Data Reduction Methods Research Articles

Stool biomarkers as measures of enteric pathogen infection in infants from Addis Ababa informal settlements.

Frequent enteric infections in children may be an important cause of growth faltering; however, we do not fully understand the mechanisms by which pathogen infections and the physiological responses to these infections result in poorer growth. Commonly used protein fecal biomarkers (anti-alpha trypsin, neopterin, and myeloperoxidase) provide broad immunological information on an inflammatory response; however, they do not provide information on non-immune processes (e.g., gut integrity) that may be important indicators of chronic end states such as environmental enteric dysfunction (EED). To explore how additional biomarkers will better inform which physiological pathways (both immune and non-immune) are impacted by pathogen exposure we added to the traditional panel of 3 protein fecal biomarkers 4 novel fecal mRNA transcript biomarkers (sucrase isomaltase, caudal homeobox 1, S100A8, and mucin 12) and analyzed stool samples from infants living in informal settlements in Addis Ababa, Ethiopia. To test how this expanded biomarker panel captures distinct pathogen exposure processes we used two different scoring systems. First, we used a theory-based approach to assign each biomarker to specific physiological attributes based on prior understanding of each biomarker. Second, we used data reduction methods to categorize biomarkers and then assign physiological attributes to those categories. We used linear models to examine the association between the derived biomarker scores (based on mRNA and protein levels) and stool pathogen gene counts to determine pathogen specific effects on gut physiology and immune responses. Inflammation scores were positively associated with Shigella and enteropathogenic E.Coli (EPEC) infection, while gut integrity scores were negatively associated with Shigella, EPEC and, shigatoxigenic E.coli (STEC) infection. Our expanded panel of biomarkers hold promise as tools to measure systemic outcomes of enteric pathogen infection. mRNA biomarkers complement established protein biomarkers by providing important cell-specific physiological and immunological consequences of pathogen carriage that can lead to chronic end states such as EED.

The Impact of Fasting Mondays and Thursdays on Self-Control of Students at Al-Mustaqim Bugel Islamic Boarding School, Jepara

This study aims to examine the impact of fasting on Mondays and Thursdays on self-control of students at the Al-Mustaqim Bugel Islamic Boarding School, Jepara. This research is motivated by the number of students who violate the rules of the cottage. The number of students who violate the rules of the boarding school is caused by the inability of students to manage their time, thoughts and feelings. Therefore, the researcher intends to examine how big the role of Monday and Thursday fasting on self-control of students at the Al-Mustaqim Bugel Islamic Boarding School, Jepara. The type of research uses field research (field research) with a qualitative approach method. The data collection techniques were obtained through observation, interviews, and documentation. The subjects in this study were 1 caregiver, 1 administrator, and 8 students at the Al-Mustaqim Bugel Islamic Boarding School, Jepara. In the process of data analysis using data reduction methods, data presentation, verification and drawing conclusions. The results of this study indicate that fasting on Mondays and Thursdays has an impact on self-control of students at the Al-Mustaqim Bugel Islamic Boarding School, Jepara. The impact felt by the students is that they are more able to be patient in facing various difficulties, become more disciplined individuals and are able to control their emotions. In addition, the greatest benefit experienced is feeling closer to Allah SWT so that thoughts, feelings and actions are more able to be controlled.

UX Framework Including Imbalanced UX Dataset Reduction Method for Analyzing Interaction Trends of Agent Systems.

The performance of game AI can significantly impact the purchase decisions of users. User experience (UX) technology can evaluate user satisfaction with game AI by analyzing user interaction input through a user interface (UI). Although traditional UX-based game agent systems use a UX evaluation to identify the common interaction trends of multiple users, there is a limit to evaluating UX data, i.e., creating a UX evaluation and identifying the interaction trend for each individual user. The loss of UX data features for each user should be minimized and reflected to provide a personalized game agent system for each user. This paper proposes a UX framework for game agent systems in which a UX data reduction method is applied to improve the interaction for each user. The proposed UX framework maintains non-trend data features in the UX dataset where overfitting occurs to provide a personalized game agent system for each user, achieved by minimizing the loss of UX data features for each user. The proposed UX framework is applied to a game called "Freestyle" to verify its performance. By using the proposed UX framework, the imbalanced UX dataset of the Freestyle game minimizes overfitting and becomes a UX dataset that reflects the interaction trend of each user. The UX dataset generated from the proposed UX framework is used to provide customized game agents of each user to enhanced interaction. Furthermore, the proposed UX framework is expected to contribute to the research on UX-based personalized services.

