rotation Forest and Random Subspace, in order to increase the effectiveness of a single learning algorithm. We have conducted experiments with other well-known ensemble methods, with 25 sub-classifiers, in order to test the proposed model. The experimental study that we have conducted is based on 35 various datasets. According to the Friedman test, the Rotation Forest of Random Subspace C4.5 (RFRS C4.5) and the PART (RFRS PART) algorithms exhibit the best scores in our resulting ranking. Our results have shown that the proposed method exhibits competitive performance and better accuracy in most of the cases.
Pharmaceutical plant machine availability prediction using Artificial Neural Network
Authors: Garg Deepika , Roy Nihar Ranjan , Khanna Ashish
Published: May 2022

During the 2nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.
Optimal trained long short-term memory for opinion mining: A hybrid semantic knowledgebase approach
Authors: Menaga D. , Ambati Loknath Sai , Bojja Giridhar Reddy
Published: May 2022

This study describes a Hybrid Semantic Knowledgebase-Machine Learning strategy for mining the domain feature-level opinions and categorizing them on a multi-point scale. The proposed work is constructed by following the six major stages: “(a) Pre-processing, (b) Domain Feature Extraction, (c) Sentiment Extraction, (d) Domain Feature-Sentiment Association, (e) Domain Feature Polarity, (f) Knowledgebase Enrichment and (g) Opinion Classification with optimized deep learning model”. The proposed method is divided into several levels, each of which is designed to solve the opinion mining challenges at “domain feature level”. Initially, from the collected reviews, the domain features as well as semantics are extracted. Subsequently, in the Domain feature-sentiment association stage, the extracted filtered domain features are associated with their corresponding extracted sentiments acquired from the Sentiment Extraction phase. Once, the sentiments and the features are associated, the polarity of the feature sentiment pairs are computed using the sentiment aggregation function, which assigns a score to the features based on its proximity. Then, in the Knowledgebase enrichment stage, the initial review knowledgebase was used to bootstrap the domain feature extraction process, which is further enriched with new semantic information related to the analyzed review and the corresponding extracted domain features. Eventually, in the Opinion classification phase, a new optimized deep learning classifier, the optimized Long Short-Term Memory (LSTM) is introduced. The optimized LSTM is trained with the semantic information acquired from the enriched knowledge base and the statistical features created by Vector Space Model. Moreover, to enhance the classification accuracy of the LSTM, an Improved Butterfly Optimization Algorithm is introduced to fine-tune the weight of the model. Finally, the performance of the proposed work is evaluated over the conventional models.
Autism detection for toddlers from behavioural indicators using classification techniques
Authors: Chan Sapphira , Thabtah Fadi , Abdel-Jaber Hussein , Guerrero Franco
Published: May 2022

Autism spectrum disorder (ASD) is a condition associated with impairments in communication, social, and repetitive behaviour; the degree of impairment varies between individuals with ASD. Since ASD has a substantial impact on the individual, caregivers, and family members due to the social and economic costs involved, early ASD screening becomes fundamental to enable faster access to healthcare resources. One of the important studied groups in ASD research is toddlers – detecting autistic traits at an early stage can help physicians develop treatment plans. This paper aims to improve the detection rate of ASD screening for toddlers using a data driven approach by identifying the impactful feature set related to ASD, and then processing these features using classification algorithms to accurately screen for ASD. To achieve the aim, a data driven framework consisting of feature selection and classification algorithms is proposed, and then implemented on a real dataset related to the ASD screening of toddlers. Empirical evaluations on the ASD screening dataset using different classification methods reveal that when support vector machine (SVM) or Naïve Bayes are integrated with the proposed framework good predictive models are constructed for toddler ASD screening. These predictive models can be adopted by different medical staff and caregivers to replace scoring functions of conventional screening methods.
The empirical analysis on the demand of salary factors for knowledge staffs of SMES
Authors: Dong Guojuan
Published: May 2022

Small and medium-sized enterprises (SMES) in China, especially knowledge-based enterprise is one of the main forces for realizing knowledge innovation and changing industrial structure, and creation, utilization and appreciation of the knowledge ultimately depend on knowledge staffs which are the carrier of knowledge to achieve. In this paper, the traditional salary model of SMES is analyzed with empirical analysis method, the traditional salary model cannot meet the salary demands of knowledge staffs, we propose a salary system that matches the demand characteristics of knowledge staffs, and there is a positive discussion how to effectively motivate the knowledge staffs.
Shipping network optimization of port equipment components based on improved genetic algorithm
Authors: Li Huaidong , Hu Jiankun , Tong Bangyu
Published: May 2022

The management of components ensures that the port equipment is in good working condition, ensure port production safety and continuity. In this paper, a port equipment component supply network optimization model that considers the characteristics of shipping network is proposed to optimize hub port selection, route choice, ship deployment, inventory optimization, etc. At the same time, an improved genetic algorithm is developed, including chromosome matrix, row column crossover genetic operation, and double-layer parallel search. The results show that the model and algorithm can effectively obtain a better solution and achieve the optimization goal.
China’s higher education development evaluation based on GA-BP neural network
Authors: Ren Yanzhou , Wang Xinyu , Li Zelong
Published: May 2022

