bluuebunny/crossref_metadata_2025 huggingface dataset:
Computational Fluid Dynamics–Discrete Element Method Numerical Investigation of Binary Particle Mixing in Gas–Solid Fluidized Bed with Different Drag Models
Authors: Han Chen , Fu Xiaoling , Guo Xiaolu , Lu Wei , Zhang Shaoqing , Wang Hui , Yang Yang
Published: November 2024

The fluidized bed is a critical reactor in the energy and chemical industries, where the mixing and agglomeration behaviors of binary particles significantly influence both the efficiency of reaction processes and the uniformity of final products. However, the selection of appropriate drag force models remains a subject of debate due to the variability in particle properties and operating conditions. In this study, we investigated the fluidization behavior of binary mixtures composed of two different sizes of Geldart-D particles within a fluidized bed, evaluating nine distinct drag force models, including Wen and Yu; Schiller and Naumann; Ergun; Gidaspow, Bezburuah, and Ding; Huilin and Gidaspow; De Felice; Syamlal and O’Brien; and Hill, Koch, and Ladd. We focused on four key parameters: particle mixing degree, migration characteristics, temperature variation, and mean pressure drop. Simulation results revealed that the choice of drag model markedly affected mixing behavior, migration dynamics, and temperature distribution. Notably, the Ergun; Gidaspow, Bezburuah, and Ding; and Hill, Koch, and Ladd models exhibited superior particle mixing uniformity. While the drag model had a relatively minor impact on particle temperature changes, its selection became critical in simulations requiring high-temperature precision. Regarding pressure drop, the Huilin and Gidaspow and Gidaspow, Bezburuah, and Ding models demonstrated smaller and more stable pressure drop fluctuations. These findings offer valuable theoretical insights into gas–solid two-phase flow under binary particle mixing and provide practical guidance for the design and operation of fluidized bed reactors.
Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach
Authors: Guo Yingjie , Kim Ji-Yeon , Park Jeonghyun , Lee Jung-Min , Park Sung-Gwan , Lee Eui-Jong , Lee Sangyoup , Hwang Moon-Hyun , Zheng Guili , Ren Xianghao , Chae Kyu-Jung
Published: November 2024

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.
Low-Cost Sensors for the Measurement of Soil Water Content for Rainfall-Induced Shallow Landslide Early Warning Systems
Authors: Pavanello Margherita , Bordoni Massimiliano , Vivaldi Valerio , Reguzzoni Mauro , Tamburini Andrea , Villa Fabio , Meisina Claudia
Published: November 2024

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.
Land-Use Impacts on Soil Erosion: Geochemical Insights from an Urban Drinking Catchment, South-Central Chile
Authors: Contreras Angela , Álvarez-Amado Fernanda , Aguilar-Gomez Maite , Campos-Quiroz Dilan , Castillo Pamela , Tardani Daniele , Poblete-González Camila , Cortés-Aranda Joaquín , Godfrey Linda , Orellana-Silva Nicolás
Published: November 2024

We investigate the influence of land use and land cover (LU/LC) changes on soil erosion and chemical weathering processes within the Nonguén watershed in the Coastal Cordillera of south-central Chile. The watershed is divided into three sub-basins, each characterized by distinct LU/LC patterns: native forest and exotic plantations. A comprehensive geochemical analysis, including trace elements and lithium (Li) isotopes, was conducted on river water and suspended sediment samples collected from streams within these sub-basins to assess how land management practices, particularly plantation activities, influence the geochemical composition of river systems. Our results show that sub-basins dominated by exotic plantations exhibit significantly higher concentrations of major and trace elements in suspended sediments compared to sub-basins dominated by native forests. The elevated trace element concentrations are primarily attributed to increased physical erosion due to forestry activities such as clear-cutting and soil disturbance, which enhance sediment mobilization. Notably, concentrations of elements such as Fe, Al, and As in plantation-dominated sub-basins are raised to ten times higher than in native-dominated sub-basins. In contrast, sub-basins with native forest cover exhibit lower levels of sediment transport and trace element mobilization, suggesting that native vegetation exerts a stabilizing effect that mitigates soil erosion. Despite the substantial differences in sediment transport and element concentrations, Li isotopic data (δ7Li) show minimal fractionation across the different LU/LC types. This indicates that land use changes impact the chemical weathering processes less compared to physical erosion. The isotopic signatures suggest that physical erosion, rather than chemical weathering, is the dominant process influencing trace element distribution in plantation-dominated areas. The study provides critical insights into how forestry practices, specifically the expansion of exotic plantations, accelerate soil degradation and affect the geochemical composition of river systems. The increased sediment loads, and trace element concentrations observed in plantation-dominated sub-basins, raise concerns about the long-term sustainability of forest management practices, particularly regarding their impacts on water quality in urban catchment areas. These results are of significant relevance for environmental management and policy, as they underscore the need for more investigation and sustainable land use strategies to minimize soil erosion and preserve water resources in regions undergoing rapid LU/LC changes.
A Data-Driven Analysis of Electric Vehicle Adoption Barriers in the Philippines: Combining SEM and ANNs
Authors: Saflor Charmine Sheena R. , Mariñas Klint Allen , Gumasing Ma. Janice , Tangsoc Jazmin
Published: November 2024