The MUSEHubbleUltra Deep Field surveys: Data release II

We present the second data release of the MUSEHubbleUltra-Deep Field surveys, which includes the deepest spectroscopic survey ever performed. The MUSE data, with their 3D content, amazing depth, wide spectral range, and excellent spatial and medium spectral resolution, are rich in information. Their location in theHubbleultra-deep field area, which benefits from an exquisite collection of ancillary panchromatic information, is a major asset. This update of the first release incorporates a new 141-h adaptive-optics-assisted MUSE eXtremely Deep Field (MXDF; 1 arcmin diameter field of view) in addition to the reprocessed 10-h mosaic (3 × 3 arcmin2) and the single 31-h deep field (1 × 1 arcmin2). All three data sets were processed and analyzed homogeneously using advanced data reduction and analysis methods. The 3σpoint-source flux limit of an unresolved emission line reaches 3.1 × 10−19and 6.3 × 10−20 erg s−1 cm−2at 10- and 141-h depths, respectively. We have securely identified and measured the redshift of 2221 sources, an increase of 41% compared to the first release. With the exception of eight stars, the collected sample consists of 25 nearby galaxies (z < 0.25), 677 [O II] emitters (z= 0.25 − 1.5), 201 galaxies in the MUSE redshift desert range (z = 1.5 − 2.8), and 1308 Lyαemitters (z = 2.8 − 6.7). This represents an order of magnitude more redshifts than the collection of all spectroscopic redshifts obtained before MUSE in theHubbleultra-deep field area (i.e., 2221 versus 292). At high redshift (z > 3), the difference is even more striking, with a factor of 65 increase (1308 versus 20). We compared the measured redshifts against three published photometric redshift catalogs and find the photo-z accuracy to be lower than the constraints provided by photo-z fitting codes. Eighty percent of the galaxies in our final catalog have an HST counterpart. These galaxies are on average faint, with a median ABF775Wmagnitude of 25.7 and 28.7 for the [O II] and Lyαemitters, respectively. Fits of their spectral energy distribution show that these galaxies tend to be low-mass star-forming galaxies, with a median stellar mass of 6.2 × 108 M⊙and a median star-formation rate of 0.4 M⊙yr−1. We measured the completeness of our catalog with respect to HST and found that, in the deepest 141-h area, 50% completeness is achieved for an AB magnitude of 27.6 and 28.7 (F775W) atz= 0.8 − 1.6 andz = 3.2 − 4.5, respectively. Twenty percent of our catalog, or 424 galaxies, have no HST counterpart. The vast majority of these new sources are high equivalent-widthz > 2.8 Lyαemitters that are detected by MUSE thanks to their bright and asymmetric broad Lyαline. We release advanced data products, specific software, and a web interface to select and download data sets.

STRATEGI PENINGKATAN NASABAH BANK SYARIAH DENGAN PENDEKATAN EXPERIENTAL MARKETING (STUDI PADA BANK MUAMALAT KCP BONE)

The objectives of this study are: (1) to find out how the form of the Experiental Marketing implementation strategy carried out by Bank Muamalat KCP Bone in increasing the number of customers (2) to find out and analyze the constraints of Bank Muamalat KCP Bone in increasing the number of customers through an Experiental Marketing approach and solutions to overcome these obstacles. This type of research is field research with qualitative research methods and descriptive approaches. Data were collected through observation, interviews, and documentation. The data obtained are analyzed using data reduction methods, data presentation and inference. At Bank Muamalat KCP Bone, the form of strategy for implementing Experiental Marketing carried out in increasing prospective customers is by approaching emotionally to prospective customers/ customers, conducting ball pick-up services to prospective customers through the "Ayo Hijrah" program, product promotion through the Muamalat DIN (Digital Islamic Network) Application, building company branding through Sourvenir promotions, collaborating with students in the procurement of Islamic banking training and seminars and find out the background of potential customers. The obstacles that are usually faced are prospective customers who lack respect for the service/arrival of bank employees when providing information, in terms of corporate culture/ organizational culture, and lack of public understanding of Islamic banks. However, these obstacles can be overcome by always following the wishes of prospective customers by doing the best service, evaluating themselves in carrying out organizational culture, and aggressively conducting the "Ayo Hijrah" campaign to invite people to switch to a sharia-based economy.