The development of higher education supplies a large number of high-level talents to the society, which is the key to building a harmonious society. At present, the development of regional higher education is extremely uneven, and it is the top priority of education development that it is urgent to clarify the situation of regional higher education. This article constructs a comprehensive evaluation index system of higher education development from a total of 19 indicators from five dimensions of talent training, teacher strength, scientific research output, infrastructure and social services, and then uses entropy and genetic algorithm-projection pursuit model to calculate the weight. GA-BP and BP neural network models are used for comprehensive evaluation. It is found that: (1) The most important factors affecting the development of higher education are technology transfer income and the application of R&D achievements in colleges and universities; (2) Compared with BP neural network, GA optimizes BP neural network in terms of effectiveness, convergence speed, and accuracy. (3) Generally speaking, during the research period, the development of China’s higher education has gradually improved, with an average annual growth rate of 3.5%. In terms of sub-regions, the provinces with excellent higher education development levels have increased from 0 in 2008. The number has increased to 5 in 2019, and the development of higher education among provinces is extremely uneven, and the differences between provinces are gradually increasing.
Advancing transformative STEM learning: Converging perspectives from education, social science, mathematics, and engineering
Authors: Elliott Rebekah , Loh Carolyn G. , Psenka Carolyn E. , Lewis Jennifer M. , Kim Kyoung-Yun , Haapala Karl R. , Neal Donald , Okudan Kremer Gül E.
Published: May 2022

Society faces emerging challenges that require re-envisioning what it means to know and use science, technology, engineering, and mathematics (STEM) and who are STEM scientists. We advocate for a transdisciplinary framework for participatory STEM learning based on the culmination of the authors’ designing and complemented by reviews of extant works in youth STEM learning and engagement. Data literacy, geospatial reasoning, and community science are cornerstones in our framework because of their power to leverage and integrate the four STEM disciplines. Youth with their families are authors and designers in community problem-solving using data literacy and geospatial reasoning through participatory community science to question, analyze, and design solutions empowered by their lived experiences. Through partnerships with community organizations, families, youth, and STEM practitioners, we discuss how to develop and use tools and methods to design and build better spaces for youths’ communities. Our aim is for more authentic, inclusive, and empowering learning opportunities that broaden youths’ STEM participation. We describe our framework and the underlying commitments, design principles, expected outcomes, and limitations.
One-of-a-kind Production in Cyber-Physical Production Systems Considering Machine Failures
Authors: Vinci Carlavan Guido , Rossit Daniel Alejandro
Published: May 2022

Industry 4.0 proposes the incorporation of information technologies at all levels of the production process. By incorporating these technologies, Industry 4.0 provides new tools for production planning processes, allowing to address problems in an innovative and efficient manner. From these technologies and tools, it is that in this work a One-of-a-Kind Production (OKP) process is approached, where the products tend to be highly customized. OKP implies working with a very large variability within production, demanding very efficient planning systems. For this, a planning model based on CONWIP-type strategies was proposed, which seeks to level the production of a shop floor configured in the form of a job shop. Even more, for having a more realistic shop-floor representation, machine failures have been included in the model. In turn, different dispatching rules were proposed to study the performance and analyze the behaviour of the system. From the results obtained, it is observed that, when the production demand is very exigent in relation with the capacity of the system, the dispatching rules that analyze the workload generated by each job tend to perform better. However, when the demand on the capacity of the production system is less intense, the rules associated with due dates are the ones that obtain the best results.
Deep ganitrus algorithm for speech emotion recognition
Authors: Shukla Shilpi , Jain Madhu
Published: May 2022

Human emotion recognition with the evaluation of speech signals is an emerging topic in recent decades. Emotion recognition through speech signals is relatively confusing because of the speaking style, voice quality, cultural background of the speaker, environment, etc. Even though numerous signal processing methods and frameworks exists to detect and characterize the speech signal’s emotions, they do not attain the full speech emotion recognition (SER) accuracy and success rate. This paper proposes a novel algorithm, namely the deep ganitrus algorithm (DGA), to perceive the various categories of emotions from the input speech signal for better accuracy. DGA combines independent component analysis with fisher criterion for feature extraction and deep belief network with wake sleep for emotion classification. This algorithm is inspired by the elaeocarpus ganitrus (rudraksha seed), which has 1 to 21 lines. The single line bead is rarest to find, analogously finding a single emotion from the speech signal is also complex. The proposed DGA is experimentally verified on the Berlin database. Finally, the evaluation results were compared with the existing framework, and the test result accomplishes better recognition accuracy when compared with all other current algorithms.
Water evaporation algorithm optimized cascade controller for frequency regulation of integrated microgrid
Authors: Raja K. , Patan Muzeeb Khan , Ahmed Md. Azahar , Ganeshan P.
Published: May 2022

