Microsoft Research Articles

Managing Risk: a Decision Tool to Enhance Yield and Quality of Peanut (Arachis hypogaea) in Ghana

Peanut cultivation in Ghana is influenced by abiotic (e.g., drought, fertilization, etc.) and biotic (e.g., pests, diseases, etc.) factors that must be managed effectively to maximize yields. These factors differ across agro-ecologies and pose significant risks to yields and aflatoxin contamination and can limit financial returns to farmers. Knowledge of interactions among key production practices such as pest management, crop rotation sequence, varietal selection, tillage systems, and chemical inputs is required to mitigate these risks and to provide quality advice to peanut farmers by Agricultural Extension Agents (AEAs) and/or similar advisory groups. This paper describes the development of a comprehensive, Microsoft (MS) Excel based risk management tool to help farmers and their advisors implement practices that minimize the risk of yield loss and aflatoxin contamination while providing production cost estimates. This tool is based on a similar one developed for peanut production in North Carolina (USA). Three risk tools were developed; one for the interior savannah zone of northern Ghana which has a unimodal rainfall pattern, and two for the forest and transition zones of southern Ghana which are characterized by a bimodal rainfall pattern. These tools will aid AEAs and farmers to make informed decisions on the best combinations of production practices that will minimize risk and increase yields in Ghana. The development process considered the differences in selection of varieties, plant density, fertilization, and pest management between savannah and the forest-transition regions as well as the impact of their interactions on peanut yield and aflatoxin contamination. Overall, this risk management tool benefits extension service providers (government and private), breeding, agronomy, and plant protection programs as well as teaching in higher education institutions.

An Update to Converting Zip Code Data into Distances Using Microsoft Excel Geography Data Type, Maps, and Including Driving Distances

In the article “Converting Zip Code Data into Distances: A Case Study for Teaching Business Analytics” [Huggins E (2019) INFORMS Trans. Ed. 19(2):105–107], the author demonstrates how to convert a column of zip code data into approximate distances from a default zip code using standard Microsoft (MS) Excel functions and a zip code database. In this case article, we update that case in three specific areas. First, we explore using the relatively new MS Excel Geography Data Type that, in theory at least, eliminates the need to use the zip code database by accessing required information about each zip code directly from the web. Second, we show how to apply the MS Excel Maps feature to this data. Third, although the original article uses approximate straight-line distances, we show how to extract driving distances between two zip codes using public maps and an application programming interface with MS Excel’s =WEBSERVICE() function. Two of these updates are not as straightforward as one might hope: The Geography Data Type returns a significant number of errors and, although driving distances can be determined, there is no “easy” way to do so yet. We discuss these issues in the case teaching note. Supplemental Material: The Teaching Note and data files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

챗(Chat) GPT의 이용과 저작권 쟁점 고찰

The development of big data analysis and deep learning technologies triggered by the 4th industrial revolution created an opportunity to expand the scope of application and application of artificial intelligence to our lives. Beyond the level where conventional artificial intelligence was merely a means of assisting human decision-making or action, it has reached a stage where it analyzes vast amounts of data to identify problems on its own, create optimal answers, and even self-examine errors. A representative example is the Chat GPT service. Recently, however, copyright infringement disputes have been reported in this regard. For example, in January 2023, Getty Images filed a copyright infringement lawsuit against Stability AI, the developer of 'Stable Diffusion', and in April 2023, Twitter CEO Elon Musk also announced its intention to file a copyright infringement lawsuit against Microsoft (MS). In this regard, in this paper, first, as an issue derived from data use, chat GPT does not disclose how to learn data, so it is difficult to confirm whether scrolling and scraping are prohibited information has been collected, and chat GPT service is fair. It was pointed out that there are elements that are difficult to see as use, that the scope of TDM exemption is different for each country, and that TDM exemption regulations have not yet been introduced in Korea. Then, as a way to determine and protect copyright infringement of chat GPT products, first, in relation to authors and rights attribution, the contribution of artificial intelligence and humans to creation is divided and marked by stages (by year), and rights attribution must be different accordingly, second, in relation to the judgment of substantial similarity, refer to the criteria for functional works, but in the case of artificial intelligence products, there is a limit to determining whether or not copyright infringement is caused only by human feelings, so plagiarism verification or copyright infringement prevention programs must be developed and applied. Third, in relation to copyright protection institutions, it is appropriate to stipulate the protection period of artificial intelligence products at 5 years, but a two-dimensional approach must be taken so as not to reduce the incentive for human creation. Fourth, in relation to civil and criminal legal responsibilities, the Product Liability Act Referring to the provisions of the Senate Civil Act, it was proposed that artificial intelligence developers and users jointly and jointly take responsibility in principle, but that responsibilities should be assigned differentially depending on whether there was a violation of the duty of care or negligence. Based on the contents examined in this paper, it is necessary to actively seek rational and systematic ways to regulate generative artificial intelligence services.

