bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediksi Churn Pelanggan Multinational Bank Menggunakan Algoritma Machine Learning
Authors: Hidayat Rifki , Syawaludin M Ainur , Nurmalitasari Nurmalitasari
Published: December 2024

Dalam menghadapi persaingan pasar yang ketat, prediksi churn pelanggan menjadi penting bagi perusahaan perbankan untuk mempertahankan loyalitas pelanggan. Penelitian ini mengaplikasikan algoritma machine learning meliputi Naive Bayes, Decision Tree, dan Random Forest untuk prediksi churn pelanggan pada ABC Multinational Bank. Data yang digunakan adalah dataset publik yang diambil dari Kaggle yang mencakup informasi 10.000 nasabah bank. Proses penelitian melibatkan beberapa tahapan yaitu pengumpulan data, preprocessing, pemodelan, prediksi, dan evaluasi. Hasil evaluasi memperlihatkan bahwa model Random Forest memberikan performa terbaik dengan akurasi 85% dan AUC 0.83. Naive Bayes dan Decision Tree masing-masing memiliki akurasi 82% dan 77%. Kesimpulan menunjukkan bahwa Random Forest lebih unggul dalam memprediksi churn pelanggan dibandingkan dua algoritma lainnya, sehingga dapat digunakan untuk strategi pemasaran yang lebih efektif dalam industri perbankan.
Paraneoplastic Cerebellar Degeneration with Anti-Yo Antibody in a Patient with Fallopian Tube Adenocarcinoma: A Case Report and Literature Review
Authors: Sae-Chan Jirayu , Yamuthai Supakorn , Amornpojnimman Thanyalak , Koonalinthip Prut
Published: December 2024

Paraneoplastic cerebellar degeneration (PCD) typically presents via acute to subacute cerebellar ataxia, dysarthria, and ocular dysmetria, resulting from tumor-induced autoimmunity against the cerebellum. In most cases, symptoms of PCD show months before a diagnosis of cancer. Therefore, it is important for clinicians to investigate the primary tumor in PCD cases in order to treat both conditions concurrently. Herein, we report a case of PCD associated with an anti-Yo antibody, leading to a diagnosis of left fallopian tube adenocarcinoma.
A new species of the genus Scolopostethus (Heteroptera: Rhyparochromidae) from Middle Asia
Authors: Neimorovets V.V.
Published: December 2024

A new species, Scolopostethus gissarensis sp. nov. (Heteroptera: Rhyparochromidae: Rhyparochrominae: Drymini), is described from Middle Asia (Kazakhstan, Uzbekistan, Tajikistan, and Kyrgyzstan). The new species is placed in the genus Scolopostethus Fieber, 1860, based on a combination of morphological characters. The structural characters of the foreleg and the paramere distinctly differentiate it from other Palaearctic species within this genus. Scolopostethus gissarensis sp. nov. is primarily compared to S. decoratus (Hahn, 1833). Colour photographs depicting the habitus and structural details of the body are provided for both S. gissarensis sp. nov. and S. decoratus (Hahn, 1833).
A new species and new faunistic records of scale insects (Homoptera: Coccinea) from Indochina
Authors: Gavrilov-Zimin I.A. , Novgorodova T.A.
Published: December 2024

A new species, Antonina kazasae sp. nov. (Pseudococcidae), is described and illustrated based on specimens collected in southern Vietnam (Cat Tien National Park). Additionally, fourteen other species from the families Coccidae, Margarodidae, and Pseudococcidae are recorded for the first time from various countries of Indochina (Myanmar, Thailand, Laos, and Vietnam).
La innovación estrategia de competitividad en micronegocios
Authors: Pereira Bolaños Cliden Amanda
Published: December 2024

Introducción: las microempresas son las unidades de negocio de mayor participación en Colombia, en esta investigación se muestra un avance del proyecto de investigación que busca a partir de conocer los principales obstáculos para la innovación empresarial, generar estrategias de fortalecimiento empresarial y competitividad para micronegocios en Bogotá. Metodología: la investigación de tipo descriptiva con un enfoque cualitativo, analiza el grado de innovación de las  empresas colombianas para acceder al mercado y ser competitivas. La investigación se plantea en tres fases: la primera, análisis de las dificultades en la innovación, la segunda, relacionada con la caracterización económica, empresarial y laboral de micronegocios en la ciudad y, finalmente, formulación de estrategias que promuevan el fortalecimiento y crecimiento en el tiempo de micronegocios en Bogotá. Discusión: La articulación de todos los actores de la política de competitividad e innovación nacional y distrital se pueden generar estrategias de fortalecimiento empresarial local y nacional. Conclusión: El avance de la investigación en su primera etapa de análisis muestra la situación actual de las mipymes en innovación, tipos innovación, dificultades de la innovación en el sector de industria manufacturera, comercio y servicios en Colombia durante los últimos cinco años.
Innovando en la Galaxia de la Inteligencia Artificial
Authors: Santamarina Sancho María , Parra Meroño Concepción , Torres Romay Emma
Published: December 2024

