bluuebunny/crossref_metadata_2025 huggingface dataset:
La lectura y la escritura creativa para el desarrollo de habilidades comunicativas en educación preparatoria
Authors: Orrala-Plúa María Alexandra , Guamangallo-Taipe Ana María , Tigua-López Laura Ángela , Sánchez-Orrala Nicole Elizabeth
Published: March 2024

Los procesos de lectura y escritura son macro destrezas que, en conjunto con la escucha y el habla, se constituyen en el principal objetivo de la escuela, sobre todo a nivel de las primeras etapas educativas, ya que son elementos vertebradores de los demás aprendizajes y fundamentales para producir y entender el proceso humano. Desde estas premisas, se presenta este articulo con el propósito de describir los procesos de la lectura y la escritura creativa como herramientas para el desarrollo de habilidades comunicativas en Educación preparatoria. El mismo es una investigación documental, orientada metodologicamente mediante un diseño bibliografico. Como resultado, se puede enunciar que el desarrollo de la lectura y la escritura creativa en estas etapas es fundamental para el desarrollo integral de los niños. Estas habilidades no solo les proporcionan las herramientas necesarias para comunicarse eficazmente, sino que también estimulan su imaginación, creatividad y pensamiento crítico y creativo.
Felicidad laboral y desempeño en docentes
Authors: Valverde-Alva Weslyn Erasmo , Pantigoso-Layza Gonzalo Ytalo , Vereau-Amaya Elvis Amado , Soto-Zavaleta Annie Rosa
Published: March 2024

El presente estudio se planteó como propósito determinar la relación que existe entre la felicidad laboral y el desempeño de los docentes de la Facultad de educación y humanidades de la UNS, 2022. Se trata de un estudio cuantitativo, no experimental y con un diseño correlacional. La población estuvo conformada por 112 docentes con carga lectiva. Para la recolección de datos se aplicó un cuestionario para la variable felicidad laboral y, una guía de análisis documental, para el desempeño docente. Los resultados evidenciaron un nivel medio de felicidad laboral (58) y un nivel bueno correspondiente a su desempeño (55.4%). Se concluyó que existe relación significativa entre la felicidad laboral y el desempeño de los docentes de la Facultad de Educación y Humanidades de la UNS, asimismo, esta relación se extiende con cada una de las dimensiones del desempeño docente: cognoscitiva, didáctica y metodológica, actitudinal y consejería y tutoría.
Capital de trabajo y su influencia en la rentabilidad de una empresa constructora
Authors: Pintado-Castillo Cesar Agusto , Valiente-Saldaña Yoni Mateo , Gonzales-Rentería Yuri Gagarín
Published: March 2024

La investigación tiene como propósito determinar la influencia del capital de trabajo en la rentabilidad de una empresa constructora en Perú. Se trabajó con un nivel descriptivo correlacional, de enfoque cuantitativo y corte transversal, la población constó de 6 empleados dedicados a labores contable-financiera. Se empleó como técnica la encuesta y siendo su instrumento el cuestionario, a fin de comprobar las hipótesis respecto a la asociación de variables se utilizó la regresión logística. Los resultados logrados, han sido desarrollados y obtenidos con la prueba de Tau-b de Kendall, estableciendo una correlación del 0.966 y una significación de 0,000; estando por debajo del 0,05; aceptando la hipótesis alternativa, obteniendo que existe influencia significativa del capital de trabajo en la rentabilidad. Concluyéndose que el capital de trabajo tiene influencia significativa en la rentabilidad de la empresa constructora.
Habilidades directivas y comunicación organizacional interna en el trabajo colaborativo
Authors: Villacorta-Méndez Jessica Esther , De La Cruz-Arteaga Gavi Sarela , Reyes-Barrios Sergio Augusto , Herrera-Espinoza Kelly Cinthya
Published: March 2024

La finalidad de la presente investigación fue determinar si el trabajo colaborativo de las instituciones educativas públicas de Barranca, 2023, se ve influenciado por las habilidades directivas y la comunicación organizacional interna. Se desarrolló un estudio cuantitativo. La muestra estuvo compuesta por 50 docentes del distrito de Barranca, que laboran en el año 2023. Para el acopio de datos se empleó la encuesta como técnica y tres cuestionarios. Para la obtención de los resultados se aplicó una prueba de regresión logística ordinal, cuyos resultados mostraron un valor R de Nagelkerke de 0.472, ello señala que las habilidades directivas y la comunicación organizacional interna explican en un 47.2% el nivel de trabajo en equipo. Estos resultados conllevan a concluir que el trabajo colaborativo de los docentes de las instituciones educativas públicas de Barranca, 2023, es influenciado por las habilidades directivas y la comunicación organizacional interna.
La cultura de investigación y el contexto de acción del investigador
Authors: Molina-Gutiérrez Teresa-de-Jesús , Mejías-de-Parra Teresa , Luzardo-Martínez Hendry
Published: March 2024