Analysis and Correlation of Visual Evidence in Campaigns of Malicious Office Documents

Many malware campaigns use Microsoft (MS) Office documents as droppers to download and execute their malicious payload. Such campaigns often use these documents because MS Office is installed on billions of devices and that these files allow the execution of arbitrary VBA code. Recent versions of MS Office prevent the automatic execution of VBA macros, so malware authors try to convince users into enabling the content via images that, e.g., forge system or technical errors. In this article, we propose a mechanism to extract and analyse the different components of the files, including these visual elements, and construct lightweight signatures based on them. These visual elements are used as input for a text extraction pipeline which, in combination with the signatures, is able to capture the intent of MS Office files and the campaign they belong to. We test and validate our approach using an extensive database of malware samples, obtaining an accuracy above 99% in the task of distinguishing between benign and malicious files. Furthermore, our signature-based scheme allowed us to identify correlations between different campaigns, illustrating that some campaigns are either using the same tools or collaborating between them.

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively.

E-Learning Software: Comparing the use Behavior Among Academicians and Schoolteachers

The COVID-19 pandemic has caused an increase in the use of e-learning software. From the perspective of the decision-makers (school/university administration), it is crucial to understand what characteristics of the software are perceived by the users (teachers) as necessary for a task (e-learning). A popular method of determining these characteristics is a technology acceptance model. In this paper, the authors aim to understand the technology acceptance of Microsoft (MS) Teams by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The authors also analyze acceptance in two distinctive groups of teachers: schoolteachers and academic teachers, to better understand differences in e-learning software acceptance and implementation. The results show that MS Teams is a well-accepted software and the users' intention to use MS Teams in e-learning is the same regardless of the teacher's group. There are significant differences in the perception of software characteristics between different groups. The authors analyze the potential causes of these differences and suggest practical implications for improving the quality of e-learning. The intent is not to validate UTAUT as a general technology acceptance model but to understand its practical importance in explaining use behavior in e-learning.

Is hybrid teaching delivering equivalent learning for students in higher education?

ABSTRACT Hybrid approaches to teaching, where there is a proportion of online (asynchronous or synchronous) teaching as well as face-to-face synchronous teaching, were adopted almost ubiquitously by UK higher education institutions throughout the 2020/2021 academic year amidst disruption caused by national and international Covid-19 restrictions. One example of this was the introduction and use of software such as Microsoft (MS) Teams as a learning tool and platform to continue education provisions. As such, this study reports on the implementation of MS Teams to deliver a foundation year physical science curriculum within a hybrid learning context over the 2020/2021 academic year and within multiple learning contexts. In-built data analytics from MS Teams were used to measure student engagement and activity on the software. Additionally, a questionnaire was used to capture student perspectives of MS Teams and their use as a learning tool. These data show that the like-for-like replacement of face-to-face teaching with an online alternative within this hybrid approach did not produce a clear like-for-like learning environment for students, except in the case of small-group collaborations where students found clear utility for the platform. Significant thought needs to be given to the use of online and hybrid alternatives to traditional face-to-face instruction and, in order to facilitate student learning and engagement effectively, a bottom-up approach to redesign may be required instead of simply substituting teaching tools.

