Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of large-scale sea-level changes on tides in the past, present and future

Tides propagate through the oceans as shallow water waves and are therefore sensitive to changes in water depth and areal extent of the ocean. In this thesis the impacts of climatologically driven sea-level changes and the resulting changes in ocean extent on the global tidal dynamics are investigated. The large global sea-level adjustments between the Last Glacial Maximum (LGM, 18,000-21,000 years BP) and the present are considered; the present day tidal changes are analysed and the impact of future collapses of the West Antarctic and Greenland Ice Sheets on the tides are investigated using a global tidal model. The 130 m glacio-eustatic sea-level drop during the LGM had a profound impact on the principal semi-diurnal tide, doubling global dissipation rates and resulting in 'megatides' in the North Atlantic region. It is shown that the magnitude of the Atlantic tides is sensitive to the grounding line location of the Antarctic Ice Sheet. Next, the impacts of the altered dissipation for LGM ocean mixing and the meridional overturning circulation (MOC) in the glacial ocean are explored using an intermediate complexity climate model. The importance of tidal mixing for the global ocean circulation is highlighted. Strong LGM tidal mixing could have provided a mechanism for sustaining a vigorous LGM MOC. During the deglacial period glacioeustatic sea-level rose rapidly and large adjustments of the global tides occurred into the early Holocene. In the late Holocene sea-level adjustments were small and tidal dynamics remained fairly constant over this period. Tide-gauge records covering the past decades show global changes in tidal amplitudes occurring in parallel with largescale sea-level changes which have been attributed to global warming. Using simulations forced with observed sea-level trends an attempt is made to reproduce the large-scale change patterns. For M2 the patterns agree well, but for K1 the model is unable to reproduce the trends, possibly due to the small magnitude of the K1 trends. A number of recent studies have highlighted an accelerated ice loss from the ice streams draining the West Antarctic and Greenland Ice Sheet together with widespread grounding line retreats in West Antarctica. It has been suggested that both ice sheets could undergo collapses under certain climate warming scenarios leading to increases in global mean sea-level of 5 m and 7 m, respectively. It is shown that the collapse of a polar ice sheet would lead to large changes in tidal dynamics and thus changes in sea-level variability. These changes have further reaching consequences for shelf-sea dynamics, ecosystems, and open ocean tidal mixing.
Early Economic Thought and The Dynamics of Economic Thought

This research aims to explore the history of the development and dynamics of economic thought, especially in the context of Islamic economics. The research was conducted using qualitative, descriptive and historiographic methods. Data were obtained through literature studies sourced from primary sources such as the Quran and Hadith, as well as secondary sources in the form of academic articles and previous research. This research identifies three main approaches that influence the development of Islamic economic thought, namely: (1) the legal approach, where classical scholars focused on legal aspects such as usury, zakat and the banking system; (2) the rational-critical approach where a number of researchers relied directly on the analysis of direct data on economic and social issues; (3) the dominant attitude of Muslim scholars towards Western economic science. The contributions of classical scholars include in-depth studies of fundamental concepts of Islamic economics such as riba, zakat, waqf, and interest-free financial systems. At the same time, modernist scholars criticized classical thinking and offered new explanations for contemporary economic problems. The mainstream approach has enriched empirical and theoretical research on Islamic economics. This study concludes that Islamic economic thought must remain grounded in its primary sources, the Quran and Hadith, while remaining open to new interpretations within the confines of Islamic teachings. Islamic economics is considered a dynamic system that continues to develop and adapt to changes in society, the needs of society, and the challenges of the times. This research is expected to provide a comprehensive understanding of the historical roots, theoretical foundations and prospects of Islamic economics in the future.
Planetary Wave Modulation of Gravity Waves Over the Andes in 2016

The Andes account for the largest source of orographic gravity waves (GWs) in the middle atmosphere. This results from persistent, strong zonal winds at the surface encountering the north‐south mountain chain, producing strong orographic lift, and resulting GWs. Here, we consider GWs in the stratosphere and mesosphere above the Andes as observed by the Cloud Imaging and Particle Size instrument, the Solar Occultation for Ice Experiment on the AIM satellite, and the Sounding of the Atmosphere using Broadband Emission Radiometry instrument onboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. GW variability is considered in the context of the location of the stratospheric wintertime westerly jet and planetary wave (PW) amplitude and phase. The occurrence of GWs in the middle and upper atmosphere depends not only on tropospheric sources, like the Andes, but also on the background winds through which they propagate. Results suggest that the propagation of GWs into the mesosphere is well correlated with winds throughout the middle and upper stratosphere. PWs cause the westerly jet to move over the Andes, resulting in an increase in GW amplitude throughout the middle and upper stratosphere. The evolution and variability of GWs are tied closely to the phase of the PW‐1 and are linked to PW‐2 when the PW amplitudes are sufficiently large. GW amplitude, as observed by all three data sets, increases in the upper stratosphere and lower mesosphere when the trough of the PW‐1 in the stratosphere is over the Andes.
Asymmetric Effects of Tax Competition on FDI vs. Budget Balance in European OECD Economies: Heterogeneous Panel Approach

