Reviewerly/exHarmony huggingface dataset:
The Perception and Education Needs of Active Seniors on Successful Aging

Focused on active seniors, this study attempted to provide basic information for developing systematic old-age preparation programs and online platforms by analyzing their overall characteristics, perceptions of successful aging, and their demand of old age preparation education.Results are as follows.Active seniors had high income levels and life satisfaction, lived with their spouse, and had a college degree or higher.Physical aging seems to be an important turning point at the beginning of old age, and the government's benefits for the elderly and retirement was also important criteria.In successful aging, active seniors were having stronger importance than inactive seniors in all three areas of daily well-being, psychosocial well-being, and self-efficacy.Although Active seniors were planning or already well-prepared for old age in terms of physical, economic, and social aspects, they were willing to participate in additional education of old age preparation, especially physical health care, leisure and hobbies, psychological health care, family relationships, and economic management.Regarding the meaning of work in old age, the active group cited personal satisfaction and growth as the biggest meaning.Based on these results, the contents of education
Development of Virtual Reality Biology Education Content Using Instructor's Gaze Visualization Interface

Since virtual reality(VR) education is based on a 3D environment, it is characterized by being able to deliver more information than traditional multimedia educational content.However, if a lot of information is delivered to learners simultaneously, a cognitive load occurs, and learning performance decreases.In this case, using the instructor's gaze visualization interface can reduce learners' distractions and learners can focus on important learning content.In addition, eye-tracking technology can be used to evaluate learning performance, and concentration can be measured through dwell time.In this paper, virtual reality biology
Optimization of Machine Learning Algorithms Through Hyper-parameter Tuning Applied for the classification of Arabic news

Abstract In the field of machine learning, hyperparameter tuning is a technique used to determine which learning algorithms have the most effective parameters. Using the Grid Search strategy as part of the machine learning algorithms, we suggest a few different ways to improve the accuracy of text categorization. In this research, we combined the TF-IDF feature selection approach with three different machine learning algorithms. These algorithms are Multinomial Logistic Regression MLR, Support Vector Machine SVM, and Artificial Neural Network ANN. According to the results of our tests, hyperparameter adjustment can significantly improve the classifier's performance.
The Role of Mothers with Adolescent Readiness which will Menarche at Jolotundo 2 Jetis Elementary School, Mojokerto: Correlation Study

Menarche is the first time menstruation that happened in early adolescent. One factor that affects the readiness of young people in the face of menarche is the source of information of the nearest person is the mother. The role of a good mother would give a good readiness also to his daughter to confront menarche. This research proposed to investigate the role of mother's relationship with the adolescent readiness to confront menarche. Design of research is an analytic correlation with cross sectional approach. The population in this research were 26 girls who have not experienced menarche at SDN Jolotundo 2 Jetis Mojokerto District. The techniques samples used a total of sampling with respondents 26 girls who have experienced menarche.The data collection is done by used a questionnaire research instrument roles of mothers and adolescents readiness. Data processing and data analysis using cross tabulation. The results of research obtained the role of a mother who less good tend to have adolescents readiness was not ready to confront of menarche is 16 people (94,1%), while respondents who have the role of a good mother is likely to have a ready readiness to confront menarche is 7 people (77.8%), which means that there is relationship readiness role of mothers with adolescent readiness confront menarche. Expected for the mother can give education about it what need to be preapared by her daughter in the confront of menarche in order to be able to form the readiness her daughter in the confront of menarche.&#x0D; Keywords: adolescent readiness, menarche, the role of mothers
Consistent Epistemic Planning for Multiagent Deep Reinforcement Learning

Abstract Multi-agent cooperation needs to reason about beliefs in the partially observable environment without communication, but the traditional Multi-agent Deep Reinforcement Learning (MADRL) algorithm struggles to handle the uncertainty of agents. Multi-agent Epistemic planning (MEP) tries to let the agent find a best plan to complete the cooperation task, so as to more effectively solve the uncertainty. However, inconsistent planning arises if the MADRL only adds MEP. We propose a MADRL-based policy network architecture called SMM-MEPP: Shared Mental Model - Multi-agent Epistemic Planning Policy. Firstly, Multi-agent Epistemic Planning and MADRL are investigated to build the "Perception-Planning-Action" multi-agent epistemic planning framework. Then, mental model in psychology is introduced and descript as a neural network. Thirdly, parameter sharing mechanism is utilized to achieve the shared mental model and maintain the consistency of epistemic planning. Finally, we apply the SMM-MEPP architecture to three advanced MADRL algorithms (i.e., MAAC, MADDPG and MAPPO) and conduct comparative experiments in multi-agent cooperation tasks. Experiments show that the proposed method can bring consistent planning for multiple agents, and improves convergence speed or training effect in partially observable environment without communication.
Zö Kramers 1978-1983

