Miracle-dz/UniFAR-FTUs huggingface dataset:
CLUSTERING PROVINCE IN INDONESIA BY COMMUNICATION TECHNOLOGY RELATED VARIABLES

Technological developments in Indonesia growth rapidly. Almost all systems used in daily life have been using the technology. One of its technology is communication technology. It because communication technology is a important tool for send information. All was done in order to communicate easier and faster. It is therefore important to research the condition of the existing communication technology in Indonesia. Communications technology also one of the focus of the government in national development. But not easy to know the state of communication technology in Indonesia because Indonesia has a large region and different geographically. The purpose of this research was to determine the grouping of provinces in Indonesia to increase the communication sector in order to support national development. The method used in this research is cluster hierarchical analysis method and criterion of determining the best method and many cluster optimal use Cubic Clustering Criterion (CCC). The data used is secondary data from the Statisctics Indonesia (BPS) and the Ministry of Communication and Information. The results showed that the number of cluster based on related communication technology variables are 3 cluster which 1st cluster members consist of 21 provinces, 2nd cluster members consist of 7 provinces and 3rd cluster members consist of 3 provinces. Key words : Communications Technology, Cluster Analysis, Hierarchical Method, Cubic Clustering Criterion (CCC)
Micropetrology: Are Inclusions Grains of Truth?

Inclusions in minerals, whether fluids, melts or crystalline phases, are small pieces of the large-scale puzzle of Nature, time-consuming to investigate and often of difficult interpretation. Yet they are windows into the past of their host mineral. Mineral inclusions provide the opportunity to unravel the genesis of their host, whilst the increasingly refined understanding of their elastic behaviour provides the basis for alternative, equilibriumindependent geobarometry. Fluid and melt inclusions reveal information about material transfer in the Earth system, from shallow mineralization to mantle re-fertilization via subduction. The study of inclusions is thus one of the most intriguing and fertile branches of © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com Downloaded from https://academic.oup.com/petrology/advance-article-abstract/doi/10.1093/petrology/egy075/5060724 by guest on 30 July 2018 2 micropetrology. In this contribution, we focus on two recent developments: the use of elasticity models to extract the formation conditions of the host crystal, and the discovery and investigation of melt inclusions in metamorphic rocks. We also discuss how to evaluate the information provided by inclusions, given that they are no longer at the pressure and temperature conditions of entrapment. We discuss how to understand and quantify the changes undergone during cooling and depressurisation, and how metastability-related phenomena in inclusions, such as crystallization of rare polymorphs, and preservation of the original content of volatiles in fluid and melt inclusions, provide direct evidence that inclusions represent closed systems. The field of study of inclusions in minerals still has a largely-untapped potential. The most fruitful avenues for future research will emerge from continuous technological innovation in analytical and imaging techniques, the application of experimental petrology, and the development and application of new theoretical models for coupled mineral behaviour under changing P-T conditions.
Aufeis of the Indigirka river basin (Russia): the database
from historical data and recent Landsat images

Abstract. Detailed spatial geodatabase of aufeis in the Indigirka River, the basin area 305 000 km2, Russia was compiled from the Cadaster of aufeis of the North-East of the USSR published in 1958, topographic maps and Landsat images for 2013–2017. The aufeis area share varies from 0.26 to 1.15 % in different river sub-basins within the studied area. Digitized Cadaster (1958) contains the coordinates and characteristics of 897 aufeises with total area of 2064 km2. The Landsat-based identification of aufeises for 2013–2017 allowed the description of 1213 aufeises on a total area of 128 km2. The combined digital database of the aufeis is available at https://doi.pangaea. de/10.1594/PANGAEA.891036. The satellite-derived total area of aufeis is 1.6 times less than in the Cadaster (1958). At the same time, more than 600 aufeis identified by Landsat images analyses are missing in the Cadaster (1958). It implies that the aufeis formation conditions may have been changed between the mid-20th century and the present. About 60 % of total area presents 10 % of the largest aufeis. Most aufeis are located in the elevation band of 1100–1300 m. The interannual variability of the aufeis area was estimated by the example of the Bolshaya Momskaya naled (aufeis) and the group of large aufeis in the basin of the Syuryuktyakh River for the period of 2001–2016. The results of analysis indicate a tendency towards a decrease in the area of the Bolshaya Momskaya naled in recent years, at the same time the reduction in the aufeis area in the basin of the Syuryuktyakh River has not occurred.
Application Prospect of Induced Pluripotent Stem Cells in Organoids and Cell Therapy