Innovando en la Galaxia de la Inteligencia Artificial explora cómo la IA está transformando diversos sectores, con un enfoque particular en la educación. Desde la optimización de procesos y la personalización de experiencias hasta la creación de contenidos y la mejora de la interacción con los usuarios, la IA está abriendo nuevas fronteras tecnológicas. En el ámbito educativo, la integración de la IA está redefiniendo la enseñanza y el aprendizaje, especialmente a través de herramientas de aprendizaje adaptativo y aplicaciones generativas. Este monográfico reúne investigaciones que exploran el papel de la IA en la educación superior, el pensamiento creativo y la creación de contenidos personalizados. Un área clave es cómo la IA puede asistir a los docentes en la planificación de lecciones, la creación de contenidos y la evaluación de estudiantes. También se examinan las aplicaciones prácticas de la IA generativa en la producción de materiales educativos, permitiendo más interacción entre maestros y estudiantes.
The New Frontier of Warfare: Evaluating the Role of International Humanitarian Law in Cyber-Crimes
Authors: Khan Saima Razzaq , - Rubab , Shah Azmat Ali
Published: December 2024

People & governments have become dependent on computer networks in the last few decades. Both the military & people use these networks to do things like shop online and control radars. A country's computer network system is very important from a military point of view in war. International Humanitarian Law is hard to follow when computers are used to help the military win a war. Schmidt (2017) there are at least two ways to look at whether or not IHL applies to cyber war. These ways of doing things are "permissive" and "restrictive." After looking at both of these ways of thinking, this article suggests that the most important thing to consider when applying the IHL to cyber-operation during a war is how it affects civilians.
Characteristics and Implications of Older Adult Education in Asian Countries: Focusing on Japan, Taiwan, and Singapore
Authors:   Korean Society of Educational Gerontology, Jun Sookoung , Kim Young Sek
Published: December 2024

This article analyzes and compares the laws, policies, and practices related to older adult education in three East Asian countries—Japan, Taiwan, and Singapore—whose populations aged 65 and over are higher or similar to that of South Korea, in order to identify the future direction for older adult education in Korean society. Primary sources, such as national legal provisions, government-issued policy documents, and newspaper articles, as well as previous studies on lifelong learning and elder education in these countries, were examined. The analysis reveals that South Korea needs to connect older adult education to older adult social contributions, establish a government agency dedicated to promoting older adult education policies comprehensively, expand older adult education programs utilizing higher education institutions, and develop more educational programs for understanding older adult and inter-generational activities.
A Study of Factors Associated with High-Risk Drinking among the Older Adults in the Community
Authors:   Korean Society of Educational Gerontology, Han HyeRi , Rhee YongJoo
Published: December 2024

This study aimed to identify high-risk drinking behaviors among community-dwelling older adults aged 65 and over and to explore the demographic and health-related factors influencing these behaviors. The analysis utilized data from the Korean Longitudinal Study of Ageing (KLoSA), specifically the 8th wave (2020), focusing on 4,405 community-dwelling older adults aged 65 and above. To determine factors influencing high-risk drinking, a binomial logistic regression analysis was conducted. The results showed that men were significantly more likely than women to engage in high-risk drinking, and that those aged 65-75 were more likely to drink at high-risk levels than those over 75. Additionally, the absence of a spouse and lower frequency of social contact were significantly associated with high-risk drinking. Among health-related factors, smoking was associated with a notably higher rate of high-risk drinking. Based on these findings, practical intervention strategies for preventing and mitigating high-risk drinking among older adults are suggested.
Development History of Elderly Education in Europe over 75 Years and Implications
Authors:   Korean Society of Educational Gerontology, Park Seong Hie
Published: December 2024

The purpose of this study is to analyze the development of elderly education in Europe, primarily focusing on Germany, over different historical periods, and to explore the direction of elderly education in South Korea. The research question is to examine the theoretical, conceptual, and practical aspects of 75 years of European elderly education from the 1950s to 2024, and to propose directions for the development of elderly education in South Korea. The research method involved analyzing prior literature from sources such as UNESCO, the European Community, and the International Conference on Aging.
The findings of the study show that, during the early stage, elderly people, who were stigmatized as socially disadvantaged, began to be recognized as subjects of education, and the concept of gerontology emerged. In the transitional period, the paradigm of elderly education shifted towards “active aging.” During the period of expansion, the concept of lifelong learning for the elderly was introduced, and the right to education for older adults began to be recognized and guaranteed. Since 2000, the paradigm of “healthy aging” has emerged, focusing on enhancing the quality of life for elderly people and building a new phase of life in old age, in line with the realization of a sustainable society.
Based on these results, the study proposes the following implications for elderly education in South Korea. First, from a macro perspective, elderly education should be implemented with public accountability, in alignment with the goals stated in various laws and policies. From a micro perspective, there is a need for educational programs that raise awareness among older adults, helping them to create a new vision of aging and navigate the new “100-year life” era.
Colecistectomía en embarazadas con patología biliar complicada en el Hospital de Clínicas
Authors: Zeoli Mariana , Pontillo Marcelo , Guillén Alison , Tarigo Nicolás , Delgado Juan , Andreoli Gustavo , Valiñas Roberto
Published: December 2024

