Miracle-dz/UniFAR-FTUs huggingface dataset:
A SPATIOTEMPORAL FUSION NETWORK TO MULTI SOURCE HETEROGENEOUS DATA FOR LANDSLIDE RECOGNITION

Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. The model takes observations such as time-series optical remote sensing images, DEM, geological formations, and meteorological data as inputs. To address the problems of non-uniform data forms and redundancy caused by time-series data, we design the temporal phase fusion module of coupled CNN-LSTM to fuse the temporal features of multi-source data based on the extraction of their spatial features. Subsequently, we design the spatial feature fusion module based on DCNN-DBN to realize the deep expression of temporal phase and spatial features of landslides and improve the recognition efficiency and accuracy of the network. Through experimental verification, the AUC value of our proposed model is 0.8976, the F1 score is 0.8352, and the MIoU is 0.8624. The evaluation results reflect that the model can provide support for large-scale landslide disaster investigation.
The promises and pitfalls of government-funded teacher staffing initiatives on teacher employment in hard-to-staff schools: Evidence from South Carolina

Situated against teacher demand problems worldwide, rising teacher turnover and declining teacher education enrollment have rendered the state of South Carolina a region commonly described as facing a “teacher shortage crisis. ” This paper reports results from an evaluation of the Rural Recruitment Initiative (RRI), a state-level teacher staffing policy for SC’s hard-to-staff districts. Based on a decade of data analysis, we assess RRI’s causal effect using a Difference-in-Differences model with Arellano–Bond maximum likelihood design and find that RRI funding reduces teacher turnover rate by 1% for fund-receiving districts. We conclude by discussing how the initiative could be improved and what policymakers can learn from our results.
Parametrization of the Tkatchenko-Scheffler dispersion correction scheme for popular exchange-correlation density functionals: effect on the description of liquid water

We present a list of optimized damping range parameters $s_R$ to be used with the Tkatchenko-Scheffler van der Waals dispersion-correction scheme [Phys. Rev. Lett. 102, 073005 (2009)]. The optimal $s_R$ are obtained for seven popular generalized-gradient approximation exchange-correlation density functionals: PBE, RPBE, revPBE, PBEsol, BLYP, AM05 and PW91. The optimization is carried out in the standard way by minimizing the mean absolute error of the S22 test set, where the reference interaction energies are taken from coupled-cluster calculations. With the optimized range parameters, we assess the impact of van der Waals corrections on the ability of these functionals to accurately describe structural and thermodynamic properties of liquid water: radial distribution functions, self-diffusion coefficients and standard molar entropies.
Liouville theory revisited

We try to develop a coherent picture of Liouville theory as a two-dimensional conformal field theory that takes into account the perspectives of the path-integral approach, bootstrap, canonical quantization and the operator approach. To do this, we need to develop further some of these approaches. This includes in particular the construction of general exponential field operators from a set of covariant chiral operators. The latter are shown to satisfy braid relations that allow one to prove the locality of the former.
Analysis of Science-Based Entrepreneurship Skills and Parents' Academic Achievement Pressure and Support of Primary School Students

This study aims to examine whether there is a relationship between the academic achievement pressure and support of parents and students' science-based entrepreneurship and how these variables change in terms of parents' activities with their children. For this purpose, one of the quantitative research approaches, the discriptive survey method, was used in the study. 142 students from 3 different primary schools in Erzurum province participated in the study, using the appropriate sampling method. Demographic information questionnaire, Parental Academic Success Pressure and Support Scale (PASPSS), and Science-Based Entrepreneurship Scale (SBES) were used as data collection tools in the study. The data obtained from the participants were analyzed with the SPSS 26.0 package program. As a result, the science-based entrepreneurship levels of the students change in terms of the length of time the parents study with their children and participate in social sports activities. The pressure on their children's academic success is enhancing in terms of parents' children spending time in the digital environment while studying. On the contrary, the higher their children's educational expectations, the more they support their academic success. Finally, weak and intermediate relationships were determined regarding students' science-based entrepreneurship, parental academic achievement pressures, and support.
Evaluating sea ice thickness simulation is critical for projecting a summer ice-free Arctic Ocean