Integration of renewable energy sources into existing grid influence the stability of the power system. This article introduces the application of cascade controller in hybrid power system which enhance the frequency stability during power perturbations of the load and generation. For this study, a thermal power unit is considered with integration of a microgrid consist of regular diesel generator, renewable power generating units, energy storage and other power managing devices. Proportional-integral and proportional-integral-derivative (PI-PID) cascade controller is provided for this hybrid power system to reduce the frequency oscillations during system uncertainties. The optimal values of the PI-PID controller are achieved by using water evaporation optimization (WEO) algorithm with fast convergence rate. Investigations are carried out in different scenarios of the IM and results are compared with the PID controller to showcase the advantages of the cascade controller for frequency regulation. Simulations are carried out in MATLAB-SIMULINK® software environment.
Research on incentive strategy based on service quality in spatial crowdsourcing task allocation
Authors: Peng Peng , Ni Zhiwei , Wu Zhangjun , Zhu Xuhui , Xia Pingfan
Published: May 2022

In order to further improve the enthusiasm of spatial crowdsourcing workers, considering the service quality of workers, different incentive strategies are proposed and tasks are assigned. Firstly, the incentive model is constructed from the unit time revenue of task and online idle time, and the evaluation function of the evaluation model is constructed; Secondly, the task allocation is transformed into a combinatorial optimization problem by delay matching, and an improved glowworm swarm algorithm is proposed to solve the problem by discrete coding, introducing six kinds of mobile modes, adaptive probability matching and infeasible solution processing; Finally, the algorithm is used to solve the task allocation. The experimental results show that compared with the travel cost minimization strategy and random allocation strategy, the positive incentive index of the proposed strategy is improved by 11.79% and 14.60% respectively, and the fair incentive index is improved by 0.83% and 0.22% respectively, which can effectively improve the positive incentive range and incentive fairness of workers.
Machine Learning-based forecasting models for COVID-19 spread in Algeria
Authors: Chebout Mohamed Sedik , Kabour Oussama
Published: May 2022

Currently, the Algerian health system is facing the fourth wave of COVID-19 in which the number of recovered cases grows exponentially each day due to the COVID-19 Omicron variant. According to the Algerian National Institute of Public Health (ANIPH), it was reported 168 668 confirmed cases and 4 189 deaths till 29 July, 2021. In this work, we aim to utilize supervised Machine Learning (ML) based models in an attempt to forecast the future trend of the disease in Algeria. To that end, we use three forecasting models: Facebook Prophet, LSTM and ARIMA. Forecasting results of the 90 future days are provided. The used dataset contains the confirmed and death cases collected from the daily Epidemiological Situation (ES), published by ANIPH, from 19 April 2020 to 29 July 2021. The forecasting accuracy of the models are assessed and compared using several statistical assessment criteria. The results show that ARIMA outperforms Facebook Prophet and LSTM in the case of confirmed cases. However, LSTM shows best performance in the case of death cases. This study shows clearly that the pandemic spread is still in progress and protection measures like contact restriction and lockdown should be strictly applied especially with the appearance of the COVID-19 Delta and Omicron variants.
Marshall olkin extended exponentiated Gamma distribution and its applications
Authors: Aguilar Guilherme Aparecido Santos , Moala Fernando A. , de Oliveira Ricardo Puziol
Published: May 2022

Different methods for obtaining new probability distributions have been introduced in the literature in recent years, for example, (Gupta et al., 1998) proposed an interesting uni-parametric lifetime distribution, Exponentiated Gamma (EG), which hazard function has increasing and bathtub shapes. In this paper, we build a new two-parameters distribution, the Marshall Olkin Extended Exponentiated Gamma (MOEEG) distribution, which is derived from the Marshall-Olkin method and the EG distribution. The hazard function of this new distribution can accommodate monotonic, non-monotonic and unimodal shapes, allowing a better fit to greater data variability. In addition to the great flexibility of fitting the data, it contains only two parameters providing a simple parameter estimation procedure, unlike other distributions proposed in the literature that have three or more parameters. Some properties of the new distribution considered in this paper are presented such as n-th time, r-th moment of residual life, r-thmoment of residual life inverted, stochastic ordering, entropy, mean deviation, Bonferroni and Lorenz curve, skewness, kurtosis, order statistics, and stress-strength parameter. We also apply two different estimation methods, maximum likelihood and Bayesian approach. Real data applications are presented to illustrate the usefulness of this new distribution.
Hybrid machine learning approach based intrusion detection in cloud: A metaheuristic assisted model
Authors: V. Murali Mohan , RM Balajee , K Mewada Hiren , BR Rajakumar , D Binu
Published: May 2022