The global trends in taxation have generated a “race to the bottom” in capital income taxation, which is intended to be stopped by OECD through the introduction of a global minimum tax rate (15% of effective average tax rate—EATR). The question is whether the defined tax competition floor would have heterogeneous implications in different economies. The aim of this paper is to examine the long-term relationship between the EATR and FDI, and between the EATR and budget balance (BB) in European OECD economies in the period 1998–2021, using non-stationary, heterogeneous panels. According to the linear PMG model, a significant negative long-term relationship was revealed between the EATR and FDI and between the EATR and BB, while the error-correction parameters are significant and heterogeneous, showing that the speed of adjustments towards equilibrium is different across the analyzed economies. However, the nonlinear PMG results revealed asymmetry as the magnitude of the influence of an EATR reduction has a greater effect on FDI attraction and deficit deepening than an increase in the EATR on the opposite tendencies of FDI and deficit. Policymakers are facing a trade-off related to FDI attraction/budget deficit deepening when making decisions in relation to the EATR, and they are mostly oriented toward FDI inflow using EATR reduction in the analyzed economies.
Mycielski ideals and uniform trees

Mycielski introduced ideals P k and C k on ω k which are called Mycielski ideals. We investigate their cardinal invariants. Strengthening results of Kamo and of Rosłanowski we prove that cof( P k ) and cof( C k ) may be singular and that their coﬁnality is > 2 ℵ 0 . We study the relation between P k and the ideal associated with Silver forcing and C k and the ideal associated with uniform Sacks forcing. We prove that consistently neither ideal is contained in one of the others, respectively. We show that P k and C k are Tukey reducible to P k +1 and C k +1 , respectively. By iterating a uniform version U k of k -dimensional Sacks forcing we produce a model in which cov( C k +1 ) = ℵ 1 and cov( C k ) = ℵ 2 . We prove that the natural amoeba A ( U k ) of U k has the Laver property, and thus, when iterated, increases the additivity of the ideal associated with U k without adding Cohen or random reals.
Efficacy of Handheld Ultrasound in Medical Education: A Comprehensive Systematic Review and Narrative Analysis

Miniaturization has made ultrasound (US) technology ultraportable and, in association with their relatively low cost, made handheld devices attractive for medical education training programs. However, performing an ultrasound requires complex skills, and it is unclear whether handheld devices are suitable for the training of novices. Our study aimed to identify to what extent handheld US devices can be employed in medical undergraduates’ and residents’ education. We selected studies that evaluate the results obtained by students and/or residents who have participated in ultrasound training programs using handheld devices. The studies were included if they reported post-test (pre-test optional) achievements or a comparison with a control group (a group of experts or novices who underwent a different intervention). Twenty-six studies were selected, and their characteristics were summarized. Handheld ultrasound devices were used in training programs to learn echocardiography, abdominal, and/or musculoskeletal ultrasound. Statistically significant progress was noted in the ability of naïve participants to capture and interpret ultrasound images, but training duration influenced the outcomes. While ultrasound training using handheld devices has proven to be feasible for various body regions and purposes (e.g., better understanding of anatomy, clinical applications, etc.), the long-term impacts of handheld education interventions must be considered in addition to the short-term results to outline guidelines for targeted educational needs.
Polymerization of recombinant tau core fragments in vitro and seeding studies in cultured cells

The relative polymerization of specific tau protein cores that define Alzheimer’s disease, Pick’s disease and corticobasal degeneration were investigated using amyloid fluorometry and electron microscopy. In addition, the relative prion-like activities of polymers comprised of these respective tau protein segments were investigated in a cell-based assay. It is demonstrated that the seeding activities of specific tau core fibrils are affected by the presence of pathogenic tau missense mutations and the microtubule binding domain composition of tau. The unique impact of tau phosphorylation on seeding propensity was also investigated by altering stretches of phospho-mimetic and phospho-null residues in the presence of Alzheimer’s disease tau core fibrils. These results have important mechanistic implications for mutation and isoform-specific driven pathogenesis.
Drift-filled hollows in Battersea: investigation of the structure and geology along the route of the Northern Line Extension, London