Machine Learning Approach to NOx Prediction for SCR Process of Thermal Power Plant

This paper presents a machine learning application, to predict the formed nitrogen-oxides (NOx) in thermal power plants, and used in the control loops of the selective catalytic reduction (SCR) process. To deal with the big plant operation data, data reduction methods are also described. The predictions are applied for the feed-forward (FF) control and show improvement of control performance in the simulation study.

Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models

Continuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves. Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activities such as early failure detection.

Coupling Effect Mechanism of Microscale Waterjet Dynamics and Manipulation Mode on Clinical Indicators

A comprehensive, direct, and clear evaluation system is fundamental to promoting the clinical development of medical waterjet technology. According to the requirements of medical waterjet safety and work efficiency, combined with the uncertain factors of clinical operation mode, a multi-factor coupling evaluation system with splash range, bleeding volume, water consumption, and operation time as indicators has been proposed, and the indicators quantification method and dynamic optimization variables suitable for clinical application were analyzed. Through the micro-scale waterjet impact tests and optical observation under different impact conditions, and using the data reduction method of the dynamic deformation process of gelatin samples, the direct or indirect feature quantification methods corresponding to each evaluation index were constructed. A multi-objective optimization model of clinical indexes was established, which reveals the coupling mechanism of micro-scale waterjet power and manipulation mode on clinical indicators.

Filter and Embedded Feature Selection Methods to Meet Big Data Visualization Challenges

This study focuses on meeting the challenges of big data visualization by using of data reduction methods based the feature selection methods. To reduce the volume of big data and minimize model training time (Tt) while maintaining data quality. We contributed to meeting the challenges of big data visualization using the embedded method based “Select from model (SFM)” method by using “Random forest Importance algorithm (RFI)” and comparing it with the filter method by using “Select percentile (SP)” method based chi square “Chi2” tool for selecting the most important features, which are then fed into a classification process using the logistic regression (LR) algorithm and the k-nearest neighbor (KNN) algorithm. Thus, the classification accuracy (AC) performance of LR is also compared to the KNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied. Consequently, the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal. After making several comparisons, the study suggests (SFMLR) using SFM based on RFI algorithm for feature selection, with LR algorithm for data classify. The proposal proved its efficacy by comparing its results with recent literature.

Perspektif Hadis Tentang Teori Belajar Behavioristik (Studi Kitab Ar-Rasul Al-Muallim)

This study examines hadiths that are directly related to behavioristic learning theory in concept and practice. The formulation of learning theories by western experts does not necessarily deny the concepts and teachings of the Prophet who have already perfected theory and practice. This research uses literature study techniques to obtain qualitative results by being described using data reduction methods from various sources, then displayed properly. The result of this study is that the application of this behavioristic learning theory has been preceded by the Messenger of Allah as the best educator of all time. The important concept of the environment as a shaper of behavior (charity) has been explained in the noble hadith. Regarding stimulus (motivation / reward or punishment) and response has also been practiced by him Saw. Likewise, the important role of learning preparation, practice and repetition as well as impact analysis, as well as rewarding and response to dislike behavior then gently reprimanding is a concept that has been used by the Prophet since long ago.

THE PRAGMATIC ANALYSIS OF DIRECTIVE SPEECH ACT TYPES IN THE MAIN CHARACTER’S UTTERANCES IN THE MOVIE “FREE GUY”

This study applied a pragmatic study to obtain the types of directive speech acts in the movie "Free Guy” directed by Shawn Levy. The purpose of this study was to analyse the types of directive speech acts contained in the movie. Then, the speech was analysed and classified by applying the theory of Searle and Vanderveken (1985) to the data source. The data source for this research was the directive speech act uttered by the main characters in the movie "Free Guy". To get the desired research results, the research method used was descriptive qualitative method. Data collection technique was using observation technique and non-participation technique. Because too many the same data the researchers used data reduction method to reduce the same data. The data from this study was analysed using pragmatic method. The researchers found utterances of directive speech acts in the speech of the main characters. The researchers found 6 types of directive speech acts, including (2) Commanding, (4) Asking, (3) Request, (2) Ordering, (3) Suggest, and (1) Begging. The asking type of directive speech act was the most frequently found because the main character in this movie used to ask questions and the begging type was the type that was spoken the least.

