Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite‐Fault Bayesian Inversion of Teleseismic Body Waves

Abstract Inverting geophysical data has provided fundamental information about the behavior of earthquake rupture. However, inferring kinematic source model parameters for finite‐fault ruptures is an intrinsically underdetermined problem (the problem of nonuniqueness), because we are restricted to finite noisy observations. Although many studies use least‐squares techniques to make the finite‐fault problem tractable, these methods generally lack the ability to apply non‐Gaussian error analysis and the imposition of nonlinear constraints. However, the Bayesian approach can be employed to find a Gaussian or non‐Gaussian distribution of all probable model parameters, while utilizing nonlinear constraints. We present case studies to quantify the resolving power and associated uncertainties using only teleseismic body waves in a Bayesian framework to infer the slip history for a synthetic case and two earthquakes: the 2011 M w 7.1 Van, east Turkey, earthquake and the 2010 M w 7.2 El Mayor–Cucapah, Baja California, earthquake. In implementing the Bayesian method, we further present two distinct solutions to investigate the uncertainties by performing the inversion with and without velocity structure perturbations. We find that the posterior ensemble becomes broader when including velocity structure variability and introduces a spatial smearing of slip. Using the Bayesian framework solely on teleseismic body waves, we find rake is poorly constrained by the observations and rise time is poorly resolved when slip amplitude is low. Electronic Supplement: Figures of histograms of slip and rise time, waveform comparisons between data and synthetics, and slip velocity along the fault plane for a synthetic case, as well as for the 2011 M w 7.1 Van, east Turkey, earthquake, and the 2010 M w 7.2 El Mayor–Cucapah, Baja California, earthquake.
Cache-Aided Multi-User Private Information Retrieval Using PDAs

We consider the problem of cache-aided multi-user private information retrieval (MuPIR). This problem consists of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> independent files that are replicated across <inline-formula> <tex-math notation="LaTeX">$S \geq 2$ </tex-math></inline-formula> non-colluding servers. There are <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cache-equipped users having caches of size equivalent to <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> files. Every user aims to retrieve a file from the servers, but servers should not obtain any information about users’ demands. The user caches are filled with some arbitrary function of the files before the users decide their demands, known as the placement phase. After deciding their demands, users cooperatively send queries to the servers to retrieve their desired files privately. Upon receiving the queries, servers broadcast coded transmissions based on these queries and the files, known as the delivery phase. Conveying queries to the servers incurs an upload cost for the users, and downloading the answers broadcasted by the servers incurs a download cost. Each file has to be split into <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> packets to implement cache-aided MuPIR schemes. In this paper, we propose MuPIR schemes that utilize placement delivery arrays (PDAs) to characterize placement and delivery. Proposed MuPIR schemes significantly reduce subpacketization levels while slightly increasing the download cost. The proposed scheme also substantially reduces the upload cost for the users. We show that our scheme is order optimal in terms of download cost for PDAs based on Ali-Niesen scheme for centralised coded caching. We recover the optimal single-user PIR scheme presented by Tian et al. in [“Capacity-Achieving Private Information Retrieval Codes With Optimal Message Size and Upload Cost,” IEEE Trans. Inform. Theory, 2019] as a special case. Our scheme also achieves optimal rate for single-user cache-aided PIR setup reported in [R. Tandon, “The capacity of cache aided private information retrieval,” Annual Allerton Conference on Communication, Control and Computing, 2017. ]
ADRIN: An Adaptive and Distributed Routing Mechanism for Challenged IoT Networks

Data and communication networks in disaster-hit areas are often crippled due to partial or complete outages of the networking infrastructures. In such challenging post-disaster scenarios, message sharing is indispensable for a timely recovery. Typically, it is done by building mobile ad-hoc networks by devices in possession of users. However, challenged networks are often constrained by intermittent connectivity, delay, and energy constraints, necessitating routing strategies that offer seamless communication in the face of node failure and mobility. In this paper, we present an adaptive and distributed routing mechanism termed ADRIN. It employs backtracking-based mobility tracing, inspired by the backward algorithm of Hidden Markov Models, to infer periodicity in mobility to determine stable routes and then constructs a spatiotemporal ad-hoc network to relay data in a multi-hop fashion to the base station. Preliminary experiments show that ADRIN approximates the underlying mobility distribution and performs data forwarding despite failures.
Review of current and planned adaptation action in Bangladesh

i Résumé . ii Acknowledgements . vi Synopsis . ix Introduction . 1 1. Current climate and projected changes . 3 1.1 Observed climate trends . 4 1.2 Climate change projections . 5 2. Vulnerability to climate change . 6 2.1 Current drivers of vulnerability . 6 2.2 Vulnerability to climate change. 9 3. Adaptation planning context . 13 3.1 National-level development policy context . 14 3.2 National-level climate policy context . 15 3.3 Institutional structure for climate governance . 21 3.4 National-level sectoral policies . 23 4. Current and planned adaptation programs and projects . 26 4.1 Adaptation projects and programs . 26 4.2 Climate finance . 32 5. Networks and communities of practice . 35 6. Conclusions . 36 7. Annexes . 39 Annex A: Methodology . 39 Annex B: Projects and programs . 46 8. References . 60 CARIAA Working Paper #6
Distribution of the Platypus in the Bellinger Catchment from Community Knowledge and Field Survey and its Relationship to River Disturbance

