Miracle-dz/UniFAR-FTUs huggingface dataset:
The surface wind field during winter smog nights in Christchurch and coastal Canterbury, New Zealand

In Christchurch and other Canterbury towns on the east coast of New Zealand's South Island, poor dispersion conditions and high emissions of particulate matter and carbon monoxide regularly lead to the build‐up of smog during anticyclonic weather conditions in wintertime. This study analyses surface wind fields during smog nights using data collected during the Christchurch Air Pollution Study 2000 (CAPS2000). Westerly land breezes and drainage winds from the foothills of the Southern Alps and the Canterbury Plains are evident in all coastal Canterbury towns, but local terrain features often complicate the wind fields. This holds particularly for Christchurch, where Banks Peninsula causes flow splitting of both the drainage winds on the Canterbury Plains and any superimposed larger scale winds. Furthermore, stagnation of airflow over Christchurch is often caused by the convergence of localized southeasterly drainage winds down the western part of the Banks Peninsula (Port Hills) with the regional‐scale northwesterly drainage winds from the Canterbury Plains. Results illustrate the interaction of cold air drainage over Christchurch, although the unusually high frequency of synoptic‐scale easterly winds during the study period appeared to reduce the effects of both these sources of cold air drainage on air pollution concentrations. The effects of topographically induced flow splitting and non‐stationary drainage wind convergence on urban air pollution dispersion identified here have not previously been studied in detail internationally and should receive greater attention in the future. Copyright © 2004 Royal Meteorological Society
Journal of Neuroengineering and Rehabilitation Modulation of Walking Speed by Changing Optic Flow in Persons with Stroke

Background: Walking speed, which is often reduced after stroke, can be influenced by the perception of optic flow (OF) speed. The present study aims to: 1) compare the modulation of walking speed in response to OF speed changes between persons with stroke and healthy controls and 2) investigate whether virtual environments (VE) manipulating OF speed can be used to promote volitional changes in walking speed post stroke.
The Connective Tissue Graft Wall Technique for treating Patients with Cairo’s RT3 Gingival Recession and Intrabony defects: A Case Series

Aim: The purpose of this trial was to assess the potential of connective tissue graft wall technique to improve the clinical and radiographic outcomes of intra-bony defects and root coverage outcomes simultaneously. Subjects and methods: Ten patients with Cairo’s RT3 gingival recession associated with intrabony defects were treated with this technique. The recession defects were treated with a coronally advanced envelope flap and simplified papilla preservation at the site of the defect. A de-epithelialized free gingival graft was harvested and positioned buccal to the defect creating a buccal wall at the defect. Clinical parameters including; recession depth, probing depth and clinical attachment level were measured at baseline and 6 months. The radiographic bone gain was evaluated at 6 months. Results: At 6 months, this study reported mean±SD in recession depth reduction of 2±0.62 mm at 6 months and mean±SD absolute reduction in probing depth of 2±0.7 mm. regarding clinical attachment level, a mean±SD gain of 2±0.67 mm was achieved after 6 months. The radiographic mean±SD depth of intrabony defect showed a statistically significant reduction of 0.20±0.24 mm at 6 months (P≤0.05). Conclusion: This study suggests that the connective tissue graft wall technique might improve root coverage and regeneration of intrabony defects simultaneously.
Nitrate Pollution Externality Associated with Increased Corn Demand for Ethanol: a Case Study of Olmsted County in Minnesota

Land use practices generate various environmental externalities. With the increased interest in biofuel production, it is imperative to understand the benefits and costs associated with resource allocation choices made for biofuel production. This paper contributes to the full cost accounting of biofuels research by estimating potential nitrate pollution cost associated with land use change that may result from increasing corn prices due to demand from ethanol producers. The increased demand for corn from ethanol producers is assumed to translate into changed spatial pattern of land use. Specifically this paper develops a regression model to empirically explain the relationship between groundwater nitrate levels in private wells in Olmsted County in Minnesota and a number of variables that may affect nitrate levels in groundwater including land use practices. Coefficients of the regression model are then employed to estimate the potential nitrate pollution levels under each land use scenario assumed. Finally percentage change in nitrate concentrations predicted under each land use scenario is applied to the observed nitrate concentrations in the private wells and a cost estimate is developed based on number of wells expected to exceed the 10 mg/L level.
Unveiling Maternal Mortality Challenges in a Resource Limited Setting, Ethiopia: A Systematic Literature Review

By combining data from five carefully chosen studies completed between 2014 and 2023, this systematic review aims to assess maternal mortality rates, risk factors, and related variables in Ethiopia.A thorough search of electronic databases was done, including PubMed, Scopus, and Google Scholar. Utilizing predetermined criteria, eligible studies were found, which led to the inclusion of studies carried out in various Ethiopian regions. A thorough quality assessment was done to make sure the chosen studies were reliable. Maternal mortality rates, primary and secondary risk variables, and systemic, socioeconomic, and cultural factors influencing maternal mortalities were all extracted from the data from these studies.In Ethiopia, the research found large regional variations in maternal mortality rates, with obstructed labor, postpartum hemorrhage, and hypertensive disorders of pregnancy emerging as the main risk factors. Disparities in education, age, living in remote areas, and restricted access to antenatal care services were secondary concerns. Notably, limited antenatal care services, rural residence, young maternal age, and a lack of education were linked to increased maternal death rates.The ongoing problem of maternal mortality in Ethiopia is highlighted by this systematic review. Even though there have been gains, particularly in metropolitan areas, there are still significant regional differences. Increasing access to high-quality healthcare, especially in rural areas, boosting education, and increasing the use of antenatal care are just a few of the multidimensional initiatives needed to address maternal mortality. In order to minimize maternal mortality and ultimately move Ethiopia closer to achieving Sustainable Development Goal 3, comprehensive and targeted policies must be put in place.
Late Oligocene-Miocene mid-latitude aridification and wind patterns in the Asian interior