Since its inception, induced pluripotent stem cell (iPSC) technology has been hailed as a powerful tool for comprehending disease etiology and advancing drug screening across various domains. While earlier iPSC-based disease modeling and drug assessment primarily operated at the cellular level, recent years have witnessed a significant shift towards organoid-based investigations. Organoids derived from iPSCs offer distinct advantages, particularly in enabling the observation of disease progression and drug metabolism in an in vivo-like environment, surpassing the capabilities of iPSC-derived cells. Furthermore, iPSC-based cell therapy has emerged as a focal point of clinical interest. In this review, we provide an extensive overview of non-integrative reprogramming methods that have evolved since the inception of iPSC technology. We also deliver a comprehensive examination of iPSC-derived organoids, spanning the realms of the nervous system, cardiovascular system, and oncology, as well as systematically elucidate recent advancements in iPSC-related cell therapies.
Research competences of early-career researchers

Development of human resources and empowerment of young scientists is one of the basic measures for achieving Serbia's current strategic scientific and technological development goals. The aim of our research was to determine which scientific production quality determinants (assumptions) of early-career researchers can strengthen their scientific research capacities and thus increase their scientific research quality. The research was performed using a descriptive method. An e-questionnaire for early-career researchers was prepared (N = 423). The results confirm that early-career researchers assess their scientific research competence differently depending on the scientific field in which they work, or have done their PhD. There are obvious differences in their assessments depending on the length of their work experience. Such results indicate that institutions and individuals need to intensify professional support for early-career researchers.
MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.
Coming Home, Staying Home: Adopters’ Stories about Transitioning Their New Dog into Their Home and Family

Simple Summary When the expectations and the reality of caring for a new dog clash, life can be challenging for adopters and their dogs. This study explored adopters’ experiences up to four years post-adoption. Many adopters in our study admitted their expectations were unrealistic and realised they were ignorant of the time and patience needed to build a positive relationship with their dog. Adopters believed building trust with the dog and learning its behavioural comfort limits in different environments was essential. Ensuring the dog’s and family members’ safety in their home was critical. Using local adopters’ stories could be a powerful tool in pre- and post-adoption support programs. Abstract Published research estimates shelter dogs’ post-adoption returns at 7–20%, with a significant percentage of these occurring in the first month post-adoption. To better understand factors that contribute to the success or failure of long-term rehoming outcomes, this study sought to understand post-adoption challenges up to four years post-adoption, targeting dogs identified as more likely to be returned. Thirty-one adopters participated in semi-interviews. Thematic analysis of their responses yielded three themes: (1) The adoption process takes time and requires patience; (2) Building trust and learning limits are essential to lasting adoptive relationships; and (3) Human–dog relationships are idiosyncratic because they involve unique individuals. These results have potential application in programs designed to guide adopters and dogs through a successful adoption process. Access to real-life adoption stories, such as those uncovered in this study, might help new adopters develop reasonable expectations and learn from others’ experiences as they work to develop lasting relationships with their dogs.
Response of short term precipitation to initial soil states in WRF-ARW model