La incidencia de patología biliar es alta en nuestro país, en la gestación es favorecida por factores hormonales y mecánicos. Siendo la patología biliar la segunda causa de cuadro agudo de abdomen no obstétrico. El manejo de las complicaciones biliares, son de manejo diverso en nuestro hospital, sobretodo con lo referente en indicar una cirugía durante la gestación.El objetivo fue comparar la oportunidad de la colecistectomía en gestantes con patología biliar complicada en nuestro centro versus la evidencia a nivel internacional. Se realizó un estudio observacional, retrospectivo, transversal y descriptivo, en el período de 1º de enero de 2017 a 31 de diciembre de 2021, en el Hospital de Clínicas Dr.Manuel Quintela, Montevideo Uruguay.Se incluyeron 57 pacientes. Media de edad 26 años.El 81% se manifestó en el segundo y tercer trimestre.Los diagnósticos fueron: colecistitis agudas (CA) en un 28%; colangitis agudas 45.6%,pancreatitis agudas (PA) 17.5% y litiasis vesicular sintomática en 5.3%. El 47 % de laspacientes incluidas en el estudio no fueron colecistectomizadas al momento del análisis de los datos.Sólo el 30 % de gestantes fueron operadas.
Conclusiones: Sólo el tercio de las gestantes son colecistectomizadas en el Hospital deClínicas. Los trabajos coinciden en que la colecistectomía en la embarazada es segura entodos los trimestres, el tratamiento será ajustado a cada caso según experiencia del equipo y recursos del medio.
The Limits of Applicability of the Gutenberg–Richter Law in the Problems of Seismic Hazard and Risk Assessment
Authors: Krushelnitskii K. V. , Shebalin P. N. , Vorobieva I. A. , Selyutskaya O. V. , Antipova A. O.
Published: December 2024

The Gutenberg–Richter law establishes a log-linear relationship between the number of earthquakes that have occurred within some spatiotemporal volume and their magnitude. This similarity property presumably reflects fractal structure of the fault system in which earthquake sources are formed. The Gutenberg–Richter law plays a key role in the problems of seismic hazard and risk assessment. Using the Gutenberg–Richter relationship, we can estimate the average recurrence period of strong earthquakes from the recurrence rate of weaker earthquakes. Since the strongest earthquakes occur infrequently, with intervals of a few hundred years or more, it is not possible to directly assess their recurrence. From indirect geologic and paleoseismic estimates it often seems that strong earthquakes on individual faults occur more frequently than expected in accordance with the Gutenberg–Richter law. Such estimates underlie the hypothesis of the so called characteristic earthquakes. This hypothesis is in many cases additionally supported by the form of the magnitude–frequency distributions for individual faults, constructed from the data of modern earthquake catalogs. At the same time, an important factor affecting the form of the magnitude–frequency distribution is the choice of the spatial domain in which the distribution is constructed. This paper investigates the influence of this factor and determines the conditions under which the Gutenberg–Richter law is applicable for estimating the recurrence of strong earthquakes.
Quantiles of Мmax and Other Characteristics of the Seismic Field Used in the Compilation of General Seismic Zoning (GSZ) Maps
Authors: Pisarenko V. F.
Published: December 2024

A new probabilistic approach to the problem of estimating the regional maximum possible magnitude and some parameters of seismic impact is proposed. The methodology of its practical application is described, which is based on considering the maximum magnitude in the future time interval T as a random quantity and using its quantile with a given level of confidence as the regional maximum magnitude.
The September 21, 2020 (Mw = 5.6) Bystraya Earthquake at the South-Western Flank of the Baikal Rift Zone: a Milestone in Macroseismology of Eastern Siberia
Authors: Radziminovich Y. B. , Novopashina A. V. , Lukhneva O. F. , Gileva N. A. , Kuz’mina E. A.
Published: December 2024

The article presents analysis of macroseismic data on the September 21, 2020 (Mw = 5.6) Bystraya earthquake, which occurred in the eastern part of the Tunka basins system on the southwestern flank of the Baikal rift zone. Macroseismic data were collected mainly through an Internet questionnaire posted on the website of the Baikal Branch of the Geophysical Survey, Russian Academy Sciences. A total of 3013 eyewitness responses were collected, which is currently an unprecedented number in the entire history of macroseismic observations in the Baikal region. In total, we collected data for 263 Intensity Data Points. The maximal shaking intensity (VI–VII MSK-64) was observed in the Bystraya village and the Kultuk settlement. The shaking intensity V MSK-64 was noted at a distance of up to ∼180 km; intensity IV MSK-64 was recorded at a distance of up to ∼550 km. Analysis of data on the Bystraya earthquake revealed significantly lower attenuation compared to that expected from the regional macroseismic equation. Due to the large volume of macroseismic data collected, as well as the high efficiency of the data collection method used, the Bystraya earthquake can be considered an important milestone in macroseismic research in East Siberia.
The Vendian and Early Cambrian of the Southwest, West and Northwest of the Eastern European Platform: categories of rivers that formed sedimentary
Authors: Maslov A. V. , Podkovyrov V. N.
Published: December 2024