As the world progresses into the peak of the Fourth Industrial Revolution, the adoption of smart and sustainable technologies, including electric vehicles (EVs), has gained significant momentum. However, the widespread acceptance of EVs is hindered by several unresolved barriers. This study investigates the factors influencing the adoption of electric vehicles in the Philippines, focusing on key barriers through an integrated approach using machine learning and structural equation modeling (SEM). Specifically, artificial neural networks (ANNs) and SEM are employed to analyze data from online surveys and the existing literature, identifying the critical obstacles that impact consumer acceptance. The findings reveal that the availability of charging stations, range anxiety, and vehicle costs are the primary deterrents to EV adoption. By incorporating a sustainability perspective, this study underscores the crucial role of electric vehicles in reducing environmental impacts and achieving carbon reduction targets. The hybrid methodology presented offers new insights to guide policymakers in promoting electric vehicle usage, thereby contributing to the global sustainable development goals.
Risk Factors for Human Contact with Bats in Northern Tanzania
Authors: Bunuma Emmanuel Kulwa , Keyyu Julius , Maziku Joseph , Bitanyi Stella , Fyumagwa Robert , Changula Katendi , Mubemba Benjamin , Simulundu Edgar , Chitanga Simbarashe , Horton Daniel L. , Ekiri Abel Bulamu , Muleya Walter
Published: November 2024

Background: Environmental changes impact bat–human interactions, heightening concerns of pathogen spillover and highlighting the need to understand human–bat interactions in the context of ecosystems. The objective of this study was to assess factors associated with human contact with bats in communities living near bat roosts in Northern Tanzania. Methods: A cross-sectional survey was used to investigate the factors associated with human contact with bats. Utilizing a standardized questionnaire administered through face-to-face interviews, relevant data were collected from respondents living in or near regions characterized by high bat density. Descriptive analyses followed by bivariate and multivariate analyses were performed to assess the association between the investigated factors and the outcome variable of interest, contact with bats. Results: Data were collected from 325 respondents. Of the respondents, 57.5% (187/325) reported bat contact, and only 4% believed bats can transmit rabies. The following factors were significantly associated with human–bat contact: being unaware that bats can transmit diseases like rabies (AOR = 8.63, CI = 1.04, 71.43, p = 0.045), feeling safe to handle bats with bare hands (AOR = 3.47, CI = 1.71, 7.05, p < 0.001), not washing hands thoroughly after bat bite or scratch (AOR = 2.43, CI = 1.29, 4.59, p = 0.006), using water from ponds, rivers or wells (AOR = 5.58, CI = 3.17, 9.79, p <0.001), age > 45 years (AOR = 1.77, CI = 1.0, 3.14, p = 0.047) and a low level of education (AOR = 5.86, CI = 2.97, 11.57, p <0.001). Conclusion: The study findings highlight the key factors to be targeted when developing strategies aimed at improving the community’s knowledge of the potential risks of zoonotic infectious diseases associated with bats and reducing the likelihood of human–bat contact and the related public health risks in communities living near bat roosting sites in Tanzania.
Iberoamérica como “narrativa democrática”
Authors: Fernández Leost José Andrés
Published: November 2024

La construcción de una “narrativa iberoamericana” en clave democrática no resulta sencilla. Por un lado, la noción identitaria de una “iberoamericaneidad” compartida no acaba de cristalizar. Por otro, la opinión que las sociedades iberoamericanas manifiestan hacia el funcionamiento de sus democracias es inquietante. Con todo, sus fundamentos liberales y pluralistas perduran, y su ejercicio persiste como el único mecanismo de acceso legítimo al poder. El legado histórico e institucional iberoamericano, junto con el soporte de su comunidad de valores, permite articular una narrativa con proyección de futuro que soslaye los relatos regresivos. Tanto en América Latina como en España las ideas de “democracia” y “comunidad iberoamericana” parecen estar perdiendo atractivo. Aún tratándose de términos cuya categorización se ubica en campos disímiles (político frente a cultural), cabe afirmar que la relación entre ambos se estrechó a finales de los años ochenta y principios de los noventa. Fue entonces cuando, al tiempo que se consumaba la llamada “tercera ola democratizadora”—y se experimentaban procesos de transición y desarrollo socioeconómicos compartidos— se creó el sistema de cumbres iberoamericanas (1991) que, entre otros efectos, institucionalizaba los valores que representa la idea de Iberoamérica, inherentemente vinculada al imperio de la ley, el pluralismo, o la democracia. Más de treinta años después, esta visión, al igual que el propio sistema de cumbres, corre el riesgo de caer en la irrelevancia. Un modo de sondear su estado consiste en analizar separadamente los conceptos de “Iberoamérica” y de “democracia”.
التغيرات في المستوى العام للأسعار في الكويت: تحليل التكامل المشترك المتعدد
Authors: المطيري نايف حمد
Published: November 2024

