1,043 research outputs found

    Enhancing the Synthesis of Biolubricant from Used Chicken Fat: Optimization of Operating Parameters Using Magnesium Oxide Nanoparticles as a Catalyst and Response Surface Methodology

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    In light of the growing concerns regarding the environmental impact and sustainability of mineral oil-based lubricants, the use of biolubricants has been advocated as a renewable alternative. The double transesterification of used chicken fat oil involves two steps of converting the triglycerides into methyl esters (UCFME) using methanol and Magnesium oxide Nano Particles (MgO NPs) as catalyst, and utilizing Trimethylolpropane (TMP) and MgO NPs to produce the final biolubricant (UCFBL). This research aimed to optimize the reaction parameters for the transesterification process involving used chicken fat methyl ester (UCFME) and TMP using response surface methodology. A series of 20 individual experiments were conducted, focusing on the variables of reaction temperature, time, and UCFME-to-TMP molar ratio. Through statistical modeling, it was predicted that the transesterification process would yield a maximum conversion rate of 97.5% under the optimized conditions of a reaction temperature 114 ¬įC, a reaction time 227 minutes, and a UCFME-to-TMP molar ratio 10.5:1. Experimental results, obtained from three independent replicates conducted under these optimal conditions, demonstrated an average yield of 98.3 % for the production of UCFBL, which aligned closely with the model's predicted range of 98.35%. The resultant biolubricant has remarkable lubrication qualities, such as a pour point of -5 ¬įC, flash point of 289¬įC, viscosity index of 213, and kinematic viscosities (KV) of 38.5 and 9.2 cSt at 40 and 100 ¬įC, respectively. These qualities revealed that the biolubricant generated fulfilled the ISO VG-32 criteria, making it an acceptable replacement for petroleum-based lubricants in industrial machine applications

    Sustainable Concrete: Exploring Fresh, Mechanical, Durability, and Microstructural Properties with Recycled Fine Aggregates

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    The growing construction industry and global population have led to increased demand for concrete, resulting in increased waste production. Recycling construction and demolition (C&D) waste as recycled fine aggregates (RFA) in concrete could help reduce waste and conserve natural resources. This research delves into the meticulous examination of particle packing density within specific cylindrical volumes under standard compacting efforts, elucidating an order of compressive strength. The study comprehensively explores various concrete properties, including workability, compressive strength, flexural strength, split tensile strength, drying shrinkage, electrical resistivity, rapid chloride penetration, and microstructural characteristics (analyzed through XRD, SEM, and EDAX). RFA particles, ranging from 0.15 to 4.75 mm, were employed as partial replacements for fine aggregates, with replacement percentages varying from 0% to 100% in increments of 25%. The empirical findings underscore that the incorporation of RFA significantly enhances concrete properties. However, it was observed that surpassing the optimum replacement percentage of 25% (RFA 25) adversely impacts the concrete’s strength and microstructure. Specifically, RFA 25 exhibited remarkable improvements, with a 14.75% increase in compressive strength, a 6.61% boost in flexural strength, and a 13.14% enhancement in split tensile strength compared to conventional concrete (RC). Furthermore, RFA 25 demonstrated a 4.16% increment in drying shrinkage, 17.65% higher electrical resistivity, and an 18.83% superior resistance to chloride penetration compared to RC. The analysis of XRD, SEM, and EDAX results elucidated that at lower replacement percentages, the pozzolanic reaction enhances strength by forming additional hydration products. Conversely, at higher replacement levels, strength diminishes

    An IoT Based pH Level Monitoring Mobile Application on Fishponds using pH Sensor and Waterproof Temperature Sensor

