Miracle-dz/UniFAR-FTUs huggingface dataset:
Wage labor in rice cultivation areas near Lake Victoria in Western Kenya

Rice is now the most rapidly growing food commodity in sub-Saharan Africa. This growth is mainly driven by urbanization. There is substantial research on the wage labor dynamics in rice production in Asia, but in Africa, there is limited analysis of the labor used in rice cultivation-the attributes, numbers, and costs which make it difficult to understand the actual market, such as how products are procured. This study aims to fill this gap in the research by comparing wage labor factors in two rice cultivation areas in Kenya: Awach and Ahero. The role of wage labor in farm household management as well as in the economy of the areas is considered. The results, based on two surveys, show that, at the Awach small-scale scheme, the yield was low and the cultivation area small, generating profit per household of only about one-half that of the more large-scale Ahero scheme. High wage expenses at Awach put pressure on rice farming management because, although family labor was used for rice cultivation, additional labor was required for transplanting and weeding, incurring expenses. These were covered by sales of rice in the previous year or sales of livestock. In both areas, wage laborers were mostly people residing in the same area. In the case of Ahero, the scale of the scheme itself was large, and the hired laborers were unknown. However, in Awach, there was a high rate of hiring neighbors, and thus, wage labor costs could also be understood as a source of income for people in the area. Therefore, it can be inferred, in the case of Ahero, that there is a mechanism of wage labor based on economic principles, whereas in the case of Awach, small-scale production using family labor may be less efficient, but there is an additional concern with community principles. Key words: Rice cultivation, western Kenya, wage labor, economic principles, community principles.
Toughening of Epoxy Nanocomposites: Nano and Hybrid Effects

In this paper, we review recent progress made in the field of epoxy-based binary and ternary nanocomposites containing three-, two-, and one-dimensional (i.e., 3D-, 2D-, and 1D) nano-size fillers with a special focus on their fracture behaviors. Despite investigations conducted so far to evaluate the crack-resistance of epoxy nanocomposites and attempts made to clarify the controlling toughening mechanisms of these materials, some questions remain unsolved. It is shown that silica nanoparticles can be as effective as rubber particles in improving the fracture toughness/energy; but incorporation of carbon nanotubes (CNTs) or clay platelets in epoxy matrices delays crack growth only modestly. The “nano” effects of silica (<25 vol.%) and rubber (>10 wt.%) nanoparticles in toughening epoxy resin are confirmed by comparison with silica and rubber micro-particles of the same loading. There is clear evidence of both synergistic and additive toughening effects in the silica/rubber/epoxy ternary nanocomposites. In addition, positive hybrid toughening effect has been observed in the nano-rubber/CNT/epoxy composites; however, a negative hybrid effect is predominant in nano-clay/nano-rubber/epoxy ternary nano-composites. Future research directions for epoxy-based nanocomposites towards multi-functional applications are discussed.
New Technologies for Optical Coherence Microscopy

According to the American Cancer Society, gastrointestinal (GI) cancers are among the most common forms of malignancies suffered today, affecting -200,000 people and causing -80,000 deaths in the United States every year. The prognosis depends heavily on the detection of earlystage lesions. The process of endoscopic surveillance, excisional biopsy, and histologic examination is the current gold standard for screening and diagnosis of many GI cancers. This process, however, is invasive, time-consuming, and can suffer from unacceptable false negative rates. Optical imaging technology that provides real-time, high-resolution imaging of human tissue in vivo with resolution at or near that of histopathology may significantly improve clinicians' capabilities to identify malignancies at curable stages. The ability to assess histologic hallmarks of GI cancer at the tissue architectural and cellular levels without excisional biopsy would be a major advance in GI cancer management. Development of techniques to reliably image cellular and subcellular structure through endoscopic devices is one of the most outstanding challenges in biomedical imaging today and holds tremendous promise for surgical applications and for early diagnostic screening and staging of epithelial malignancies. Optical coherence microscopy (OCM) is an in vivo cellular imaging technique that combines OCT with confocal microscopy. Due to the unique feature of using two distinct optical sectioning techniques, OCM can provide superior imaging depth in highly scattered tissues and can overcome important imaging probe design limitations that hinder confocal microscopy. Two novel designs for OCM systems are proposed and developed for high resolution cellular imaging. The first uses Fourier domain optical coherence detection, and the second implements line-field illumination and detection. Differences in performance from the standard time-domain optical coherence microscopy systems will be studied. Thesis Supervisor: James G. Fujimoto Professor of Electrical Engineering and Computer Science
Secure and Efficient Fire-fly Data Routing Algorithm for Wireless Sensor Networks in IoT Monitoring Systems