Zö Kramers was an orchestra in a mix of punk, reggae, ska, marching music, blues, pop, rock but not jazz. Participating in various roles: Calle Ottestam / bass, vocals, lyrics, music Per Sommarström / guitar, vocals, lyrics, music Martin Fälthammar / drums, text Olof Halldin guitar, keyboard, music, voice, percussion, mixing Mikael Södersten / keyboard, vocals, lyrics, music in addition Peter Söderblom / synth Persa Jacobson / text Katte Salomon / song Joakim Pirinen has drawn and written texts For individual bets - click on "Vilka Vem När Var Hur Vad". History: Zö Kramer's original composition was Calle Ottestam, Per Sommarström, Olle Halldin, Martin Fälthammar and initially Peter Söderblom. Added after about a year did Mikael Södersten. First on drums was Jolly Bod but was soon replaced by Martin. Katte Salomon also sang on the recordings from 1983. Music and lyrics were written by the band members but lyrics were also written by Joakim Pirinen and Persa Jacobson. The band's first name was "Arne & his anabolic steroids". The name "Zö Kramers" comes from Kramer-Pollnow's hyperkinetic syndrome - (dance disease) a psychotic condition characteristic of the band's dance-inspired style. Between 1978-1983, about forty songs were written. About twenty concerts were mostly held on Lidingö. The band's biggest "hit" was Tyska marionetter (German puppets). Live recordings have been available since the beginning and about 25 concerts were performed, but more studio mixes were made in four rounds where the first was done in 1979. Recording was done with a four-channel TEAC tape recorder and TEAC mixer table. Sound improvements were made in 2002 at the same time as digitization and in 2020 before the launch of this website.
GEOTEK: Extracting Marine Geological Data from Publications

In Marine Geology, scientists persistently perform extensive experiments to measure diverse features across the globe, hence to estimate environmental changes. For example, Mass Accumulation Rate (MAR) and Sedimentation Rate (SR) are measured by marine geologists at various oceanographic locations and are largely reported in research publications but have not been compiled in any central database. Furthermore, every MAR and SR observation normally carries i) exact locational information (Longitude and Latitude), ii) the method of measurement (stratigraphy, 210Pb), iii) a numerical value and units (2.4 g/m2/yr), iv) temporal feature (e.g. hundred years ago). The contextual information attached to MAR and SR observations is heterogeneous and manual approaches for information extraction from text are infeasible. It is also worth mentioning that MAR and SR are not denoted in standard international (SI) units.We propose the comprehensive end-to-end framework GEOTEK (Geological Text to Knowledge) to extract targeted information from marine geology publications. The proposed framework comprises three modules. The first module carries a document relevance model alongside a PDF extractor, capable of filtering relevant sources using metadata, and the extraction module extracts text, tables, and metadata respectively. The second module mainly comprises of two information extractors, namely Geo-Quantities and Geo-Spacy, particularly trained on text from the Marine Geology domain. Geo-Quantities is capable of extracting relevant numerical information from the text and covers more than 100 unit variants for MAR and SR, while Geo-Spacy extracts a set of relevant named entities as well as locational entities, which are further processed to obtain respective geocode boundaries. The third module, the Heterogeneous Information Linking module (HIL), processes exact spatial information from tables and captions and forms links to the previously extracted measurements. Finally, the all-linked information is populated in an interactive map view.
Deep Learning for volcanic risk assessment

The large amount of lava outflows during effusive eruptions can cause profound morphological changes, affecting both the natural and inhabited environment, destroying buildings, agricultural fields and important infrastructures such as roads, power lines, aqueducts and even modified the coastline. The ongoing demographic congestion around volcanic structures increases the potential risks and costs that lava flows represent and leads to a growing demand for implementing effective risk mitigation measures. Therefore, it is important to assess the elements at risk in volcanic areas to establish the mitigation actions to reduce the lava flow risk. Risk management for volcanoes is not just an emergency response to save lives but is also important in terms of economic loss. However, the collection of data regarding exposed elements surrounding the volcanoes is a lengthy and time-consuming process but utilizing the satellite images together with several machine learning techniques helps address this goal. We propose a cloud based platform in Google Colab using Land Use and Land Cover (LULC) classifiers to automatically assess the elements at risk by exploiting freely available high spatial resolution satellite images. This procedure allows to get an updated map of elements at risks in volcanic areas worldwide and will allow to routinely update the exposure map and thus risk map. In fact, up-to-date risk maps are fundamental to reaching the optimal decision in case of any hazard and crisis and can help us add or delete critical zones around the volcano. Using the freely available Sentinel 2-Multispectral Instrument (MSI) images and deep learning models, we aim to test the LULC applicability to a variety of volcanic areas whilst comparing the performances of two Convolutional Neural Network (CNN) architectures, namely VGG16 and ResNET50.
Performance analysis of a U-Net landslide detection model