Despite agreement at large-scale and synoptic scales in numerical weather prediction models, uncertainties in initial conditions lead to significant scatter in predictions of precipitation associate with mesoscale phenomena. Of particular importance for the disagreement are errors in initial soil state, which modulates evaporation and other energy and water exchange between the atmosphere and land. Errors in soil state lead to incorrect surface water and energy partitioning and mesoscale circulation. Observed soil state (especially soil moisture) data are limited in spatial and temporal scales. soil properties (moisture and temperature) are also very much model-dependent variables (Koster et al., 2004). Therefore it is still a challenging issue to prescript accurate initial soil state in numerical models. Most current practices in soil properties initialization are to prescribe soil state with so-called observed climatology, or to use other model’s prediction through interpolations or aggregations. These practices, however, may not be suitable to short to medium-range weather forecasting models because the errors in the initial soil properties may make the forecasting useless. In this study, we explore the impacts of initial soil conditions on precipitation using the state-of-art WRFARW model and the HRLDAS through a set of ensemble forecast under prefect-model assumption, whereby the same model used to generate a ‘true’ solution is used to generate ensemble forecasts. Though such ‘true’ state is not a direct analog to real numerical weather prediction where error contributed by model deficient may be significant or grater than that resulted from errors in initial conditions. However prefect model experiments do offer how best to design an ensemble of initial conditions in the absence of model error. Because the ‘true’ state and ensemble forecasts are on the same model grids, no additional errors will be induced in comparison and statistical verification. The generation of initial soil conditions and model configurations are discussed in section 2, followed by results presentation in section 3. General discussion and conclusion are given in section 4.
Metal Boundary Effect Mitigation by HKMG Thermal Process Optimization in FinFET Integration Technology

In this work, the influence of high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>/metal gate (HKMG) thermal processes such as post dielectric annealing (PDA), post metal annealing (PMA), and post amorphous Si cap annealing (PCA) on metal boundary effect (MBE) in FinFET is investigated. It is revealed that the PDA temperature increase leads to more severe MBE. On the contrary, the increase of soak or spike temperature of PMA is beneficial to reduce MBE. A higher PCA spike temperature also reduces MBE. It is demonstrated that by using an optimized combination of these three anneals, the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {t}}{)}$ </tex-math></inline-formula> shift induced by MBE can be reduced by about 50% without sacrificing device performance and reliability.
Experimental Analyses and Predictive Modelling of Ultrasonic Welding Parameters for Enhancing Smart Textile Fabrication

This study aims to illustrate the design, fabrication, and optimisation of an ultrasonic welding (UW) machine to join copper wires with non-woven PVC textiles as smart textiles. The study explicitly evaluates UW parameters’ impact on heat generation, joint strength, and electrical properties, with a comprehensive understanding of the process dynamics and developing a predictive model applicable to smart textiles. The methodological approach involved designing and manufacturing an ultrasonic piezoelectric transducer using ABAQUS finite element analyses (FEA) software and constructing a UW machine for the current purpose. The full factorial design (FFD) approach was employed in experiments to systematically assess the influence of welding time, welding pressure, and copper wire diameter on the produced joints. Experimental data were meticulously collected, and a backpropagation neural network (BPNN) model was constructed based on the analysis of these results. The results of the experimental investigation provided valuable insights into the UW process, elucidating the intricate relationship between welding parameters and heat generation, joint strength, and post-welding electrical properties of the copper wires. This dataset served as the basis for developing a neural network model, showcasing a high level of accuracy in predicting welding outcomes compared to the FFD model. The neural network model provides a valuable tool for controlling and optimising the UW process in the realm of smart textile production.
A systematic review of empirical evidence on migration influenced by environmental change in Africa

BACKGROUND Despite an increase in scholarly and policy interests in the impacts of environmental and climate change on migration, empirical knowledge in the field remains varied, patchy and limited. Generalized discourse on migration influenced by environmental change frequently leads to an oversimplification of the complex channels through which environmental change influences the migration process. The role of environmental and climate change in driving migration reported in existing studies seems to vary from one extreme to the other ‒ from limited and rather indirect role to significant impacts ‒ preventing us from drawing a conclusive evidence. OBJECTIVE This paper seeks to systematize the existing empirical evidence on migration influenced by environmental change with a focus on Africa, the continent most vulnerable to climate change. METHODS We combine elements of a systematic evidence assessment with a more reflexive form of evidence-focused literature review. 53 qualitative and quantitative studies selected from the comprehensive “Climig database” on the influence of environmental change on migration are systematically analyzed based on the framework of the multi-dimensional drivers of migration. RESULTS Environmental change influences migration in Africa in an indirect way i.e. through affecting other drivers of migration including sociodemographic, economic and political factors. How and in what direction environmental change influences migration depends on socioeconomic and geographical contexts, demographic characteristics and type and duration of migration. CONCLUSIONS It is not possible to draw a universal conclusion whether environmental change will increase or suppress migration in Africa since it is context-specific. CONTRIBUTION The review provides a first systematic and comprehensive summary of empirical evidence on environmental driver of migration in Africa considering direct and indirect pathways through which environmental change influence internal and international migration.
Changes in diagnostic patterns and resource utilisation in Swiss adult ICUs during the first two COVID-19 waves: an exploratory study