In the Electronics world the sensor is used in IoT applications. The sensed data need to be transfer to the appropriate devices as input for further processing. Clustering used to group the sensors which could form cluster and select the nodes head from the cluster. The head of each cluster receives the forwarded data through the cluster member and pass on to nearest permanent fixed station. Identifying cluster head and shortest route identification is a major challenge. This paper proposed a novelty on hybrid decision making algorithm with firefly routing algorithm (HDMFRA) for Cluster Head selection. This research work focusing of three main criteria which could save the energy and extend the life activation of the node, through the usage of energy, amount of nodes adjacent and energy consumption from permanent fixed station. To aggregate the data in optimized manner and to transfer the data in efficient manner Fire Fly routing algorithm was used. Simulation results show that proposed algorithm HDMFRA network in homogeneous environment is effective and prolonging the life time of the node by 25%.
Combining support vector machine with radial basis function kernel and information gain for sentiment analysis of movie reviews

A movie review can be considered as an essential data source for both movie producers and potential consumers. The reviews are usually used as a benchmark for knowing the movie’s quality and are utilized as references to whether the film is worth watching. The study of sentiment analysis has received more attention from researchers. This current study aims to classify the sentiment analysis of movie reviews obtained from the IMDb site. The support vector machine (SVM) method was employed to classify the movie review’s sentiments. Meanwhile, the radial basis function (RBF) kernel and information gain (IG) were used to enhance classification. Feature selection was conducted by removing irrelevant features and selecting features with a strong correlation to classification. The IG algorithm was used for feature selection. The current study revealed that the classification accuracy of movie review sentiment analysis using the SVM algorithm, SVM + RBF kernel, and SVM+RBF Kernel+IG are 81.50%, 82.25%, and 87.25%, respectively.
Development of simulation integrated learning model with mikir approach to school for disaster mitigation

Indonesia is a country that is prone to disasters. Therefore disaster mitigation needs to be introduced to students. This study aims to: (1) develop a model of disaster mitigation through learning in schools, (2) test the validity of disaster mitigation learning models, (3) test the effectiveness of disaster mitigation learning models in increasing understanding student skills towards the concepts, principles and practice of self-rescue in the event of a natural disaster. This research is a research and development (R&D). The research design starts with a theoretical exploration and review of disaster mitigation material to be taught, validation of the model, test the effectiveness of the model and ends with a practicality test of the model. Data analysis techniques with descriptive and quantitative. The results of the study are disaster mitigation models through learning with integrated simulation models with MIKiR approach that have been validated by experts (academics, practitioners, and policy makers), and effective to increase of. understanding student skills towards the concepts, principles and practice of self-rescue in the event of a natural disaster with N-g = 0.62 in middle catogory
Evaluation performance recall and F2 score of credit card fraud detection unbalanced dataset using SMOTE oversampling technique