The rapid decline of Arctic sea ice, including sea ice area (SIA) retreat and sea ice thinning, is a striking manifestation of global climate change. Analysis of 40 CMIP6 models reveals a very large spread in both model simulations of the September SIA and thickness and the timing of a summer ice-free Arctic Ocean. The existing SIA-based evaluation metrics are deficient due to observational uncertainty, prominent internal variability, and indirect Arctic response to global forcing. Given the critical roles of sea ice thickness (SIT) in determining Arctic ice variation throughout the seasonal cycle and the April SIT bridging the winter freezing and summer melting processes, we propose two SIT-based metrics, the April mean SIT and summer SIA response to April SIT, to assess climate models’ capability to reproduce the historical change of the Arctic sea ice area. The selected 11 good models reduce the uncertainty in the projected first ice-free Arctic by 70% relative to 11 poor models. The chosen models’ ensemble mean projects the first ice-free year in 2049 (2043) under the shared socio-economic pathways (SSP)2-4.5 (SSP5-8.5) scenario with one standard deviation of the inter-model spread of 12.0 (8.9) years.
A Quantum Approach to Condensed Matter Physics

1. Semiclassical introduction 2. Second quantization and the electron gas 3. Boson systems 4. One-electron theory 5. Density functional theory 6. Electron-phonon interactions 7. Superconductivity 8. Semiclassical theory of conductivity in metals 9. Mesoscopic systems 10. The quantum Hall effect 11. Kondo effect and heavy fermions.
Distribution of dark matter in the spiral galaxy NGC 3198.

Two-component mass models, consisting of an exponential disk and a spherical halo, are constructed to fit a newly determined rotation curve of NGC 3198 that extends to 11 disk scale lengths. The amount of dark matter inside the last point of the rotation curve, at 30 kpc, is at least 4 times larger than the amount of visible matter, with M/L(B)tot = 18 solar M/L(B). The maximum mass-to-light ratio for the disk is M/L(B) = 3.6. The available data cannot discriminate between disk models with low M/L and high M/L, but arguments are presented which suggest that the true mass-to-light ratio of the disk is close to the maximum computed value. The core radius of the distribution of dark matter is found to satisfy R(core) of between 1.7 and 12.5 kpc. 31 references.
What determines the shape of the local (z < 0.1) infrared galaxy luminosity function?

We investigate what shapes the infrared luminosity function of local galaxies by comparing it to the local infrared AGN luminosity function. The former corresponds to emission from dust heated by stars and AGN, whereas the latter includes emission from AGN-heated dust only. Our results show that infrared emission from AGN starts mixing into the galaxy luminosity function in the luminous infrared galaxy (LIRG) regime and becomes significant in the ultraluminous infrared galaxy (ULIRG) regime, with the luminosity above which local ULIRGs become AGN-dominated being in the log(LIR/Lsun)~12.2-12.7 range. We propose that as a result of the AGN contribution, the infrared galaxy luminosity function has a flatter high luminosity slope than UV/optical galaxy luminosity functions. Furthermore, we note that the increased AGN contribution as a function of LIR is reflected in the average dust temperature (Tdust) of local galaxies, and may be responsible for the local LIR-Tdust relation. However, although our results show that AGN play a central role in defining the properties of local ULIRGs, we find that the dominant power source in the local ULIRG population is star-formation.
Scalable Influence-Aware Profit Maximization Over Livestreaming Marketing Network

Profit maximization (PM), with the purpose to select the appropriate set of initial seed users to maximize the effectiveness of diffusion, has become the focus of Social Network Analysis with broad prospects of applications such as social opinion propagation and Internet marketing. Under the condition of ensuring great performance, the existing PM models are faced with the challenge of time complexity and universality because they take too long to execute and their working conditions come-= with harsh restrictions. In this paper, we propose a new algorithm named CirclePrune (CP) which optimizes the runtime in large-scale network and loosens the constraints by warming up, and apply it to the scenario of livestreaming marketing. Experimental results confirm the effectiveness and efficiency for the CP algorithm.
A Multiple Observer Probability Analysis for Bell Scenarios in Special Relativity

Here we present a Multiple Observer Probability Analysis (MOPA) for the purpose of clarifying topics in experimental Bell scenarios. Because Bell scenarios are interested in quantum effects between nonlocal measurement devices, we assign an observer to each device: Alice and Bob. Given that the observers are stationary and space-like separated, each observer is privy to different information along their shared equi-temporal lines due to permutations in the order they observe events. Therefore, each observer is inclined to assign different probability distributions to the same set of propositions due to these informational differences. The observers are obligated to update their probability distributions on the basis of locally observed events, and in this sense, factuality is informational locality. In this framework, only local variables or detections may be factual, but nothing prevents an observer from inquiring or making if-then inferences on the counterfactual basis of a nonlocal proposition being true. Indeed the objects pertaining to these nonlocal counterfactual propositions may be far outside an observer's light cone. The MOPA arrives at the conclusion that the CHSH inequality is only nonlocally violated "counterfactually" by each observer whereas local violations of the CHSH may be factual or counterfactual. We believe the MOPA to better gel probability theory (and thus QM) with Special Relativity than does the standard locality conditions imposed in the Bell and CHSH inequalities. The no-signaling condition is reinterpreted, and perhaps further clarified, in the MOPA and statements about counterfactuality and observer dependent QM are made.
Knowledge Integration for Commonsense Reasoning with Default Logic