El uso de un término o concepto requiere de emprender cierta peregrinación teórica. Este artículo propone un recorrido para la conceptualización del término cultura de investigación. Se ha fijado una travesía que primero incursiona en el concepto de conocimiento, de cultura general y la problemática de la cultura de investigación. Segundo, enmarca la investigación como proceso y producto intelectual para dar paso a la actitud del investigador, caracterización necesaria para ubicar al actor de la cultura de investigación. Siguiendo una investigación documental, con diseño bibliográfico, se ofrece un recorrido teórico que asigna un acercamiento al concepto en cuestión, como aporte para su construcción desde la perspectiva del contexto social donde se desarrolle. Por último, se afirma que los principios y valores que orientan la cultura de investigación se presentan asentados en los aspectos positivos de su proyección en una integración que le atribuye características particulares para perfilar su definición.
Seguimiento de la enseñanza y evaluación formativa en docentes de educación primaria en el Perú
Authors: Armas-Rebaza Laura Giovanna
Published: March 2024

El presente estudio buscó establecer la incidencia del seguimiento de la enseñanza en la evaluación formativa en docentes de educación primaria en el Perú. Para ello, se asumió, metodológicamente, una investigación básica, enfocada cuantitativamente y con un diseño descriptivo correlacional. Se trabajó con una muestra de 236 docentes de educación primaria que laboraban en la jurisdicción de una Unidad de Gestión Educativa. Se recogió información mediante dos cuestionarios diseñados por la investigadora, los cuales fueron sometidos a confiabilidad empleando el Alfa de Cronbach y la validez por juicio de expertos. Para contrastar las hipótesis, se aplicó el Rho de Spearman. Con la sistematización de los datos, los resultados muestran que el seguimiento de la enseñanza influye significativamente en la evaluación formativa de los docentes del nivel primara en el Perú, concluyéndose que existe una incidencia regular, positiva y significativa que comprueba la hipótesis de investigación.
Aprendizaje tecnológico desde los primeros años de escolaridad en la era de la inteligencia artificial
Authors: Duque-Rodríguez Jennifer Andreina , Piña-Ferrer Lenys Senovia , Isea-Argüelles Josía Jeseff , Comas-Rodríguez Raúl
Published: March 2024

La presente investigación tuvo como propósito indagar sobre el aprendizaje tecnológico, desde los primeros años de escolaridad, en la era de la inteligencia artificial. El estudio se llevó a cabo bajo la metodología documental, seleccionada para la comprensión ontológica de las experiencias de aprendizaje tecnológico desde los primeros años de escolaridad. Como resultado, se precisó que la era digital forma parte de la vida cotidiana, lo cual se constituye en un escenario propicio para que la Inteligencia Artificial (IA) se convierta en una herramienta favorable dentro del ámbito educativo. En conclusión, se requiere iniciar el proceso formativo a través de IA desde los primeros años de escolaridad, por cuanto esta brinda la oportunidad de desarrollar competencias orientadas a promover el desarrollo de habilidades sociales, la capacidad de comunicación, la creatividad y el pensamiento crítico.
Chondroitin Sulfate for Osteoarthritis – Is There a Risk for Cancer: an Analysis of Osteoarthritis Initiative Data
Authors: Lambova Sevdalina Nikolova , Shirinsky Valery , Shirinsky Ivan
Published: March 2024

The aim of this study was to evaluate the association between chondroitin   sulfate (CS) use and incidence of self-reported cancer during 6-year follow up in the     publicly available Osteoarthritis Initiative (OAI) cohort participants.

Materials and Methods: The current study included analysis of 6-year longitudinal data obtained from the OAI progression (n= 1390) and incidence (n = 3284) subcohorts that are publicly available at https://nda.nih.gov/oai/. The inclusion criteria for OAI were the following: age between 45 and 79 years for both subcohorts, symptomatic tibiofemoral knee osteoarthritis (OA) for the progression subcohort, and the presence of established or putative risk factors for incident knee OA for the incidence subcohort. The OAI participants were recruited and enrolled between February 2004 and May 2006 at four recruitment centres in the United States. This study received ethical approval from each recruitment centre. All participants provided written informed consent. To reduce the risk of bias, only participants who did not take CS at baseline were included in the analysis (a “new-user” design).

Results: 570 (18%) new users of CS and 2597 (82%) CS non-users were compared. In the CS user group, 294 (51.6%) participants received CS for more than one year, 291 (33.5%) for more than 2 years, 123 (21.6%) for more than 3 years, and 60 (10.5%) for more than 4 years. None of the participants received CS for more than 5 years. CS users and non-users were of the same age, had similar male/female ratio, and similar body mass index. In the non-user group, there were more African-American participants and there were more current smokers. 160 (6.2%) and 37 (6.5%) cases of incident self-reported cancer in the non-users and user groups were identified. There was no association between the use of CS and incident self-reported cancer in the non-adjusted analyses. The adjustments for age, race and smoking status did not change the results.