Unbalanced data becomes an interesting research and continues to be studied because of its uniqueness. Unbalanced data requires special treatment prior to making the data balance. In this paper, our study to investigate the performance of unbalanced dataset using diverse oversampling proportion. We use SMOTE to gerentae new syntethic data, then we classify using random forest algorithm. In our experiment we generate new sampling with start 20%, 40%, 60%, 80%, and 100% of majority class, so that the data balancing until 50%: 50%. Each new generated data, we train the data using classification technique. Then, evaluate each algorithm performance. We show that the highest F2 score i.e: 85.34 and 84.93. The new data generated is 60% of majority class, result F2 score 85.34, then the new data generated from 100% of majority class result F2 score 84.93.
Implementation of MVC (Model-View-Controller) architecture in online submission and reporting process at official travel warrant information system based on web application

An official travel warrant (SPPD) is a type of assignment letter needed by an employee as evidence of an order from the manager in carrying out work assignments to other areas. At the end of the assignment, employees must make a report on the tasks assigned to their manager. In general, SPPD services are still carried out manually, both in the process of submission, approval, reporting and data storage. This manual service requires a long processing time, large resources and must be performed in rigid places and times. In addition, storing letters manually creates difficulties when retrieve data is required. This research aims to provide an online official travel warrant information system based on a web application. With web-based applications, information system services are faster and can be done anytime anywhere, not limited by time and place. The storage of letter data in the database concept also facilitates the retrieving process. The information system is designed to serve the complete service flow of official travel warrant starting from submission, approval to reporting. The design of the application system is carried out by PHP programming with the MVC (Model-View-Controller) concept using the Codeigniter framework. Data is stored on the MYSQL database. The design results are tested for functionality using the black-box method. From the test results, it was found that all the functions designed including submission, approval, reporting and retrieval of data could go well as expected. The assignment letter create by system and send to employee online. The task report can create online on the system. Approval of the task report is also carried out online in the designed information system.
A Comprehensive Analysis of Multimodal Speech Emotion Recognition

Emotion recognition is critical in dealing with everyday interpersonal human interactions. Understanding a person’s emotions through his speech can do wonders for shaping social interactions. Because of the rapid development of social media, single-modal emotion recognition is finding it difficult to meet the demands of the current emotional recognition system. A multimodal emotion recognition model from speech and text was proposed in this paper to optimize the performance of the emotion recognition system. This paper, explore the comprehensive analysis of speech emotion recognition using text and audio. The results show that enhancement of accuracy compared to either audio or text. Here, results were obtained using the deep learning model I.e. LSTM. The experiment analysis is done for RAVDESS and SAVEE datasets. This implementation is done by python programming.
Interfacial Reactions and Electrical Properties of Ti/n-GaN Contacts

The phase equilibria in the ternary Ti-Ga-N have been investigated. Interfacial reactions in Ti/GaN contacts have been studied by diffusion couple experiments. The ternary phase Ti 2 GaN was confirmed by x-ray diffraction in bulk samples as well as in massive Ti/GaN diffusion couples and annealed Ti thin films on GaN. The diffusion path in samples, annealed at 850 (cid:176) C in Ar gas, is GaN/TiN/Ti 2 GaN/Ti 3 Ga/Ti. A planar TiN layer forms in direct contact to GaN and governs the electrical properties of annealed Ti/GaN contacts. Thin film contacts were fabricated by sputtering Ti on MOVPE grown n-GaN (5x10 17 cm -3 ) and subsequent rapid thermal annealing in an Argon atmosphere. Initially non-linear current-voltage characteristics become ohmic after annealing and a specific contact resistance of approximately 10 -2 W cm 2 , measured with the circular transmission line method, was found after annealing at 900 0 C for 1 min.
Synergistic photothermal antimicrobial therapy using graphene oxide/polymer composite layer-by-layer thin films