: Commonsense reasoning in artiﬁcial intelligence is the problem of inferring decisions and answers regarding mundane situations. Several research groups have built large knowledge graphs with the goal of capturing some aspects of commonsense knowledge. Using these knowledge graphs for problem solving and question answering is a subject of active research. Our contribution is encoding and integrating knowledge graphs like Quasimodo, ConceptNet, and Wordnet for symbolic reasoning. A major challenge in symbolic commonsense reasoning is coping with contradictory and uncertain knowledge, which we handle by extending ﬁrst order logic with numeric conﬁdences and default logic rules. To our knowledge this is the ﬁrst large scale commonsense knowledge base seriously using default logic. We give several examples of the proposed representation and solving questions on the basis of the knowledge base built.
NONCOMMUTATIVITY FROM THE SYMPLECTIC POINT OF VIEW

The great deal in noncommutative (NC) field theories started when it was noted that NC spaces naturally arise in string theory with a constant background magnetic field in the presence of D-branes. In this work we explore how NC geometry can be introduced into a commutative field theory besides the usual introduction of the Moyal product. We propose a nonperturbative systematic new way to introduce NC geometry into commutative systems, based mainly on the symplectic approach. Further, as example, this formalism describes precisely how to obtain a Lagrangian description for the NC version of some systems reproducing well-known theories.
Averaging Facial Images

The variation and change in the form and size of biological objects are studied by the branch of bioinformatics called morphometrics. Traditional methods include measuring distances, angles, areas and volumes.1,2 In the last three decades, more advanced methods have been developed based on the analysis of landmarks, points that have specific geometric locations and associated biological names. A morphometric study aims to describe a biological shape in the simplest possible way, removing extraneous information and facilitating comparison between different objects.3,4 The whole set of data describing the shape of an object is essentially replaced by a small number of landmarks, which are further analysed by statistical methods. This approach uses the power of mathematics to study complex shapes in a rigorous fashion and permits their objective analysis rather than subjective assessment.
Frequent Flare Events on the Short-period M-type Eclipsing Binary BX Tri

We present long-term, multicolor photometric monitoring and spectroscopic observations of the short-period M-type eclipsing binary BX Tri. Six flare events were recorded over four years from 2014 to 2017. Three of them were detected on one night within an orbital cycle. The strongest one was identified on 2014 December 23. With the amplitudes ΔB = 0.48 mag, ΔV = 0.28 mag, ΔR = 0.10 mag, and ΔI = 0.02 mag, the total energy due to the flare event was measured to be 4.08 (±0.24) × 1034 erg, exceeding the superflare level (1034). Based on the observations, the evolutionary status of the binary system and the long-term orbital period changes were analyzed. It reveals that BX Tri is probably a semidetached system with the primary component filling its Roche lobe. The extremely high occurrence of flare events in the binary could be related to rapid mass transfer between the components.
Acceptance and Commitment Training Focused on Psychological Flexibility for Family Members of Children with Intellectual Disabilities

The objective of the study was to analyse the effect of a psychological flexibility intervention programme based on Acceptance and Commitment Therapy (ACT) on 36 family members of children with intellectual disabilities. The 6-PAQ (parental psychological flexibility), PSS-14 (perceived stress), GHQ-12 (psychological health), and WBSI (suppression of unwanted thoughts) were used as measurement instruments before the programme (pre), after (post), and at follow-up (after two months). Possible change in family interactions due to the family intervention was also assessed through self-monitoring. A decrease in psychological inflexibility, a reduction in stress, an improvement in psychological well-being, and a reduction in the tendency to suppress thoughts and emotions were observed after the programme. Furthermore, the effects seem to extend to family interactions, with an increase in positive interactions and a decrease in negative ones. The study leads us to think about the importance of psychological flexibility in children with chronic conditions as a process that mediates the impact of stress and family well-being.
User Experience Dimensions for E-procurement