Platypus distribution in the Bellinger catchment was investigated using a combination of field and community surveys. The field survey in 1996 consisted of netting and observations from the river bank and a canoe. The community-based wildlife survey consisted of a questionnaire and colour maps on which respondents were asked to mark the locations of sightings. Platypuses were observed or caught in 36 locations from all three rivers of the catchment. Two of the three platypuses captured were lactating females. The community recorded 123 locations of platypuses. The fact that the wildlife survey yielded similar results to the field surveys in identifying the location of individuals highlights the value of community records for platypus surveys. There were major floods in 2001, after which we contacted respondents who had reported seeing platypuses three years before. Of the 21 respondents who had been near the river since the flood, 7 had seen platypuses, principally in the tributaries of the Bellinger River. The habitat quality of the rivers was evaluated for platypuses and records were related to disturbance and rehabilitation. The species has survived in this system, but its future can only be assured by strategies which prevent further degradation of its habitat and institute proactive rehabilitation of the damaged sections of these streams.
Investigation of virtual synchronous generator control parameters for small signal stability at different levels of renewable resource penetration

The increasing integration of renewable resources is transforming a modern power system into one that is entirely reliant on inverter-based resources (IBR). The grid forming (GFM) inverters, especially virtual synchronous generators (VSG), have gained high popularity as they are capable of closely emulating the inherent characteristics of synchronous generators (SG). A detailed sensitivity analysis of the VSG-based GFM inverter under different system conditions has been investigated. The impact on the small signal stability (modal analysis) and time domain active power response due to the variations in inertia coefficient (H) and damping coefficient (D) have been observed. The settling point and damping ratio are both affected by changes in the damping coefficient. The settling time remains the same even when the inertia coefficient changes, although the settling point changes. The impact of changes in H and D parameters in different case studies is analyzed using the DIgSILENT Power Factory simulation platform.
Current Crafting: Mastering Low-Power Configuration Methods

Current Crafting: Mastering Low-Power Configuration Methods” presents a comprehensive exploration of three distinct yet interrelated techniques: Lector, TSPC (True Single Phase Clock), and SAPON (Self-Aligned Pass-Gate-Only N-Logic). This paper delves into the intricate art of low-power system design, emphasizing the significance of these methods in achieving optimal power efficiency. The Lector technique offers a unique approach to reducing power consumption in integrated circuits by employing novel transistor configurations. TSPC, known for its efficiency in dynamic logic circuitry, is investigated for its contributions to minimizing power dissipation through efficient clocking methodologies. Furthermore, the SAPON technique, focusing on pass-gate logic design, is discussed for its potential in reducing power consumption without compromising performance. Through a comprehensive comparative analysis, this reveals that the Lector technique exhibits a 20% average reduction in power consumption compared to conventional methods, showcasing its efficacy in achieving energy efficiency. Similarly, the TSPC method demonstrates a 15% decrease in power dissipation, emphasizing its potential for enhancing power efficiency and speed in dynamic logic circuits. Furthermore, the SAPON technique reveals a remarkable 25% decrease in power usage, illustrating its significant advantage in N-logic circuits through pass gate logic. In addition to the direct power reduction percentages, a thorough examination of Mean Time to Failure (MTBF) percentages associated with these techniques has been conducted, demonstrating Lector's 18% improvement, TSPC's 12% enhancement, and SAPON's 22% increase in reliability over traditional configurations.
Machine Learning Combining Expert Features to Differentiate Infection Types Based on Physiological and Biochemical Indicators of Malayan Pangolin

This research retrospectively analyzed blood parameters of rescued Malay pangolins from 2018 to 2021 to differentiate between diseased individuals and those with pathogenic infections, aiming to enhance clinical diagnosis and treatment strategies. We employed clustering, machine learning, ensemble learning, and expert mechanisms for classification and training. Under supervised conditions, we combined this model with ensemble learning to distinguish healthy from diseased samples, achieving 73.08% accuracy. By integrating expert mechanisms for infection data, we identified bacterial, viral, and parasitic infections from the diseased dataset with 87.5% accuracy, a 14.42% improvement. The naive Bayes and random forest models stood out in performance.
Mid- to late Holocene climate variability of the Maritime Continent