تستخدم هذه الدراسة أسلوب تحليل التكامل المشترك المتعدد، ونموذج متجه تصحيح الخطأ المتعدد للبحث في مصادر التغيرات في المستوى العام للأسعار. واستخدمت الدراسة نموذجاً نقدياً لتحديد السعر؛ حيث يظهر جليًّا كل من عرض النقود والطلب على النقود. ويختلف هذا النموذج النقدي المستخدم عن غيره من النماذج المستخدمة في الدراسات السابقة في أنه يعطي أهمية للقوى الخارجية المؤثرة، مثل الأسعار الخارجية. وتؤكد النتائج التي تم التوصل إليها أن المستوى العام للأسعار في الكويت له ارتباط مع كل من الدخل الحقيقي وعرض النقود وأسعار الواردات؛ نظراً لوجود علاقة توازنية طويلة الأجل بين هذه المتغيرات.
تأثير ارتفاع أسعار النفط الخام على النمو الاقتصادي في الهند
Authors: الجبشة فاتن يوسف
Published: November 2024

تعد جمهورية الهند ثاني أكثر بلد تزايد استيرادها للنفط الخام في العالم، ويبلغ معدل النمو الاقتصادي السنوي للهند خلال السنوات الماضية 6%، ومن المتوقع أن تصبح جمهورية الهند في عام ۲۰۲۰ رابع أكبر اقتصاد بتماثل القوة الشرائية في العالم بعد الصين وأمريكا واليابان. وقد أوضحت الدراسة أن كثافة استهلاك الطاقة في الناتج المحلي الإجمالي للهند كان أعلى خلال الثمانينيات وبداية التسعينيات، بسبب تزايد الاستثمارات في الصناعات والنشاطات الاقتصادية التي تستهلك الطاقة بكثافة. وانخفضت هذه النشاطات خلال السنوات العشر الماضية بسبب المكاسب الناتجة عن زيادة كفاءة الإنتاج، وهيمنة دور القطاع الخدمي على الاقتصاد الهندي. ويبين التحليل الرباعي لمواطن القوة والضعف والفرص والمخاطر لاقتصاد جمهورية الهند أنها تتمتع بعناصر مناسبة وفرص متاحة لامتصاص الزيادة في تكاليف النفط المستهلك خلال فترات ارتفاع أسعار النفط الخام. وتمت مناقشة تأثير السياسة الاقتصادية القومية في إطار نظرة إستراتيجية لإدارة ارتفاع أسعار النفط.
Exploring AI Literacy Competency Elements in Music Education
Authors:   Education Research Institute, Oh Jihyang
Published: November 2024

This study examines the direction of AI literacy education in music education in the context where the concept of AI literacy is being discussed and the necessity of AI education is being emphasized. To this end, the conceptual meaning and competency elements of AI literacy were reviewed through literature analysis on AI literacy, and the AI literacy competency elements required for music education were derived. The AI literacy competencies in music education were proposed as follows: first, expanding musical perspective and insight through understanding AI, second, expressive and communicative ability through AI, third, creative and convergent media utilization through AI learning experience, and fourth, AI ethics competency that can responsibly and ethically utilize AI tools in various music activities. The AI literacy competency elements suggest directions on what content and level should be focused on when including AI in music classes, and the significance of this study can be found in that it provided basic data for understanding the essential 'AI literacy’ technology and its contents required in music education.
Needs Analysis of Pre-service Teachers for Digital Competence
Authors:   Education Research Institute, Yoo Mina , Jin Sung-Hee
Published: November 2024