Drift-filled hollows (DFHs) are a major subsurface hazard for engineering in London. They are characterized by a steeply inclined cone-shaped hollow into (sometimes through) the London Clay Formation, filled with unconsolidated fine- to coarse-grained drift and often covered by terrace gravels, making them difficult to identify at the surface. Their origin remains uncertain but most probably formed towards the end of glacial epochs by meltwater scouring, perhaps of collapsed pingos. Usually associated with tributaries to the Thames, DFHs are particularly prevalent in the Battersea area, through which the Northern Line Extension (NLE) is to be built. This study uses 283 public borehole records and site reports to build a 3D geological ground model of two known DFHs in the Battersea area to develop a more complete understanding of their origin. We show that DFHs are probably older than previously assumed, dating from the end-Anglian (Marine Isotope Stage (MIS) 12), c. 300 kyr ago, before the deposition of the River Terrace Deposits. The two DFHs modelled fall into distinct types: a small shallow DFH that is probably a purely scour feature in origin, and a larger, deeper DFH that probably formed by the scouring of a perhaps fault-controlled pingo. It is unclear whether the faults controlled pingo formation passively by acting as a conduit for water, or in a more active sense by driving ground movements. Both DFHs represent a significant hazard for the NLE and require more detailed investigations to properly constrain their extent.
F-ROADNET: Late Fusion-Based Automotive Radar Object Detection

Road user categorization is essential for autonomous driving perception. In challenging traffic situations including unfavorable weather (such as fog, snow, and rain) and dim lighting. There are several kinds of sensors that need to be researched in order to achieve the precision and resilience that autonomous systems demand. Currently, to create a depiction of the environment surrounding the vehicle, principally cameras and laser scanners (LiDAR) are commissioned. Despite their enticing qualities, Radar sensors are currently underutilized for autonomous driving, even though, they have been employed in the automobile industry for a long time. Radar’s ability to measure the relative speed of obstacles and to operate even in adverse weather conditions makes it a front line contender for road user detection. This study proposes F-ROADNET, a multi-object classification method for vulnerable road users based on raw Radar data. The model is trained on Range Angle and Range Doppler maps based on a late fusion architecture. F-ROADNET has a detection accuracy of 99.01%, precision of 99.3% and recall of 99% on the CARRADA dataset and detection accuracy of 91.62%, precision of 87.2% and recall of 90.2% on the RADDet dataset. The findings exhibit that F-ROADNET outperforms established methods in terms of average precision.
The many ways to Transparency

This article explores a sample of the literature on transparency in the 1984-2020 period through a systematic review. The sample consists of 242 works (articles, books, and book chapters) collected from different academic databases. Latent dirichlet allocation (LDA) probabilistic topic modelling – an unsupervised machine learning approach – is employed in order to classify and construct a typology of topics within the literature. This approach is complemented by a structured overview of the varieties of transparency framework and is aimed at addressing three research questions: a) What analytical approaches are identified in the literature? b) How is transparency conceptualised through such analytical approaches? And, c) where has transparency’s focus been placed in relation to an event-process framework? The findings show unequal methodological approaches, topics, and issues investigated. These insights and the novel approach utilised outline key challenges and opportunities for future transparency research.
Amiodarone induced lung toxicity: a radiological overview that simulating COVID19 infection disease

Amiodarone-induced pulmonary toxicity (AIPT) is among the most serious adverse effects and is one of the leading causes of death associated with its use. It is a clinical pathology that is conditioned by dose, patient’s age, and pre-existent pulmonary pathologies. Those effects reach a plateau at a cumulative dose bigger than 150g. Patient’s comorbidities; oxygen therapy, invasive procedures or surgical interventions can trigger the pulmonary symptoms induced by amiodarone toxicity. The increased risk of developing amiodarone-induced pulmonary fibrosis is directly related to the dose and the duration of the intake. Despite significant advances in the understanding of AIPT, its aetiology and pathogenesis remain incompletely understood. The role of steroids in the management of pulmonary toxicity from amiodarone is debatable, however, most reports of improvement after amiodarone withdrawal di"er little from those in which concomitant steroid therapy was employed. Therefore, the addition of therapeutic doses of corticosteroids in amiodarone induced pneumopathy may be indicated. Typically, prednisone is started in doses of 40 to 60 mg/day orally and slowly reduced. Again, the pharmacodynamics of amiodarone dictate a treatment period of four to 12 months. The case report describes a patient with AIPT who after therapy with Prednisone at a dosage of 50mg/day by gradually scaling down the doses as reported in the above clinical studies, had a clinical, functional and CT radiological picture that was markedly improved with disappearance of most of the scattered ground glass areas and the previously reported thickening with associated bi-apical fibrotic outcomes.
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems

Although Physics-Informed Neural Networks (PINNs) have been successfully applied to various differential equations, accurately solving perturbed convection-diffusion-reaction problems is still extremely challenging for PINNs. This paper investigates the source of the learning difficulties and finds that the rapid transition of potential solution in the layer region causes the failure of convergence. Based on this finding, we present a curriculum learning method that encourages neural networks to “prioritize the learning on easier nonlayer regions”. The method helps PINNs to dynamically adjust the training data weights, speed up the learning procedure, and ultimately significantly improve the accuracy of the network approximation. Extensive evaluation on multiple typical model equations shows that the proposed approach accurately captures the resolution of the layer regions, and achieves multiple orders of magnitude lower root-mean-squared error than ordinary PINNs. We provide our PyTorch code at https://github.com/WYu-Feng/CLPINN.
Analisis time series untuk memprediksi jumlah penduduk miskin di Cilacap

