Rapid Mineralization Research Articles

Eligibility Recipients Determination of El-Nino Direct Cash Aid Using C4.5 Algorithm

The El Nino phenomenon, which causes increased temperatures and prolonged drought, is predicted to continue to hit Indonesia until the end of 2023. The significant impacts of El Nino include reducing the productivity of agricultural products, including food crops such as rice. This condition will certainly greatly affect the stability of domestic food prices. A total of 420 people from Bernah Village, Kota Alam Subdistrict received it. In the selection process for BLT El Nino recipients in Bernah Village, problems often occur such as problems in identifying aid recipients. This creates a society where aid recipients are still relatively well-off. By applying the C4.5 Algorithm, the C4.5 Calculation can determine feasibility thereby increasing efficiency in determining El Nino Direct Cash Assistance receipts. Calculations using Microsoft Excel 2010 revealed that there were 252 eligible recipients while 168 were not eligible. And from the calculation results using Rapid Miner 10.1. It is known that the accuracy rate is 100.00% with a total of 420 people receiving aid data.

Classification of Social Assistance Recipients Using Machine Learning

Social assistance is assistance funds provided by local governments. In the Minister of Home Affairs Regulation No. 32 of 2011, it is explained that social assistance is the provision of assistance in the form of money/goods from local governments to individuals, families, groups, and communities which is not continuous and selective in nature. One of the villages in North Lampung still often experiences problems, including high poverty rates and low education levels. The Naive Bayes algorithm method was chosen to classify aid recipients based on employment, age, and income. The spreadsheet calculations show that the Family Hope Program Assistance (PKH) class is 135 people and the Direct Cash Assistance (BLT) class is 39 people with a total of 176 people in the social assistance recipient data. From the results of Rapid Miner calculations, the accuracy value for the PKH and BLT classes is 100.00%.

Comparison of Social Media BOT Functions Using the K-Nearest Neighbor Method Against User Satisfaction

Social media is currently an alternative medium in conveying messages in the form of news and can be used as a tool to exchange news from different places. Many people use social media to express opinions, express feelings, as well as experiences and things that can be of concern. In this study, the data processing used was the K-Nearest Neighbor Algorithm with the classification method as a media for comparing the functions of the two bots, namely the WhatsApp and Telegram bots. Using SPSS (Statistical Product and Serive Solutions) and Rapidminer as a place to perform calculations and analysis. Based on the results of testing data mining with Rapid Miner, the calculation results are obtained which will be used as information to support user satisfaction in using Social Media Bots. User satisfaction is found in WhatsApp Bot users 72.22% and Telegram Bot users 28.57%. Calculations are carried out with a data mining process obtained from the K-Nearest Neighbor algorithm to make it easier to find Bot user satisfaction on both WhatsApp and Telegram social media. The WhatsApp bot is the best choice and has several useful functions as a digital communication media tool on the Internet of Things.

An integrated experimental-modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks.

Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramafic rocks. However, unlike saline aquifers and depleted oil and gas reservoirs historically considered for GCS, these formations can have extremely low porosities and permeabilities, limiting storage volumes and reactive mineral surfaces to the preexisting fracture network. As a result, coupling between geochemical interactions and the fracture network evolution is a critical component of long-term, sustainable carbon storage. In this paper, we summarize recent advances in integrating experimental and modeling approaches to determine the first-order processes for carbon mineralization in a fractured mafic/ultramafic rock system. We observe the critical role of fracture aperture, flow, and surface characteristics in controlling the quantity, identity, and morphology of secondary precipitates and present where the influence of these factors can be reflected in newly developed thermo-hydro-mechanical-chemical models. Our findings provide a roadmap for future work on carbon mineralization, as we present the most important system components and key challenges that we are overcoming to enable GCS in mafic and ultramafic rocks.

Rapid phenol mineralisation in a low-pressure dead-end light transmitting photocatalytic membrane reactor (LT-PMR)

Photocatalytic membrane reactors (PMRs) are widely studied for wastewater decontamination but reactions are too slow for scaled up application. This PMR study proposes a novel operation involving rapid decontamination in just one pass through the confined space within the membrane. This approach was demonstrated for the first time by light transmitting PMR (LT-PMR), conveniently receiving UV LED light (λpeak = 365 nm) via the permeate side to the photocatalyst P25 TiO2 microfiltration membrane coated on porous glass. A 93 % reduction in model contaminant phenol concentration from a 10 mg/L feed occurred at a flux of 4.7 L/m2/h. The phenol degradation occurred without any additional residence time from external vessels or recirculation. Phenol was almost entirely mineralised, confirmed by total organic carbon (TOC) measurements, with trace by-products identified by high pressure liquid chromatography (HPLC) analysis. Mass transfer and reaction rate constants from Langmuir-Hinshelwood modelling were comparable to literature. Highest reaction rates occurred at lowest permeate phenol concentrations, highlighting potential to destroy recalcitrant pollutants persisting through established treatments. LT-PMR is therefore an effective and practical process that very rapidly destroys phenol. Further work to explore improved flux by enhanced light transmission, tailored catalytic materials and demonstration on other pollutants and water matrices is warranted.