Cloud computing provides various cost-effective on-demand services to the user and so it is rising like a real trend in the IT service model. However, due to its open and distributed architecture, it is highly vulnerable to attackers. The security and privacy of cloud users has become a major hurdle. The most prevalent approach for detecting attacks on the cloud is the Intrusion Detection System (IDS). Scalability and autonomous self-adaptation weren’t features of contemporary IDS deployed in traditional Internet or Intranet contexts. Furthermore, they lack determinism, making them inappropriate for cloud-based settings. This necessitates the development of new cloud-based IDS capable of fulfilling the firm’s security standards. Therefore, in this research work, we have introduced a new IDS model for the cloud environment. Our research work is made up of four major phases: “data pre-processing, optimal clustering, feature selection, and attack detection phase”. Initially, the collected raw data are pre-processed to enhance the quality of the data. Then, these pre-processed data are segmented with the newly introduced K-means clustering model, where we’ve optimally selected the centroids by introducing a new hybrid optimization model referred as Spider Monkey Updated with Sealion Optimization (SMSLO), which is the conceptual hybridization of standard SeaLion Optimization (SLnO) and Spider Monkey Optimization (SMO), respectively. At the end of segmentation, two clusters (attack data and non-attack data) will be formed. The data available in both clusters seems to be huge in dimensions, so we’ve lessened the dimensions of the data in the clusters by applying the “Principal Component Analysis (PCA)” algorithm. Subsequently, these dimensionality-reduced features pass into the attack detection phase. The attack detection phase is modeled with the optimized Deep Belief Network (DBN), which portrays the type of attack (Dos, Botnet, DDoS as well) that intruded into the network. Since the DBN makes the final detections; it is ought to be less prone to errors. We have lessened the detection errors such as the Mean Square Error (MSE) of DBN by fine-tuning its weight using a new hybrid optimization model (SMSLO). Finally, the result acquired from the proposed work (DBN+SMSLO) is validated.
Multi-objective secure task scheduling based on SLA in multi-cloud environment
Authors: Jawade Prashant Balkrishna , Ramachandram S.
Published: May 2022

The appliances that are received at a cloud data centre are a compilation of jobs (task) that might be independent or dependent on one another. These tasks are then allocated to diverse virtual machine (VM) in a scheduled way. For this task allocation, various scheduling policies are deployed with the intention of reducing energy utilization and makespan, and increasing cloud resource exploitation as well. A variety of research and studies were done to attain an optimal solution in a single cloud setting, however the similar schemes might not operate on multi-cloud environments. Here, this paper aims to introduce a secured task scheduling model in multi-cloud environment. The developed approach mainly concerns on optimal allocation of tasks via a hybrid optimization theory. Consequently, the developed optimal task allotment considers the objectives like makespan, execution time, security parameters (risk evaluation), utilization cost, maximal service level agreement (SLA) adherence and power usage effectiveness (PUE). For resolving this issue, a novel hybrid algorithm termed as rock hyraxes updated shark smell with logistic mapping (RHU-SLM) is introduced in this work. At last, the superiority of developed approach is proved on varied measures.
Comparison of short-term outcomes of 35-weeks’ gestation infants cared for in a level II NICU vs mother-baby, a retrospective study
Authors: Weimer K.E.D. , Bidegain M. , Shaikh S.K. , Couchet P. , Tanaka D.T. , Athavale K.
Published: May 2022

BACKGROUND: Late preterm infants are at high risk for medical complications and represent a growing NICU population. While 34-weeks’ gestation infants are generally admitted to the NICU and 36-weeks’gestation infants stay in mother-baby, there is wide practice variation for 35-weeks’gestation infants. The objective of this study was to compare short-term outcomes of 35-weeks’ gestation infants born at two hospitals within the same health system (DUHS), where one (DRH) admits all 35-weeks’ gestation infants to their level II NICU and the other (DUH) admits all 35-weeks’ gestation infants to mother-baby, unless clinical concern. METHODS: We conducted a retrospective cohort analysis of 35-weeks’ gestation infants born at DUHS from 2014–2019. Infant specific data were collected for birth, demographics, medications, medical therapies, LOS, ED visits and readmissions. 35-weeks’ gestation infants at each hospital (DRH vs DUH) that met inclusion criteria were compared, regardless of unit(s) of care. RESULTS: 726 infants of 35-weeks’ gestation were identified, 591 met our inclusion criteria (DUH –462, DRH –129). Infants discharged from DRH were more likely to receive medical therapies (caffeine, antibiotics, blood culture, phototherapy, NGT), had a 4 day longer LOS, but were more likely to feed exclusively MBM at discharge. There were no differences in ED visits; however, more infants from DUH were readmitted within 30 days of discharge. CONCLUSIONS: Our findings suggest admitting 35-weeks’ gestation infants directly to the NICU increases medical interventions and LOS, but might reduce hospital readmissions.
Is walking maintained following discharge from prosthetic rehabilitation?
Authors: Curtin Kate , Walsh Mary , Stow Jacqui , Malone Ailish
Published: May 2022

PURPOSE: To determine if people who walked with a lower limb prosthesis on discharge from rehabilitation continued to 20 weeks after discharge. MATERIALS AND METHODS: An observational cohort study design. People with major lower extremity amputation (LEA) who were discharged from rehabilitation using a prosthesis to walk and were due for prosthetic review appointment (20 weeks following discharge) during the period of data collection were eligible to be included. The primary outcome was the continuation of walking with a lower limb prosthesis at this time point (T2). Secondary outcome measures were: Timed Up and Go (TUG), Two Minute Walk Test (2MWT), SIGAM score, Locomotor Capability Index-5 (LCI-5), and Orthotic Prosthetic User Survey- Quality of Life (OPUS-QOL). Secondary outcomes were assessed at time of discharge from rehabilitation (T1) and repeated at T2. Statistical analysis was carried out comparing the results of the secondary outcome measures at T1 and T2. RESULTS: 22/28 participants were still walking with their prostheses at T2. Five of the six who abandoned walking with their prostheses did so because of issues with the skin on their residuum or contralateral foot. Participants who continued to walk with their prostheses demonstrated a statistically significant improvement in TUG (5.01 seconds, p < 0.01) and 2MWT (11.35 metres, p < 0.01) between the two time points. No statistically significant difference was found in LCI-5 or OPUS QOL scores between the two time points. CONCLUSION: Most participants continued to walk with their prosthesis and experienced further improvements in their exercise tolerance and decrease in their risk of falls after discharge.
Anthropometric data of an elderly Iranian population provides insights for system design
Authors: Mokhtarinia Hamid Reza , Maleki-Ghahfarokhi Azam , Rafiee Mahsa , Mohammadi Sharareh , Gabel Charles Philip
Published: May 2022