Based on data on the geochemical characteristics ((La/Yb)N, Eu/Eu* and content of Th) of clay rocks of Podolian Transnistria, South-Eastern Poland, Belarus and Volyn, Lithuania, the vicinity of St. Petersburg, South-Eastern White Sea region and Arctic Norway, categories of rivers that transported fine-grained aluminosiliconclastics to sinks that existed during the Vendian and Early Cambrian were reconstructed in the west of the East European Platform. The distribution of data points of clay rocks of the Volynian time on the (La/Yb)N–Eu/Eu* diagram with the fields of the composition of pelitic fraction of bottom sediments in the estuary of modern rivers of various categories allows us to assume that for Lithuania and Podolian Transnistria the feeding provinces were, among other things, paleowatersheds composed of volcanic formations. For Eastern Belarus and the South-Eastern White Sea region, the influence of erosion products supplied by rivers flowing through igneous/metamorphic terranes (crystalline basement) is noticeable. A significant part of the clastic material was carried by rivers that drained sedimentary formations, as well as large rivers, i.e. rivers whose length exceeded 1000 km and whose drainage area was more than 100,000 km2. The existence of the latter is confirmed by the presence of detrital zircon in the rocks of the Zhukov Formation of South-Eastern Poland, borrowed possibly from the rocks of Fennoscandia. In the Redkinian time, along with large rivers (rivers category 1) and rivers fed by the products of erosion of sedimentary formations (rivers category 2), transport of fine-grained clastics was also carried out by rivers that drained rocks of the crystalline basement ((rivers category 3; this is typical of Belarus and Volyn, the South-Eastern White Sea and Arctic Norway) and by rivers flowing through areas of distribution of volcanic associations (rivers category 4). The distribution of data points of the clay rocks of the Kotlin stage on the (La/Yb)N–Eu/Eu* diagram suggests that the main agents of transport of fine-grained aluminosiliconclastics to sinks at that time were large rivers and rivers fed by fine particulate matter due to the erosion of predominantly sedimentary rocks. The Early Cambrian paleowatersheds were apparently composed not only of crystalline rocks, but also of sedimentary formations. All of the above is in fairly good agreement with the previously established fact of a gradual increase in the contribution of erosion products of sedimentary rocks to the formation of Vendian–Early Cambrian sedimentary sequences from Podolian Transnistria to Arctic Norway.
PENGUJIAN ALGORITMA YOLO UNTUK DETEKSI CACAT PADA PRODUK HASIL ALUMUNIUM CASTING PADA INDUSTRI OTOMOTIF
Authors: Lilansa Noval , Pratama Rizqi Aji , Tanadi Richard , Natasasmita Renold Nindi Kara
Published: December 2024

This research addresses the challenges faced by one of the automotive parts manufacturing plants in Indonesia, in detecting leaks in car components manufactured through aluminum die casting. Existing manual monitoring methods are time-consuming, prone to human error, and pose risks to product quality and operational safety. To overcome these challenges, this research proposes the application of machine learning, specifically computer vision techniques, such as Object Detection to identify and localize gas bubbles using a Leak Tester Machine equipped with a camera sensor equipped with the YOLO algorithm to improve the efficiency of the detection process. As technology develops, there is a shift from manual approaches to more efficient automated systems, utilizing computer vision algorithms, hardware such as Nvidia Jetson and Digital Camera. The YOLO algorithm shows good accuracy in overcoming defect detection in aluminum casting products, achieving a precision of 0.94, recall of 0.82, and mAP@0.5 of 0.90. The performance of the YOLO algorithm in detecting gas bubbles is 100 - 120 ms per frame from thestreaming camera so that the fps performance is 10 fps (frames per second).
PENERAPAN TEKNOLOGI MESIN PENCETAK BAKSO BERBASIS PNEUMATIK PADA UMKM
Authors: Suwandi Dedi , Sifa Agus , Supriatun Evi
Published: December 2024

Bakso atau Baso merupakan salah satu makanan terfavorit masyarakat Indonesia. Penamaan bakso tergantung dari tempat asal pembuatannya, daging yang digunakan, cara penyajian, cara pembuatan, ukuran dan bentuknya sehingga penamaanya sangat bervariasi. Bakso pada umumnya berbentuk bulat dengan diameter rata-rata 15-35 mm. Selama ini proses pembuatan bakso dimasyarakat masih menggunakan metode manual, caranya dengan membentuk adonan bakso menggunakan tangan dan sendok. Adonan digenggam dengan tangan kemudian diremas sehingga keluar diantara lubang jari telunjuk dan ibu jari, adonan yang keluar berbentuk bulat kemudian diambil menggunakan sendok dan dimasukan kedalam wadah berisi air panas. Proses pencetakan bakso manual ini membutuhkan keahlian, ketelatenan dan memiliki bahaya terkena air panas sehingga tidak semua orang bisa mengerjakannya. Dengan peluang tersebut dibuat sebuah mesin pencetak bakso dengan sistem pneumatic. Mesin ini menggunakan sumber tenaga dari kompresor minimal 10 bar yang menggerakan dua sistem pneumatic. Sistem pneumatik pertama menggerakan piston cylinder pneumatic TBC 40-400 yang menekan adonan kebawah menuju cetakan. Sistem pneumatic kedua menggerakan dua piston pneumatic cylinder 25X300. Kombinasi kedua sistem pneumatic tersebut mampu menghasilkan 60 bakso permenit dengan diameter 30 mm.
PENERAPAN TEKNOLOGI MESIN PENGAYAKAN TIPE-ROTARY UNTUK PEMISAHAN DEDAK HALUS, DEDAK KASAR, DAN MENIR PADA INDUSTRI UMKM PENGOLAHAN PAKAN TERNAK
Authors: B Badruzzaman , M Azwar M Azwar , Aziz Aziz , PB Pandu
Published: December 2024