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    Climate change vastly affect the demand of human consumption such as natural resources due to the destruction of the natural ecosystem. Due to the increasing population in many countries, people discovered innovative ways to produce supplies specifically food that can suffice the demandfor food consumption without compromising natural resources. Most people rely on terrestrial land including farms for plant based foods, there are artificial made food harvested in a controlled environment and creating artificial environment for animal culture both in land and body of water that serves as mass production of raw product such aschicken, beef, pork and fish meat. Since climate change is oneof the major factors that can affect the production of food, developers and experts provided assistance in innovative ways such as designing application and devices that can provide efficient process in mass producing raw products in the market. The emergence of Internet of Things in the different industries has vastly improved in present time. Internet of Things changed the way people execute their tasks from customer service, manufacturing large amount of materials, and even creating a smarter and energy efficient home for families that can be controlled through smartphone devices remotely. Internet of things were integrated in animal culture in several countries and it provides efficient and reliable assistance for animal farmers such as fishpond and poultry owners. This proposed research contributes on providing alternative and innovate way of aquaculture on artificial body of water specifically on fishponds. The proposed study can recommend specific aquatic species that can be culture in a specific season in the Philippines throughanalysis of the temperature and pH level of the artificial bodyof water

    Classificação multiclasse de sinais de eletroencefalograma para tarefas de imaginação motora utilizando processamento estatístico de sinais e deep learning

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    Research Interests: Efficient classification of electroencephalogram (EEG) signals is crucial for the development of brain-computer interface systems. However, the complexity and variability of EEG signals pose significant challenges for accurate classification. Additionally, this study has social relevance as it can contribute to the development of assistive brain-computer interfaces, benefiting individuals with severe motor impairments, such as those who have experienced a stroke. These interfaces have the potential to improve the quality of life for these individuals by enabling communication and device control through brain activity. Objectives: This study aimed to compare the performance and computational cost of an artificial neural network using different signal processing techniques for the classification of resting state and left/right wrist movement imagination states from EEG signals. Three statistical signal processing techniques, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Singular Spectrum Analysis (SSA), were explored in conjunction with a Convolutional Neural Network (CNN) to enhance the classification of EEG signals. Results Obtained: The results revealed that the PCA technique led to a reduction in training time of up to 63.5% without significantly compromising performance in terms of classification accuracy. PCA proved to be a promising approach, capturing relevant information from the EEG signals and improving the CNN‚Äôs ability to classify accurately. On the other hand, both ICA and SSA techniques did not yield promising results. ICA had negative effects on feature extraction, resulting in decreased classification accuracy by the CNN. SSA, on the other hand, showed consistently low performance across all evaluated metrics, indicating challenges in capturing discriminative information from the EEG-IM signals.Interesses de pesquisa: A classifica√ß√£o eficiente dos sinais de eletroencefalograma (EEG) √© fundamental para a constru√ß√£o de sistemas com interface c√©rebro-computador. No entanto, a complexidade dos sinais de EEG e sua variabilidade entre indiv√≠duos apresentam desafios significativos para a classifica√ß√£o precisa. Este estudo tem relev√Ęncia social, pois pode contribuir para o desenvolvimento de interfaces c√©rebro-computador assistivas, beneficiando pessoas com severos danos motores, como aquelas que sofreram acidente vascular cerebral (AVC). Essas interfaces t√™m o potencial de melhorar a qualidade de vida desses indiv√≠duos, permitindo a comunica√ß√£o e o controle de dispositivos atrav√©s da atividade cerebral. Objetivos: Este estudo teve como objetivo comparar o desempenho e o custo computacional de uma rede neural artificial utilizando diferentes t√©cnicas de processamento de sinal na classifica√ß√£o de estados de repouso e imagina√ß√£o do movimento do punho esquerdo e direito a partir de sinais de EEG. Foram exploradas tr√™s t√©cnicas estat√≠sticas de processamento de sinais: An√°lise de Componentes Principais (PCA), An√°lise de Componentes Independentes (ICA) e An√°lise Espectral Singular (SSA), em conjunto com uma Rede Neural Convolucional (CNN). Resultados obtidos: Os resultados obtidos revelaram que a t√©cnica de PCA proporcionou uma redu√ß√£o no tempo de treinamento de at√© 63,5%, sem comprometer significativamente o desempenho em termos de acur√°cia na classifica√ß√£o. A PCA demonstrou ser uma abordagem promissora, permitindo a captura de informa√ß√Ķes relevantes nos sinais de EEG e aprimorando a capacidade da CNN em realizar a classifica√ß√£o com precis√£o. Por outro lado, as t√©cnicas de ICA e SSA n√£o apresentaram resultados promissores. A ICA teve efeitos negativos na extra√ß√£o de caracter√≠sticas, resultando em uma diminui√ß√£o na acur√°cia da classifica√ß√£o realizada pela CNN. A SSA, por sua vez, mostrou um desempenho geralmente baixo em todas as m√©tricas avaliadas, indicando uma dificuldade em capturar as informa√ß√Ķes discriminativas presentes nos sinais de EEG-IM