EMPOWERING ACCOUNTING EDUCATION: A STUDY ON MICROSOFT EXCEL KNOWLEDGE AND SKILLS

Purpose- The aim of this research is to know the level of Microsoft (MS) excel knowledge and skills in the broad field of accounting education. 
 Design/methodology/approach- The survey questionnaire is designed as a research instrument and distributed to graduate students enrolled in an accounting course from Oman universities. 142 questionnaires were gathered using Google Forms and analyzed using the partial least-squares structural equations modeling (PLS-SEM) technique.
 Findings- The findings show that both MS Excel knowledge and skills are most important and developing MS excel skills in accounting education suggest that the Omani accounting education system might do more to give opportunity for students to thrive in their future careers.
 Research limitations/implications- The findings provide MS knowledge and skills of accounting graduates in Oman's higher educational institutions (HEIs). In particular, this study sheds light on the skills that accounting students want and how relevant these skills for Omani graduates.
 Practical Implications- Some modifications to accounting education are proposed to enhance accounting students' MS skills.
 Originality/value- Because there is a lack of specific research on MS excel knowledge and skills in accounting education in Gulf Cooperation Council (GCC) countries, this study contributes to the body of expertise on MS skills in GCC countries, especially in Oman.

An Efficient GAN-Based Multi-classification Approach for Financial Time Series Volatility Trend Prediction

Deep learning has achieved tremendous success in various applications owing to its robust feature representations of complex high-dimensional nonlinear data. Financial time-series prediction is no exception. Hence, the volatility trend prediction in financial time series (FTS) has been an active topic for several decades. Inspired by generative adversarial networks (GAN), which have been studied extensively in image processing and have achieved excellent results, we present the ordinal regression GAN for financial volatility trends (ORGAN-FVT) method for the end-to-end multi-classification task of FTS. An improved generative model based on convolutional long short-term memory (ConvLSTM) and multilayer perceptron (MLP) is proposed to capture temporal features effectively and mine the data distribution of volatility trends (short, neutral, and long) from given FTS data. Meanwhile, ordinal regression is leveraged for the discriminator to improve the multi-classification performance, making the model more practical. Finally, we empirically compare ORGAN-FVT with several state-of-the-art approaches on three real-world stock datasets: MICROSOFT(MSFT), Tesla(TSLA), and The People’s Insurance Company of China(PAICC). ORGAN-FVT demonstrated significantly better AUC and F1 scores, at most 20.81% higher than its competitors.

Remote Interactive Surgery Platform (RISP): Proof of Concept for an Augmented-Reality-Based Platform for Surgical Telementoring.

The "Remote Interactive Surgery Platform" (RISP) is an augmented reality (AR)-based platform for surgical telementoring. It builds upon recent advances of mixed reality head-mounted displays (MR-HMD) and associated immersive visualization technologies to assist the surgeon during an operation. It enables an interactive, real-time collaboration with a remote consultant by sharing the operating surgeon's field of view through the Microsoft (MS) HoloLens2 (HL2). Development of the RISP started during the Medical Augmented Reality Summer School 2021 and is currently still ongoing. It currently includes features such as three-dimensional annotations, bidirectional voice communication and interactive windows to display radiographs within the sterile field. This manuscript provides an overview of the RISP and preliminary results regarding its annotation accuracy and user experience measured with ten participants.

Short Stature for Age in Children of 5 to 16 Years: The First Research from the Northern Himalayan Region of India.