UMKM yang bergerak di bidang pengolahan pakan ternak di daerah Indramayu masih menggunakan proses pengayakan dedak menggunakan 2 mesin yaitu mesin pengayakan dan mesin pengipasan. Proses ini berfungsi memisahkan bahan baku bekatul menjadi dedak halus, dedak kasar, dan menir. Hal tersebut perlu solusi mesin yang menyatukan 2 mesin tersebut. Tujuan dari pembuatan mesin ini yakni dapat menghasilkan mesin yang lebih efisien dan efektif agar kualitas produk akhir meningkat dan waktu produksi yang lebih singkat. Metode pelaksanan dimulai dari identifikasi permasalahan, studi literatur, observasi, desain, simulasi, persiapan, pembuatan, perakitan mesin, uji coba mesin, penerapan pada UMKM, serta evaluasi kegiatan dan sosialisasi/pelatihan. Hasil yang didapat yakni mesin ini memiliki dimensi rangka panjang 2100 mm × lebar 790 mm × 1250 mm dengan material adalah besi siku ASTM A36, motor listrik 1 phase 1 HP, putaran motor 2800 rpm dan ditransmisikan oleh v-belt ke gearbox dengan rasio 1:10 menghasilkan putaran 280 rpm. Proses pembuatan produk telah berhasil dilakukan dengan melalui 2 uji mesin yakni uji fungsi yang menyatakan semua komponen dapat berfungsi dengan baik, sedangkan uji performa menyatakan pemisahan dedak kasar yang dihasilkan 54 %, dedak halus 35,4 %, dan menir 10,6 %. Pemisahan dedak telah berhasil dilakukan dengan maksimal, dan proses pemisahan dedak 5 kg membutuhkan waktu 3,09 menit, kapasitas mesin pengayak adalah 97 kg/jam. Akhirnya, mesin pengayakan tipe-rotary ini diharapkan dapat meningkatkan produktivitas dan kualitas produktivitas industri pengolahan pakan ternak di Kabupaten Indramayu.
АВТОМАТИЗАЦІЯ КОНТРОЛЮ ЯКОСТІ НАНЕСЕННЯ ПОЛІМЕРНИХ ПОКРИТТІВ НА МАТЕРІАЛАХ ЛЕГКОЇ ПРОМИСЛОВОСТІ
Authors: ГОРЯЩЕНКО СЕРГІЙ
Published: December 2024

The paper considers the problems of automatic application quality control of polymer coatings on materials of light industry. The methods that allow identification, segmentation and analysis of areas of polymer application, as well as to determine the texture and orientation of textile material fibers are considered. Recognizing a polymer coating on materials is a challenging task due to variations in textile texture, fiber orientation, and characteristics of the coating itself (such as transparency or thickness). We use various image processing and machine learning algorithms for efficient automated inspection. Image preprocessing includes brightness normalization, noise filtering, and grayscale conversion to facilitate further analysis. Converting to shades of gray simplifies image processing, since the program will work with a single-channel image. The use of filters such as Gaussian Blur helps to reduce the effect of small obstacles and defects on the textile base. The program for image recognition of polymer coatings is based on the use of OpenCV, Tkinter and NumPy libraries. It includes image loading, image preprocessing, segmentation, and coverage area analysis. The program allows interactive use through a graphical interface and provides visualization of results in the form of color masks. The Polymer Coating Analyzer developed in Python, using modern image processing techniques, is an effective tool for automated coating analysis. The integration of such systems into production lines allows to reduce the number of defects, improve product quality and optimize control processes. Recognition of images with different coverage quality was carried out. The successful integration of such systems into production allows to increase the efficiency of quality control, reduce the number of defects and optimize the coating process.
ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПРОГНОЗУВАННЯ СПОЖИВАННЯ ЕЛЕКТРОЕНЕРГІЇ
Authors: ГЕНТОШ ЛЕСЯ , ЛЕВКОВИЧ РОМАН
Published: December 2024

Predicting energy consumption is critical for efficient energy management and cost reduction. While recurrent neural networks (RNNs) have traditionally been used for time series processing due to their ability to model time dependencies, convolutional neural networks (CNN) can offer significant performance advantages due to parallel data processing and the ability to detect local patterns in sequences. In this paper, we analyze the performance of these two types of neural networks, evaluating their efficiency and accuracy in the context of electricity consumption forecasting using ANNs, temporal convolutional networks (TCN), and long-term short-term memory (LTSM). As a result, it was found that convolutional neural networks are 30% more accurate and 25% faster.
АСПЕКТИ ДЕФОРМАЦІЙНОЇ СТІЙКОСТІ КОМПОЗИТІВНА ОСНОВІ ВУЛКАНІЧНИХ ПОРІД
Authors: ШНИРУК ОЛЕГ , МЕЛЬНИК ЛЮБОВ , ЧЕРНЯК ЛЕВ
Published: December 2024

Наведено результати досліджень по отриманню композитів на основі гранично опіснених мас. Об’єктами досліджень були обрані композиції різновидів вулканічних порід, що виконували функцію армуючих, а рідке скло – функцію зв’язуючого – матриці. Зразки композитів виготовляли напівсухим пресуванням із наступним структурним зміцненням – випалом при тривалому та швидкісних режимах. Методика роботи включала комплекс хімічного, ретгенофазового аналізів силікатних матеріалів і стандартизованих тестувань кераміки та композитів. Показано особливості складу відсівів видобутку природного цеоліту та андезиту та їх характеристик при випалі. Встановлено ефективність використання композиції різновидів вулканічних порід для регулювання фізико-механічних показників, в тому числі мінімізації усадки як фактору деформаційної стійкості виробів.
 