Penerapan Algoritma Naïve Bayes Untuk Analisis Pengaruh Faktor Pendidikan Terhadap Peningkatan Kesehatan Masyarakat

Health and Education are the main capital in realizing the nation's progress. Because quality and productive human resources are those who have a good level of health. People who have low education usually cannot receive current information about health, they prefer traditional health care such as going to a shaman or healer rather than going to the doctor. The aim of this research is to determine the influence of educational factors on public health. Data obtained through surveys and data collection carried out in the Sustainable Development Goals (SDGs) program or what we can interpret as sustainable development goals. 4429 data were obtained which included: economic data, health data, poverty data, gender data, education data and environmental data. The data obtained is used by researchers as attributes or variables to support decision making. This research uses a classification method with the Naïve Bayes algorithm with the accuracy value as the final value of the research. Data processing uses Rapid Miner software. From the test results it turns out to have an accuracy rate of 65.19%. Testing was carried out using k-fold validation, starting from k-fold validation 2 to 10. And the highest accuracy value was found at k-fold 6

Electrothermal mineralization of per- and polyfluoroalkyl substances for soil remediation

Per- and polyfluoroalkyl substances (PFAS) are persistent and bioaccumulative pollutants that can easily accumulate in soil, posing a threat to environment and human health. Current PFAS degradation processes often suffer from low efficiency, high energy and water consumption, or lack of generality. Here, we develop a rapid electrothermal mineralization (REM) process to remediate PFAS-contaminated soil. With environmentally compatible biochar as the conductive additive, the soil temperature increases to >1000 °C within seconds by current pulse input, converting PFAS to calcium fluoride with inherent calcium compounds in soil. This process is applicable for remediating various PFAS contaminants in soil, with high removal efficiencies ( >99%) and mineralization ratios ( >90%). While retaining soil particle size, composition, water infiltration rate, and cation exchange capacity, REM facilitates an increase of exchangeable nutrient supply and arthropod survival in soil, rendering it superior to the time-consuming calcination approach that severely degrades soil properties. REM is scaled up to remediate soil at two kilograms per batch and promising for large-scale, on-site soil remediation. Life-cycle assessment and techno-economic analysis demonstrate REM as an environmentally friendly and economic process, with a significant reduction of energy consumption, greenhouse gas emission, water consumption, and operation cost, when compared to existing soil remediation practices.

Poly-m-aminophenol/CoFe2O4 (p-n) heterojunction: Sustainable decontamination of BPA, ciprofloxacin pollutants from water and its study of antimicrobial performance

The remarkable ability of magnetic p-n heterojunction catalyst constructed with p-type poly-m-aminophenol (PmAP)/n-type Cobalt ferrite (CoFe2O4) catalyst to degrade BPA, ciprofloxacin drug (CIF) and its antibacterial activity in killing the microbes was explored on Gram-positive (S. aureus) and Gram-negative (E. coli). The p-n heterojunction photocatalyst was synthesized easily via hydrothermal reaction followed by chemical oxidative polymerization process. PXRD, FTIR, SEM, HRTEM, XPS, EDX, PL/TRPL, BET surface area, VSM and Uv-DRS techniques were used to confirm the synthesized catalyst's physicochemical characteristics and structural details. At ambient conditions, the synthesized catalyst showed rapid mineralisation of the organic pollutants by nearly 100 % at a concentration of 50 ppm in just one hour, with a catalyst loading of 0.1 g. The mineralisation pathways of the organics were illustrated through the GC–MS and TOC analysis. Furthermore, the disinfection capacity of the heterojunction had showed 5 fold quicker than the individual catalysts. This p-n heterojunction catalyst show excellent activity and quick magnetic separable quality which make it a catalyst for sustainable water purification process in future.

Effect of Bi2MoO6 Morphology on Adsorption and Visible-Light-Driven Degradation of 2,4-Dichlorophenoxyacetic Acid.