ANALYSIS TYPES OF CODE-MIXING IN AZKA & DEDDY CORBUZIER UTTERANCES IN “DEDDY CORBUZIER” PODCAST: SOCIOLINGUISTIC APPROACH

This study applied a sociolinguistic study to obtain the types of Code mixing in the Podcast "Deddy Corbuzier". The purpose of this study was to analyse the types of code mixing contained in the podcast. Then, the speech was analyzed and categorised using the Musyken (2000) theory to the data source. The data source for this research was the code mixing uttered by Azka as guest star and Deddy Corbuzier. To get the desired research results, the research method used was descriptive qualitative method. Data collection technique was using observation technique and non-participation technique. Furthermore, because there was a vast volume of comparable data, the researchers applied the data reduction method. The data from this study was analysed using sociolinguistic approach. The researchers found utterances of code mixing in the speech of the guest star. As the result of the research, the researchers found 3 types of code mixing, including (9) Insertion, (4) Alternation, (2) Congruent Lexicalization. Insertion is the most common kind found in the data source, followed by Alternation and Congruent Lexicalization

Financial auditing during crisis: Assessing and reporting fraud and going concern risk in Lebanon

Research Question: What is the impact of the Lebanese crisis on the financial auditing mission, mainly during assessing and reporting fraud and going concern risk? Motivation: So far, the academic research covering the Lebanese crisis is still rare. Hence, the spillover of the financial distress is an under researched area. Our aim to investigate the impact of financial and economic disruption on financial auditing mission, together with assessing and reporting fraud and going concern risk is an important contribution to risk literature. Idea: The article examines the impact of the Lebanese crisis on financial auditing mission, mainly during assessing and reporting fraud and going concern risk. More specifically, based on fraud triangle theory and bankruptcy theory, this study investigates the response of Lebanese financial auditors to the increase of fraud and going concern risk during crisis. Data: The questionnaire survey was used to collect responses from 239 external and internal auditors. Tools: Univariate descriptive statistics have been used to describe the respondents’ profile, the Principal Component Analysis (PCA) as a data reduction method and the Kendall’s Tau B, Anova and Logistic Regression for hypotheses testing. Findings: The empirical study suggests the presence of a significant relationship between the propagation of fraud and the going concern risk. Facing this situation, Lebanese financial auditors modified the audit reports, by issuing a going concern opinion through the adoption of a hierarchical system based on the severity of the assessed risk. Contributions: The research illustrates how regulators, shareholders and several bodies can effectively evaluate financial auditors’ roles during crisis, especially in terms of fraud and going concern risk. Additionally, the findings invite several bodies to never separate fraud from going concern principle during the evaluation of the trustworthiness of financial statements, and the overall credibility of business continuity.

Multivariable fuzzy rule-based models and their granular generalization: A visual interpretable framework

Fuzzy rule-based models have been widely used due to their interpretability and effectiveness. However, they still encounter challenges when dealing with multivariable and large-scale data. In this study, we first propose a novel approach to establish a selective sampling and mapping data reduction method. The method focuses on reducing data variables while decreasing the number of samples, and an appropriate scaling size can be chosen for different situations. Then, a multivariable data-driven fuzzy rule-based model is developed based on the processed data. Moreover, the data projection approach using the distance metric helps to preserve the structural characteristics of the original data. The results are visually presented to facilitate an interpretable description of the subsequent rule-based modeling. Furthermore, due to the inevitable inaccuracy in the projection process of numeric modeling, we introduce the allocation of information granularity to extend the model to a granular form at a more abstract level. Experimental studies on both synthetic and publicly available datasets demonstrate that the proposed method has superior effectiveness and efficiency compared to the existing state-of-the-art regression algorithms.