The recent digital transformation in school education, including advancements in g enerative AI and digital technologies, highlights the need for digital competence a mong teachers. This study investigates the digital competence needs of pre-service elementary school teachers using Importance-Performance Analysis (IPA), Borich's Needs Assessment Model, and The Locus for Focus Model. The study participants were 98 first-year pre-service teachers from University A. The results showed th at pre-service teachers perceived all four sub-competencies of digital competence a s highly important. Among these, the sub-competency with the highest priority wa s the capability to utilize digital technology in education, specifically in areas such as digital-based curriculum reconstruction, design of personalized learning using di gital technology, evaluation, selection, and development of digital technologies, data, services, and content, and design of practical learning experiences based on digital technology. The findings suggest that teacher education curriculum should be impr oved to reflect the high-priority competencies identified by pre-service teachers. B ased on the results, discussions and implications for research are proposed.
Longitudinal analysis of teacher-student rapport, individualized instruction, academic self-concept and career maturity using latent growth models
Authors:   Education Research Institute, Lee Hyo–Young
Published: November 2024

This study utilized the longitudinal data from the Korean Education Longitudinal Study (KELS) 2013 (Ⅲ), specifically the 6th to 8th waves, to analyze the structural relationships among teacher-student intimacy, individualized instruction, academic self-concept, and career maturity. The study participants included a total of 7,324 students, with 3,623 boys (49.5%) and 3,701 girls (50.5%). The analysis method utilized was the Multivariate Latent Growth Model (MLGM). The research results showed that first, the analysis of the trajectories of the variables indicated that the fit of the linear change model was better than that of the no-change model, and there was a negative correlation between the initial values and the rates of change of the variables. Second, it was confirmed that teacher-student intimacy and individualized instruction have a significant impact on academic self-concept and career maturity. Third, academic self-concept was found to mediate the related variables. Based on these findings, implications for academic and career development were derived.
A Study on the Perception of Pre-service Science Teachers regarding Online Classes
Authors:   Education Research Institute, Lim Wanchul , Han Hwa-Jung , Shim Kew-Cheol
Published: November 2024

The purpose of this study is to survey the perception of pre-service science teachers regarding online classes. The subjects of this study were 76 pre-servide science teachers in the Chungcheong and Gyeongsang provinces, and an online survey was conducted. The survey instruments consisted of 13 questions in three categories: experience & class using digital tools, the learning effectiveness of online classes, and online and in-person experimental classes. Pre-service science teachers were found to lack the ability to use digital tools and the experience of class using digital tools. Pre-service science teachers perceived that in-person classes were the highest effective, but they had very positive perception of blended teaching methods. They perceived the advantages of online learning as learning at a desired location, controlling learning speed, and repeatable learning, while the disadvantages included the lack of interaction, difficulty in self-regulating learning, and poor online learning environment. In addition, they were found to have a very negative perception of online experimental classes, but showed a somewhat positive perception of learning basic concepts. In order to prepare for future school science education and enhance digital capabilities of pre-service science teachers, it is necessary to improve the curriculum for them and develop and utilize teaching-learning methods and strategies.
Effects of Aulim programs for school violence prevention on Preschool Cildrend`s pro-social behavior
Authors:   Education Research Institute, Cheon Euna , Kim Hyeonsu
Published: November 2024

The purpose of this study is to investigate the impact of the Aulim program on prosocial behavior in Seven-year-old children. The study was conducted with 56 Seven-year-old children enrolled in a private kindergarten in Y City, using a teacher-administered prosocial behavior assessment before and after the implementation of the program. For data analysis, one-way ANOVA and ANCOVA were performed using SPSS 25.0. The results showed that children who participated in the Aulim program exhibited significant improvements in prosocial behavior, particularly in the subdomains of sharing, helping, empathy, self-regulation, and leadership, compared to both the control and comparison groups. These findings highlight the necessity for active implementation of the Aulim program in early childhood education settings. Furthermore, the results suggest the need for developing early childhood education programs that utilize the characteristics of the Aulim program, enhancing teachers' professional competencies, establishing developmentally appropriate terminology, and fostering cooperation with parents.
A Study of the Transition Process of Baekje Gilt-Bronze Shoes and the Restoration of Their Production Technology
Authors:   The hoseo Archaeological Society, Kang Minjeong , Lee Hyunsang , Chung Kwangyong
Published: November 2024

This paper aims to understand the development of Baekje gilt-bronze shoes through gilt-bronze shoes, one of the funerary ritual items that appeared in the 5th and 6th centuries, and to examine the Baekje goldsmithing technology applied to gilt-bronze shoes. The authors have studied the restoration of the original Baekje gilt-bronze shoes and comprehensively reviewed the results of the actual restoration process.
Chapter Ⅱ outlined the transition process of gilt-bronze shoes based on previous research.
Baekje gilt-bronze shoes were produced from around the 5th century, the late Hansung period, to the Ungjin period. Initially, the “凸” pattern and a lattice pattern are represented by cut-curving techniques. Later, dragon and lotus patterns appeared, and cut-curving and engraving techniques were used. In the Ungjin period, the gilt-bronze shoes were replaced by simpler patterns such as lattice patterns and wavy dot patterns, which shows that the view of gilt-bronze shoes has changed a lot from the previous period.
Chapter Ⅲ described in detail the production techniques of each type of gilt-bronze shoe and examined how the four goldsmithing techniques found on gilt-bronze shoes—cut-curving, engraving, plating, and assembly-were used in different ways. Chapter Ⅳ described process by process and fabrication techniques that the author has observed in restoring numerous gilt- bronze shoes. By studying the production techniques of Baekje gilt-bronze shoes, this study has endeavored to share an aspect of research on the restoration of the production techniques of ancient plate-metal gilt-bronze artifacts.
The aspect of handicraft workshop and the Oegyeongbu in Sabi Capital
Authors:   The hoseo Archaeological Society, Lee Hwayoung , Choi Hyeongwoon , Seong Jeongyong
Published: November 2024