Conclusion: The results of the current study did not show an association between CS use and self-reported cancers. Considering the complex function of CS in the development and progression of malignant tumors, there is a need for further epidemiological studies evaluating CS effects on specific types of cancers and in different genetic backgrounds.
The vitamin D story – a 2023 update on fractures and falls
Authors: Boyanov Mihail Anguelov , Boyanova Mira Emilova
Published: March 2024

Vitamin D is more than just a vitamin – it is a real hormone with great importance for the bone-muscle unit and for many other tissues and biological processes in the human organism. This narrative review is focused on the available evidence for skeletal effects of vitamin D – possible reduction of falls and fractures in elderly people. We follow the natural path of the vitamin D story with the growing interest and hopes followed by some disappointment and then by mere realism. A number of meta-analyses are discussed in an attempt to better describe the available knowledge and the up-to-date consensus on vitamin D. In short, the existing evidence shows a modest effect of vitamin D supplements on fractures and falls, which may be comparable to that of some older antiresorptives. Current consensus statements identified the following subgroups that would benefit most from vitamin D supplements: the elderly, the institutionalized, those with vitamin D insufficiency/deficiency or secondary hyperparathyroidism, those at higher risk of fractures and falls (osteoporosis included). As fractures and falls seem to be dependent from the baseline levels of vitamin D, serum levels should be measured and supplementation should be tailored accordingly (800 – 2000 IU daily). A combination with daily calcium of 600 – 1200 mg is highly recommended to enhance the skeletal effects of vitamin D. The clinician should be aware of the low patients’ adherence with long-term supplements. In conclusion, vitamin D is a cheap and quite effective option to improve bone health with possible beneficial pleiotropic extra-skeletal effects.
COVID-19 and post-COVID-19 syndrome as an occupational disease
Authors: Doncheva - Dilova Jaklin Svetoslavova , Boyadzhieva Vladimira , Kuneva Tanja
Published: March 2024

An occupational disease is one that occurs exclusively or mainly under the influence of the harmful factors of the working environment and is included in the list of occupational diseases. Since the early stages of the pandemic, it has been proposed that COVID-19 be recognized as an occupational disease worldwide, but to date the situation varies from country to country. Even more serious turn out to be the complications of the disease (the so-called long COVID or post- COVID-19 syndrome (PCS). Patients may experience symptoms for up to 3 months or more after the onset of infection, which significantly reduces their ability to work and impairs their quality of life. To date, COVID-19 and long-term COVID-19 are not included in the lists of occupational diseases provided by international organizations (International Labor Organization, World Health Organization (WHO), European Union (EU)) and are not recognized as an occupational disease in Bulgaria and in most countries. This leads to a number of economic complications both for the patients (and their families), as well as for the employer and the state.
Results of radiowave surgery for benign eyelid tumors
Authors: Tsurova Leila M. , Malov Vladimir M. , Eroshevskaya Elena B. , Kirnosov Sergei V.
Published: March 2024

Aim – to evaluate and compare the effectiveness of the radiowave and traditional surgical treatment for the eyelid benign neoplasms.
Material and methods. The study involved 87 patients with various benign eyelids pathologies: chalazion, xanthelasma, keratopapillomas, hemangioma, atheroma keratosis pilaris. The patients were operated using either radiowave or traditional method. The diagnoses were confirmed by clinical, cytological and histological examination. In both groups of patients, we evaluated the treatment clinical effect, the wound surface healing time, and the cosmetic results for traditional surgery and radio wave treatment.
Results. The radiowave surgery, in contrast to the classical surgical method, demonstrated low traumatism, high cosmetic results, the healing occurred by primary intention in 100% of cases, no recurrence of the pathology was observed in all the cases.
Conclusion. The use of the radio wave surgical method demonstrated high efficiency, safety and a good cosmetic effect for treatment of benign eyelid neoplasms.
TINDAK PIDANA KORUPSI PADA PEKERJAAN SWAKELOLA
Authors: Hairwansyah Arif  , Ali Rizky  , Abdul Jabar Rahim
Published: March 2024

The purpose of this writing is to determine criminal acts of corruption in self-managed work, especially misuse of funds and manipulation of data, requiring a holistic approach. This research uses normative legal research methods. Normative research is research carried out by examining library materials. The data used in this research is secondary data which includes primary legal materials, namely statutory regulations. Then this research also uses secondary legal materials, namely legal materials that provide an explanation of primary legal materials. The research results show that in the face of criminal acts of corruption using data manipulation, steps that strengthen supervision and control over the reporting and project documentation process are very important. Providing an integrated and transparent information system will help minimize the risk of data manipulation by providing easier and more transparent access to project information for related parties. Apart from that, increasing the capacity and integrity of auditors and law enforcers in detecting and taking action against data manipulation practices will be key in tackling criminal acts of corruption.
Simulação numérica de um escoamento laminar incompressível em uma cavidade quadrada com tampa deslizante
Authors: Eisermann Jonatan Ismael
Published: March 2024