BACKGROUND AND AIM: The coronavirus disease 2019 (COVID-19) outbreak deeply affected intensive care units (ICUs). We aimed to explore the main changes in the distribution and characteristics of Swiss ICU patients during the first two COVID-19 waves and to relate these figures with those of the preceding two years. METHODS: Using the national ICU registry, we conducted an exploratory study to assess the number of ICU admissions in Switzerland and their changes over time, characteristics of the admissions, the length of stay (LOS) and its trend over time, ICU mortality and changes in therapeutic nursing workload and hospital resources in 2020 and compare them with the average figures in 2018 and 2019. RESULTS: After analysing 242,935 patient records from all 84 certified Swiss ICUs, we found a significant decrease in admissions (–9.6%, corresponding to –8005 patients) in 2020 compared to 2018/2019, with an increase in the proportion of men admitted (61.3% vs 59.6%; p <0.001). This reduction occurred in all Swiss regions except Ticino. Planned admissions decreased from 25,020 to 22,021 in 2020 and mainly affected the neurological/neurosurgical (–14.9%), gastrointestinal (–13.9%) and cardiovascular (–9.3%) pathologies. Unplanned admissions due to respiratory diagnoses increased by 1971 (+25.2%), and those of patients with acute respiratory distress syndrome (ARDS) requiring isolation reached 9973 (+109.9%). The LOS increased by 20.8% from 2.55 ± 4.92 days (median 1.05) in 2018/2019 to 3.08 ± 5.87 days (median 1.11 days; p <0.001), resulting in an additional 19,753 inpatient days. The nine equivalents of nursing manpower use score (NEMS) of the first nursing shift (21.6 ± 9.0 vs 20.8 ± 9.4; p <0.001), the total NEMS per patient (251.0 ± 526.8 vs 198.9 ± 413.8; p <0.01) and mortality (5.7% vs 4.7%; p <0.001) increased in 2020. The number of ICU beds increased from 979 to 1012 (+3.4%), as did the number of beds equipped with mechanical ventilators (from 773 to 821; +6.2%). CONCLUSIONS: Based on a comprehensive national data set, our report describes the profound changes triggered by COVID-19 over one year in Swiss ICUs. We observed an overall decrease in admissions and a shift in admission types, with fewer planned hospitalisations, suggesting the loss of approximately 3000 elective interventions. We found a substantial increase in unplanned admissions due to respiratory diagnoses, a doubling of ARDS cases requiring isolation, an increase in ICU LOS associated with substantial nationwide growth in ICU days, an augmented need for life-sustaining therapies and specific therapeutic resources and worse outcomes.
Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors

In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated.
Predictors of environmental guilt, and its role as a mediator of the association between human-nature relation and pro-environmental behavior intentions

The aim of the two studies (N = 245 and N = 199) was to investigate the predictors of environmental guilt and analyze its mediating role between human-nature relationship and pro-environmental behavior intentions. In the first study, the connectedness to nature and social dominance orientation emerged as predictors of environmental guilt. In addition, guilt was an important mediator of the relationship between the connectedness and individual pro- environmental behavior. In the second study, guilt was predicted by gender, by locating the causes of the climate crisis in human activities rather than in the Earth's natural cycles, and by environmental nostalgia. In addition, guilt mediated the relationship between environmental nostalgia and willingness to engage in collective action and support systemic changes. Thus, environmental guilt seems to be an important factor in predicting a wide range of environmentally friendly activities: individual behavioral intentions, willingness to engage in collective actions, and support for systemic changes.