    RHEOLOGY AS A TOOL FOR TECHNOLOGICAL ADVANCEMENTS IN SELF-COMPACTING LIGHTWEIGHT CONCRETE MIXES

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    This paper outlines a methodology aimed at enhancing the technological performance of self-compacting concrete using lightweight expanded clay aggregate. One of the primary challenges encountered when employing concrete with lightweight aggregate involves displacing expanded clay grains within the solution's liquid phase to ensure necessary fluidity (workability) while upholding high stability (structural viscosity and segregation resistance). To achieve this objective, the rheological parameters of the self-compacting cement matrix have been regulated by deliberately adjusting the functional groups of additives and the microfine mineral filler, employing analytical methods from computer materials science. Through the analysis of rheometric results obtained from investigating various solution mixtures, the most suitable model that describes their rheological behaviour has been identified. The impact of finely dispersed fly ash excipient, a carboxylate superplasticiser, and a stabiliser additive on the rheological parameters of self-compacting lightweight concrete mixes has been established. The inclusion of these complex additives in the composition has enabled substantial alterations in the flow index across a wide range (0,030-0,798). This adaptability allows for the adjustment of the mortar mixture's rheological behaviour throughout a spectrum ranging from ¬ęabnormally viscous liquid¬Ľ to ¬ęNewtonian liquid¬Ľ. This approach, examining the singular-factor dependencies' analysis on the coefficients of rheological behaviour models, aids in addressing the primary challenge encountered when using concrete with lightweight aggregate

    Instructional Quality and Faculty Behaviors in Virtual Exchange Programs: A Study of Higher Education Institutions from Philippines and Indonesia

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    This study evaluated the effectiveness of a virtual faculty and student exchange program between a higher education institution (HEI) in the Philippines and Indonesia. Data were collected from selected student participants from the higher education institutions from both countries using a descriptive research design. The study found that the program was successful in terms of instructional quality and behaviors of instructors, with respondents viewing the quality of course materials, structure, and delivery as high. The instructors were also observed to exhibit positive and great behaviors, creating a favorable learning environment for their students. Moreover, there were no significant differences in the assessment based on demographic profiles and the evaluation of the quality of course materials, structure, and delivery, as well as the views on the behaviors of the instructors. These results can serve as a foundation for improving future similar international endeavors using ICT as a medium. The study suggested that ICT can be a promising tool for international exchange programs, providing high-quality education for students from different countries. These findings can aid in the design and implementation of future international endeavors in higher education institutions

    Analisis Faktor Employee Engagement terhadap Kinerja Karyawan di PT Sinkona Indonesia Lestari

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    Length of work is one of the assessments or determinants that can show that the company has engaged employees. PT Sinkona Indonesia Lestari (PT SIL) has employees who have worked for more than 16 years as much as 62,25 percent, this shows that many employees at PT SIL feel engaged with the company. Based on this background, this research was conducted at PT SIL which aimed to find out and analyze what factors influence employee engagement on performance or employee performance at PT SIL. The sample for this study includes 100 of his employees with a stratified random sample. The variables used to describe employee engagement factors are leadership, compensation, and organizational culture. Other potential variables are employee engagement and employee performance. Organizational culture (organizational culture) has been shown to have a significant positive impact on both employee engagement and employee performance (employee performance)