Image segmentation algorithms are a type of image classifier that assigns a label to each individual pixel in an image. U-Nets, initially developed for the analysis of biomedical images and now widely used in a variety of fields, are an example of such algorithms. It has been shown that U-Nets are specially interesting when working with small training datasets and combined with data augmentation techniques.In this study, we used satellite images with labelled landslide masks from known events to train a U-Net to identify areas of potential landslide. These landslide masks are time-consuming to create, resulting in a small initial training set. Even when working with U-Nets, the success of machine learning and AI tools depends on the availability and quality of training data, as well as the algorithm settings during the training process. Tuning machine learning models to achieve the best performance possible from limited amounts of data is important to generate trustworthy results that can be used to advance the knowledge of landslide events around the world.Here, we show the differences in algorithm performance as we use different types of data augmentation and model parameters. We also explore and assess the effects on performance of options such as including different satellite bands, terrain information and alternative colour band representations.
CAT4KIT: A cross-institutional data catalog framework for the FAIRification of environmental research data

A contemporary and flexible Research Data Management (RDM) framework is required to make environmental research data Findable, Accessible, Interoperable, and Reusable (FAIR) and, hence, provide the foundation for open and reproducible earth system sciences. While data-sets that accompany scientific articles are typically published via large data repositories like Pangaea or Zenodo, intermediate, day-to-day, or actively-used data (e.g., data from research projects or prototypical data) is still exchanged via simple cloud storage services and email. And while the FAIR principles require data to be openly findable and accessible, it is often only available within closed and restricted infrastructures and local file systems.Our research project Cat4KIT hence aims to develop a cross-institutional catalog and RDM framework for the FAIRification of such day-to-day research data. This framework is comprised of four modules / services for providing access to data on storage systems through well-defined and standardized interfaces&amp;#160; harvesting and transforming (meta)data into standardized formats making (meta)data accessible to the public using well-defined and standardized catalog services and interfaces enabling users to search, filter, and explore data from decentralized research data infrastructures. We develop, implement and evaluate each of these four modules within an inter-institutional consortium consisting of scientists, software developers and potential end-users. This allows us to include a wide-range of research data from multi-dimensional climate model outputs to high-frequency in-situ measurements. We emphasize the application of existing open-source solutions and community standards for data interfaces (THREDDS, STA, S3), (meta)data schemes, and catalog services (Spatio-Temporal Assets Catalog - STAC) in order to ensure an easy integration of research data into the Cat4KIT-framework and a straightforward extension to further research data infrastructures.In our presentation, we demonstrate the current status of our Cat4KIT-framework as an inter-institutional research data management and catalog platform for the FAIRification of day-to-day research data.
The OneGeochemistry initiative as a CODATA Working Group; bringing together international geochemical data systems for easy data discovery

As geochemical data enable understanding of the Earth system and help to address critical societal issues the organisation thereof is important. Questions asked about processes affecting our environment and geological past become more complex and interdisciplinary in nature as well as multidimensional. To help answer these questions within the geochemistry research capabilities and data compilations are required to be comprehensive and both human and machine readable. Various international organisations are building infrastructure to capture and distribute geochemical data in a consistent manner adhering to the FAIR principles.&amp;#160;Since May 2021 the OneGeochemistry initiative has officially started efforts towards aligning these organisations&amp;#8217; data frameworks in order to standardise how geochemical data is reported around the globe. In November 2022 the OneGeochemistry initiative applied and was granted to become the OneGeochemistry CODATA Working Group as part of the International Science Councils Committee on Data. The initiative has now also been endorsed by the Geochemical Society, the European Association of Geochemistry and the Working Group has been endorsed by the IUGS Commission on Global Geochemical Baselines. Coordination of the OneGeochemistry initiative is funded through the WorldFAIR project where it is one of the work packages in the larger &amp;#8216;WorldFAIR: Global cooperation on FAIR data policy and practice&amp;#8217; project. A FAIR Implementation Profile analyses of the geochemistry communities of Australia (AusGeochem), USA (EarthChem, AstroMat) and Europe (GEOROC-DIGIS, EPOS-MSL, NFDI4EARTH) resulted in recognition of the need for common vocabularies for geochemistry data reporting as one of the most important actions to undertake towards international geochemistry data interoperability. A task adopted by EarthChem-DIGIS(GEOROC)-GFZ(DataSystems) collaboration and Research Vocabularies Australia.Here we will present an overview of the current OneGeochemistry initiative and its preliminary outcomes with regards to FAIR Implementation Profiles and processes that will help enable geochemical data interoperability between various stakeholders.
The WorldFAIR project: enabling global interdisciplinary cooperation on integrating FAIR Data policy and practices in geochemistry with ten other disciplinary groups.

