Reviewerly/exHarmony huggingface dataset:
Crustal Deformation of Biga Peninsula and Structural Controls on Porphyry Cu-Au and Epitermal Au Mineralization in Kirazlı Gold Deposit (Türkiye)

Kirazl&amp;#305; porphyry Cu-Au and epitermal Au mineralization is located in Biga peninsula where the region hosts numerious porphyry- and epithermal- style Au mineralizations within the Tethyan orogenic belt. Crustal deformation in the region is resulted by Cretaceous collusion during the closure of northern branch of Neotethys Ocean, related subduction, post-collusion, Cenozoic extension and following dextral strike-slip deformation regime which is emerged during the westward migration of Anatolian plate. The study includes regional fault mapping, slip data collection from regonal and district scale faults for paleostress analysis, oriented surface sampling of vein hosted deformational zones and micro-structural thin section examinations of oriented samples. Paleostress findings and fault orientations indicates two seperate character of deformations as nearly E-W trending extensional fault systems and subsequent NE-SW striking, steeply dipping dextral strike-slip faults with accompanying NNW-SSE trending left-lateral strike slip and ENE-WSW trending dextral strike-slip and oblique-slip faults. Later tectonic phase related with N-E Dextral strike-slip faults establishes the main deformational trend with accompanying district scale&amp;#160; R (synthetic) ENE-WSW trending dextral and NNW-SSE trending R' (antithetic) sinistral strike-slip faults. Slip data related to&amp;#160; E-W and ENE-WSW faults indicate that these faults are subjected to both N-S trending extensional and NE-SW trending dextral strike-slip tectonic regime. The petrographic and textural studies of oriented thin sections resulted in identification of two predominant vein directions as ENE-WSW and NNW-SSE of porphyry mineralization within the project area. ENE-WSW trending syntaxial, streched-blocky quartz bearing veins indicates multiple N-S extension and crack-seal events and postdated by NNW-SSE trending quartz veins. Also the veins with same orientation which were observed during field studies share similar orientations.This study presents the early results off Ph.D. thesis "Crustal Extension and its Relationship to Porphyry Cu-Au and Epithermal Au Mineralization in the Kirazl&amp;#305; Gold Deposit (&amp;#199;an, &amp;#199;anakkale, T&amp;#252;rkiye)" and supported by Alamos Gold Inc..
Towards the European Green Deal Data Space

The European Commission has a program to accelerate the Digital Transition and is putting forward a vision based on cloud, common European Data Spaces and AI. As the data space paradigm unfolds across Europe, the Green Deal Data Space emerges. Its foundational pillars are to be built by the GREAT project.&amp;#160;Data Spaces will be built over federated data infrastructures with common technical requirements (where possible) taking into account existing data sharing initiatives. Services and middleware developed to enable a federation of cloud-to-edge capacities will be at the disposal of all data spaces.GREAT, the Green Deal&amp;#160;Data Space&amp;#160;Foundation&amp;#160;and its Community of Practice, has the ambitious goal of defining how data with the potential to help combat climate and environmental related challenges, in line with the European Green Deal, can be shared more broadly among many stakeholders, sectors and boundaries, according to European values such as fair access, privacy and security.&amp;#160;The project will consider and incorporate community defined requirements and use case analyses to ensure that the resulting data space infrastructure is designed and built with and for the users.&amp;#160;An implementation roadmap will guide the efforts of multiple actors to converge toward a blueprint technical architecture, a data governance scheme that enables innovative business cases, and an inventory of high value datasets, that will enable proof of concept, implementation and scale-up of a minimum viable green deal data space.This roadmap will identify the resources and other key ingredients needed for the Green Deal Data Space to be successful. Data sharing by design and data sovereignty are some of the main principles that will apply from the early stages ensuring cost effective and sustainable infrastructures that will drive Europe towards a single data market and green economic growth.&amp;#160;This talk will present how to engage with the project, the design methodology, progress towards the roadmap for deployment and the collaborative approach to building data spaces in conjunction with all the sectoral data spaces and the Data Space Support Centre.
Implementing FAIR metrics and assessments for the Earth and Environmental Sciences

