Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales

Folk tales are strong cultural and social inﬂu-ences in children’s lives, and they are known to teach morals and values. However, existing studies on folk tales are largely limited to European tales. In our study, we compile a large corpus of over 1,900 tales originating from 27 diverse cultures across six continents. Using a range of lexicons and correlation analyses, we examine how human values, morals, and gender biases are expressed in folk tales across cultures. We discover differences between cultures in prevalent values and morals, as well as cross-cultural trends in problematic gender biases. Furthermore, we ﬁnd trends of reduced value expression when examining public-domain ﬁction stories, extrin-sically validate our analyses against the multicultural Schwartz Survey of Cultural Values, and ﬁnd traditional gender biases associated with values, morals, and agency. This large-scale cross-cultural study of folk tales paves the way for future studies on how literature in-ﬂuences and reﬂects cultural norms.
Community Disaster Resilience

Background This cross sectional study on“ Perception of teachers on community disaster resilience; Basis for the development of capacity building program’ was explored in this paper. Objectives To determine the profile of the teachers; To compare the membership to community organization of teachers; To compare the degree of perception of teachers on community resilience in terms of; 1. Connectedness 2. Risk of Vulnerability 3. Preparedness 4. Resources To compare the percentage perceptions of teachers on community resilience in terms of; Connectedness; level of risk and vulnerability; preparedness; resources. Methods Purposive sampling was used to 37 teachers from the 3 adopted barangays of University of the Philippines Open University which were part of UPOU Earth Ambassadors using an adopted questionnaire. Survey was carried out using a paper and pencil guided technique.
Effect of Ionic Liquid Structure on the Oxygen Reduction Reaction Under Humidified Conditions

The oxygen reduction reaction (ORR) is widely studied in room-temperature ionic liquids (RTILs) but typically in dry environments. Because water is known to affect diffusion coefficients and reaction outcomes, the influence of water on the ORR is expected to be significant. We have therefore studied the effect of RTIL structure on the ORR at different relative humidity (RH) levels using cyclic voltammetry. A broad range of cations including imidazolium, ammonium, pyrrolidinium, pyridinium, sulfonium, and phosphonium, and anions such as [BF4]−, [PF6]−, [NTf2]−, and [FAP]− were employed. The cation was found to have a large effect on the reduction current of oxygen, even at low humidity levels ( 65 RH%). Consequently, the choice of cation needs to be carefully considered when selecting a suitable RTIL solvent for oxygen reduction in humidified environments. The size, structure, and hydrophobicity of the ions were found to .
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution

Federated learning (FL) is a framework which allows multiple users to jointly train a global machine learning (ML) model by transmitting only model updates under the coordination of a parameter server, while being able to keep their datasets local. One key motivation of such distributed frameworks is to provide privacy guarantees to the users. However, preserving the users' datasets locally is shown to be not sufficient for privacy. Several differential privacy (DP) mechanisms have been proposed to provide provable privacy guarantees by introducing randomness into the framework, and majority of these mechanisms rely on injecting additive noise. FL frameworks also face the challenge of communication efficiency, especially as machine learning models grow in complexity and size. Quantization is a commonly utilized method, reducing the communication cost by transmitting compressed representation of the underlying information. Although there have been several studies on DP and quantization in FL, the potential contribution of the quantization method alone in providing privacy guarantees has not been extensively analyzed yet. We in this paper present a novel stochastic quantization method, utilizing a mixed geometric distribution to introduce the randomness needed to provide DP, without any additive noise. We provide convergence analysis for our framework and empirically study its performance.
Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments

This paper introduces a no-code, machine-readable documentation framework for open datasets, with a focus on responsible AI (RAI) considerations. The framework aims to improve comprehensibility, and usability of open datasets, facilitating easier discovery and use, better understanding of content and context, and evaluation of dataset quality and accuracy. The proposed framework is designed to streamline the evaluation of datasets, helping researchers, data scientists, and other open data users quickly identify datasets that meet their needs and organizational policies or regulations. The paper also discusses the implementation of the framework and provides recommendations to maximize its potential. The framework is expected to enhance the quality and reliability of data used in research and decision-making, fostering the development of more responsible and trustworthy AI systems.
Transformer as Linear Expansion of Learngene