IV Abstract The Maritime Continent is located within the Indo-Pacific Warm Pool (IPWP) which is described as the largest area of warm sea surface temperatures with the highest rainfall on Earth. Serving as the largest source of atmospheric water vapor and latent heat, it is of crucial importance to global atmospheric and hydrologic circulation. The regional climate is controlled by large-scale phenomena such as the seasonal migration of the Intertropical Convergence Zone (ITCZ), the Australian-Indonesian monsoon system as well as climate anomalies like the Indian Ocean Dipole (IOD) and or oscillations such as El Niño-Southern Oscillation (ENSO). Due to the complex topography of the Maritime Continent and very local air-sea interactions, the environmental influence of these phenomena strongly varies across the region today and may have done so during the past. Environmental reconstructions have been conducted by using proxy records from speleothems, corals as well as marine and lacustrine sediments revealing a complex history of climate variations of the Maritime Continent during the Holocene. Mid-to late Holocene sea surface conditions were reconstructed by using trace elements and stable isotopes as well as relative abundances of planktic foraminifera. Additionally, the amount, composition and grain size distribution of bulk sediment was investigated. Based on multi-proxy reconstructions from four marine sediment cores from western, central, and southern Indonesia, this thesis outlines variations in rainfall and upper water column conditions in response to large-scale climate phenomena as the IOD, the Australian-Indonesian monsoon system and ENSO from midto late Holocene. In the eastern tropical Indian Ocean (western Indonesia), thermocline temperatures, the thermal gradient of the upper ocean as well as the relative abundances of planktic foraminifera reveal a deeper thermocline between 8 and 3 ka and an uplift of the thermocline for the past 3 ka off western Sumatra. The recorded shoaling of the thermocline and the deduced increased occurrence of upwelling likely results from a more-positive IOD-like mean state of the Indian Ocean during the late Holocene. A transient Holocene climate model simulation reproduces anomalous surface easterly winds over the equatorial eastern Indian Ocean that support the interpretation of more positive IOD-like mean state during the late Holocene compared to the mid-Holocene. In the Makassar Strait (central Indonesia) cooler and fresher sea surface coincide with an increase in terrigenous runoff from eastern Borneo and point to an intensified AustralianIndonesian summer monsoon (AISM) during the late Holocene. Contemporaneously, periods
Research on the backstepping adaptive control strategy for swing nozzle based on virtual nonlinear load compensation

In order to achieve high-precision control of the attitude of swinging nozzle, considering the presence of nonlinear load moments on the swinging nozzle, this paper proposes a control method based on a permanent magnet synchronous motor (PMSM) for driving the swinging nozzle. An online parameter adaptive approach is employed to estimate the motor parameters as well as the various load moment parameters of the swinging nozzle. Utilizing a backstepping control algorithm, the control voltage for the permanent magnet synchronous motor is determined. This approach allows for rapid updates of system parameters based on adaptive methods when disturbances occur in the load, changes in load moments, or variations in system parameters. Consequently, it effectively enhances the robustness, response speed, and control precision of the servo system.
Contribution of Enjoyment and Browsing Activity On Urge to Buy Impulsively In E-Commerce: Gender Roles as Mediators

The existence of e-commerce is predicted to continue to increase along with the development of information technology and also change consumer behavior patterns in shopping. Technology users are not clustered by gender but differently regarding online purchasing behavior. Therefore, this study aims to investigate the enjoyment and browsing activity in e-commerce with the role of gender in moderating the relationship between impulsiveness and the urge to buy impulsively. This study uses a quantitative approach with an online survey method of 244 consumers in 5 cities around Jakarta who adopt e-commerce for shopping activities. A purposive sampling technique was applied in this study. Component-based structural equation modeling statistical technique was used to answer the research hypotheses. This study has proven that perceived enjoyment affects impulsiveness (p-value = 0.016), browsing activity (p-value < 0.001), and the urge to buy impulsively (p-value = 0.003) positively and significantly. Browsing activity also influences impulsiveness (p-value < 0.001) and the urge to buy impulsively positively and significantly (p-value < 0.001. In addition, impulsiveness positively and significantly affects the urge to buy impulsively in e-commerce (p-value < 0.001). This study also proved that gender moderates positively and significantly in the relationship between impulsiveness and the urge to buy impulsively (p-value = 0.005). Finally, this study informs that gender strongly influences unplanned buying behavior in e-commerce.
Improved Sample Volume in Cylindrical Perturbed Cavity for Permittivity Calculation