The uneven flow of deformation processes in the volume of products leads to distortion defects, technological losses and a decrease in quality. This especially applies to the manufacture of complex-shaped and flat products, the geometric parameters of which are characterized by a significant preponderance of the surface area over the thickness. A promising direction for minimizing shrinkage and the degree of deformation is the use of composites based on finitely described masses, which became the subject of research in this paper. Compositions of various types of volcanic rocks were chosen as objects of research, which performed the function of reinforcing, and liquid glass - the function of binding matrix. Samples of composites were made from mixtures having the same type and quantitative content of binder and the same granulometric composition of volcanic rocks (wt. %): fraction 3÷2 mm – 10; 2÷1 mm – 10; 1÷0.5 mm – 25, 0.5÷0.2 mm – 25, < 0.2 mm – 20. Composite samples were produced by semi-dry pressing followed by structural strengthening - firing in long-term and high-speed modes. The work methodology included a set of chemical and X-ray analysis of silicate materials and standardized testing of ceramics and composites. Features of the composition and characteristics of composites based on screenings of natural zeolite and andesite extraction during firing are shown. The effectiveness of using the composition of varieties of volcanic rocks for the regulation of physical and mechanical parameters – minimization of shrinkage as a factor of deformation flow of products during heat treatment, including in an industrial roller kiln at a maximum temperature of 1125 °C, has been established. The relationship between the differences in indicators of physical and mechanical properties of composites and the results of their structure analysis is shown. This is manifested in the peculiarities of the qualitative composition and degree of development of crystalline phases and glass phase, in the change in the ratio of the number of open and closed pores.
МЕТОД ОПТИМІЗАЦІЇ РЕФЛЕКСІЇ В .NET ЗА РАХУНОК ВИКОРИСТАННЯ ДИНАМІЧНОЇ КОМПІЛЯЦІЇ ЛЯМБДА ФУНКЦІЙ
Authors: ПОЗУР МИХАЙЛО , ВОЙТКО ВІКТОРІЯ
Published: December 2024

Reflection optimization methods in .NET are analyzed. The main method of optimization is the use of dynamic compilation of lambda functions. One approach to achieve the desired result is to use Expression compilation. Calling a compiled lambda function is faster than calling reflection, but the compilation itself takes some time. To avoid this drawback, it is necessary to store the delegate of the compiled function in memory so that it can be called again. In the course of experiments, it was determined that on versions .NET7 and higher, searching in the cache and calling such a function takes the same amount of time as calling reflection. Thus, to improve the speed of the method, it is necessary to optimize the data structure that stores the lambda function. Two-level hash-table was used to optimize search and storage of lambda functions. According to the results of the experiments, it was established that such optimization made it possible to improve the performance of the method. The new data structure also made it possible to use implementations of hash tables optimized for working with unchanged data, which allowed to further increase the performance of the method. However, this approach is only possible for use in .NET8 and higher versions. To optimize the method in cases of successive calls for the same data type, caching of information about the last data type for which the method was called was used. Experiments were conducted to determine the speed of the developed method in comparison with reflection and methods using a similar principle. The Benchmark.NET library was used to conduct experiments. According to the results of the experiments, it was established that the developed method has a better speed compared to reflection and analogues.
КОНСТРУКТИВНО-ТЕХНОЛОГІЧНА СХЕМА АВТОМАТИЗОВАНОЇ СИСТЕМИ ПРОМИВАННЯ ДОЇЛЬНИХ УСТАНОВОК ІЗ ПОВІТРЯНИМИ І ГІДРО- ІНЖЕКТОРАМИ
Authors: КАЛЕТНІК ГРИГОРІЙ , ЯРОПУД ВІТАЛІЙ , БАБИН ІГОР
Published: December 2024

The creation of automated technical and technological support for the milking plant washing system, which intensifies the process without additional costs, becomes of primary importance when solving the task of improving the quality of milk. The purpose of the research is to develop a structural and technological scheme of an automated system for washing milking units with air injectors and photosensors for determining the contamination of the milk line.
The analysis of the factors of the decrease in the quality of milk as a result of bacterial contamination showed that this is the result of poor performance of the technological operation of washing milking units and the formation of milk deposits on the internal surfaces of the milk duct system. As a result of the analysis of the existing constructions of technical and technological provision of washing, it was established that the most effective are the circulation systems of washing with the regulated formation of a traffic jam. In order to increase efficiency and save resources (by reducing the consumption of air, hot water, energy and operating costs), the process of washing milking units should be adaptive based on data obtained from monitoring tools for assessing the condition of the surfaces of the milk-conducting system and the hydrodynamic parameters of the movement of a two-phase washing solution, which is achieved by using air and hydraulic injectors based on automated control.
The cleaning system must be designed and manufactured to avoid contact of cleaning and sanitizing solutions with milk, as required by ISO 3918:2007, ISO 5707:2007 and ISO 6690:2007. The efficiency of the cleaning circulation system depends on the following factors: the design and installation must ensure the proper volume, speed and duration of action of detergents, as well as the appropriate temperature and concentration corresponding to the types of solutions used.
АНАЛІЗ МАЛИХ ВИБІРОК БАГАТОВИМІРНИХ ДАНИХНА ПРИКЛАДІ ТЕРМІНІВ ОДУЖАННЯ ПАЦІЄНТІВ ВІД COVID-19
Authors: КАМІНСЬКИЙ РОМАН , ШАХОВСЬКА НАТАЛІЯ , ДМИТРІВ ГАЛИНА
Published: December 2024