This paper examined the location, structure, and operation of handicraft workshops in Sabi Capital in Baekje, and attempted a poetic review of Oegyeongbu location around the workshops. During the Three Kingdoms period, handicrafts include weaving such as sericulture and weaving, ceramics manufacturing earthenware and tile, mining gold, silver, copper, and iron, metallurgy to refine them, and various manufacturing industries to produce household goods, tools, and weapons. It is known that it was operated as a government-operated crust industry as the scale gradually increased over time. In Sabi Capital, metal workshops and ceramics workshops are mainly identified. It is understood that ceramics workshops are mainly distributed across the Baekma River, which is the western boundary of the capital, while metal or glass workshops are distributed at different times around specific points in the city.
It is understood that the workshop in the early stages of Sabi-period was located in the south of Buso-Mountain, centered on the ruins of Gwanbuk-ri, and then moved to the northeast area of the capital. The northeastern area of Sabi Capital is vulnerable to running water because it corresponds to the alluvial land, but it is a place where abundant water sources can be supplied and smooth logistics and distribution is possible as it is close to a transportation route to enter from the outskirts of the capital. It is understood that this movement of the workshop facilities is due to the stabilization of the capital system after the capital city and the gradual development of wetlands inside the capital city.
Along with the spatial movement of the workshop, changes in the structure are also confirmed.
The initial Gwanbuk-ri workshop is a structure that simultaneously produces various goods within a dense space, while the workshop in the northeast of the capital divides individual ranges into a certain range and shows a structure in which production and accessories are organized within it.
Based on underground storage facilities, hierarchical building structures, distribution patterns of various building sites, and high wooden slips in Sabi Capital, it is estimated that there was a space with functions as a part of the Oegyeongbu, as a government office for the operation and management of the workshop system in the west and north areas of the workshop.
The Impact of Diversity and Participatory Decision-Making on School Innovativeness: Focusing on Principals' Perceptions
Authors:   Korean Society for  Holistic Convergence Education, Kim Eun-Jeong , Kang Ho-Soo
Published: November 2024

Considering the reality that the diversity of school members is increasing due to the influx of multicultural students into school organizations, it is necessary to analyze the impact of diversity on schools from an organizational perspective. Thus, this study aims to analyze the impact of organizational diversity on school innovativeness, examining whether participatory decision-making mediates these two variables. To achieve this, survey responses from school principals working in elementary and middle schools in South Korea, as provided by TALIS 2018, were utilized. Using structural equation modeling to explore the relationships among three latent variables, the analysis yielded the following results: First, acceptance of diversity had a positive effect on school innovativeness. Second, participatory decision-making among school members also had a positive effect on school innovativeness. Third, the indirect effect of participatory decision-making on the relationship between diversity acceptance and school innovativeness was found to be significant, demonstrating its role as a mediating variable in managing organizational diversity. These results have policy implications in that they empirically suggest ways to achieve school innovation in terms of diversity and decision-making.
Features of the epidemiology of esophageal cancer among the population of Ukraine for the period from 2016 to 2023
Authors: Skyba D.V. , Monakova O.S.
Published: November 2024

Esophageal cancer is an extremely serious and highly relevant issue with substantial medical and social significance. The significance is primarily due to its aggressive nature, as esophageal cancer progresses rapidly and is often diagnosed in its late stages. Late detection limits treatment options and generally leads to poorer survival outcomes, making it one of the most challenging cancers to manage. A study of data from the National Cancer Registry of Ukraine examined trends in standardized morbidity and mortality rates from 2016 to 2021. Over this period, the standardized morbidity rate decreased by 16.3%, while the standardized mortality rate dropped by 15.6%, suggesting some improvements in early detection and treatment approaches during those years. However, the outbreak of hostilities in Ukraine in 2022 created substantial barriers to healthcare operations, particularly in cancer centers. According to official data, military actions disrupted data collection, making it impossible to calculate standardized morbidity and mortality rates per capita for years 2022–2023. By the end of 2023, only data on the total number of patients registered in cancer care institutions were available. This lack of comprehensive data limits understanding of current trends in esophageal cancer and complicates planning for healthcare resources in the coming years. Wartime conditions pose unique challenges, as access to quality medical care may be restricted or delayed. For conditions like esophageal cancer, where early detection is crucial, limited access can lead to a higher likelihood of advanced-stage diagnoses, ultimately impacting prognosis and survival rates. These circumstances underscore the importance of maintaining continuity in cancer care, even under adverse conditions, through targeted healthcare planning.