Initiation and propagation fracture toughness of injection-moulded short fibre composites under different environmental conditions

Injection-moulded short-fibre composites combine lightweight and manufacturability; however, their fracture behaviour and how it is affected by the microstructure and environmental conditions are yet to be fully characterised. The initiation and propagation fracture toughnesses of injection-moulded short glass-fibre reinforced polyamide 6.6 composites were characterised through compact tension testing under the combined effect of fibre orientation, moisture level and temperature. Full R-curves were calculated using either an FE-based compliance calibration method, or the J-integral method based on full-field measurements from Digital Image Correlation; both data reduction methods provided consistent propagation values for the fracture toughness, although only the J-integral method can characterise the initiation toughness and the shape of R-curves reliably. This work revealed that the material became tougher with increasing fibre orientation along the loading direction, increasing moisture content, and/or increasing temperature; the corresponding increase in toughness was related to changes in failure and toughening mechanisms, identified through fractography. FE simulations of the compact tension tests have demonstrated the need to consider both initiation and propagation values of fracture toughness to accurately predict the response of notched specimens. The thorough characterisation of fracture toughness presented in this paper can contribute to design safer and more efficient damage-tolerant IM-SFRP components.

A retrospective analysis of mid-infrared observations of the Comet D/Shoemaker-Levy 9 and Wesley impacts on Jupiter

We present a retrospective analysis of Earth-based mid-infrared observations of Jupiter capturing the aftermath of the impacts by Comet D/Shoemaker-Levy 9 (henceforth SL9) in July 1994 and the unknown object previously termed the “Wesley impactor” in July 2009. While the effects of both impacts on Jupiter’s atmosphere have been reported previously, we were motivated to re-examine both events using consistent data reduction and analysis methods to enable robust, quantitative comparisons. To study the aftermath of the SL9 impacts, we examined infrared (7.8–20.5μm) spectrophotometry of Jupiter measured by the MIRAC (Mid-Infrared Camera) on NASA’s Infrared Telescope Facility on July 20, 21 1994 and low-resolution (R = 100) N-band (7–13μm) spectroscopy measured by SpectroCam-10 on the Palomar Telescope on July 22 1994. To study the aftermath of the Wesley impact, we examined low-resolution (R = 100) N- and Q-band (R = 80, 17–25μm) spectroscopy recorded by Gemini South Telescope’s T-ReCS (Thermal-Region Camera Spectrograph). We analyzed the observations with two independent analyses: (1) a least-squares search over a grid of candidate mineral species to determine the composition of impact residue and (2) a radiative transfer analysis to derive atmospheric information. We observe that the SL9 impact sites are enhanced in stratospheric CH4 emissions at 7.9μm, due to shock heating and adiabatic compression from plume re-entry, and from 8.5–11.5μm due to stratospheric NH3 emission and non-gaseous cometary material, in agreement with previous work. In the G impact site, we derive NH3 concentrations of 5.7−2.8+4.5 ppmv at 30 mbar. In new findings, we find that the SL9 impact sites also exhibit a non-gaseous emission feature at 18–19μm. The non-gaseous emission at 8.5–11.5μm and 18–19μm emission result is best reproduced by predominantly amorphous olivine and (obsidian) silica at similar abundances. The Wesley impact site exhibits enhanced emissions from 8.8–11.5μm and 18–19μm. We found this could be reproduced by predominantly amorphous olivine and stratospheric gaseous NH3 at concentrations of 150−121+338 ppbv retrieved at 30 mbar. Stratospheric abundances of NH3 are a factor of ∼40 higher in the SL9 impacts compared to the Wesley impact, which confirms the former reached deeper, NH3-richer altitudes of Jupiter’s atmosphere compared to the latter. The absence of silicas in the Wesley impact would place an upper limit of 10 km/s on the incident angle and ∼9∘ on the entry angle of the impactor such that shock heating associated with the impact did not reach temperatures required for silicates to be converted.

Mixed-mode I + II fracture characterisation of a honeycomb/carbon-epoxy sandwich panel using the asymmetric single-leg bending test

The Asymmetric Single-Leg Bending (ASLB) test was employed to obtain the fracture energy of a honeycomb/carbon-epoxy sandwich panel under mixed-mode I + II loading. A data reduction method lying on beam theory, specimen compliance and equivalent crack length concepts was employed to get the experimental Resistance-curve (R-curve), which allows determination of the mixed-mode fracture energy. Numerical analysis including cohesive zone modelling was used for validation and as a suitable partition method useful to estimate the mode ratio. In view of the observed non-linear behaviour, a mixed-mode I + II trapezoidal cohesive zone model and the quadratic energetic criterion were considered in the simulations. Overall, it can be concluded that the ASLB test is appropriate for mixed-mode I + II fracture characterisation of sandwich panels under predominant mode I.