We propose expanding the shared Transformer module to produce and initialize Transformers of varying depths, enabling adaptation to diverse resource constraints. Drawing an analogy to genetic expansibility, we term such module as learngene. To identify the expansion mechanism, we delve into the relationship between the layer's position and its corresponding weight value, and find that linear function appropriately approximates this relationship. Building on this insight, we present Transformer as Linear Expansion of learnGene (TLEG), a novel approach for flexibly producing and initializing Transformers of diverse depths. Specifically, to learn learngene, we firstly construct an auxiliary Transformer linearly expanded from learngene, after which we train it through employing soft distillation. Subsequently, we can produce and initialize Transformers of varying depths via linearly expanding the well-trained learngene, thereby supporting diverse downstream scenarios. Extensive experiments on ImageNet-1K demonstrate that TLEG achieves comparable or better performance in contrast to many individual models trained from scratch, while reducing around 2× training cost. When transferring to several downstream classification datasets, TLEG surpasses existing initialization methods by a large margin (e.g., +6.87% on iNat 2019 and +7.66% on CIFAR-100). Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19× parameters stored to initialize these models and around 5× pre-training costs, in contrast to the pre-training and fine-tuning approach. When transferring a fixed set of parameters to initialize different models, TLEG presents better flexibility and competitive performance while reducing around 2.9× parameters stored to initialize, compared to the pre-training approach.
A Stochastic Simulation Method for Fractional Order Compartment Models

Fractional order compartment models derived from an underlying physical stochastic process introduce a more nuanced and phenomenological approach to system modelling than compartment models obtained by replacing integer order derivatives with fractional order derivatives. In these models, inherent stochasticity becomes important, particularly when dealing with the dynamics of small populations; far from the continuum limit of large particle numbers. The need for stochastic simulations is motivated by such deviations from mean states of governing equations. This work presents an exact stochastic simulation algorithm designed for fractional order compartment models, based on a semi-Markov process. We have considered a fractional re-susceptibility epidemic model as an illustrative example, highlighting pronounced disparities between deterministic and stochastic dynamics when the total population is small. Beyond this singular example, the algorithm presented is a general tool for modelling fractional order differential equations via Monte Carlo simulations.
Contraction analysis of time-varying DAE systems via auxiliary ODE systems

This paper studies the contraction property of time-varying differential-algebraic equation (DAE) systems by embedding them to higher-dimension ordinary differential equation (ODE) systems. The first result pertains to the equivalence of the contraction of a DAE system and the uniform global exponential stability (UGES) of its variational DAE system. Such equivalence inherits the well-known property of contracting ODE systems on a specific manifold. Subsequently, we construct an auxiliary ODE system from a DAE system whose trajectories encapsulate those of the corresponding variational DAE system. Using the auxiliary ODE system, a sufficient condition for contraction of the time-varying DAE system is established by using matrix measure which allows us to estimate an lower bound on the parameters of the auxiliary system. Finally, we apply the results to analyze the stability of time-invariant DAE systems, and to design observers for time-varying ODE systems.
Macrotyloma uniflorum seed aqueous extract exhibits anticoagulant, antiplatelet and clot dissolving properties