Keywords: analysis of dynamics, cancer, mortality and morbidity.
EFFECT OF EMPLOYEE ADAPTABILITY ON SUCCESSFUL IMPLEMENTATION OF STRATEGIC PLAN IN PUBLIC SECTOR: A CASE OF SELECTED PUBLIC ORGANIZATIONS UNDER THE MINISTRY OF HEALTH IN TANZANIA
Authors: Nkone Bahati , Matoka Dr. Chacha , Ngirwa Dr. Charles
Published: November 2024

This study through the aid of BSC aimed to determine the effect of employee adaptability on implementation of strategic plan in public sector with the objective to examine its effect on successful implementation of strategic plan at selected public sector organizations in Tanzania. The study was positivist research with a quantitative approach under both descriptive and correlational research design through a survey research strategy. Population of the study was 541 permanent staff at MSD, NHIF, TMDA, NIMR and NPHL where a sample of 230 staff was selected through systematic random sampling. Data was collected through questionnaires with a response rate of 96.9 percent and analysed through regression analysis. The study has found that independent variable affects significantly the dependent variable (F=152, p-value=0.0<<0.05) and the coefficient is positive and statistically significant from zero. Consequently, employee adaptability has positive and significant effect on successful implementation of strategic plan in public sector. This finding indicates that employee adaptability has significant effect on successful implementation of strategic plan in public sector. The study therefore recommends PSOs to strengthen identification and implementation of initiatives geared toward developing and or improving employee adaptability competencies which will enhance employees’ capabilities to adjust themselves and adopt accordingly to the changes as they take place within their organizations, which is critical for effective implementation of Strategic Plan.
THE ROLES OF INFORMATION COMMUNICATION TECHNOLOGY IN COMBATING CORRUPTION PRACTICES IN ZANZIBAR PUBLIC SECTOR
Authors: Maulid Haji Ibrahim , Shaaban Hussein Khamis , Shaame Abdalla , Maweu Paul Muinde , Padayachee Premilla , Myslimi Olta
Published: November 2024

Corruption is highly taunted that it wears down trust, deteriorates democracy, hinders economic progress and aggravates inequality, poverty, social division. Any study attempting to understand corruption controll is of immense importance. This study was about the roles of ICT in combating corruption practices in Zanzibar public sectors. Descriptive research design which involved qualitative and quantitative approach was applied. The study population was 150 employees of ZAECA a government institution responsible for combating corruption in Zanzibar. The sample was selected using purposive and random sampling. Data was gathered using interview and questionnaire. The analysis of data was done using frequency and percentages distribution: mean and standard deviation and correlation. The study found that ICT tools were used in either low or very low manner in combating corruption in the public sector. The study found that the investigator practices which capitalized three roles of ICT (Provision of more information to public, bringing more accountability and Promoting transparency) were used in low manner in combating corruption in Zanzibar public sectors. The study also, found that provision of more information to public, bringing more accountability and promoting transparency had correlation coefficients: 0.781, 0.767 and 0.855 respectively with combating corruption. The study concluded that there was a significant positive relation between ICT roles (Provision of more information to public, bringing more accountability to authorities, promoting transparency) and combating corruption. The study suggested that the government of Zanzibar should develop a policy which would increase the use of ICT tools in combating corruption in the public sector.
Evaluation of the optical and electrical properties of (C8BTBT)(F4TCNQ) charge-transfer complexes according to film formation temperatures during solution coating
Authors: Uchiyama Naomi , Matsuura Yuji
Published: November 2024

This study focused on (C8BTBT)(F4TCNQ) charge-transfer complexes, where C8BTBT is 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene and F4TCNQ represents fluorinated derivatives of 7,7,8,8,-tetracyanoquinodimethane. These complexes exhibit excellent thermal and atmospheric stability and near-infrared absorption, serving as transparent thin films for photoelectric conversion. Here, we sought to improve the photoelectric conversion efficiency (and thus the quantum efficiency, QE) of (C8BTBT)(F4TCNQ) thin films by altering the film formation temperatures during solution coating; we evaluated the optical and electrical properties of the films. We found that increased film coverage of a substrate surface (leading to continuous film formation) enhanced optical absorption and improved the photocurrent extraction efficiency when these films were incorporated into devices. C8BTBT and its components, when present alone, negatively affected both the device fabrication yield and photocurrent extraction. It is thus important to maximize (C8BTBT)(F4TCNQ) formation when seeking to improve the QE.
Impact of post-deposition annealing on SiO2/SiC interfaces formed by plasma nitridation of the SiC surface and SiO2 deposition
Authors: Fujimoto Hiroki , Kobayashi Takuma , Watanabe Heiji
Published: November 2024