The use of e-procurement is needed for business transactions, especially regarding procurement activities. However, system usersalways demand and expect to use the system without problems. Existing studies on e-procurement do not focus on user experience(UX). Only a few studies have identified dimensions for UX evaluation; however, they are for e-government online services and construction. Identifying the UX dimensions for e-procurement is important for measuring user experience to provide better services. Therefore, this study attempted to investigate and determine the dimensions of user experience for e-procurement. The method for selecting articles was adopted from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The study analysed the data using thematic analysis based on the Systems and software Quality Requirements and Evaluation (SQuaRE) standards, such as ISO 25022:2016 and ISO 25023:2016, as guidance. The findings showed that among the most used UX dimensions in the e-procurement literature were satisfaction, security, transparency, efficiency, and reliability. Other UX-related dimensions identified from the review were usability, compatibility, effectiveness, performance efficiency, functional suitability, attractiveness, explainability, fairness, and visibility. The study was conducted to identify the UX dimensions for e-procurement from literature studies by organising them using ISO 25022:2016 and ISO 25023:2016 standards. This study could serve as a guideline for designers, developers, and researchers to develop an e-procurement system by referring to the proposed UX dimensions to produce a positive user experience. Moreover, the findings are beneficial to practitioners on software quality attributes.
Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)

Abstract This paper presents comparative performance analysis of GAs, PSO and BFA technique-based AGC regulators with MSPPs in each system. Each control area consists of hydro, thermal and gas power plants (HTGPPs) for generation of electricity. In this work, proportional integral derivative (PID) control structure-based AGC regulators are designed by using the above intelligent techniques. However, the cost function for designing of these intelligent controllers is formulated using the integral square error (ISE) performance index. In the previous AGC studies, simulation time for tuning of above intelligent AGC controller to analyze performance was not reported. Therefore, this research work proposes a new study to investigate the performance of these intelligent AGC controllers based upon performance indicators: (i) simulation time of these techniques, (ii) first peak value of transient response, (iii) oscillations in transient response and (iv) settling time of transient response. The parameters of the controllers are evaluated using these techniques and investigations are carried out to find the best performance of the system. Besides, system dynamic performance of BFA-tuned AGC controllers are also demonstrated for +1% to +4% step load disturbance in the respective control areas.
RESEARCH ON THE CONSTRUCTION OF GEOGRAPHIC KNOWLEDGE GRAPH INTEGRATING NATURAL DISASTER INFORMATION

Abstract. Natural disasters have a significant impact on the environment and economies of all countries around the world, and a large amount of multi-source heterogeneous geographic information data is generated every day. However, due to a lack of knowledge transformation capabilities, these nations continue to struggle with the issue of "a large amount of data and little knowledge". Therefore, it is of great significance how to extract geographic knowledge related to disasters from the vast data and construct a geographic knowledge graph integrating disaster information. Based on the theory related to knowledge extraction, this paper proposes a method to construct a natural disaster knowledge graph integrating geographic information. The core of this knowledge graph is to construct the association relationship between natural disaster concepts, research areas, and spatial data. The vocabulary and relationships associated with disaster concepts are primarily transformed by an existing word list of geographic narratives, which then provide rich semantic relationships of domain concepts for the entire knowledge graph. The research areas and spatial data types are mainly obtained through knowledge entity extraction and disambiguation methods. This disaster knowledge graph can support applications well such as natural disaster visualization and analysis, data recommendation systems, and intelligent Q&A systems, which can further improve the intelligence of natural disaster knowledge services and is expected to promote the sharing and reuse of domain knowledge graphs to a certain extent.
Enhancement of electric field of bowtie nanoantenna

A nanoantenna is designed to transform high frequency into energy. The proposed antenna is made of aluminuim. are shaped with a pair of nanoparticles brought in close nearness. Antennas separated by small gaps printed on a Si layer, which is designed as a flat-edge bowtie, with a ground plane at the under most of the substrate with a feeding putting in the gap of the bowtie antenna. The proposed antenna is designed using 3D-electromagnetic solver (CST) programs and analysed for the optimisation of metal thickness, gap size, and geometrical length. Simulations are conducted to investigate the behaviour of the nanoantenna illuminated by the linearly polarized plane wave. The nanoantenna parameters such as substrate thickness, feeding size, feeding type, and feeding material were changed to select the most efficient nanoantenna with a large directivity in our reaserch find the nanoantenna make a high electric field enhancement in their gap region. This specialty can be employed for SERS or biosensing to improve the detection limit and measure the presence of single molecules. For this, it is necessary to create antennas with enough small gaps, to be capable to recompense for the defects created during the fabrication process and reach antenna characteristics that are close to the ones presage by simulations. The numerical simulations are studied to improve the best E-field of the antenna within the 250–700 THz frequency range. The proposed antenna offers multiple-resonance frequencies and good return loss in the frequency band of 310 THz, as well as an output electric field of 5.48 v/m to 7.8 v/m. Upon changing the type of feeding in the gap (without feeding, air feeding, dielectric silicon, or feeding), and we find that when using the air between the gap, the S-parameter is (-12.9) dB at the resonance frequency of (531.3) THz and the directivity is 7.41 dB at 666 THz incident frequency.