Anthropometric parameters play vital role in monitoring growth in pediatrics. Many etiological factors lead to short stature. So, before assessing the etiological factors short stature needs to be addressed. This study aimed to screen short stature for age in school-going children aged 5 to 16 years in Uttarakhand. In this cross-sectional observational study, the height (through stadiometer) and weight (through weight machine) of 4189 students of government and private school in Rishikesh (Uttarakhand) aged 5-16 years were measured after the verbal assent of the students and individual's height is in the 3rd percentile for the mean height of a given age, sex, and population group and was considered short stature. The data collection was performed from October 2019 to July 2021. The data were categorized according to different age groups to 5-8 years, 9-12 years, and 13-16 years. The data were recorded in Microsoft (MS) Excel spreadsheet program. Statistical Package for the Social Sciences (SPSS) v23 (IBM Corp.) was used for data analysis. Descriptive statistics were elaborated in the form of means or standard deviations and medians or Interquartile range IQRs for continuous variables and frequencies and percentages for categorical variables. The Chi-square test was used for group comparisons for categorical data. 7.1% of children were short stature (height 143.16 ± 15.09 cm) in the Himalayan belt, and males were more prone to short stature at age of 9-12 years. In the growing phase of children, the etiology of short stature has to be rectified, so the children can achieve such proper growth. Parents and physicians have to assess and monitor the growth of children timely. This study can be a stepping stone for further epidemiological studies.

An exploratory cybercrime analysis and its impact on india

In the modern era, the rapid use of the internet has led to reports of cybercrimes. The number of cybercrimes is constantly increasing. The purpose of this paper is to identify the root causes of cybercrime and find insights that can be used by the administration to take fruitful decisions to overcome cybercrime offenses. It has collected data related to the registered cybercrime cases in various Indian states on different parameters for the years 2016 to 2019. The preprocessing was applied to individual datasets, and a common data set was prepared. Data analysis using Power BI (business intelligence) and Microsoft (MS) Excel tools was performed for the Indian states, and its impact was analyzed through year-wise analysis. The outcome of this paper can be utilized by the Indian administration, cyber experts, working professionals, and users to take care of the affecting factors and limit cybercrimes.

Role of Two Antibodies Panel High Molecular Weight Cytokeratin and Alpha-MethylacylCoA Racemase in Diagnosing Prostatic Lesions: A Cross-sectional Study

Introduction: Prostatic diseases cause significant morbidity and mortality. Although histopathological examination is the gold standard for diagnosing prostatic lesions but diagnosis may be challenging in the presence of benign mimickers or a very small focus of malignancy. Immunohistochemical aid to morphology helps in making a timely and accurate diagnosis. Aim: This study was done to evaluate the role of two antibodies panel High Molecular Weight Cytokeratin (HMWCK) and AlphaMethylacyl-CoA Racemase (AMACR) in improving the diagnostic accuracy of prostatic lesions. Materials and Methods: This was an observational cross-sectional study conducted in the Department of Pathology, Shri Guru Ram Rai Institute of Medical and Health Sciences (SGRRIM and HS), Dehradun, Uttarakhand, India, from May 2019 to October 2020. Haemotoxylin and Eosin (H&E) stained sections of prostatic biopsies were classified into benign and malignant. Amongst malignant lesions, prostatic adenocarcinomas were graded according to Gleason’s grading system and Gleason’s scores were noted. One section from each was subjected to AMACR and HMWCK antibody tests. HMWCK was interpreted as negative/positive and continuous/ discontinuous. For AMACR, both location and intensity of stain was observed. The parameters studied were Gleason’s score, group grade, expression of HMWCK and AMACR. Categorical data was presented in form of frequency and percentage. Independent t-test, Yates Chi-square test were used. Data was entered in Microsoft (MS) Excel sheet and analysis was done using CRAN R 2.1. Results: Total of 80 prostatic biopsies were taken, 24 were malignant and 55 were benign and one was Benign Prostatic Hyperplasia (BPH) with a focus suspicious for malignancy showing atypical small acinar proliferation on histopathological examination. The mean age of non neoplastic cases was 67.68±8.56 years, while that of neoplastic lesions was 75.41±9.34 years. Amongst benign, 56.3% (31/55) cases were BPH, 43.6% (24/55) cases were BPH with associated lesions which included 62.5% (15/24) cases of BPH with non specific prostatitis; 29.2% (7/24) cases of BPH with adenosis and 8.3% (02/24) cases of BPH with basal cell hyperplasia. Of malignant cases, 24 cases were of adenocarcinoma with maximum cases having Gleason’s score 9 (11/24;45.8%) and Group grade V (18/24;75%). The sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) of HMWCK and AMACR were calculated using histopathology as the gold standard. Conclusion: Although histopathology is the gold standard in prostatic biopsies but immunohistochemistry is additional diagnostic aid in confirmation of diagnosis. Immunohistochemistry not only confirms the histological diagnosis but is of great help in challenging cases. It has markedly increased the diagnostic accuracy