Macrotyloma uniflorum is a robust pulse crop of semi-arid tropics that has been least studied. It is a novel legume belongs to the family Fabacea and the phylum Leguminosae (Kumar, e 2006). It is an underutilized food crop largely grown in India, Africa, Australia, Burma, Malaysia & Srilanka. Despite high quality of nutritional supplements there are embed prejudice against the usage of in the Macrotyloma uniflorum population (Nezamuddin, ). The reason is, 1970 Macrotyloma uniflorum has been considered as a low grade food (Kadam et al., 1985; Ambasta, 1986; Smartt,1985). In spite of such prejudice, (horsegram) Macrotyloma uniflorum is the fifth most extensively growing pulse in India. Macrotyloma uniflorum seed aqueous extract exhibits anticoagulant, antiplatelet and clot dissolving properties Chethana Ramachandraiah , Sharath Kumar M. Nandish , Jayanna Kengaiah , Chandramma a a a
A study of different cloud detection methods for the JEM-EUSO atmospheric monitoring sys- tem

The observation of the atmosphere is a crucial task for the JEM-EUSO mission, and a module for the atmospheric monitoring is included in the design of the whole system. In this paper the retrieval of cloud coverage in the field of view of the telescope is addressed considering both radiative methods commonly used in the meteorological field and methods of image analysis, with the aim of studying the feasibility of these approaches to the data that the JEM- EUSO infra red camera will provide. The complementarity of the two approaches will be further investigated, together with a different set of techniques, to contribute to achieve the best cloud estimation in JEM-EUSO.
A dynamical approach to study the time dependent behavior ofthe Kuhbanan fault zone (Kerman – Iran)

Abstract. In this research a proposed methodology is improved to identify how the stresses increase between two Earthquakes in Kuhbanan fault zone (Iran). Using the Mohr circles of the Earthquake we could calculate the main stress (σ1), hydrostatic stress, normal and shear stresses and the initial and final Coulomb stresses for all individual Earthquakes. For the relation of the whole fault we need the initial and the final Coulomb stress as well as the time during which the stress reaches from initial value to the final Coulomb failure value. The initial Coulomb stress is chosen as the least value, to be 30 megapascal. For the final Coulomb stress we used the average final Coulomb stress of all Earthquakes and for the time between this two initial and final stress we use the average time between Earthquake that is 3377 days. Using the Coulomb stresses at selected times, one can see how the stress increase with time between Earthquakes. The best fit of points of stress versus time is a polynomial relation. The model will help to estimate the stress accumulation with time until the next event, this means one can estimate the approaching time to the next main shock.
Seasonal Variability of Wind Stress Curl and Vorticity of Surface Currents in the North Atlantic

Seasonal variability of spatial distribution and values of the wind stress curl and vorticity of the surface currents in the North Atlantic are analyzed based on the ORA-S 3 reanalysis data for 1959– 2011. It is revealed that placement of two constant cyclonic and anticyclonic spacious areas in the surface currents’ vorticity distribution corresponds, in general, to the positions of the main oceanic gyres – the North Atlantic Sub-polar cyclonic and Subtropical anticyclonic gyres. As for distribution of the wind stress curl, the areas with cyclonic and anticyclonic surface currents’ vorticity are of more complex heterogeneous structure as compared to the similar ones. Strengthening of the cyclonic wind stress curl and vorticity of the surface currents in the sub-polar zone takes place in January, weakening – in July. Strengthening of the anticyclonic wind stress curl and vorticity of the surface currents in the subtropical zone is observed in January and June–July and weakening in May and September–October. Correlation between the intra-annual changes of the wind stress curl and the surface currents’ vorticity values averaged over the area of the North Atlantic Subtropical Gyre is 0.86, whereas the same value resulted from averaging over the area of the North Atlantic Sub-polar Gyre – 0.98. The values of the wind stress and its curl obtained by S. Hellerman and M. Rosenstein in 1983 are confirmed (though in some later papers they were characterized as overestimated).
Chaparral recovery following a major fire with variable burn conditions