We examined the impact of post-deposition annealing (PDA) on SiO2/SiC structures formed by plasma nitridation of the SiC surface followed by sputter deposition of SiO2. The interface state density near the conduction band edge of SiC was reduced from about 2 × 1012 to 1 × 1011 eV−1 cm−2 as the CO2-PDA temperature increased from 1050 °C to 1250 °C. In addition, the sample treated by CO2-PDA exhibited substantially higher immunity against positive gate bias stress than the standard NO nitridation. Our findings indicate that defect passivation by CO2-PDA plays a crucial role in improving the performance and reliability of SiC MOS devices formed by sputter-SiO2 deposition.
Epitaxial growth of Ge1−xSnx thin film with Sn composition of 50% and possibility of Ge–Sn ordered bonding structure formation
Authors: Shibayama Shigehisa , Shibata Kaito , Sakashita Mitsuo , Kurosawa Masashi , Nakatsuka Osamu
Published: November 2024

We examined the epitaxial growth of Ge1−x
Sn
x
thin film with x ∼ 0.50 on GaSb(111) substrate using a low-temperature MBE. X-ray diffraction and Raman scattering spectroscopy analyses revealed that the Ge1−x
Sn
x
thin film with x ∼ 0.50 with no strain was successfully grown on GaSb(111). Furthermore, in the Raman analysis, a sharper Ge-Sn peak compared to Ge-Ge and Sn-Sn peaks was clearly observed and showed an improved thermal stability than Ge1−x
Sn
x
with a smaller Sn composition. This suggests that the Ge1−x
Sn
x
with x ∼ 0.50 would have an ordered Ge–Sn bonding like a zinc blend system, that is significantly different from Si1−x
Ge
x
case.
Large Language Model Agents as Prognostics and Health Management Copilots
Authors: Lukens Sarah , McCabe Lucas H. , Gen Joshua , Ali Asma
Published: November 2024

Amid concerns of an aging or diminishing industrial workforce, the recent advancement of large language models (LLMs) presents an opportunity to alleviate potential experience gaps. In this context, we present a practical Prognostics and Health Management (PHM) workflow and self-evaluation framework that leverages LLMs as specialized in-the-loop agents to enhance operational efficiency without subverting human subject matter expertise. Specifically, we automate maintenance recommendations triggered by PHM alerts for monitoring the health of physical assets, using LLM agents to execute structured components of the standard maintenance recommendation protocol, including data processing, failure mode discovery, and evaluation. To illustrate this framework, we provide a case study based on historical data derived from PHM model alerts. We discuss requirements for the design and evaluation of such “PHM Copilots” and formalize key considerations for integrating LLMs into industrial domain applications. Refined deployment of our proposed end-to-end integrated system may enable less experienced and professionals to back-fill existing personnel at reduced costs.
SYSAI for System Health Management - a Statistical Framework for the Analysis of Diagnosis Systems
Authors: He Yuning , Schumann Johann
Published: November 2024

On-board failure diagnosis and health management systems (HMS) are crucial for the operation of complex autonomous aerospace systems. False alarms (false positives, FPs) or false negatives (FNs) can lead to lower system performance or even loss of mission or the autonomous vehicle. Therefore, a careful verification and validation (V&V) is important. Due to the high dimensionality of the system’s state space, however, exhaustive testing of the HMS is usually not possible.
In this paper, we present how our SYSAI (System Analysis for Systems with AI components) framework can support intelligent analysis and testing of HMS on the system level. SYSAI’s capabilities to efficiently explore high-dimensional state and parameter spaces and to identify diagnosability regions and their boundaries, makes a comprehensive analysis of the diagnosis system possible and can provide feedback to the designer. We will illustrate our approach using the ADAPT (Advanced Diagnostics and Prognostics Testbed) redundant power storage and distribution system.
Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery
Authors: De Fabritiis Fabrizio , Gryllias Konstantinos
Published: November 2024

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan.
In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications.
In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks.
The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.
Comparing Feature and Trajectory-Based Remaining Useful Life Modeling of Electrical Resistance Heating Wires
Authors: Mählkvist Simon , Söderkvist Vermelin Wilhelm , Helander Thomas , Kyprianidis Konstantinos
Published: November 2024