Although there has been great acceptance across disciplines for years to make research data FAIR (findable, accessible, interoperable, and reusable), there is still no consensus in most communities on how to implement this concretely on a discipline-specific basis. Available metrics are exclusively domain agnostic, and there are few approaches to formulate binding metrics and tests specifically for particular domains&amp;#160; (e.g. earth and environmental science disciplines) and to implement them in assessment tools.In this presentation, we will introduce new approaches being developed in the FAIR-IMPACT project, based on domain specific use case partners and their communities, including those from the earth and environmental sciences (e.g. collaboration with the communities involved in the FAIR-EASE project), to extend and thus adapt existing FAIR metrics for assessing data objects and the F-UJI FAIR Assessment Tool to more fully incorporate the disciplinary, 'geo' context. A particular focus here will be on incorporating geo-specific metadata standards, covering data formats and semantic artefacts&amp;#160; within FAIR metrics, and the detection or verification of these standards by the F-UJI FAIR Assessment Tool. We will finally report also on the collaboration with one of the EIDA Data Centers (as part of the European Infrastructure for seismic waveform data in EPOS) where the F-UJI FAIR Assessment Tool has been further developed to be aware of the very domain specific standard data and metadata as well as services.&amp;#160;
Near-real-time microseismic monitoring with machine-learning and waveform back-projection at the Utah FORGE geothermal site

Deep geothermal energy exploitation necessitates establishing effective fluid circulation paths for heat transfer and managing induced earthquake risk. By detecting and characterizing induced microseismic events, we can provide insights into the fracture network growth and the induced earthquake risk during hydraulic stimulation and geothermal production in enhanced geothermal systems (EGS). During hydraulic stimulation, monitoring has to be performed in near-real-time to provide timely information for assessing potential earthquake risk and for adjusting the stimulation plan. In addition, high-precision microseismic event location is vital for evaluating the connectivity of the stimulated reservoir and designing the trajectory of the production wells. However, achieving real-time monitoring and high-resolution location in a single monitoring workflow is challenging due to the low signal-to-noise ratio and short inter-event time of microseismic events.To address these challenges in microseismic monitoring, we build a near-real-time monitoring workflow that integrates machine-learning (ML) techniques for efficient event detection and waveform back-projection methods for high-precision event location. The proposed workflow is designed to utilize various pre-trained ML models to deal with the scarcity issue of training datasets in new EGS sites. We apply the proposed workflow to the microseismic dataset collected at the Utah FORGE geothermal site in a playback mode. Because most pre-trained ML models are trained on local earthquake datasets having larger event magnitudes and lower data sampling rates, we implement and evaluate various strategies, such as re-scaling, re-sampling, and filtering, to enhance the performance of pre-trained models on the microseismic dataset. We compare the obtained ML catalog with a reference catalog built from a conventional workflow consisting of automatic phase picking and manual refinement. Due to the application of ML and waveform back-projection techniques, our workflow can nicely separate microseismic events with very short inter-event times (in terms of a second) and cope with events with significant magnitude/amplitude differences, leading to more reliable event detections. Detailed comparisons show that the accuracy of ML phase identification is comparable to and sometimes even superior to manual picking (with a difference in milliseconds), which contributes to precise event locations.
Bridging the gap of Jose Magsaysay elementary school learners in numeracy: Makati localized numeracy assessment tool for stage 1 (Grades 1-3)

Numeracy is an essential skill needed in everyday life. The study aimed to assess the numeracy of Jose Magsaysay Elementary School learners using the Makati Localized Assessment Tool for Stage 1 (Grades 1-3). Specifically, the study determined the numeracy gaps encountered by the learners, the numeracy levels of the learners, and how the school can address the needs of the learners in numeracy. Utilizing a quantitative research design, the results of an eight-week intervention utilizing the Makati Localized Numeracy Assessment Tool indicated no statistically significant differences between pre-and post-tests for students in Grade 2 and Grade 3, although a relatively significant difference was seen for students in Grade 1. Furthermore, paired correlations showed no significant association between pre-and post-test results across all grade levels, suggesting the intervention for students' numeracy abilities did not succeed in closing numeracy gaps Findings also indicate that the majority of Grade 1, Grade 2, and Grade 3 students need major support for all eight numeracy skills examined and that the school must address the weakness of students in numeracy skills through better interventions such as exposing students to more drills and exercises, differentiated instruction, and using better instructional materials together with parent-student conferences for numeracy and one-on-one tutoring. It is also recommended that the school conduct new intervention strategies to determine what would assist in addressing the learners' numeracy gaps. In addition, as the intervention in the current research lasted just eight weeks, the school may potentially explore an extended numeracy intervention period that lasts longer than eight weeks for future interventions. Furthermore, future researchers might investigate interventions that incorporate parental participation, particularly for kids in Grades 1, 2, and 3, and examine the different factors that influence the eight dimensions of numeracy skills by the student to provide better numeracy gaps interventions for students.
eo-grow - Earth Observation framework for scaled-up processing in Python