Neste trabalho é realizada a simulação numérica de um escoamento laminar incompressível no interior de uma cavidade quadrada com tampa deslizante. Para tanto, um modelo matemático bidimensional baseado na formulação de função corrente e vorticidade das equações de Navier-Stokes é deduzido e, em seguida, discretizado sobre uma malha computacional por meio do método das Diferenças Finitas. Para a resolução das equações discretizadas a partir das condições iniciais e de contorno do problema, um código computacional baseado em um processo iterativo semi-implícito é implementado no programa Matlab e disponibilizado no texto. Por meio desse código são feitas simulações para escoamentos incompressíveis, com número de Reynolds 100, 400 e 1000. A validação do código é feita por meio de comparações dos resultados numéricos obtidos com resultados de referência disponíveis na literatura. Análises decorrentes da simulação evidenciam que os escoamentos provocam a criação de uma grande zona de recirculação próxima ao centro da cavidade e duas menores nos cantos inferiores, cuja intensidade e tamanho dependem diretamente do número de Reynolds correspondente.
A Rancang Bangun Aplikasi Kas Rute12 Berbasis Web pada RT 012 RW004 Tanah Merdeka Jakarta Timur
Authors: Lestari Sri , Ramadhanty Irviani Liestian , Rachmawati Dea Noer
Published: March 2024

The monthly contribution payment system at RT 012 RW 004 Tanah Merdeka, East Jakarta, still relies on a manual process using a contribution book, making it prone to errors and discrepancies between the money collected and the recording report. To overcome this problem, a web-based application has been developed called RUTE12. This application aims to simplify the tasks of RT officers in recording and managing finances while increasing community involvement. With the introduction of digitalization, it is hoped that it can reduce calculation errors, align the amounts collected with reports, and increase service efficiency. RUTE12 also plays a role in increasing community participation in various RT/RW activities, providing positive benefits for improving the financial system at the RT level, and being an inspiration for similar developments in various regions.
Copper ion sorption ability of Alternaria alternata (Fr.) Keissl. and Fusarium oxysporum Schltdl. micromycetes
Authors: Volkova V. D. , Fedoseeva E. V. , Terekhova V. A.
Published: March 2024

Our analysis of metabolic and ecological features of fungi indicates a high bioremediation potential of fungal mycelium. The relevance of the problems solved in this work is conditioned by insufficiently studied mechanisms and conditions of micromycetal activity on heavy metal detoxication. The effect of copper on the development of two micromycete species, namely, melanised Alternaria alternata (Fr.) Keissl., 1912 and hyaline  Fusarium oxysporum Schltdl., 1824 cultivated on Czapek agar (with 2 or 3% sucrose) was compared;  the ability of mycelium to sorb copper cations when grown in a liquid culture with 0, 0.05, 0.1, 0.25, and 0.5 mg Cu2+ / L was evaluated. F. oxysporum had noticeable advantages in terms of  growth rate, tolerance, conidia production on medium with Cu2+. The effective concentration of copper (EC50), indicating the resistance of the fungus, increased for  F. oxysporum by 1.5 times with increasing sucrose content in the medium. The melanised culture of  A. alternata  was superior to  F. oxysporum in terms of sorption capacity. The percentage of Cu2+ extraction by fungal mycelium from the medium reached 40% in the case of  F. oxysporum and twice as much in the case of  A. alternata . The vast majority of copper was sorbed by the cell walls of hyphae and washed away by water. A small amount (a maximum of 0.16 µg Cu2+ /g dry mycelium of  A. alternate ) penetrated inside the mycelial cells. F. oxysporum, which had no intracellular protective melanins, accumulated Cu2+ (2 to 14 times) less. The results show that the mechanisms of resistance to Cu2+ and sorption are different in the studied species: in Fusarium they are  determined mainly by the barrier functions of hyphae cell walls, while in Alternaria melanin plays a significant role in protection against Cu toxic action.
Organization of forest geosystems in the Lower Amur region by a case study of the Komsomol’sky Nature Reserve
Authors: Van P. S.
Published: March 2024