Industrial heating significantly contributes to global greenhouse gas emissions, accounting for a substantial portion of annual emissions. The transition to fossil-free operations in the heating industry is closely linked to advancements in industrial electrical heating systems, especially those using resistance heating wires. In this context, Prognostics and Health Management is crucial for enhancing system reliability and sustainability through predictive maintenance strategies.
The integration of machine learning technologies into Prognostics and Health Management has significantly improved the precision and applicability of Remaining Useful Life modeling. This improvement enables more accurate predictions of component lifespans, optimizes maintenance schedules, and enhances operational efficiency in industrial heating applications. These developments are essential for reducing greenhouse gas emissions in the sector.
This paper serves as a guide for conducting Remaining Useful Life modeling for industrial batch processes. It evaluates and compares two methodologies: deep learning-based approaches using full time-series data, such as recurrent neural networks and their variants, and feature-engineering-based methods, including random forest regression and support vector machines. Our results show that the feature-oriented approach performs better overall in terms of predictive accuracy and computational efficiency. The study includes a detailed sensitivity analysis and hyperparameter estimation for each method, providing valuable insights into developing robust and transparent Prognostics and Health Management systems. These systems are crucial in supporting the heating industry’s move towards more sustainable and emission-free operations.
The findings reveal that feature-oriented methods are both performant and robust, particularly excelling in handling outliers. The random forest regression model, in particular, demonstrated the highest performance on the test dataset according to the chosen evaluation metrics. Conversely, trajectory-oriented methods exhibited less bias across varying levels of degradation, a helpful characteristic for Prognostics and Health Management systems. While feature-oriented methods tend to systematically underestimate Remaining Useful Life at high true values and overestimate it at low actual values, this issue is less pronounced in trajectory-oriented models. Overall, these insights highlight the strengths and limitations of each approach, guiding the development of more effective and reliable predictive maintenance strategies.
Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles
Authors: Du Xinyu , Mu Huaizheng , Corr Kevin , Nowak Matt , Wong Hong , Chang Tung-Wah Frederick , Rahimifard Sara
Published: November 2024

Each electric vehicle (EV) requires a low-voltage (e.g., 12V) auxiliary battery to provide electric power to onboard electronic control units, lighting systems, and various sensors during power off. Therefore, when the low-voltage battery is in low state of health (SOH) or low state of charge (SOC), it may cause no-start events. The existing OnStar Proactive Alert service can effectively predict low SOC or low SOH events for low-voltage batteries in Internal Combustion Engine vehicles using cranking signals. However, it does not work for EVs since there is no cranking event. In this work, a diagnostic and prognostic solution for the low-voltage battery of EVs is proposed. Four novel health indicators (HIs) along with the decision-making system are developed based on equivalent circuit models. Furthermore, the selection process of appropriate HIs tailored to various operational states of the vehicle is described. The validation results based on GM test EV data have demonstrated the effectiveness and robustness of the proposed solution.
Process for Real-Time Monitoring of a Turboshaft Engine
Authors: Bechhoefer Eric , Hajimohammadali Fatemeh
Published: November 2024

For helicopters engaged in sling loads or heavy lift, there is a need to report current turboshaft engine health (e.g., margin) and contingency power available from the engine in realtime. Displaying this information allows the pilot in command of the aircraft to make more informed decisions about the safety of continuing a mission. For engine margin, when aircraft parameter data is recorded by a health and usage monitoring system (HUMS) or flight data monitoring system (FDM), this functionality allows maintainers to be notified of the engines’ degraded performance to initiate an inspection/maintenance action to restore the engine to its designed performance. However, this does not help the pilot make mission-critical decisions during the flight. The paper covers the method to use HUMS/FDM data to calculate, in real-time, the power available to the pilot.
Elastodynamics based Modelling of Acoustic Emission for Earlier Bearing Damage Detection
Authors: Bhattacharyya Anurag , Thyagarajan Krishnan , Yan Jin , Pintong Kevin , Chen Qiushu , Lee Joseph , Kiesel Peter , Goebel Kai
Published: November 2024

It is crucial for many applications to detect bearing damage as early as possible to allow for scheduling of maintenance with lead times that minimize operational disruption. State of the practice is the detection of spalling but damage initiates prior to spalling as subsurface and surface cracks. Such damage is much harder to detect and to model. This study proposes a unique application of the nanofrictional Prandtl-Tomlinson model to predict macroscopic acoustic emission (AE) signals that occur at cracked interfaces under relative motion. The study integrates large deformation modelling of structures with elastodynamic simulations to investigate early AE signals generated under different bearing rotational speeds. Experimental studies are carried out to measure acoustic vibrations from metal-metal surface friction using fiber optic sensors and compared to those predicted by the model. Broad agreement of results highlights the validity of this framework.