BACKGROUND: Anthropometric data should be considered for each major population age category, specifically because of the dimension changes occurring during ageing. In elderly subjects, the physical activities and their capabilities become limited. Therefore anthropometric reference data for equipment and system designs is necessary. OBJECTIVES: The aim of the study was to collect data for the body dimensions of an elderly Iranian population and evaluate the related gender and age correlations. METHODS: Nineteen body dimensions were manually measured on a sample size of 317 subjects (167 male and 150 female) from four provinces of Iran. A pilot preliminary test for assessing test-retest reliability on ten body dimensions using the Intraclass Correlation Coefficient (ICC2 :1)  was performed. Descriptive statistics were reported based on gender and independent samples t-tests to compare the anthropometric dimensions of both genders and age. RESULTS: Results indicated higher dimensions in males, except for hip-breadth (p = 0.87). In all subjects, increasing age corresponded with: decreased standing shoulder height, eye height, and elbow height; and with sitting height and sitting knee height; while hip-breadth increased. Reliability was acceptable (ICC2.1 >0.88). In the elderly, as with adult subjects, there are significant gender differences in body dimensions, and this should be considered in equipment and system design. Furthermore, this study demonstrated both genders differences and the consequences of aging. CONCLUSIONS: From this pilot data, product designers can consider the anthropometric characteristics for elderly Iranian subjects for equipment and system design.
Post-basic education training, age and gender of healthcare workers for elderly patients in Germany and Canada: A comparison
Authors: Mehdi Zafar , Nasser Ramzi , Theobald Hildegard
Published: May 2022

BACKGROUND: This study compares Canadian and German healthcare workers employment status and wages based on age, gender, and educational training. The German and Canadian healthcare systems are rarely compared, that if such a comparison between the two countries is available, can provide an insight of health workers employment status and how that might affect the wellbeing of elderly. OBJECTIVES: The study investigates the relation of age, gender and post-basic training with annual employment and income of health care workers in Canada and Germany. METHODS: Secondary data on age, gender, post-basic education training, employment status and average monthly wages/salary was obtained from the German Socioeconomic Panel and the Canadian Survey of Labor and Income Dynamics data. The German dataset comprised 571 healthcare workers, including 219 nurses, 231 elder carers and 121 care assistants. The Canadian dataset comprised 2,580 healthcare workers, including 947 nurses, 493 elder carers and 1,140 care assistants. RESULTS: Primarily, there was a strong relationship between post-basic training and wages for both the Canadian and German samples among elder carers and care assistants. Older healthcare workers (46 years old and above) were generally employed, and age had no predictive power on annual labor earnings. This difference in age was significant among the Canadian than the German nurses. Post-basic training had a significant relation with annual earnings and monthly wages in both the Canadian and German samples. CONCLUSIONS: This study suggests that the socio-demographics of healthcare workers as age, gender and advanced training are associated with their employment opportunities, financial rewards, and better career opportunities. The relationship between demographic variables helps understand the relation of healthcare workers employment dispositions and how it might improve the quality of life of older people in nursing homes, hospitals and elderly private homes in Canada and Germany.
Comparison of Injection Characteristics and Droplet Distribution on Crude Palm Oil and Diesel Using CFD
Authors: Suprianto Teguh , Darmansyah Darmansyah , Hasbi Muhammad
Published: May 2022

CPO (Crude Palm Oil) is a potential renewable
fuel as a substitute for fossil fuels used in internal combustion
engines. An important parameter that affects the combustion of
an engine with an injection system is the characteristics of the
fuel spray. This research was conducted using Computational
Fluid Dynamics (CFD) software to model the atomization
process that occurs when CPO and diesel fuel are injected with
a pressurized atomizer. The modeling shows the characteristics
of the spray which include the shape of the spray and the droplet
diameter distribution. These characteristics are presented with
spray images, droplet diameter distribution graphs, and velocity
distribution graphs. Simulations show that at temperatures
above 75°C and 100°C, CPO spray produces droplet shapes and
distributions that are almost the same as diesel spray. On the
other hand, diesel injection spray produces a lower droplet size.
The highest droplet size distribution is at 12-16 microns.
FAKTOR FAKTOR YANG MEMPENGARUHI PRESTASI NON AKADEMIK SISWA PADA EKSTRAKURIKULER PASUKAN KHUSUS (PASSUS) PRAMUKA DI SMP ISLAM TERPADU AL KAHFI BOGOR
Authors: Nasyirudin Ilyas , Sa'diyah Maemunah
Published: May 2022