Analysing EO data is a complex process, and solutions often require custom tailored algorithms. On top of that, in the EO domain most problems come with an additional challenge: How can the solution be applied on a large scale? Within the H2020 project Global Earth Monitor (GEM) we have updated and extended&amp;#160;eo-learn&amp;#160;with additional functionalities that allow for new approaches to scalable and cost-effective Earth Observation data processing. We have tied it with the Sentinel Hub&amp;#8217;s unified main data interface (Process API), the Data Cube processing engine for constructing analysis-ready adjustable data cubes using Batch Process API, and, finally, the Statistical API and Batch Statistical API to streamline access to spatio-temporally aggregated satellite data. As part of GEM processing framework, we have built eo-grow which facilitates extraction of valuable information from satellite imagery. eo-grow tackles the issues of scalability by enabling coordination of clusters to run the EO workflows over large areas using &amp;#160;Ray. At the same time the framework provides reproducibility and traceability of the experiments using schemed input configurations and their validation. In eo-grow a workflow based solution is wrapped into a pipeline object, which takes care of parametrization, logging, storage, multi-processing, data management and more. The pipeline object is configured via a well-defined schema allowing straightforward experimentation and scaling up: going to larger area of interest, running on different time interval, or tweak of any other pipeline parameter becomes just a matter of updating (json) configuration, which additionally serve as record of the experiment. eo-grow library has been publicly released on GitHub: https://github.com/sentinel-hub/eo-grow. The documentation available in the repository provides the overview of the eo-grow general structure, its core objects, and instructions on installation and using eo-grow with command line interface. Additional repository, https://github.com/sentinel-hub/eo-grow-examples showcases eo-grow on a few use-cases. In the presentation we will introduce the framework and showcase its usability on concrete examples. We will illustrate how eo-grow is used in large-scale research experiments, explain its role in reproducibility and show how the no-code approach and code reuse facilitate the productionalization of the workflows.
Probabilistic Machine Learning of the Natural Variability of Climate

Because of natural or internal variability, the behavior of processes ranging from unresolved small-scale physical and dynamical processes to the response of the climate system at the largest scales is probabilistic rather than denterministic. Indeed, it is also the case that while climate models are skilful at predicting the response of the climate system to external forcing, they are less skilful when it comes to predicting natural variability. A variety of probabilistic machine learning techniques ranging from Reservoir Computing to Generative Adversarial Networks to Bayesian Neural Networks are considered in the context of modeling natural variability. At the large scales, these models are seen to improve upon the Linear Inverse Modeling (LIM) approach which has itself been sometimes thought of as capturing the bulk of the predictable component of natural variability.&amp;#160;
Exploring New Instrument deGradation Models and Algorithms (ENIGMA)

The Solar Radiation and Climate Experiment measured Total Solar Irradiance (TSI)&amp;#160;and Solar Spectral Irradiance (SSI) from 2003 to 2020. The Solar Irradiance Monitor (SIM)&amp;#160;instrument measured SSI from 200nm to 2400 nm on a daily basis. The current SORCE-SIMinstrument degradation correction uses a measurement equation derived fromaccessible telemetry, the known instrument refraction geometry, inter-detector comparisons,and inter-spectrometer comparisons. While the current degradation model captures much of thelong-term trending, some of the parameters were adjusted without well-defined physicalinterpretations.The degradation model used for SORCE-SIM is not unique. We're reporting on work to Explore NewInstrument degradation Models and Algorithms (ENIGMA) to address issues with the currentmodel and with the derived corrected irradiances. The development of enhanced SORCE-SIMmeasurement equations permits the evaluation, and potential inclusion, of degradationmechanisms not captured in the present model.We present an initial updated degradation model which improves our ability to construct a&amp;#160;composite irradiance time series in combination with TSIS-SIM, whose measurements overlap&amp;#160;with SORCE for 2 years and has well-defined uncertainties. Combining the two datasets, allowedthe construction of a consistent composite irradiance time series from 2003-2023.&amp;#160;
Generalized Person Re-identification by Locating and Eliminating Domain-Sensitive Features

In this paper, we study the problem of domain generalization for person re-identification (re-ID), which adopts training data from multiple domains to learn a re-ID model that can be directly deployed to unseen target domains without further fine-tuning. One promising idea is removing the subsets of features that are not beneficial to the generalization of models. This can be achieved by muting the subset features that correspond to high back-propagated gradients as these subsets are easy for the model to overfit. But this method ignores the interaction of multiple domains. Therefore, we propose a novel method to solve this problem by comparing the gradients from two different training schemes. One of the training schemes discriminates input data from their corresponding domain to obtain back-propagated temporary gradients in the intermediate features. At the same time, another scheme discriminates input data from all domains to obtain the temporary gradients. By comparing the temporary gradient between the two schemes, we can identify the domain-generalizable subset features from those domain-specific subset features. We thus mute them in the subsequent training process to enforce the model to learn domain-generalizable information and improve its generalization. Extensive experiments on four large-scale re-ID benchmarks have verified the effectiveness of our method. Code is available at https://github.com/Ssd111/LEDF.git .
Six-Map Planning Method of Digital Transformation