ABSTRACT Wildfires are a common occurrence in California shrublands, maintaining ecosystem functions with the regeneration of key shrub species. The Cedar Fire of 2003 in southern California was unique in that a portion of it burned with wildfire accelerated by dry, strong northeasterly Santa Ana winds that later subsided, while the remaining area burned under an onshore, westerly wind of lower velocity and higher humidity. These nearby areas, having similar terrain, fuel type, and environments, burned under these different conditions. Our goal is to understand the connection between vegetation response to extreme fire events by analysing life-form regrowth in chaparral from the Santa Ana wind driven, Santa Ana backing, and non-Santa Ana fire types. Study sites representing these three fire conditions were based on fire progression maps generated from Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data. Shrub cover before and six years after the fire were mapped based on a spatial contextual classifier applied to colour infrared orthoimagery, and analysed per slope aspect and angle, elevation, and fire characteristic categories to isolate shrub regrowth patterns. Six years after the fire, shrub cover in the Santa Ana wind driven site was substantially lower than in the other two sites. Such differences in shrub cover at the landscape scale may have resulted from different wind speed, direction, and humidity during the fire, coupled with terrain differences on wildfire behaviour and different rates of recovery associated primarily with moisture availability to plants. The information gathered from this study can help land managers assess shrub regrowth and possibility of vegetation type change after extreme fire events in southern California shrubland ecosystems.
Characterizing Drought in India Using GRACE Observations of Terrestrial Water Storage Deficit

AbstractFrequent recurrences of drought in India have had major societal, economical, and environmental impacts. While region-specific assessments are abundant, exhaustive appraisal over large spatial scales has been insubstantial. Here a new drought index called Water Storage Deficit Index (WSDI) is devised and analyzed for holistic representation of drought. The crux of the method is the employment of terrestrial water storage (TWS) variations from Gravity Recovery and Climate Experiment (GRACE) for quantification of drought intensity and severity. Drought events in recent times are well identified and quantified using the approach over four homogenous rainfall regions of India over the period from April 2002 to April 2015. Among the four regions, the highest peak deficit of −158.00 mm is observed in January 2015 over central India. While the drought of 2002–04 is prominent in peninsular and west-central India, the drought of 2009–10 and 2012–13 is conspicuous in almost all four regions of India. The lo.
Electrical Bioimpedance for Cardiac Output Measurement

) guided treatment strategy on improving blood pressure (BP) control. The study included 102 adults with a mean age of 54 ±14 years who were seen in an outpatient hypertension clinic. The patients were randomized 1:1 to either a hemodynamic group (n = 51) or to a standard care group (n = 51). All participants had ICG performed at each visit, but the ICG findings were not revealed in the standard arm to physicians or patients. There were no statistically significant differences in the number and class of anti-hypertensive medications, patient demographic, clinical, BP or ICG variables at baseline between the
Late Quaternary sea level and coastal settlement in the European North

The primary aim of this article is to comment on the earliest coastal human settlement in the European North, especially along the coast of northern Norway. Did the first settlers migrate to these coastal areas from the west or the east? Did dry land exist beyond the outermost coastal islands on the submerged strandflat and the continental shelf during the postglacial time? Knowledge about the deglaciation of the last ice sheet and relative sea level changes in the area is a useful tool in a scientific approach when focusing on early human migration. An interactive computer modelling programme comprising sea level data and diagram presentations facilitates the investigation of coastal settlement through time.
Implementation and study of switched impedance boost and KY-boost converters for electric vehicle

A DC/DC converter based on photovoltaic (PV) grid connections is proposed here. This work focuses on the switched impedance source (S¬_ZSC) for electric vehicles. Every PV grid-connected system require step-up DC/DC converters to boost voltage range from low to high. Connecting an extra diode and a switch to the terminals of a standard quasi-impedance source DC/DC converter raises the step-up factors. In this proposed converter, this capacitor does more than just filter noise. Saturated inductors, on the other hand, can cause instability that needs not occur. In any case, the modulation index's H-bridge at its extremes is adjustable to a broader range when the circuit is used for DC-AC conversion. A larger boost factor is achieved with a shorter duty time as compared to currently available Z-source based systems. Both a buck-boost charging station and a KY-boost charging station were modeled for this investigation. PI or fuzzy logic controllers are utilized in DC-DC converters to keep the output voltage stable.
Strategi Influencer Marketing Dalam Meningkatkan Customer Engagement