This article is devoted to the structural-functional organization of the forest geosystems of Komsomol’sky Nature Reserve located in the south of the Lower Amur Region. It considers a landscape organization model built using the information theory methods. The model is represented as a block diagram consisting of digraphs, in  whose center there are landscape features. This model is a modified version of the previously published one for the studied territory. It is built on the basis of richer field data and with the addition of previously unused landscape features. Weak links of medium density predominate in the model, which may indicate the stability of the forest geosystems formed there. Our analysis shows that  the main factors determining the structure of Komsomol’sky Nature Reserve’s forest geosystems are altitudinal-exposure zonality, altitude, lithology of parent rocks, and soil  hydrothermal conditions. It is established that the stand of timber is directly related to the humus soil horizon lithomass: it decreases with an increase in  lithomass. Plants biomass is highly dependent on the steepness of slopes: it decreases with its increase. Separate relationships have been revealed for plant layers. The herbaceous layer height decreases with an increase in the altitude and lithom ass in the soil. The herbage floristic diversity differs greatly in land facie groups and has a maximum  in the ridge-top areas of the slopes and at the foot hill. The shrub layer height depends on soil moisture content, lithology of parent rocks and lithomass: it de-creases with increasing soil moisture content most of all and increases with increasing lithomass in the soil. A relationship has been found only with the lithomass in the soil for all tree layer features. A lithomass increase has a depressing effect on trees.
Spread of antibiotic-resistant actinobacteria in soils exposed to various types of anthropogenic impact
Authors: Shirokikh I. G. , Gembitskaya E. A. , Ashikhmina T. Ya.
Published: March 2024

The work investigated the spectra of resistance to a certain set of antibiotics and the frequency of occurrence of resistant strains among typical soil bacteria – actinomycetes. Equal samples of isolates from sod-podzolic soil were used,  selected at sites typifying various types of economic activity, namely: the production areas of a plywood factory and a chemical plant, the territory of a medical institution, and a solid waste collection and storage site. A total of 58 isolates with features characteristic of actinomycetes of the genus Streptomyces of the sections Cinereus, Helvolo-Flavus and Albus were obtained. It has been established that various types of economic activity lead to specific changes in the antibiotic resistance, which characterizes the soil of a natural grass-grass meadow (back-ground). Isolates with resistance to nalidixic  acid, amoxicillin and ceftriaxone were found in the sample taken on the territory of the chemical plant more often than in the soil of the background site. The proportion of strains resistant to nalidixic acid, lincomycin and azithromycin was significantly (P<0.05) higher in the soil samples  taken at the MSW collection site and the territory of the medical institution, while those resistant  to nalidixic acid and amoxicillin were in the sample from the plywood factory Groups of antibiotics (quinolones – nalidixic acid, and  β-lactams – amoxicillin and ceftriaxone) were identified, the resistance to which in streptomyces isolates from soils involved in economic activity  was significantly higher than in soil isolates from the background territory. The data obtained in  the work indicate the need to monitor the spread of antibiotic resistance in soils that are not only at risk of accumulation of residual concentrations of antibiotics, but also other  consequences of anthropogenic influences.
Kandungan Selulosa, Hemiselulosa dan Lignin Jerami Padi yang Difermentasi dengan Berbagai Probiotik
Authors: Bahri Syamsul
Published: March 2024

ABSTRAKIlham Karim, 2022. Kandungan Selulosa, Hemiselulosa dan Lignin Jerami Padi yang difermentasi dengan Berbagai Probiotik. Dibimbing oleh Syamsul Bahri dan Syahruddin.Pemanfaatan jerami padi merupakan solusi mengatasi kebutuhan pakan ternak ruminansia, namun terdapat kendala pemanfaatan jerami padi sebagai pakan tunggal ialah kandungan nutrisinya yang rendah, sehingga perlu pengelolaan sebelum digunakan sebagai pakan ternak yaitu dengan metode fermentasi. Penelitian ini bertujuan untuk mengetahui kandungan selulosa, hemiselulosa dan lignin jerami padi yang difermentasi dengan berbagai probiotik. Penelitian ini dilaksanakan dengan menggunakan Rancangan Acak Lengkap (RAL) dengan lima perlakuan dan tiga ulangan. Perlakuan terdiri dari: P0 = jerami padi 90%  + konsentrat 10%, P1 = jerami padi 90% + konsentrat 10% + probiotik bio EM+, P2 = jerami padi 90% + konsentrat 10% + probiotik starbio, P3 = jerami padi 90% + konsentrat 10% + probiotik winprob, P4 = jerami padi 90% + konsentrat 10% + Kombinasi probiotik (bio EM+, starbio, dan winprob). Parameter yang diukur adalah kandungan selulosa, hemiselulosa, dan lignin. Hasil penelitian menunjukkan bahwa perlakuan berpengaruh sangat nyata (P0,01) terhadap kandungan selulosa dan hemiselulosa tetapi tidak berpengaruh nyata (P0,05) terhadap kandungan lignin jerami padi. Disimpulkan bahwa perlakuan P1 (probiotik EM+) dapat menurunkan kandungan selulosa jerami padi. Sedangkan penambahan berbagai probiotik pada fermentasi jerami padi tidak dapat menurunkan kandungan hemiselulosa dan lignin secara signifikan.Kata Kunci: Fermentasi, Hemiselulosa, Jerami Padi, Lignin, Probiotik, Selulosa
Gambaran Studi Kasus; Analisis Keuntungan Usaha Ternak Babi di CV Anugerah
Authors: Oroh Franky N.S , Osak Richard E. M. F , Iroth Filandre Imanuel
Published: March 2024