Association of Clinicopathological Profile and Immunohistochemical Expression of KRAS and BRAF in Colorectal Carcinoma: A Cross-sectional Study

Introduction: Colorectal Carcinoma (CRC) is a multi-step process that occurs due to the accumulation of several genetic alterations. The most important alterations are related to the rat sarcoma viral oncogene homolog (RAS) and Rapidly Accelerated Fibrosarcoma (RAF), which have been implicated as key intermediates in the RAS-mediated signalling cascade. However, the levels of Kristen Rat Sarcoma Virus (KRAS) and v-raf Murine Sarcoma Viral Oncogene Homolog (BRAF) protein expression and their prognostic evaluation in CRC patients remain unknown. Aim: To investigate the immunohistochemical expression of KRAS and BRAF proteins in CRC. Materials and Methods: The present institutional-based cross-sectional observational study was conducted in a tertiary care centre in West Bengal, specifically in the Department of Pathology in collaboration with the Department of Surgery at Murshidabad Medical College and Hospital, Berhampore, West Bengal, India. A total of 26 CRC cases were enrolled in the present study, received over a period of one and a half years from January 2021 to June 2022. The parameters studied included demographic and clinical information of the patients, histopathological findings, pathological grade and stage of carcinoma, and immunohistochemical findings for KRAS and BRAF. For statistical analysis, data were entered into Microsoft (MS) Excel. Descriptive measures such as mean±Standard Deviation (SD), range, and percentage were used. The Chi-square test was used to determine the significance of the study. Results: A total of biopsy-proven CRC specimens were studied, consisting of 17 male patients (65.38%) with a male-to-female ratio of 1.9:1. The most common age group involved was 51-60 years (38%). Conventional adenocarcinoma accounted for the majority of cases (85%), with mucinous carcinoma comprising the remaining 15%. Among the 26 cases, 15 (58%) showed KRAS positivity, which was significantly associated with tumour grade and stage. Most of the cases were BRAF-negative. Out of the 21 cases where either KRAS or BRAF or both were positive, 20 cases showed high T stage (T3 and T4) and/or metastatic lesions (p-value 0.001). All four cases that were negative for both BRAF and KRAS belonged to the low T stage. Conclusion: A significant correlation was observed between the expression of KRAS and high-grade, high pathological Tumour, Node, Metastasis (TNM) T stage (T3 and T4) CRC. Therefore, KRAS Immunohistochemistry (IHC) biomarkers should be included in the standard diagnostic protocol for colorectal cancer, as they help identify KRAS-positive CRC cases that are resistant to targeted immunotherapy.

Health Economic Evaluation Using Markov Models in R for Microsoft Excel Users: A Tutorial.

A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

Levels, Trends, and Causes of Adolescent and Young Adult Maternal Mortality in Nigeria During 1990–2017: Analysis of the 2017 Global Burden of Disease Study Database