The analysis of multidimensional data presented in small volumes - small samples is quite often used in publishing, pedagogy, sociology mainly for the purpose of understanding the existing situation and choosing and making appropriate decisions. The main goal of such an analysis is to identify the impact of various factors on the object of attention in order to adjust further steps in one or another type of activity. The authors of this article worked with the material, namely with the medical and biological indicators of 19 patients with the disease of Covid-19, who underwent a course of treatment and at the time of recovery, their condition is presented with relevant data. These data include: duration of recovery of each patient in bed days and 29 indicators of physical and physiological state, divided into five groups. These are the following groups: physical characteristics (age, height, weight) and cardiovascular system, respiratory system, immune and circulatory systems. The data analysis started with the recovery term regarding the normality of the distribution. The following methods were used for this: quartile-quartile graph, as well as Shapiro-Wilk, Kolmogorov-Smirnov and Anderson-Darling tests. Based on the results of the application of these methods, it was concluded that the data are subject to the normal distribution law. The presence of both women and men in the data sample, and the latter are twice as few, required clarification of the gender difference between them. Here, the authors used a visual comparison using a boxplot and Student's T-test. By visual comparison, the differences between the minimums and the first quartiles are greater than the differences between the medians, third quartiles, and maximums, but according to the results of the T-test, the average values ​​of these samples are equal and differ only by chance. The relationship of signs with the terms of recovery was determined in the following way. For each group of features, their individual multivariate averages were determined, and the recovery time interval was divided into four subintervals. According to these subintervals, patients were divided along with their multivariate averages for each group of indicators. Each subinterval is matched with the average value of the individual multivariate averages. The data are presented in a table - subintervals as levels of the main factor and groups of averages from individual multivariate averages as variables. One-factor variance analysis was applied to the data in the table, the equality of the average groups of indicators, as well as the weakness of the influence of diagnostic indicators on recovery time. The work has a scientific and practical nature and can be useful in similar situations.
USAGE OF MACHINE LEARNING IN INVENTORY AND WAREHOUSE MANAGEMENT SYSTEMS
Authors: MOLCHANOV BOHDAN
Published: December 2024

The management of inventory and warehouse is a critical component of supply chain operations. This article explores the impact of machine learning on inventory and warehouse management systems. This research is important because machine learning is gaining increasing recognition for its potential to increase operational efficiency and accuracy across industries.
The purpose of this research is to examine the hypothesized potential of implementing machine learning to solve challenging problems in inventory management systems in an attempt to improve their efficiency. In addition, the study aims to evaluate current applications of these capabilities to provide a basis for future research.
The research is grounded in a comprehensive review of existing literature on the use of machine learning in inventory and warehouse management. To explore practical implementations, the study also analyses how leading companies have integrated machine learning into their products.
The result of this study is a review of existing research and practical applications of machine learning in inventory and warehouse management. This paper provides a detailed analysis of how machine learning can be used to improve demand forecasting, inventory levels, warehouse planning, and order fulfillment processes. This material serves as a basis for further study and research in this area.
The scientific novelty of this research is the study of machine learning methods applied to inventory and warehouse management.  The study expands the understanding of how machine learning can be used to solve problems in these areas.
The practical implications of the research provide a basis for the implementation of mentioned technologies in enterprises. Firstly, the study highlights the potential for significant cost savings, efficiency gains, and increased customer satisfaction. Secondly, it offers valuable material for further research in supply chain management.
МЕТОД КЛАСИФІКАЦІЇ ТЕКСТІВ ЗА ВМІСТОМ ПРОПАГАНДИ НЕЙРОМЕРЕЖЕВИМИ МОДЕЛЯМИ ГЛИБОКОГО НАВЧАННЯ
Authors: МОЛЧАНОВА МАРИНА
Published: December 2024