Pig farm is a type of business that is mostly carried out in North Sulawesi Province because pigs have a large market potential in the North Sulawesi market because socially the majority of the population in several districts in North Sulawesi has likes to consume pork side dishes. The purpose of this study was to analyze the profitability of CV. Anugerah pig farm. This research was carried out at CV. Anugerah pig farm from August to September 2022. The research method used was a case study research method.  This study uses primary data and secondary data.  Primary data were obtained from direct interviews with farmers using a questionnaire. Secondary data obtained from related agencies, as well as related research results. The results of profit analysis with investment criteria using NPV, Net B/C and IRR criteria show that CV  Anugerah  pig  farm  in  Kanonang  III  Village received  a  revenue  of  Rp.  3,076,947,000  a  year  with  a  profit  of  Rp.564,671,420   a   year.   Financial   analysis   shows   an   NPV   value   of   Rp.976,112,575, the Net B/C value is 1.96 and the IRR value is 22.89 worthy of continuing. The results of the financial feasibility analysis of CV. Anugerah pig farm were declared feasible for development, based on the results of calculating a positive NPV value at an interest rate of 12%, a Net B/C value of greater than one and an IRR value of the prevailing interest rate (12%)
Pengaruh Kapasitas Kelembagaan Kelompok Tani Terhadap Kemandirian Petani di Kabupaten Gorontalo
Authors: Pateda Sry Yenni , Fathan Suparmin , Zakaria Fauzan
Published: March 2024

This research aims to analyze the level of farmers' institutional capacity and the influence of institutional capacity on the independence of beef cattle farmers. This research was conducted in Gorontalo Regency from February to November 2018. The research used a survey method with a quantitative approach. The sample was determined in stages (multi stages sampling), namely by selecting districts, sub-districts, villages and groups. The determination of farmers as respondents was carried out in an exhaustive manner. The number of samples used was six groups with 120 group members. Data sources include primary and secondary data. The analysis used is descriptive analysis and quantitative analysis using structural equation modeling (SEM) statistics from the Analysis of Moment Structures (Amos) program. The research results explain the level of institutional capacity is good (3.18) which consists of indicators of goal achievement (3.24), institutional function and role (3.15), institutional innovativeness (3.12) and group institutional sustainability (3.18). Institutional capacity influences the independence of beef cattle farmers by 79%.
Uncertainty Quantification in Fatigue Crack Growth Prognosis
Authors: Sankararaman Shankar , Ling You , Shantz Christopher , Mahadevan Sankaran
Published: March 2024

This paper presents a methodology to quantify the uncertainty in fatigue crack growth prognosis, applied to structures with complicated geometry and subjected to variable amplitude multi-axial loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. Crack growth under variable amplitude loading is modeled using a modified Paris law that includes retardation effects. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis. The effect of different types of uncertainty – physical variability, data uncertainty and modeling errors – on crack growth prediction is investigated. The various sources of uncertainty include, but not limited to, variability in loading conditions, material parameters, experimental data, model uncertainty, etc. Three different types of modeling errors – crack growth model error, discretization error and surrogate model error – are included in analysis. The different types of uncertainty are incorporated into the crack growth prediction methodology to predict the probability distribution of crack size as a function of number of load cycles. The proposed method is illustrated using an application problem, surface cracking in a cylindrical structure
Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals
Authors: Wu Fangji , Lee Jay
Published: March 2024

Gearbox is a very complex mechanical system that can generate vibrations from its various elements such as gears, shafts, and bearings. Transmission path effect, signal coupling, and noise contamination can further induce difficulties to the development of diagnostic system for a gearbox. This paper introduces a novel information reconstruction approach to clustering and diagnosis of gearbox signals in varying operating conditions. First, vibration signal is transformed from time domain to frequency domain with Fast Fourier Transform (FFT). Then, reconstruction filters are employed to sift the frequency components in FFT spectrum to retain the information of interest. Features are further extracted to calculate the coefficients of the reconstructed energy expression. Then, correlation analysis (CA) and distance measurement (DM) techniques are utilized to cluster signals under diverse shaft speeds and loads. Finally, energy coefficients are used as health indicators for the purpose of fault diagnosis of the rotating elements in the gearbox. The proposed method was used to solve the gearbox problem of the 2009 PHM Conference Data Analysis Competition and won with the best score in both professional and student categories.*
Model Adaptation for Prognostics in a Particle Filtering Framework
Authors: Saha Bhaskar , Goebel Kai
Published: March 2024