Abstrak

                                                                    

Tujuan dari penelitian ini adalah untuk mengetahui faktor faktor yang mempengaruhi prestasi non akademik siswa dalam kegiatan ekstrakurikuler pramuka, penelitian ini menggunakan metode studi lapangan atau lebih dikenal dengan field research. Dari kajian lapangan di SMPIT Al Kahfi bogor peneliti menemukan beberapa faktor yang mempengaruhi prestasi siswa pada kegiatan ekstrakurikuler pramuka yang diwakili oleh pasukan khusus (passus Al Kahfi), passus adalah sekumpulan siswa pilihan yang dididik dan dibina secara khusus oleh pembina pramuka untuk mewakili SMPIT Al kahfi dalam lomba kepramukaan, secara umum ada dua faktor yang mempengaruhi prestasi passus Al kahfi dalam lomba kepramukaan, yang pertama adalah faktor eksternal yang terdiri dari dukungan dari pembina, teman teman angkatan, orang tua, dan kepala sekolah, yang kedua adalah faktor internal yang terdiri dari kekuatan niat dan spiritual., latihan keras, kompak dan saling menguatkan, disiplin tepat waktu dan pembiayaan.

 

Kata Kunci: Passus Al Kahfi, Prestasi, Ekstrakurikuler, Pramuka

 

 

Abstract

 The purpose of this study was to find out about the factors that influence students' non-academic achievement in scout extracurricular activities, this study used the field study method or better known as “field research”. From a field study at SMPIT Al Kahfi Bogor, researcher found several factors that affect student achievement in scout extracurricular activities represented by special forces (Passus Al Kahfi), Passus is a selected group of students who are specially educated and fostered by scoutmasters/instructor to represent SMPIT Al Kahfi In the scouting competition, in general there are two factors that affect the achievement of Passus Al Kahfi in the scouting competition, the first is an external factor consisting of support from the coach, friends in the same level, parents, and the school principal. The second is an internal factor, consisting of strength of intention and spirituality, hard training, compactness and mutually reinforcing, disciplined on time and financing.

 

Keywords: Passus Al kahfi, Achievement, extracurricular, Scout
PENDIDIKAN HIMPUNAN ANAK MASJID (HAMAS) DAN PERANANNYA DALAM PENDIDIKAN BERBASIS MASJID
Authors: Iman Ma'danil , Nurulrosidin Didin , Suryatna Yayat
Published: May 2022

Abstrak

Tempat penelitian dilaksanakan di Masjid Jogokariyan kampung Jogokariyan Kelurahan dan Kecamatan Mantrijeron Kota Yogyakarta. Ada beberapa masalah yang dihadapi oleh masjid-masjid secara keseluruhan yaitu masjid hanya sebatas tempat shalat dan tidak ramah anak. Dengan demikian banyak anak-anak memilih tempat-tempat yang menyenangkan dan memberikan ruang yang bebas untuk mereka. Dari masalah tersebut maka peneliti menemukan salah satu masjid ramah anak yang menyelenggrakan pendidikan berbasis masjid. Metode yang digunakan dalam penelitian adalah metode deskriptif dengan studi kasus dan pendekatan kualitatif. Pengumpulan data dilakukan melalui wawancara, observasi, dan dokumentasi. Hasil penelitian dapat disimpulkan bahwa masjid Jogokariyan selain berperan sebagai tempat bersujud dan beribadah, juga berperan aktif sebagai lembaga pendidikan untuk anak-anak. Pendidikan berbasis masjid yang dilaksanakan di masjid Jogokariyan  Yogyakarta memberikan pendidikan agama, umum, mental, sejarah dan kepemimpinan semuanya dibungkus atas nama Himpunan Anak-Anak Masjid (HAMAS). Masjid Jogokariyan menjadi masjid ramah anak sehingga mendapatkan kepercayaan dari masyarakat untuk menitipkan dan menyerahkan pendidikan non formalnya di masjid. Prestasi masjid Jogokariyan dalam mengelola dan menyelenggarakan pendidikan ramah anak berbasis masjid dapat pengakuan dari warga Yogyakarta dan Kementerian Agama Republik Indonesia.

 

Kata Kunci: Pendidikan, Anak, Masjid

 

Abstract

The research was conducted at the Jogokariyan Mosque, Jogokariyan Village, and Mantrijeron District, Yogyakarta City. There are several problems faced by mosques as a whole, namely the mosque is only a place of prayer and is not child-friendly. Thus many children choose places that are fun and provide free space for them. From this problem, the researcher found one child-friendly mosque that held mosque-based education. The method used in this research is a descriptive method with case studies and a qualitative approach. Data was collected through interviews, observation, and documentation. The results of the study can be concluded that the Jogokariyan mosque in addition to acting as a place of prostration and worship, also plays an active role as an educational institution for children. Mosque-based education held at the Jogokariyan mosque in Yogyakarta provides religious, general, mental, historical and leadership education all wrapped up in the name of the Mosque Children's Association (HAMAS). The Jogokariyan Mosque is a child-friendly mosque so that it gains the trust of the community to entrust and submit their non-formal education at the mosque. The achievements of the Jogokariyan mosque in managing and providing mosque-based child-friendly education have been recognized by the residents of Yogyakarta and the Ministry of Religion of the Republic of Indonesia.