The development of highly efficient and stable visible-light-driven photocatalysts for the removal of herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) from water is still a challenge. In this work, Bi2MoO6 (BMO) materials with different morphology were successfully prepared via a simple hydrothermal method by altering the solvent. The morphology of the BMO material is mainly influenced by the solvent used in the synthesis (H2O, ethanol, and ethylene glycol or their mixtures) and to a lesser extent by subsequent thermal annealing. BMO with aggregated spheres and nanoplate-like structures hydrothermally synthesized in ethylene glycol (EG) and subsequently calcined at 400 °C (BMO-400 (EG)) showed the highest adsorption capacity and photocatalytic activity compared to other synthesized morphologies. Complete degradation of 2,4-D on BMO upon irradiation with a blue light-emitting diode (LED, λmax = 467 nm) was reached within 150 min, resulting in 2,4-dichlorophenol (2,4-DCP) as the main degradation product. Holes (h+) and superoxide radicals (⋅O2-) are assumed to be the reactive species observed for the rapid conversion of 2,4-D to 2,4-DCP. The addition of H2O2 to the reaction mixture not only accelerates the degradation of 2,4-DCP but also significantly reduces the total organic carbon (TOC) content, indicating that hydroxyl radicals are crucial for the rapid mineralization of 2,4-D. Under optimal conditions, the TOC value was reduced by 84.5% within 180 min using BMO-400 (EG) and H2O2. The improved degradation performance of BMO-400 (EG) can be attributed to its particular morphology leading to lower charge transfer resistance, higher electron-hole separation, and larger specific surface area.

Analysis of k-means clustering algorithm in advanced country clustering using rapid miner

In the era of globalization, the understanding of developed countries is no longer limited to the level of per capita income alone. As part of the analysis of developed countries based on aspects of government revenue, income balance, national savings, and domestic output based on sales. This research aims to cluster and to find out how these economic indicators are interrelated and affect the status of a country as a developed country. The K-means algorithm is used to identify patterns of countries with similar economic characteristics. From the research conducted, there are 4 clusters generated based on the characteristics of developed countries.

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner

Penerapan Metode Association Rule Menggunakan Apriori untuk Rekomendasi Penyusunan Rak Buku di Perpustakaan Universitas Muhammadiyah Bengkulu

The library is one of the facilities provided by Muhammadiyah University. The Muhammadiyah University Library can provide various kinds of library materials such as textbooks, text books, literature for practicums, general magazines and various other collections. Some book collections can be borrowed or can only be accessed on site. Apriori is an algorithm that is well known for searching frequent item sets using association rule techniques. The a priori algorithm uses knowledge about previously known frequent itemsets to process further information. In the a priori algorithm, to determine the candidates that may appear, it is done by paying attention to the minimum support. The data collection techniques used by the author in this research are interview techniques, observation and literature study. To carry out a bookshelf search application in a library, it begins with the process of inputting the book title, author's name, and publisher through inputting search data. Next, the system will look for a match string which will be processed by the application. If each string is found it will display it as a whole and will provide bookshelf information. The conclusion that can be drawn from the application of the association rule method using a priori for recommendations for arranging bookshelves in the Muhammadiyah University of Bengkulu library, is that you can find the placement of books in the library by applying the association rule method, can distribute books grouped according to scientific discipline categories, and the rapid miner application By using the association rule method, you can also read scientific discipline categories from the same book and the title entered contains the same characters but different writing.

Predicting Motivation among GKP Recipients using Regression Techniques in Machine Learning: An Implementation of Rapid Miner

Recently, predictive analytics has found a place in many research areas. From business to healthcare, the revolutions of predictive analytics studies are constantly evolving to help decision makers identify the problem and make a wise decision. While a bigger impact has been reported on the development of predictive models in business studies, there has been very little effort that investigates the deployment of predictive models by using machine learning approaches specifically involving SMEs. Small business entrepreneurs (SMEs) are among the entities most affected because of the COVID-19 pandemic. Hence, this study aims to develop a model that could predict the motivation score of GKP recipients based on three factors: satisfaction, perceived value, and perceived expectation. Four different regression models, namely Linear, Ridge, Lasso, and SVR, were developed and evaluated as the best model by using Rapid Miner machine learning software tools. The GKIPP grant dataset has been used as a case study for estimating the motive of the GKP grant to evaluate the outcomes of various regression models. The findings indicate that linear and SVR models have produced highly accurate predictions about the motivation of GKP recipients. They have also produced a high proportion of R-square scores across all regression models, which is highly encouraging.