    The microbial biodiversity at the archeological site of Tel Megiddo (Israel)

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    IntroductionThe ancient city of Tel Megiddo in the Jezreel Valley (Israel), which lasted from the Neolithic to the Iron Age, has been continuously excavated since 1903 and is now recognized as a World Heritage Site. The site features multiple ruins in various areas, including temples and stables, alongside modern constructions, and public access is allowed in designated areas. The site has been studied extensively since the last century; however, its microbiome has never been studied. We carried out the first survey of the microbiomes in Tel Megiddo. Our objectives were to study (i) the unique microbial community structure of the site, (ii) the variation in the microbial communities across areas, (iii) the similarity of the microbiomes to urban and archeological microbes, (iv) the presence and abundance of potential bio-corroding microbes, and (v) the presence and abundance of potentially pathogenic microbes.MethodsWe collected 40 swab samples from ten major areas and identified microbial taxa using next-generation sequencing of microbial genomes. These genomes were annotated and classified taxonomically and pathogenetically.ResultsWe found that eight phyla, six of which exist in all ten areas, dominated the site (>99%). The relative sequence abundance of taxa varied between the ruins and the sampled materials and was assessed using all metagenomic reads mapping to a respective taxon. The site hosted unique taxa characteristic of the built environment and exhibited high similarity to the microbiome of other monuments. We identified acid-producing bacteria that may pose a risk to the site through biocorrosion and staining and thus pose a danger to the site’s preservation. Differences in the microbiomes of the publicly accessible or inaccessible areas were insignificant; however, pathogens were more abundant in the former.DiscussionWe found that Tel Megiddo combines microbiomes of arid regions and monuments with human pathogens. The findings shed light on the microbial community structures and have relevance for bio-conservation efforts and visitor health

    Redução da utilização de xilol no processamento histológico de amostras biológicas de animais para comparação com a técnica padrão/convencional: Reduction in the use of xylene in the histological processing of biological samples from animals compared to the standard/conventional technique

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    T√©cnicas histol√≥gicas auxiliam no processamento de amostras biol√≥gicas aplicadas ao estudo e diagn√≥stico, utilizando reagentes e corantes que favorecem a an√°lise de c√©lulas e tecidos. O tempo de preparo das amostras √© longo e o uso de reagentes qu√≠micos potencialmente t√≥xicos exp√Ķe os manipuladores e gera res√≠duos qu√≠micos ambientais, inclusive o xileno, um hidrocarboneto arom√°tico amplamente utilizado como agente clarificante e desparafinante. Este estudo visa reduzir o uso de xileno na etapa final da colora√ß√£o de hematoxilina e eosina em compara√ß√£o com o protocolo padr√£o para colora√ß√£o em diferentes amostras de tecidos de animais necropsiados, sem perder a qualidade da amostra e o diagn√≥stico. Sessenta e seis l√Ęminas foram confeccionadas a partir de 3 blocos de parafina, divididas em dois grupos e coradas em HE com xileno e com redu√ß√£o ou sem xileno. As l√Ęminas foram analisadas por 25 profissionais das √°reas biol√≥gica e veterin√°ria, e os resultados expressos por estat√≠stica descritiva, apresentando os valores m√©dios. O teste de correla√ß√£o do Coeficiente de Spearman foi realizado no programa estat√≠stico Jamovi. As l√Ęminas do grupo de teste apresentaram melhores m√©dias est√°ticas do que as do grupo de controle. Concluindo que o xileno pode ser reduzido e exclu√≠do da √ļltima etapa de colora√ß√£o, mantendo a qualidade estrutural do tecido e diagn√≥stico, reduzindo custos operacionais e sem prejudicar a sa√ļde do trabalhador
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