Kemiskinan merupakan salah satu aspek yang harus ditanggulangi pemerintah agar setiap warga negara mendapatkan kesejahteraan yang sama dan perekonomian yang merata. Pentingnya kebijakan untuk memberantas kemiskinan telah dilakukan pemerintah indonesia dan khsususnya pemerintah daerah cilacap telah berupaya besar untuk dapat mengurangi tingkat kemiskinan. Diketahui bahwa di Cilacap tercatat tingkat kemiskinan pada tahun 2022 sebesar 11.02%. Untuk itu, sebagai upaya untuk dapat mengurangi jumlah kemiskinan di cilacap perlu adanya data prediksi yang diharapkan dapat menjadi referensi ilmiah untuk menyusun kebijakan dan strategi penanganan yang tepat. Hal tersebut menjadi dasar adanya penelitian ini yang bertujuan untuk melakukan prediksi jumlah penduduk miskin di cilacap dengan analisis time series berbantuan software POM-QM untuk komputasinya. Metode time series yang digunakan ialah moving average, weighted moving average, dan exponential smoothing. Data jumlah penduduk miskin di Cilacap yang digunakan ialah data pada tahun 2005 sampai dengan 2022 berdasarkan data BPS. Diperoleh hasil bahwa, metode exponential smoothing merupakan metode terbaik time series untuk peramalan jumlah penduduk miskin di cilacap dengan tingkat kesalahan sebesar 6.77% atau akurasi sebesar 93.23% yang berarti sangat akurat. Selanjutnya, hasil prediksi untuk periode 2023 didapatkan bahwa jumlah penduduk miskin di cilacap sebesar 191668 jiwa.Kata kunci:Time Series, Jumlah Penduduk Miskin, Exponential Smoothing, Moving Average
Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images

(1) Background: Computed tomography (CT) imaging challenges in diagnosing renal cell carcinoma (RCC) include distinguishing malignant from benign tissues and determining the likely subtype. The goal is to show the algorithm’s ability to improve renal cell carcinoma identification and treatment, improving patient outcomes. (2) Methods: This study uses the European Deep-Health toolkit’s Convolutional Neural Network with ECVL, (European Computer Vision Library), and EDDL, (European Distributed Deep Learning Library). Image segmentation utilized U-net architecture and classification with resnet101. The model’s clinical efficiency was assessed utilizing kidney, tumor, Dice score, and renal cell carcinoma categorization quality. (3) Results: The raw dataset contains 457 healthy right kidneys, 456 healthy left kidneys, 76 pathological right kidneys, and 84 pathological left kidneys. Preparing raw data for analysis was crucial to algorithm implementation. Kidney segmentation performance was 0.84, and tumor segmentation mean Dice score was 0.675 for the suggested model. Renal cell carcinoma classification was 0.885 accurate. (4) Conclusion and key findings: The present study focused on analyzing data from both healthy patients and diseased renal patients, with a particular emphasis on data processing. The method achieved a kidney segmentation accuracy of 0.84 and mean Dice scores of 0.675 for tumor segmentation. The system performed well in classifying renal cell carcinoma, achieving an accuracy of 0.885, results which indicates that the technique has the potential to improve the diagnosis of kidney pathology.
Partitioning Attention Weight: Mitigating Adverse Effect of Incorrect Pseudo-Labels for Self-Supervised ASR

The performance of automatic speech recognition (ASR) models has been significantly improved owing to advances in deep learning and end-to-end approaches. However, these require a large amount of labeled data, which are expensive to obtain. Semi-supervised learning techniques, such as pseudo-labeling and self-supervised learning, have emerged as potential solutions to reduce the reliance on labeled data. Recently, some studies have combined self-supervised learning and pseudo-labeling to further enhance ASR performance. However, these methods suffer from incorrect pseudo-labels that propagate errors and reduce ASR performance. In this paper, we propose a novel method called partitioning attention weight (PAW) to mitigate the adverse effects of incorrect labels without requiring additional language models. Our proposed method isolates audio segments by partitioning a fully connected attention weight into sub-attention weights to prevent adverse effects that the model learns the wrong context for the entire attention weights from incorrect labels as well as overfitting. The proposed method is simple, requiring few changes to existing learning frameworks, and leverages the alignment information obtained during the pseudo-labeling process. Our experimental results show consistent performance improvements in ASR performance across various semi-supervised learning scenarios.