Implementation Of Decision Tree Algorithm For Classification Of Eligibility In Social Assistance Fund Distribution

This study examined the use of decision tree algorithms, specifically C4.5, in classifying the eligibility for social assistance fund distribution in Kelurahan Bangka Belitung Laut, Kecamatan Pontianak Tenggara. Inaccuracies in distribution were caused by recipient selection based on village officials' recommendations, which only considered the type of occupation. To address this issue, this study developed a system based on the C4.5 algorithm to process economic census data. This research used a quantitative method with a descriptive analytical approach, collecting data through literature studies and economic census, and processing data using the Rapid Miner application. The classification model generated was evaluated using 10-fold cross-validation to ensure high accuracy. The results showed that the C4.5 algorithm achieved 100% accuracy, precision, and recall. The decision tree model indicated that the main attributes determining eligibility were occupation and income. Some rules derived from this model, such as those who are unemployed with an income below Rp29,500 being eligible for assistance, provided clear guidelines for policymakers. The implementation of this algorithm is expected to improve fairness and effectiveness in the distribution of social assistance funds in Kelurahan Bangka Belitung Laut, reduce public dissatisfaction, and prevent potential social conflicts. This study recommended adopting the model in other areas with adjustments to local data to enhance broader fairness in aid distribution.

Rapid and efficient mineralization of methylene blue dye (above 95 % in 20 min) catalyzed by ZnO nanoparticles under sunlight

This novel communication reports rapid mineralization of methylene blue dye, reaching above 95% in 20 min under cloudy sunlight catalyzed by ZnO nanoparticles, which were synthesized from N-Dipentene as a reducing agent, at different zinc acetate concentrations from 0.1 M to 0.4 M. The synthesized ZnO nanoparticles structural, morphological, compositional, and optical characterizations were done using X-ray diffraction (XRD), high resolution scanning microscopy (HRSEM), energy dispersive spectroscopy (EDS) and photoluminescence (PL) respectively. From these characterizations ZnO wurtzite crystalline structure, average crystallite size (18–25 nm) with heterogeneous elongated nanoparticles mixed to triangle like particles and low content of byproducts as well as the presence of oxygen Vacancies transitions at the red-orange bands, which contributes to the PL main emission related to the visible region at the electromagnetic spectrum were obtained, these results were correlated to the photocatalytic activity. Meanwhile direct sunlight photocatalyst using 17 mg of ZnO for 10 ppm of methylene blue dye (MB) degradation was performed, the efficiency was analyzed by Fourier transform infrared spectroscopy (FT-IR) and UV–Vis spectroscopy, obtaining the kinetic adsorption ratio fitted to the pseudo second order (PSO) and the pseudo first order (PFO) to determine the mechanism that governs this process. All those analyses were correlated to the material characteristics and synthesis concentration. A 95.38 % efficiency was reached at 20 min for the champion sample D3R followed by D4R, D2R and D1R reaching an 89.19, 80.40 and 66.86 % for the same time. The maximum mineralization efficiency for D3RA was 98.08 % at 30 min, albeit it was observed that the adsorption kinetics obeys the Pseudo second order (PSO) for D3R, D4R and D2R meanwhile for D1R is governed by the Pseudo First Order mechanism (PFO), mainly by the diffusive chemical mechanism.

Comparison Of K-Means and K-Medoids Algorithm for Clustering Data UMKM in Pagar Alam City

The aim of this research is clustering MSME data in Pagar Alam City using the K-Means and K-Medoids algorithms. This research is motivated by the lack of further management of MSME data collection, which can hinder the development and improvement of Pagar Alam City MSMEs. Meanwhile, this data is considered necessary for agencies to develop and improve Pagar Alam City MSMEs. Apart from agencies, this data is also useful for sub-districts, sub-districts and RT/RW to find out what interests, talents and potential the community has in what business fields. MSME data is processed using Rapid Miner and Python, the system development method in this research uses the Cross Industry Standard Process for Data Mining (CRISP-DM) method, where the stages include Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The test method uses the Davies-bouldin index, a DBI value that is close to 0 results in good clustering. The results of this research obtained 3 clusters. In 2020 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 1 and C2= 3 sub-district. In 2022 K-Means C0= 1, C1= 3 and C2= 1 sub-district, K-Medoids C0= 1, C1= 3 and C2= 1 sub-districts. The results of the 2020 sub-district DBI clustering calculation are DBI k-means = 0.134 and k-medoids = 0.523. In 2022 DBI k-means = 0.277 and k-medoids = 0.496. So it can be concluded that the K-Means algorithm in the case of grouping MSMEs in Pagar Alam City has better performance, because the DBI value is close to 0. From the results of the grouping it can help provide an overview for related parties in encouraging or providing assistance to sub-districts that are included in the low cluster.