An estimate of the maximum volume of a dielectric material, based on the cavity perturbation technique, is performed for accurate calculation of the complex permittivity. It is observed that a cylindrical perturbed cavity makes it possible to obtain a maximum volume ratio of dielectric sample to cavity which is more appreciable in comparison with a rectangular perturbed cavity. Some geometries and natures of the sample have been tested and a low loss material with a low dielectric constant and a thin disk shape ensures the best maximum volume ratio.
Distance-Based Fuzzy MCDM Methods for a Supplier Selection Decision-Making Model

Making the decision to select the best suppliers is a critical process within a company as it impacts all aspects from upstream to downstream. However, this decision-making process is often challenging due to the involvement of multiple factors and suppliers. Multiple criteria decision-making (MCDM) methods are commonly proposed to address this issue. In this study, two MCDM methods, namely fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy evaluation based on distance from average solution (FEDAS), were chosen to select suppliers and were tested on a small-scale retail company in Indonesia. These two methods are categorized as distance-based MCDM methods that have been widely applied due to their practical capabilities. In this decision-making model, five criteria have been identified: service (C1), delivery (C2), quality of product (C3), price of product (C4), and production capacity (C5), involving three suppliers. By using the best-worst method (BWM), the most important criterion was C4, with a weight of 0.503. The comparison results of both methods recommended Supplier A as the best supplier. FTOPSIS and FEDAS indicate significant performance scores for the best supplier of 0.783 and 0.837, respectively. Notably, both methods produced relatively similar and stable results. This demonstrates the reliability of both methods for use in this case. Nonetheless, future studies need to be extended to involve a larger system scale and sensitivity analysis to make the decision-making model more comprehensive.
Data-Driven Efficient Medical Image Segmentation with Object-Oriented Deep Neural Networks

In the domain of medical diagnostics, medical images serve as invaluable tools for elucidating various pathological conditions. However, the process of image segmentation, which involves partitioning these images into distinct regions of interest, is profoundly challenging due to the intricate and diverse pixel features inherent to medical imagery. While Convolutional Neural Networks (CNNs) have exhibited proficiency in this task, they come at a significant cost in terms of computational resources and the requisite sizeable datasets for training, rendering them computationally demanding. To mitigate these impediments, this study introduces an innovative paradigm rooted in Object-oriented Programming (OOP) principles, denoted as the Object-oriented Pixel Descriptor (OOPD). This methodology augments the information encapsulated within medical images, synergizing with a deep neural network architecture to yield the Object-oriented Deep Neural Network (OODNN) segmentation model. In a rigorous evaluation encompassing three distinct medical image datasets, each characterized by a limited dataset size, OODNN demonstrates substantial prowess, achieving a notable balanced accuracy of 0.771. This performance is analogous to contemporary state-of-the-art CNN-based models, albeit with a marked reduction in training duration and computational demands. These findings present a promising avenue for the advancement of medical image segmentation, with potential ramifications for augmenting the diagnostic capabilities of healthcare practitioners.
MFTr_Locator: a Novel Transformer Model Decoding Multi-Label Protein Subcellular Locations in Multi-Field ImmunohistoChemistry Image

Proteins are a kind of biological macromolecules that play a vital role in building the cytoskeleton, being a catalyst in biological processes, and participating in the biological immune process. The loss and incorrect of the subcellular location of proteins prone to suffer from pathological disease. Therefore, predicting accurately subcellular location of proteins has become a hot research topic. In particular, proteins that occur in multiple subcellular locations have received little attention. Based on this, a novel protein multi-label subcellular location predictor called MFTr_Locator was developed to predict the subcellular location of multi-label proteins in multi-field immunohistochemistry (IHC) images. The proposed method consists of four main steps, namely, splitting the IHC images into multi-field patches, image feature extraction, feature selection, and multi-label classification. First, an IHC image was initially split into three scales using three-size slide windows, which represent the region with abundant protein expression in the current field size. In addition, subcellular location features (SLFs) and frequency domain operator were employed to quantify the multi-scaled IHC patch images, and stepwise discriminant analysis (SDA) was adopted to reduce feature dimension. Finally, a concatenated feature that included multi-field features from an IHC image was fed into the decoder of the Transformer to recognize subcellular locations of multi-label proteins. To validate the proposed method, a benchmark dataset comprising 2100 single-label and 1200 multi-label IHC images from the human protein atlas (HPA) was collected. The main contributions of our method are as follows: Firstly, we employ multi-field images instead of single-field images for subcellular location recognition. Secondly, we use Transformer's decoder as a multi-label classifier. From the experimental results, the subset accuracy of mixed labels reaches 79.7%. The promising results show that the developed model substantially outperforms current in-field methods and may provide protein subcellular location with model analysis tools effectively.