Background: Despite the global progress made in reducing maternal mortality in the past two decades, the health of adolescents and young adult women (10-24 years) has been neglected based on the wrong assumption that they are healthy, especially in a developing country like Nigeria; this is despite the fact that adolescents and young adults (AYA) constitute over 16% of Nigeria’s population. Thus, this study aimed to determine the levels, trends, and causes of maternal mortality among AYA women aged 10-24 years in Nigeria. Methods: This was a cross-sectional study that utilized secondary data sets running from 1990 to 2017 from the Global Burden of Disease Study’s (GBD) online interactive tool downloaded from the Global Health Data Exchange (GHDx) platform. From the database, we extracted the numbers of maternal deaths (10-49 years), adolescent and young adults’ maternal deaths (10-24 years), the proportion of maternal deaths (10-24 years), and both maternal mortality ratio and rate for 15-49 years old and 10-24 years old in the form of Comma separated values (CSV) and converted into XLS file in Microsoft (MS) Excel format for the analysis. Frequencies, proportions, and charts were generated using MS Excel. Result: A total of 122,107 maternal deaths occurred among adolescents and young adults (AYA), which is 26% of all deaths occurring among 10-49 years old. Year-to-year changes in the absolute numbers of maternal deaths among AYA showed that it ranged from 3,866 (1990) to 4,451 (2017), showing a slight upward increase over the period; however, both AYA maternal mortality ratio and rate show consistent declines over the period. AYA maternal mortality ratio (MMR) for adolescents aged 15-19 years was 17% higher than for young adults aged 20-24 years. Sepsis was the leading cause of adolescent maternal death (MMR of 168), followed by maternal hypertensive disorder, obstructed labor, maternal hemorrhage, ectopic pregnancy, and abortion. A total of 460,155 maternal deaths occurred in women aged 10-49 years during the study period. Maternal deaths in the age group 10-24 years ranged from 3,866 (29.8%) in 1990 to 4,451 (25.1%) in 2017. The maternal mortality ratio in women aged 10-49 years declined by about 30% for the 1990 – 2017 period. Conclusion and Implications for Translation: The study showed that adolescents and young adults are at increased risk of maternal deaths in Nigeria. Therefore, multi-faceted maternal health programs in the country need to target this age group to prevent maternal deaths. Copyright © 2022 Dahiru et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

A comparative study of radiofrequency micro-needling with platelet rich plasma and fractional LASER in acne scar management

<p class="abstract"><strong>Background:</strong> Acne scars (AS) results in permanent scarring causes facial disfigurement to the patients. The aim of the study was to assess the comparative effectiveness and outcomes of radiofrequency micro needling and fractional carbon dioxide laser methods in treating acne scars.</p><p class="abstract"><strong>Methods:</strong> This comparative observational study was conducted in the Ibn Sina diagnostic and consultancy center, Dhaka, Bangladesh during the period from January 2019 to December 2020. In total 30 patients with acne scar were selected as the study population. Among total participants, 15 patients who underwent radiofrequency micro needling treatment method were denoted as group-1 and the rest 15 patients who underwent carbon dioxide laser treatment method were denoted as group-2 patients. All data were processed, analyzed and disseminated by Microsoft (MS) office and statistical package for the social sciences (SPSS) version 26.<strong></strong></p><p class="abstract"><strong>Results:</strong> In this study, in group A as per Goodman and Baron scale at the baseline 40% had grade-3 and 60% had grade-4 status. In the same group, after treatment 23.33, 30.00%, 20.00% and 13.33% patients improved up to grade 1, grade 2, grade 3 and grade 4 status respectively. On the other hand, in group-2, after treatment 26.67%, 50.00%, 20.00% and 13.33% patients improved up to grade 1, grade 2, grade 3 and grade 4 status respectively. Finally, according to the mean Goodman and Baron scale scores in both the groups, we found extremely significant correlations between both the baseline and after treatment status where <0001.</p><p><strong>Conclusions:</strong> Both radiofrequency micro needling and fractional CO<sub>2</sub> laser treatment methods are effective for the treatment of acne scars. </p>

Expansion of the EZSTATSG1 Microsoft Excel Tool for ANSI/ASB Standard 036 Method Validation by the Addition of Weighted Quadratic External Calibration Models Utilizing Five-, Six- or Seven-Point Curves.