We report an efficient and a simple synergistic antimicrobial therapy for lysing pathogenic E. coli K12 MG1655 using layer-by-layer (LbL) films of graphene oxide (GO) and poly(allylamine hydrochloride) (PAH). Two kinds of antimicrobial therapies have been tested in this study. The first therapy involves the inherent antimicrobial property of GO studied by incubating the bacteria with glass slides coated with GO/PAH films up to 16 h (varying number of layers from 20 to 80). The optical density (OD) measurements at 600 nm revealed that the antimicrobial activity of the films increased with an increase in the number of layers, which can be attributed to an increase in the roughness of the films and also the amount of GO in the films. The second therapy involves exploiting the photothermal property of GO by exposing the films to a near-infrared (NIR) laser at 1064 nm (Nd:YAG, 10 ns pulse, 10 Hz, 85 mW) for 15 min and incubation for 4 or 16 h. The photothermal therapy results revealed the enhanced antimicrobial activity compared to incubation of bacteria with films in the absence of a NIR-laser for 4 or 16 h. The enhanced antimicrobial activity of GO/PAH films is because of the synergistic effect resulting from membrane-stress induced on bacteria by GO sheets and photothermal heating in the presence of NIR-light. Further, we have also performed a fluorescence-based live/dead assay using confocal fluorescence microscopy to verify the viability of bacterial cells after incubating the bacteria and GO/PAH films with and without a NIR laser. All the results suggest that the synergistic antimicrobial effect of GO/PAH films causes increased lysis of E. coli compared to the individual effects.
Comparative study between KNN and maximum entropy classification in sentiment analysis of menstrual cup

The rise of reuse, reduce, recycle movement impacts both in pollutants management and waste. Including menstrual waste by using the menstrual cup. However, the use of menstrual cups in Indonesia is not yet popular. Sentiment analysis is needed to see how the public thinks about the menstrual cup. Using 1108 data from Twitter, which was then labeled into positive and negative manually, the sentiment analysis stage was carried out using Maximum Entropy and K-Nearest Neighbor. These two were chosen because maximum entropy works by obtaining the best probability distribution, which closest to reality, and k-nearest neighbors work by classifying new objects based on attribute examples and training data. The implementation program uses PyCharm IDE and python programming language. From the research results, maximum entropy and k-nearest neighbors accuracy each are 84.6% and 83.7% with 1108 tweets. In the next research, a lexicon dictionary can be used to replace the manual labeling process.
Influence of the adiabatic process on a quantum system demonstrated using the energy conservation principle

The adiabatic process is an old topic, which does not excite energy transitions and entropy changes in quantum systems. For undergraduate students, these two properties are easily accepted intuitively, but the general proof is complicated. Unlike ordinary textbooks, we use the principle of energy conservation in a one-dimensional infinite well to prove these properties. This paper will enable students to deepen their views on quantum mechanics and statistical physics.
Integration of 4C competencies in online mathematics learning in junior high schools during the covid-19 pandemic

Critical thinking, collaboration, creativity, and communication (4C), need to be owned by students. The purpose of this study: To analyse the role of junior high school mathematics teachers in developing 4C competencies. The methods: A qualitative approach is used. The research team collaborated with SMPN 24 Semarang. The research subjects were mathematics teachers of SMPN 24 Semarang. Data analysis: data reduction, data exposure, data interpretation, and conclusion drawing. The data were obtained from observations, interviews with research subjects, and FGD. The results: (1) The role of the Mathematics teacher in cultivating 4C was carried out by providing open-ended questions to students and inviting students to discuss them in small groups. (2) Teachers need to be trained to develop online learning so that mathematics material and 4C competencies can be integrated and absorbed by students optimally. In conclusion, (1) open-ended questions can be used by mathematics teachers as a means of interpreting Mathematics lessons with 4C competencies through student discussions in small groups. (2) During the Covid-19 pandemic, teachers need to be trained to develop online learning so that mathematics materials and 4C competencies can be integrated and absorbed by students.
Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter

Back In 2020, World Health Organization (WHO) has announced COVID-19 as a pandemic. From the many public responses, especially those on Twitter regarding the #newnormal campaign, a sentiment analysis process needs to be carried out to find out the perceptions that exist in society through social media. In this study, data were obtained through the crawling process on Twitter using the Twitter API. The method used in the sentiment analysis process is lexicon-based. The lexicon-based method works by labeling words containing sentiments based on a lexicon dictionary that already has weight on each word or doesn’t have weight on words in a lexicon dictionary. The classification results using lexicon-based are also used to make training data in the testing process using the naive Bayes classifier algorithm. In general, the research stages in this sentiment analysis include data crawling, text preprocessing, feature extractions, and the classification process. The sentiment analysis process results showed that the percentage of social media users on Twitter about #newnormal was 33.19% containing negative sentiments and 66.36% containing positive sentiments. Meanwhile, for testing the naive Bayes classifier algorithm in the sentiment analysis process got an accuracy of 79.72%.
Optimization of Naïve Bayes uses the genetic algorithm for classification data

Classification is one of the statistical methods to classify data systematically. However, if there is a large amount of data and various features, it often results in low accuracy. For this reason, methods are needed that can handle the data with various types. One method that can handle this problem is Naïve Bayes. Naïve Bayes is one of the methods used for classification data. This method requires a stage of selection of independent variables in increasing the accuracy of the model from Naïve Bayes. So we need an excellent method to fix these deficiencies uses a Genetic Algorithm (GA). Genetic algorithm is one of the metaheuristic methods used in optimization techniques. The data used are septic tank data in East Surabaya with eleven independent for classification data. The result of classification accuracy using Naïve Bayes is 72.7%. When Naive Bayes was used with a genetic algorithm, the classification accuracy was increased is 90.9%
Implementing AskMe3 Approach to Adult Learning Through the Kalamazoo Literacy Council

Background: Ask Me 3 was developed as a tool to help individuals successfully communicate with their healthcare providers (Institute for Healthcare Improvement, n.d.). This was designed to study the effectiveness of this tool in educating adult learners of Kalamazoo, MI, through the Kalamazoo Literacy Council (KLC) Virtual Learning Center. Objective: The objective was to determine whether learners of the Kalamazoo Literacy Council gained confidence communicating with a healthcare provider through the implementation of a distance education Ask Me 3 program. Methods: Learners associated with the KLC were sent a pre-lesson survey asking them to assess their confidence levels with using the Ask Me 3 tool and communicating with their healthcare provider. They were then asked to attend an Ask Me 3 lesson presented virtually by the Kalamazoo Literacy Council, and a post-lesson survey followed asking the same questions as the initial survey. Based on the results of these surveys, we can conclude whether implementing a lesson on Ask Me 3 into literacy classes is beneficial to the learners. Purpose: The purpose of this study was to educate adult learners on Ask Me 3 in order to help lower the health literacy barrier within Kalamazoo County. Results: Results were not able to show whether learners gained confidence communicating with a healthcare provider, and no statistical analysis was conducted due to a lack of participants completing data collection. Conclusion: Ask Me 3 is a simple educational program that can easily be implemented within health literacy adult education, but further research is needed to determine the effect of this program on patient outcomes.
Application of OpenPose deep learning algorithm for gait parameter identiﬁcation

The systematic study of human gait dynamics has allowed medical professionals to offer personalized treatment to individuals suffering from varying degrees of gait degeneration. Currently, marker based motion capture is regarded as the gold standard of motion analysis [1][2][3]. Nevertheless, it is a very time consuming and fatiguing process, as a multitude of markers need to be carefully positioned on an individuals body. The use of Inertial Measurement Units (IMUs) has facilitated a faster process of recording limb accelerations and velocities during locomotion, allowing the reconstruction and personalized study of the gait dynamics. However, IMUs cannot be used to reconstruct the limbs’ positions, unless the individual’s initial body pose is known. Currently, the initial body pose is gained via a motion capturing systems, overturning the time benefit of the IMUs.