&amp;#8216;WorldFAIR: Global cooperation on FAIR data policy and practice&amp;#8217; is a European Commission funded project composed of 11 discipline and cross-discipline case studies drawn together by CODATA, the Committee on DATA of the International Science Councils Committee on DATA, and is supported by the Research Data Alliance. WorldFAIR is a diverse, global community effort that currently has 19 partners located in Africa, Australasia, Europe, and North and South America, representing organisations from research, government and industry. The 11 individual case studies are drawn from Chemistry, Nanomaterials, Geochemistry, Social Surveys, Population Health, Urban Health, Biodiversity, Agriculture, Oceans, Disaster Risk Reduction and Cultural Heritage. The WorldFAIR project aims to focus on the interoperability and reusability of research data products from both within and across disciplines by creating a Cross-Domain Interoperability Framework (CDIF).The foundation of the CDIF will be a series of FAIR Implementation Profiles (FIPs) which will be used as a methodology for individual communities to express their FAIR practices and decisions for each of the 15 individual FAIR guiding principles.&amp;#160;As an example of how this will work, the WorldFAIR&amp;#8217;s Geochemistry case study is led by OneGeochemistry, an international network of national geochemical data infrastructure organisations. Initially an informal network with representatives from AuScope (Australia), GEOROC (Germany), EPOS Multi-scale Laboratories (Europe), EarthChem (US) and AstroMaterials (US). With the advent of WorldFAIR, OneGeochemistry has formalised it&amp;#8217;s governance structure and is now a CODATA Work Group. Over the life of WorldFAIR, OneGeochemistry will work towards developing a community prototype FAIR Implementation Profile(s) for individual geochemical techniques, including the minimum defined variables, through workshops and consultations, and subsequently be responsible for their communication, publication and dissemination. The Geochemistry case study will work closely with the Chemistry case study and leverage relevant chemical standards and vocabularies wherever possible.Through the development of community lead FAIR Implementation Profile(s) for geochemistry within a global Cross-Domain Interoperability Framework (CDIF), WorldFAIR and OneGeochemistry are both advancing the adoption of the FAIR data principles within Geochemistry and simultaneously enabling interoperability of geochemical research data products across the other ten discipline case studies.
Using Bayesian network to build a participatory framework for sustainable agriculture water management in Yunlin, Taiwan

Agricultural water use comprises the major part of the total water consumption in many countries, and Taiwan is no exception. However, urbanization and industrialization have triggered the competition for water among different sectors. Water is transferred to satisfy the daily need and industrial need, especially the need of high-tech industries, from the agricultural sector. Groundwater hence becomes an alternative water resource for agriculture, but the over-exploitation of groundwater resources also leads to some problems such as environmental degradation and land subsidence, and climate change has worsened the situation in the recent years.In Taiwan, groundwater is one of the vital water resources for irrigation, especially when the first crop rice begins being cultivated in the late dry season in central Taiwan. Yunlin County located in central Taiwan is chosen as the study area, which is now facing severe issues about groundwater over-exploitation and suffering from land subsidence threatening the safety of Taiwan High Speed Rail. Because of the high water consumption, groundwater extraction from agriculture is deemed to be the major cause of the land subsidence and should be well monitored and reduced. However, farmers&amp;#8217; pumping behaviors are highly related to the national water allocation policy, food policy and the socioeconomic factors in the rural area. The top-down agricultural water management might not be sufficient and sustainable. Hence, in this study, we propose a participatory framework for agricultural water management using a Bayesian network. The framework tries to incorporate the main factors that affect decision making among different stakeholders, including the Water Resources Agency, Irrigation Agency, Agriculture and Food Agency, farmers, etc., and represent the causal relationship among factors through Bayes&amp;#8217; theorem, or the conditional probability tables (CPTs). The CPTs are constructed based on data, literature reviews and interviews with stakeholders. The key issues concerning different stakeholders are considered in the framework as well, such as surface water shortage for agriculture, land subsidence, and sustainability of agriculture in Yunlin. The network can be used to hold discussions with stakeholders and show the interactions of their decisions among others. The aim of this framework is to facilitate the discussions and formulate the strategies for sustainable agricultural water management with the aid of the intuitive and transparent structure of the Bayesian network.