Morphological Traits Evaluated with Random Forest Method Explains Natural Classification of Grapevine (Vitis vinifera L.) Cultivars.

There are hundreds of morphologic and morphometric traits available to classify and identify grapevine (Vitis vinifera L.) genotypes, while statistical evaluation of those has certain limitations, especially when we have no information about the traits that are discriminative to a certain sample set. High numbers of investigated characters could cause redundancy, while reducing those numbers may result in data loss. Grapevine is one of the most important horticultural crops, with many cultivars in production. The characterization of the genotypes is of undeniably high importance. In this study, we analyzed a dataset of scientific and historical importance with 125 morphological traits of 97 grapevine cultivars described by Németh in 1966. However, the traits are not independent in a set of a large number of categorical traits with too few cultivars. Therefore, the number of traits was first reduced using a simple and effective algorithm to eliminate traits with redundant information content using the asymmetric measure of association Goodman and Kruskal's λ. We reduced the number of traits from 125 to 59 without any information loss. For the classification, we applied a random forest (RF) method. In this way, 93% of the cultivars were correctly classified using only four traits of the data set. To our knowledge, only a few studies applied a trait elimination algorithm similar to ours in ampelography that can be used for other biological data sets of similar structure. The classification results give a morphological explanation to several cultivars from the Carpathian Basin, a territory where all three Vitis vinifera L. geographical groups, occidentalis, orientalis and pontica, are represented. We found that the information-loss-avoiding data reduction method we applied in our study solved the redundancy-caused interdependencies and provided a suitable dataset for classifying grapevine genotypes. For example, this method may successfully be applied in digital image analysis-based traditional morphometric investigations in ampelography.

Evaluation of the cracked lap shear test for mixed-mode fracture toughness estimation of adhesive joints

Cohesive zone models are a widely used method in adhesive joint strength predictions, but it requires the knowledge of fracture toughness of the adhesive in the different loading modes, together with the mixed-mode behaviour. The cracked lap shear test is a simple but seldom studied mixed-mode test and, as such, a deeper knowledge about its accuracy and of the available data reduction methods is required. This work consists of the experimental and numerical analysis of the cracked lap shear test to estimate the mixed-mode fracture toughness of two structural adhesives. The experimental work consisted of testing cracked lap shear specimens with the different adhesives. Numerically, cohesive zone model were used to predict the behaviour of bonded joints under mixed mode, while assessing the validity of the available data reduction methods to estimate fracture toughness. While the experimental analysis showed that the Brussat’s method led to offset results compared to the other methods, the cohesive zone model analysis confirmed the inadequacy of the Brussat's method, and the suitability of the Grady and Kinloch's models for mixed-mode fracture analysis.

Manajemen Pembelajaran Guru Dalam Meningkatkan Hasil Belajar Siswa Selama Pandemi Covid-19

With the outbreak of the covid-19 pandemic, social dynamics are changing rapidly, including in the world of education. Where the transition of learning that was previously carried out face-to-face has shifted to a virtual space. The purpose of this study is to find out and analyze the planning, implementation and evaluation of online learning management at SDN Sawahan Mojosari during the pandemic.The type of research in this research is qualitative with a case study approach. Data collection techniques by means of observation, interviews and documentation. analysis using data reduction methods, data presentation and drawing conclusions. Techniques for data validity are credibility, transferability, dependability, and confirmability. The research results are. 1). The planning stage is carried out through several stages, namely first a small meeting of the principal with the school committee, as well as teachers, then the results of the meeting are socialized to parents and students 2). The implementation of online learning during the COVID- 19 pandemic at SDN Sawahan Mojosari was handed over to the implementation by the principal to each class teacher, namely by using the Whatsapp group Mapel, Google Classroom and Google Form to send materials, be it photos, videos, or LKPD 3). Evaluation of online learning during the COVID-19 pandemic at SDN Sawahan Mojosari was carried out by the Principal together with class teachers directly into the class group or through the teacher's personal WA