The first generation of this Microsoft (MS) Excel (Redmond, WA, USA) tool for method validation, EZSTATSG1, was designed for methods utilizing only linear calibration curves requiring seven calibration levels, and quadratic calibration models were not supported. This significantly improved version, EZSTATSG2, includes all of the features of the original template such as weighted linear calibration models, bias and precision data, dilution integrity and ion suppression. New features in this version include flexible five-, six- or seven-point calibration curves; six weighted quadratic calibration models; standardized residuals by use of frequency plots overlaid with the normal distribution function along with five-number summary data and processed sample stability. The implementation of Visual Basic for Applications in Excel UserForms prevents accidental alteration of existing formulas and also ensures that pertinent cells are relocked every time a file is reopened. The quadratic models feature the 95% confidence intervals for checking the significance of the second-order term and are fully characterized by providing the equations for the axis of symmetry, directrix and coordinates for vertex and focus. Example data of α-hydroxymidazolam demonstrate that the quadratic calibration curves fit the data more adequately than the linear models for this method. This second-generation tool summarizes all of the validation parameters of a method for both linear and quadratic calibration models. Models with the lower average sum of relative errors and higher R2 values are color-coded green, indicating the likelihood of a better fitting model. Like the EZSTATSG1, the redesigned EZSTATSG2.xltm MS Excel self-actuating validation tool and a completed PDF example are available to the scientific community for download as supplementary data. Updates to the template can be found at www.EZSTATS4validation.com.

A Successful Model of Microsoft Teams Online Learning Platform in Vocational High School

An excellent online learning platform requires a success model in its utilization. During the pandemic, a new adjustment (new normal) was needed in the educational field. It was initiated by providing numerous online learning platforms. Therefore, there are only a few successful model evaluations. Thus, this research aims to (1) determine the effect of Microsoft (MS) Teams and (2) develop a successful model as an online learning platform in Vocational High Schools (SMK). This research used a quantitative approach with the PLS-SEM technique in the data analysis. The collection of data was also carried out via the use of a questionnaire, with a total sample of 430 participants (85 teachers and 345 students) using MS Teams at Vocational High Schools. The results showed that four factors were significantly affecting the success of MS Teams, and these includes the information & system qualities, use, and user's satisfaction. However, these findings were based on the factors which were insignificant to the success of the MS Teams. It was also reported that the factors that supported the MS Teams' operation included the Internet and software specifications. Moreover, additional observations also indicated that one of the supporting factors (SF2) was considered invalid.

Develoment of The Computer Simulation of Oscillation in Physics Learning

The simplest mechanical system that shows such motion oscillation is a simple pendulum, which consists of a mass suspended from a rope, so that it can swing freely in response to the force of gravity. This teaching material is in the Fundamental Physics subject. Other teaching materials related to oscillation motion are simple linear harmonic oscillations. This research aimed to develop the computer simulation of oscillation in physics learning by using MATLAB. The use of simulations within teaching process had a positive impact on students’ being able to predict and explain the phenomena. It also designed to provide possibility to explore physics phenomenon which cannot be realized in school conditions. The simulation can direct the student to reveal the existence of equilibrium between the two sides of an equation rather than causal relations. The research method used was Research and Development (RD) developed by Borg Gall (1983) for developing educational products. They are the pre-product form was developed by creating computer programs based on algorithms, validation through forum group discussion carried out by several lecturers to provide validation of the pre-product, major product revision, the pre-trial by 10 students, operational product revision, the operational product trial carried out by a class of students at the computer laboratory, final product revision, and dissemination.The steps were research and information collection, planning, develop a preliminary form of product, preliminary testing, main product revision, main field testing, and operational product revision. In the step of the main field testing, it was applied to the class of students. The 33 students and 6 lecturers were asked to respond about it. A qualitative test review was conducted through expert judgement for validation. They provided an assessment of suitability between the item with the indicator in the form of a Likert scale with five answer options. Based on expert judgment realated to the feasibility of the test, V Aiken index is also calculated. The data analysis techniques were descriptive analysis conducted using Micros oft Excel and continued by looking for effective constributions to the relationship of each independent variable to work readiness. According to the statements of questionnaire given from the lecturers, the computer simulation of oscillation felt into a good and very good category. Meanwhile, the students’ responses showed that it was fair, good, and very good. The oscillation simulation software developed has good criteria and is suitable for use in learning oscillation systems. The benefit of this research is to determine the form of the wave propagation simulation and its superposition which later can be used for physics research and suitable for students to learn physics.