The six maps in the planning method of digital transformation include the strategy map, business map, requirement map, application map (data intelligence), algorithm map, and data map. Companies can sort out the business map according to their strategy map. The requirement map is generated from the business map. After sorting out the requirement map, the application map (data intelligence) is formed by integrating the digital transformation path.
SOCIAL MEDIA USER PERSONALITY CLASSIFICATION BASED ON HOW USER LIVE AND MAKE DECISION

Personality classification is one of the ways in the field of Natural Language Processing (NLP) with a collection of data that can describe the user's personality through input sets of text documents such as status uploads. Social media is one way to interact online that can provide convenience for users, such as interacting, expressing themselves, and expanding friendships. Status posts on social media can be extracted into useful information in the personality classification process. This research performs classification based on how social media users live their lives and make decisions, which is a representation of the "Thinkers/Feelers" and "Judgers/Perceivers" class attributes of the Myers-Briggs Type Indicator (MBTI) model. Researchers are encouraged to develop a personality classification system with feature extraction that can improve system performance. In this research, there are three main experiments conducted, experiments using data with oversample techniques in the Thinker/Feelers (TF) and Judgers/Perceivers (JP) classes provide the best results compared to other experiments with f1-score and accuracy of 92% using the Random Forest classification method and Glove as the extraction feature.
ENTERPRISE ARCHITECTURE DESIGN BLUEPRINT FOR SMART VILLAGE IMPLEMENTATION IN SUMUR BANDUNG SUB DISTRICT (CASE STUDY : SMART LIVING DIMENSION)

Sumur Bandung District is one of Bandung City's sub-districts that provides public services and empowerment to its residents. The sub district faces a number of issues in carrying out its duties and providing services. One of the issues confronting Sumur Bandung Sub District is the lack of Final Waste Processing Sites (TPPAS). With the current waste management model, there will be more garbage piles in TPPAS, causing environmental problems for its resident. As a result, The implementation of one of the dimensions in the smart village, namely smart living, is required. by improving current waste management model and supported by technological innovation. However, technological innovation itself will cause new problems if management is seen only as an activity provision of software/hardware for automation needs. Based on these issues, it is necessary to create a Enterprise architecture design of waste management that aims to align technology strategy with business strategy, TOGAF ADM was chosen as the framework because it provides a method for developing, managing, and implementing enterprise architecture maintenance. The results of the design are in the form of a blueprint that can be used as a reference for Sumur Bandung Sub District to overcome issues.
Web Scraping with HTML DOM Method for Website News API creation

Information is one of the important things in this era, one of the information that always exists every day is news. The amount of news that appears every day becomes a new problem when news websites do not provide API (Application Programming Interface) services to get the news. This is an obstacle for researchers who will analyze news topics. The copy and paste method is less effective in getting news every day on news websites because it takes a long time. In this research, web scraping is done with the HTML (Hypertext Markup Language) DOM (Document Object Model) method to retrieve data from news sites. The results of web scraping are in the form of datasets which are then entered into the database and made into an API. The API that has been created is tested using black box testing and testing the suitability of the data, between the data obtained when scraping and the data on the news website at the time of testing. The results of testing using black box testing show that the filters on the API created run according to their functions and get a high percentage of data conformity. The Tribunnews.com news website has a conformity rate of 99.2%, Detik.com of 97.9% and Li-putan6.com of 98.6%.
THE IMPACT OF PUBG ON STUDENTS OF SARGODHA UNIVERSITY

This study concentrates on the impact of PUBG on students of the University of Sargodha. The study’s purpose is to find out how PUBG affects students' physical and mental health. In the 21st century, there is massive growth in technology with huge progress in the digital world. The supercomputers of the 1960s are replaced by 21st-century laptops, and telephones are replaced by Tech mobile phones. So as the games of computers are replaced by mobile gaming. Especially in the late 2010-decade, mobile online gaming sees massive growth. The games like PUBG mobile, Call of Duty mobile, free fire make a huge impact among students, but especially PUBG has taken mobile gaming to the next level. PUBG aka Player unknown’s Battleground is online gaming in which 100 players participate in a game as an individual, Duo, or as a squad of 4 players. The last player or squad alive wins the game (PUBG, 2021). This study checks the impact of PUBG on students of the University of Sargodha. The researcher has selected 100 students while using convenient sampling technique. The respondents answer the questionnaire through a google form.