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.*
Applying the General Path Model to Estimation of Remaining Useful Life
Authors: Coble Jamie , Hines J. Wesley
Published: March 2024

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed effects-based, or Type III, prognostics. A unit-specific prognostic model, called the General Path Model, involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08.
A Multiple Model Prediction Algorithm for CNC MachineWear PHM
Authors: Chen Huimin
Published: March 2024

We present a multiple model approach for wear depth estimation of milling machine cutters using dynamometer, accelerometer, and acoustic emission data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The performance  evaluation procedure and the resulting scores from the submitted predictions are also discussed.
An Auto-Associative Residual Processing and K-means Clustering Approach for Anemometer Health Assessment
Authors: Siegel David , Lee Jay
Published: March 2024

This paper presents a health assessment methodology, as well as specific residual processing and figure of merit algorithms for anemometers in two different configurations. The methodology and algorithms are applied to data sets provided by the Prognostics and Health Management Society 2011 Data Challenge. The two configurations consist of the “paired” data set in which two anemometers are positioned at the same height, and the “shear” data set which includes an array of anemometers at different heights. Various wind speed statistics, wind direction, and ambienttemperature information are provided, in which the objective is to classify the anemometer health status during a set of samples from a 5 day period. The proposed health assessment methodology consists of a set of data processing steps that include: data filtering and pre-processing, a residual or difference calculation, and a k-means clustering based figure of merit calculation. The residual processing for the paired data set was performed using a straightforward difference calculation, while the shear data set utilized an additional set of algorithm processing steps to calculate a weighted residual value for each anemometer. The residual processing algorithm for the shear data set used a set of auto-associative neural network models to learn the underlying correlation relationship between the anemometer sensors and to calculate a weighted residual value for each of the anemometer wind speed measurements. A figure of merit value based on the mean value of the smaller of the two clusters for the wind speed residual is used to determine the health status of each anemometer. Overall, the proposed methodology and algorithms show promise, in that the results from this approach resulted in the top score for the PHM 2011 Data Challenge Competition. Using different clustering algorithms or density estimation methods for the figure of merit calculation is being considered for future work.
Automated Fault Detection of Wind Turbine Gearbox using Data- Driven Approach
Authors: Praveen Hemanth Mithun , Anilkumar Tejas , Geetha Rajasekharan Sabareesh
Published: March 2024

Wind turbine manufacturers have adopted condition monitoring systems to monitor and report a turbine is health and operating parameters to ensure that the system operates within its design specifications. While the present systems use specialized condition monitoring hardware to detectabnormal acoustic or vibration signals, it is not capable of pinpointing the exact location of the fault apart from isolating the system from which the signal originated. This drawback can be attributed to the requirement of powerful signal processors in order to decode the signal and efforts to train a system to identify the signal emitted by a faulty component. In the light of recent advancement of datadriven approaches and signal processing, these drawbacks can be overcome with increased computation power and sophisticated algorithms that foray into every integratedsystem. This paper reports such an investigation conducted on a miniature wind turbine planetary gearbox subjected to multi-component failures. The vibration signals were acquired using two accelerometers placed inside the gearbox. The speed of the gearbox was varied according to asimulated wind flow pattern. The primary goal of the study was to investigate the practicality of implementing datadriven approaches to categorise multi-component faults from a composite non-stationary signal. Short time Fourier transforms ( STFT ) coefficients were used as attributes by aset of data-driven algorithms to build machine learning models. Each model built was tested with a randomised set of instances which was reserved from the main dataset and tested multiple times by means of cross validation. The novelty in the paper entails a methodology which has been devised to classify faults using a randomised vibration dataset with little human intervention by means of machine learning algorithms. The authors propose that this methodology can also be used for real-time fault detection and classification for various machinery and components.
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information
Authors: Michau Gabriel , Fink Olga
Published: March 2024

The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets aredesigned and maintained to be highly reliable and faults are rare. Fault detection can thus not be solved with supervised learning. Second, complex industrial systems usually have long lifetime during which they face very different operating conditions. In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model. In this paper, we propose a methodology to monitor the systems in their early life. To do so, we enhance the training dataset with other units from a fleet, for which longer observations are available. Since each unit has its own specificity, we propose to extract features made independent of their origin by three unsupervised feature alignment techniques. First, using a variational encoder, we impose a shared probabilistic encoder/decoder for both units. Second, we introduce a new loss designed to conserve inter-point spacial relationships between the input and the learned features. Last, wepropose to train in an adversarial manner a discriminator on the origin of the features. Once aligned, the features are fed to a one-class classifier to monitor the health of the system. By exploring the different combinations of the proposed alignment strategies, and by testing them on a real case study, a fleet composed of 112 power plants operated in different geographical locations and under very different operating regimes, we demonstrate that this alignment is necessary and beneficial.
Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data
Authors: Hemmer Martin , Klausen Andreas , Khang Huynh van , Robbersmyr Kjell G. , Waag Tor I.
Published: March 2024