Penelitian ini bertujuan untuk menjelaskan dan menganalisis strategi influencer marketing yang digunakan oleh akun Instagram sotoayamcakson dalam meningkatkan keterlibatan pelanggan (customer engagement). Dalam era pemasaran digital dan media sosial, influencer marketing telah menjadi alat yang berpengaruh dalam membangun interaksi yang kuat antara merek dan pelanggan potensial. Studi deskriptif dalam penelitian ini akan menggambarkan bagaimana sotoayamcakson berkolaborasi dengan influencer Instagram untuk menciptakan konten yang menarik dan memikat. Analisa dilakukan dari jenis-jenis konten yang dihasilkan,pengumpulan data melalui pattern matching dan mengidentifikasi strategi pemasaran yang digunakan, serta respons dan interaksi pelanggan terhadap konten influencer. Hasil dari penelitian ini menunjukkan bahwa sotoayamcakson berusaha untuk mengkombinasikan mega influencer hingga nano influencer. Kombinasi tersebut ditujukan guna untuk mendatangkan kesadaran terhadap produk dan keterlibatan dari para calon maupun para pelanggan loyal sotoayamcakson. Diharapkan hasil dari penelitian ini dapat memberikan dampak akademis mengenai strategi influencer marketing yang dilakukan oleh sotoayamcakson.
Automatic detection of outliers in world music collections

In big data collections it is often important to identify outlier behaviour that should be ﬁltered out or treated diﬀerently. In music this could help us identify sound recordings that stand out in a recorded music collection. We call these recordings ‘outliers’ and perform a computational analysis to detect them. We focus on world and traditional music collected from available sound archives. Using signal processing tools we extract audio features that denote rhythmic, melodic, harmonic, and timbral aspects of each recording in our collection. Outliers in the dataset are detected with data mining techniques. A sound recording is an outlier depending on how distinct its musical characteristics are compared to other recordings in the collection. However, outlier recordings can also be detected in cases where the descriptors have failed to capture the correct attributes. To evaluate our ﬁndings we perform a listening test with music experts. From preliminary results we are able to capture, amongst other, songs with distinct patterns of timbre and rhythm but also speech samples as outliers. The proposed methodology can help identify sound recordings that have a unique musical character or ﬁlter irrelevant audio from music collections.
MOF-199 and Ni-BTC: Synthesis, Physicochemical Properties, and Catalytic Activity in Oxidation of 5-Hydroxymethylfurfural

Platform chemical 2,5-furandicarboxylic acid (FDCA) has potential applications to replace petroleum-based chemicals. Metal Organic Framework (MOF) can be used as a catalyst to oxidize 5-hydroxymethylfurfural (HMF), producing FDCA. MOF-199 and Ni-BTC were synthesized using solvothermal method with trimesic acid (benzene 1,3,5-tricarboxylic acid/H3BTC) as a linker and Cu or Ni as a metal nod. The physical and chemical properties of catalysts were discovered through characterization using X-ray Diffraction (XRD), Fourier Transform Infra Red (FT-IR), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy - Energy Dispersive X-ray (SEM-EDX), and Ammonia Temperature-programmed Desorption (NH3-TPD). FDCA and its intermediate compounds were produced by converting HMF to FDCA in a small glass batch reactor. The yields of products were then determined by High-Performance Liquid Chromatography (HPLC). HPLC results indicated that there was no DFF (2,5-diformylfuran) signal, indicating that FDCA was formed by FFCA (5-formylfuroic acid) and HMFCA (5-hydroxymethylfuroic acid) formation reaction pathway. The maximum conversion (71%) was obtained using Ni-BTC as a catalyst at 130 °C for 5 h, with FDCA yield of 61.8%. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).