Airline Attitudes: Mining Data for Service Sentiment Insights

Published user reviews and opinions underlie sentiment analysis, thereby enabling extraction of essential data about service providers and indicating their market status. This study investigates smell sentimental grouping precision of standard information mining systems against six Indian carriers related social media microblog datasets. The sentiment analysis with classifiers, namely Bayes Net and SVM, achieved good results in their accuracies. The analysis centers on how well passenger sentiments can be separated out by the six airlines. On WEKA tool, Bayes Net gained the highest accuracy whereas on Rapid Miner tool, SVM yields better classification than all others. A look at passengers experience with the airlines According to reviews, Go Air has been one of the top-knotch in terms of service received by customers and turns out to be highest rated airline based on passenger feedback.

Sclerostin modulates mineralization degree and stiffness profile in the fibrocartilaginous enthesis for mechanical tissue integrity.

Fibrocartilaginous entheses consist of tendons, unmineralized and mineralized fibrocartilage, and subchondral bone, each exhibiting varying stiffness. Here we examined the functional role of sclerostin, expressed in mature mineralized fibrochondrocytes. Following rapid mineralization of unmineralized fibrocartilage and concurrent replacement of epiphyseal hyaline cartilage by bone, unmineralized fibrocartilage reexpanded after a decline in alkaline phosphatase activity at the mineralization front. Sclerostin was co-expressed with osteocalcin at the base of mineralized fibrocartilage adjacent to subchondral bone. In Scx-deficient mice with less mechanical loading due to defects of the Achilles tendon, sclerostin+ fibrochondrocyte count significantly decreased in the defective enthesis where chondrocyte maturation was markedly impaired in both fibrocartilage and hyaline cartilage. Loss of the Sost gene, encoding sclerostin, elevated mineral density in mineralized zones of fibrocartilaginous entheses. Atomic force microscopy analysis revealed increased fibrocartilage stiffness. These lines of evidence suggest that sclerostin in mature mineralized fibrochondrocytes acts as a modulator for mechanical tissue integrity of fibrocartilaginous entheses.

Data mining for predictive analysis in gynecology: a focus on cervical health

Currently, data mining based on the application of detection of important patterns that allow making decisions according to cervical cancer is a problem that affects women from the age of 24 years and older. For this purpose, the Rapid Miner Studio tool was used for data analysis according to age. To perform this analysis, the knowledge discovery in databases (KDD) methodology was used according to the stages that this methodology follows, such as data selection, data preparation, data mining and evaluation and interpretation. On the other hand, the comparison of methodologies such as the standard intersectoral process for data mining (Crips-dm), KDD and sample, explore, modify, model, evaluate (Semma) is shown, which is separated by dimensions and in each dimension both methodologies are compared. In that sense, a graph was created comparing algorithmic models such as naive Bayes, decision tree, and rule induction. It is concluded that the most outstanding result was -1.424 located in cluster 4 in the attribute result date.

Developmental milestones in captive Galago moholi.

Systems of the body develop in a modular manner. For example, neural development in primates is generally rapid, whereas dental development varies much more. In the present study, we examined development of the skull, teeth, and postcrania in a highly specialized leaping primate, Galago moholi. Eighteen specimens ranging from birth to adult were studied. Bones, teeth, and the cranial cavity (i.e., endocast) were reconstructed with Amira software based on microCT cross-referenced to histology. Amira was also used to compute endocast volume (as a proxy for brain size). Reconstructions of the wrist and ankle show that ossification is complete at 1 month postnatally, consistent with the onset of leaping locomotion in this species. Endocranial volume is less than 50% of adult volume at birth, ~80% by 1 month, and has reached adult volume by 2 months postnatal age. Full deciduous dentition eruption occurs by 2 weeks, and the young are known to begin capturing and consuming arthropods on their own by 4 weeks, contemporaneous with the timing of bone and ankle ossification that accompanies successful hunting. The modular pattern of development of body systems in Galago moholi provides an interesting view of a "race" to adult morphology for some joints that are critical for specialized leaping and clinging, rapid crown mineralization to begin a transitional diet, but perhaps more prolonged reliance on nursing to support brain growth.