The industry is moving towards maintenance strategies that consider component health, which require extensive collection and analysis of data. Condition monitoring methods that require manual feature extraction and analysis, become infeasible on an industrial scale. Machine learning algorithms can be used to automatically detect and classify faults, however, obtaining sufficient data for training is required for deep learning and other data-driven classification approaches. Data from healthy machine operation is generally available in abundance, while data from representative fault- and operating conditions is limited. This limits both development and deployment of deep learning-based CM systems on an industrial scale. This paper addresses both the challenges of automated analysis and lack of training data. A deep learning classifier architecture utilizing 1-dimensional dilated convolutions is proposed. Dilation of the convolution kernel allows for analysis of raw vibration signals while simultaneously maintaining the receptive field of the classifier enough to capture temporal patterns. The proposed method performs classification in time domain on signal segments of 1 second or shorter. With knowledge of the bearing specification, artificial vibration signals with similar characteristics as an actual bearing fault can be created. In this work, generated fault signals are combined with healthy operational data to obtaintraining data for a deep classifier. Parameters of the vibration model is chosen as distributions rather than fixed values. By using a range parameters in the vibration model, the classifier learns to recognize temporal features from the training data that generalize to unseen data. The effectiveness of the proposed method is demonstrated by training classifiers on generated data and testing on real signals from faulty bearingsat both low and high speed. One dataset containing seeded faults and three run-to-failure tests are used for the demonstration.
Deep Detector Health Management under Adversarial Campaigns
Authors: Echauz Javier , Kenemer Keith , Hussein Sarfaraz , Dhaliwal Jay , Shintre Saurabh , Grzonkowski Slawomir , Gardner Andrew
Published: March 2024

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce turbidity detection as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROCtheoreticdesign guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model’s degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model’s own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.
Trends and Recent Advances of Industrial Big Data Analytics and Cyber Physical Systems for PHM Applications
Authors: Lee Jay
Published: March 2024

In today’s competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity and business innovation. Many product and manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. U.S. has been driving the Cyber Physical Systems (CPS), Industrial Internet, and Advanced Manufacturing Partnership (AMP) Program to advance future manufacturing. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical Production System (CPPS). It is clear that as more predictive analytics software and embedded IoT are integrated in industrial products, predictive technologies can further intertwine smart IoT to predict product performance autonomously and further optimize the smart service systems. The presentation will address the trends of predictive big data analytics and CPS for future industrial PHM application. First, predictive analytics and Cyber-Physical System (CPS) enabled industrial systems will be introduced. Second, advanced predictive analytics technologies for self-aware industrial systems with case studies will be presented. Finally, business innovation based on industrial big data will be introduced using case studies.
Failure Prognosis of Coal Pulverizer Gearbox Using Autocorrelation- Wavelet Packet Decomposition Method
Authors: Lee Guesuk , Kim Sooho , Song Joowhan , Kim Taejin
Published: March 2024

For Asia pacific conference of the prognostics and health management society 2017, the committee of the prognostics and health management data challenge has organized a case study problem for exploring various prognosis methods. The challenge aims at predicting the date of upcoming failure along with a failure component of coal pulverizer gearboxes. The committee provided raw signal data from the real-time operating system of coal pulverizer gearboxes, but with several issues including inconsistent loading condition or mixed signal data from different sources. In this paper, autocorrelation method and wavelet packet decomposition method are integrated to devise health indices which state degrading health conditions of the gearbox. Using the health indices, a prediction model is constructed to predict the failure date and the failure components. This paper includes the results of prediction.
A Prognostics Approach for Gearbox based on Spectrogram and Deep Learning
Authors: Song Dengwei , Su Hua , Ma Jian , Chao Likun , Ding Yu , Lu Chen
Published: March 2024

This paper presents a method and solution for Asia Pacific Conference of the Prognostics and Health Management 2017 Data Challenge. Considering the complex of gearbox signal, a novel approach utilizing spectrogram and deep learning is proposed. Spectrogram can transform the raw signal into time-frequency space that is an effective method to process vibration signal of gearbox. Deep learning is a useful method that has the ability of automatically feature learning. The spectrogram is obtained by using short-time Fourier transform (STFT). Then, the way of the stacked auto-encoders (SAE), is trained to auto-extract feature and realize the prediction. For diagnosis, the cepstrum algorithm is performed to simplify the spectrum and find the most severely-degraded component. This solution earned the 3rd highest score of the results from all teams in the competition, which demonstrate the effective of proposed method.