 

Keywords: Education, Children, Mosques
C-Reactive Protein Level in Diagnosis of Acute Appendicitis
Authors: Kumar Pramod
Published: May 2022

Background: To study the c-reactive protein in acute appendicitis.

Methods: The source of data was from pretested proforma which takes into account clinical history, general physical examination, relevant investigations, imaging modalities. All data were analyzed by Epi-info software.

Results: Mean CRP level in 0-6 mm appendix diameter patients was 1.63±2.32mg/dl, in 6-8 mm diameter cases was 2.89±3.67mg/dl, 8-10 mm cases CRP level was 3.60±3.24 mg/dl and in 10-12 mm diameter cases CRP level was 3.61±2.28 mg/dl.

Conclusion: Raised serum C-reactive protein reducing the rate of negative explorations.

Keywords: CRP, Appendix, Pain abdomen
Caracteres anatômicos e micromorfológicos da epiderme foliar de Emilia sonchifolia (L.) DC. ex Wight. (Asteraceae)
Authors: Furlan Marcos Roberto , Aoyama Elisa Mitsuko , Elias Letícia
Published: May 2022

O uso de plantas medicinais é tão antigo quanto às civilizações. Contudo, as regionalidades e sinonímias muitas vezes levam ao uso errôneo dessas plantas. Dessa forma, o objetivo do trabalho foi descrever a anatomia e micromorfologia da epiderme foliar de Emilia sonchifolia (L.) DC. ex Wight. (Asteraceae) a fim de contribuir com a correta identificação da espécie. O material vegetal foi coletado na Universidade Federal do Espírito Santo, sendo coletadas cinco folhas completamente expandidas de cinco indivíduos. Para as análises micromorfológicas o material foi fixado em FAA 50 e desidratado em série etílico. As análises e digitalização das imagens foram realizadas ao microscópio eletrônico de varredura. Para as análises anatômicas, o material foi fixado em FAA 50 e, posteriormente, transferido para álcool 50%. Foram realizadas seções paradérmicas das superfícies foliares, as quais foram coradas com azul de alcião e safranina. Em ambas as superfícies foram evidenciadas células epidérmicas com paredes celulares sinuosas, estômatos anomocíticos e anisocíticos, além de grandes tricomas tectores pluricelulares. As análises no MEV mostraram deposição de cera epicuticular em ambas as superfícies. Conclui-se que os longos tricomas tectores pluricelulares e os estômatos anomocíticos e anisocíticos são um conjunto de caracteres que permitem a identificação de E. sonchifolia.
Inhibition of digestive enzymes (α-amylase, α-glucosidase, lipase, trypsin) by aqueous Hibiscus sabdariffa L. (Malvaceae) extract
Authors: Carvalho Caio Eduardo de , Mancini Daiana Teixeira , Pereira Chrystian Araújo , Thomasi Sérgio Scherrer , Pereira Luciana Lopes Silva
Published: May 2022

A considerable portion of the world population uses medicinal plants to treat chronic diseases. In this context, hibiscus (Hibiscus sabdariffa L. - Malvaceae) stands out for its supposed hypoglycemic and slimming effect. The present work aimed to carry out enzyme inhibition tests for aqueous hibiscus extracts, as a possible mechanism of action related to the supposed slimming and hypoglycemic effects. The inhibition of four digestive enzymes (α-amylase, α-glucosidase, lipase and trypsin) was tested before and after exposure to a simulated gastric fluid. In addition, a molecular anchorage study was carried out, in order to highlight possible molecular interactions between target and ligand. The results showed that the aqueous extract of hibiscus, in the proportion 1:10 (w/v), inhibits only α-glucosidase. It was observed that cyanidin-3-sambubioside, has interaction with the enzyme with properties similar to acarbose, which corroborates the possibility of the presence of an inhibitory effect in the aqueous extract of hibiscus.
Isolation of active antiphytopathogen compound from extracts of Anadenanthera colubrina var. cebil (Griseb.) Altschul
Authors: Silva-Bessa Cibele Maria Alves da , Malafaia Carolina Barbosa , Mercês Paula Fernanda Figueiredo das , Araújo Daniel Rodrigo Cavalcante de , Silva Ana Paula Sant'Anna da , Lima Vera Lúcia de Menezes , Batista Fabiane Rabelo da Costa , Lima Cláudia Sampaio de Andrade , Souza Elineide Barbosa de , Correia Maria Tereza dos Santos , Oliva Maria Luiza Vilela
Published: May 2022