The method for classifying texts by propaganda content by neural network models of deep learning is proposed, based on combining traditional recurrent neural networks with long-term memory with transformers, which can provide a deeper understanding of sequence and context in text content. The peculiarity of proposed method is that it allows detecting both explicit and hidden propaganda messages, based on combining the capabilities of traditional recurrent neural networks with long-term memory and neural networks-transformers, as well as using the mechanism of training text data augmentation, which allows expand the number of training samples.
To evaluate the effectiveness of developed method of classifying texts by the propaganda content using deep learning neural network models, the software implementation was created, which consists of notebooks implemented in the cloud service "Google Colab" and the application for user interaction with the model. Notebooks are used to train the hybrid architecture neural network model and to expand the obtained data set by method of text augmentation. The graphical user interface application developed by Python using the PyCharm development environment.
The dataset of more than 25,000 records was created to train the neural network and the corresponding software was developed to investigate the method effectiveness. It was established that with the use of augmentation, better performance is achieved with larger number of epochs, which is explained by the expansion of the training sample, which leads to the need for a larger number of epochs. At the same time, when using augmentation, it was possible to achieve an accuracy of 97.83%, while without augmentation this indicator reached the maximum level of 96.94%. The obtained results demonstrate the ability of the proposed method to effectively classify texts based on the content of propaganda by neural network models of deep learning, and the use of the additional category "suspicious text" made it possible to raise the Precision and Recall indicators, which in turn makes it possible to automate the moderation of texts on the subject of propaganda with errors of no more than 1.83 % for false propaganda detection.
METHODS FOR ANALYZING SOCIO-ECONOMIC DATA OF TERRITORIAL COMMUNITIES FOR ADAPTIVE RESOURCE MANAGEMENT
Authors: LIPIANINA-HONCHARENKO Khrystyna
Published: December 2024

The socio-economic development of territorial communities in modern conditions requires adaptive approaches to resource management based on the intelligent analysis of large volumes of data of various types. Effective decision-making depends on the ability to integrate structured, semi-structured, and unstructured data, enabling the prediction of dynamic processes, identification of cluster groups of objects, and evaluation of key development indicators. The proposed information technology integrates modern methods of machine learning, natural language processing, and computer vision for socio-economic data analysis, ensuring accuracy, speed, and flexibility in decision-making.Based on the proposed approach, methods for cluster analysis, forecasting, and hybrid analysis have been improved, allowing consideration of the specifics of territorial communities and adaptation to crisis conditions. The obtained results lay the foundation for creating an innovative decision-support system that promotes sustainable community development, efficient resource management, and improved quality of life for the population.
Implementation of the ASICC learning model to improve the metacognitive students' based on lesson study extracts
Authors: Jannah Ro'ikatul , Purwoko Budi , Herawati Elysabet , Primandiri Poppy Rahmatika , Santoso Agus Muji
Published: December 2024

This research is driven by observations from the eighth-grade class at Junior High School 5 Kertosono, which revealed that lesson planning, reading comprehension, and problem-solving strategies have not been effectively utilized. Therefore, this research aims to enhance students' awareness and metacognitive skills through the application of the ASICC learning model. The focus of this study is on 18 eighth-grade students at Junior High School 5 Kertosono. This research is classroom action research based on a lesson study conducted over two cycles. The data analysis used is the scores of metacognitive awareness and skills in the first cycle compared to the second cycle, tested using N-Gain analysis. The research results reveal that metacognitive awareness in the first cycle showed that 11.1% of students were in the "medium" category, which increased to 22.2% in the second cycle. Meanwhile, metacognitive skills experienced a significant increase in the "high" category, starting from 16.7% in the first cycle and rising to 33.3% in the second cycle. The ASICC learning model can be applied to enhance students' metacognition. Another finding reveals that students with good metacognitive awareness do not always possess good metacognitive skills
The learning strategy for biodiversity concept: Pedagogical analysis based on technological pedagogical and content knowledge
Authors: Umar Mohammad Farhan , Bariroh Ghurrotul , Jannah Novi Dewi Khadikatul , Rahmat Adi , Riandi Riandi
Published: December 2024

Sustainable Development Goals (SDGs) emphasize quality education and environmental protection.  Biodiversity education in Phase E requires students to solve problems related to biodiversity. However, complex terminology and limited learning tools create challenges. This research aims to analyze effective learning strategies for teaching biodiversity using a TPACK framework. The method used in this research is a literature review carried out systematically by PRISMA. The analysis will map pedagogical approaches, content based on CoRes analysis, and technological aspects. Findings will inform the development of effective learning strategies for high school biodiversity education, contributing to achieving SDGs, particularly SDG 15 on terrestrial ecosystems. The finding shows that The use of CoRes to narrow down the information is required to instruct students and promote the attainment of the 15th SDG goal. Based on the findings of the analysis, the pedagogical review of the approach used, namely local wisdom, the field trip method, and the discovery learning model, as well as the use of technology to assist students in identifying PlantNet, Picture This, and iNaturalist, is considered effective to be implemented in learning biodiversity material.
Student’s scientific argumentation skills in biology: A gender-based analysis
Authors: Nisak Hoirun , Antika Linda Tri
Published: December 2024

Scientific argumentation skill is important to develop in learning because it empowers students' way of thinking in forming new knowledge, stimulates activity, learns to solve problems, build socio-cultural activities through presentations and criticism, encourages students to express their thoughts, understand concepts more easily and get used to critical thinking and logical. This research aimed to determine the significance of differences between scientific argumentation skills based on gender. Research data was obtained from sample class data, namely class XI Science at Bustanul Mubtadiin Proppo Pamekasan High School with 22 students. Measurement of scientific argumentation skills based on gender using an essay writing test with a scientific argumentation rubric using the Osborne Argument Model. Aspects measured were based on the quality of rebuttal, data, collateral, support, refutation, or presence of conflicting claims. The hypothesis test used was an independent sample T-Test using the Jamovi Statistics program. This study reported that the scientific argumentation skills of male and female students were significantly different, which scientific argumentation skills of female students were higher than males. This research provided implications for students to be skilled in scientific argumentation, especially in biology.