Several microorganisms are responsible for great economic losses in world agriculture. Preventive and treatment methods are applied to avoid contamination of crops by these microorganisms, however, the use of chemical antimicrobials damages health and the environment. Secondary plant metabolites are safe natural sources of antimicrobials for this application. Fabaceae family has its history described in the literature as a potential source for obtaining antimicrobial bioactive. The objective of this work was to isolate bioactive compounds guided by antimicrobial assays against bacteria and fungi in vitro. Organic extracts were prepared by eluotropic series of leaves of Anadenanthera colubrina var. cebil and were tested against six bacteria and six fungi phytopathogenic. The antimicrobial assays of minimum inhibitory concentration (MIC) and minimum microbicidal concentration (MMC) were performed at each purification step that occurred through HPLC-DAD, Flash Chromatography and HPLC-preparative analysis, to confirm the isolation of the bioactive. Through bioguided isolation, the compound p-hydroxybenzoic acid was obtained, which showed activity against the phytobacteria Xanthomonas campestris pv. campestris and Acidovorax citrulli.
Mikania glomerata Spreng. (Asteraceae): seu uso terapêutico e seu potencial na Pandemia de COVID-19
Authors: Souza Juliana Oliveira de , Oliveira Emilly Flores , Santos Maria Eduarda Spier dos , Kirsten Camila Nedel
Published: May 2022

A Mikania glomerata Spreng. (Asteraceae), conhecida como Guaco, é amplamente utilizada na terapia de doenças respiratórias, apresentando ação expectorante, antitussígena e broncodilatadora. Esta espécie consta na Relação Nacional de Medicamentos Essenciais (RENAME) e, pode ser utilizada na atenção primária à saúde no Sistema Único de Saúde (SUS). No final de 2019, em Wuhan, na China, surgiu o surto de um novo tipo de Coronavírus, o SARS-Cov-2 (COVID-19), com uma disseminação acelerada, logo se tornou uma pandemia. Não existe ainda uma terapia específica para tratar a doença. O que se pode afirmar é que grande parte dos infectados pelo vírus apresentam como sintoma uma doença respiratória leve a moderada. Por isso, este estudo constituiu uma revisão da literatura e teve como objetivo avaliar as propriedades terapêuticas do Guaco e seu respectivo uso na pandemia de COVID-19. No total, foram encontrados duzentos e sessenta e seis artigos, nas seis bases de dados utilizadas, dos quais nove foram selecionados para fins de revisão devido ao seu conteúdo que se enquadrou com o tema e objetivo desse trabalho. Verificou-se que a Mikania glomerata, sendo uma planta medicinal popular no tratamento da tosse e problemas respiratórios, pode ser utilizada como adjuvante no tratamento da COVID-19.
Preterit-imperfect acquisition in L2 Spanish writing: Moving beyond lexical aspect
Authors: Minnillo Sophia , Sánchez-Gutiérrez Claudia , Carando Agustina , Davidson Samuel , Fernández Mira Paloma , Sagae Kenji
Published: May 2022

While research on second language (L2) tense-aspect acquisition has flourished, most studies have focused on lexical aspect as an explanatory variable (Bardovi-Harlig and Comajoan-Colomé 2020). However, the role of the features of first language (L1) production in L2 Spanish preterit-imperfect acquisition has never been tested before. Prior research has found that the frequency and distinctiveness of verb forms in corpora of L1 English production predict L2 English learners’ tense-aspect production (Wulff et al. 2009). The present study aims to replicate these findings and test the predictions of hypotheses of L2 tense-aspect acquisition in another group of learners: English-dominant, instructed Spanish learners. Analyses were performed on longitudinal data from the Corpus of Written Spanish of L2 and Heritage Speakers (COWS-L2H; Yamada et al. 2020) and cross-sectional data from the Corpus Escrito del Español L2 (CEDEL2; Lozano 2021). Results indicate that L1 verb frequency and distinctiveness predict learners’ emergent use of the preterit and the imperfect.
Contrôle glycémique strict : une revue de la littérature
Authors: Ortiz Jonathan Tomaz
Published: May 2022

Le diabète sucré est une maladie difficile à contrôler dans le monde. Depuis la meilleure compréhension de la physiopathologie de la maladie par la médecine moderne, plusieurs voies de contrôle de l’évolution de la pathologie en cause ont été proposées. A titre d’exemple, on peut citer une étude publiée en 1993 par le Diabetes Control and Complications Trial (DCCT) et, plus tard, en 1998, validée par la United Kingdom Prospective Diabetes Study (UKPDS), qui établit l’hémoglobine glyquée comme l’un des principaux évolution des marqueurs et pronostic de la maladie. Depuis la publication de ces études, des objectifs tels que : un contrôle glycémique strict (correspondant à des objectifs d’hémoglobine glyquée inférieurs ou égaux à 6,5 mg/dL), ont été acceptés par la communauté médicale mondiale comme des références dans le contrôle et le suivi de la maladie. Dans ce contexte, le présent article a pour fil rouge : faut-il rechercher un contrôle glycémique strict pour tous les patients ? Viser à savoir, par une revue de la littérature, si le contrôle glycémique devrait être requis pour tous les patients. Par conséquent, une revue de la littérature a été réalisée sur les articles publiés sur le sujet en question, visant à se concentrer sur la prévention quaternaire et l’importance des bonnes pratiques cliniques en collaboration avec la médecine factuelle. Les résultats indiquent que, dans le cas d’un contrôle glycémique strict, il n’y a aucune preuve de bénéfices en général dans son utilisation, étant rapporté dans la littérature que, dans certaines populations, en particulier celles de plus de 80 ans, son utilisation est liée à une augmentation les effets indésirables et la morbidité causés par la maladie.