The Asian interior has the largest mid-latitude arid zone in the Northern Hemisphere, and so has become increasingly attractive for studying the initiation and the past extent of aridification in this zone. Given the enormousness of the Asian interior, it remains unclear how old and extensive the eolian deposits might have been, and what wind regimes have been responsible for the formation of the mid-latitude arid zone. Here we report new eolian records of widespread Tertiary eolian deposits in a region far from the Chinese Loess Plateau, the giant Junggar inland basin of northwestern China. Our results demonstrate that the earliest eolian deposition initiated ca. 24 Ma. We interpret that the Tertiary eolian dust in the Junggar Basin was transported by westerly winds, possibly from areas in Kazakhstan; the dust differs from the airborne dust transported by winter monsoon winds from the deserts of Mongolia and northern China that accumulated on the Loess Plateau. These results further reveal that the climate pattern, similar to that of the present, has prevailed at least since the latest Oligocene in Central Asia.
On the statistical characteristics of drought events

Several statistical tests are performed on streamflow series for purposes of analyzing multiyear drought events. These statistical tests on both high-flow and drought event parameters include (1) stationarity in terms of their linear trend; (2) randomness in terms of lag-l serial correlation; (3) correlation and cross correlation between these parameters (duration, magnitude and severity). The test results and their implications are discussed in relation with the characterization of high-flow and drought event series. Two types of envelopes for drought duration and severity are presented which use Tschebycheff's inequality. These envelopes give an excellent indication of the maximum response of a watershed in terms of drought duration and severity during the period of record.
High‐resolution VHF radar observations of turbulence structures in the mesosphere

Results from a study of mesospheric turbulence, using a VHF radar with a height resolution of 150 m, are presented. Three types of turbulence structures have been identified: blobs, sheets and layers. Blobs and sheets occurred sporadically, predominantly at heights of 60–70 km. Above 70 km, layers thicker than 1 km were dominant, which were advected with the background wind. Velocity fluctuations in these structures were proportional to their thickness. The role of partial or Fresnel reflections is discussed and possibility of diffuse reflections below 70 km is noted. Problems in estimating transport coefficients from radar data are outlined.
Measuring Trustworthiness in Neuro-Symbolic Integration

Neuro-symbolic integration of symbolic and subsymbolic techniques represents a fast-growing AI trend aimed at mitigating the issues of neural networks in terms of decision processes, reasoning, and interpretability. Several state-of-the-art neuro-symbolic approaches aim at improving performance, most of them focusing on proving their effectiveness in terms of raw predictive performance and/or reasoning capabilities. Meanwhile, few efforts have been devoted to increasing model trustworthiness, interpretability, and efficiency—mostly due to the complexity of measuring effectively improvements in terms of trustworthiness and interpretability. This is why here we analyse and discuss the need for ad-hoc trustworthiness metrics for neurosymbolic techniques. We focus on two popular paradigms mixing subsymbolic computation and symbolic knowledge, namely: (i) symbolic knowledge extraction (SKE), aimed at mapping subsymbolic models into human-interpretable knowledge bases; and (ii) symbolic knowledge injection (SKI), aimed at forcing subsymbolic models to adhere to a given symbolic knowledge. We first emphasise the need for assessing neuro-symbolic approaches from a trustworthiness perspective, highlighting the research challenges linked with this evaluation and the need for ad-hoc trust definitions. Then we summarise recent developments in SKE and SKI metrics focusing specifically on several trustworthiness pillars such as interpretability, efficiency, and robustness of neuro-symbolic methods. Finally, we highlight open research opportunities towards reliable and flexible trustworthiness metrics for neuro-symbolic integration.
The anatomy of recent large sea level fluctuations in the Mediterranean Sea

During the boreal winter months of 2009/2010 and 2010/2011, Mediterranean mean sea level rose 10 cm above the average monthly climatological values. The non‐seasonal anomalies were observed in sea surface height (from altimetry), as well as ocean mass (from gravimetry), indicating they were mostly of barotropic nature. These relatively rapid basin‐wide fluctuations occurred over time scales of 1–5 months. Here we use observations and re‐analysis data to attribute the non‐seasonal sea level and ocean mass fluctuations in the Mediterranean Sea to concurrent wind stress anomalies over the adjacent subtropical Northeast Atlantic Ocean, just west of the Strait of Gibraltar, and extending into the strait itself. The observed Mediterranean sea level fluctuations are strongly anti‐correlated with the monthly North‐Atlantic‐Oscillation (NAO) index.
Adaptive Time–Frequency-Supported Chirp Component Decomposition

There are a lot of complex amplitude modulated–frequency modulated (AM–FM) signals with strong noise in the real world. The decomposition of complex signals can effectively reveal the variation laws of each subsignal. However, current methods cannot effectively decompose complex signal with strong noise. To solve this problem, we propose a novel adaptive time–frequency-supported chirp component decomposition (ATCCD) in this article. ATCCD establishes a new adaptive signal decomposition approach by constraining the smoothness of demodulated signal and the subsignal decomposition error. Then, ATCCD establishes a new instantaneous frequency (IF) update optimization model by integrating time–frequency transform (TFT) iterative optimization and IF arctangent demodulation optimization. The new IF update model enhances the level of IF iterative optimization with the support of signal’s time–frequency feature information, which can effectively filter out the interference of strong noise and accurately estimate IF. ATCCD bridges the gap between signal decomposition and TFT, makes them promote each other’s performance, and effectively solves the mode aliasing problem. In this way, ATCCD can accurately estimate the IFs and reconstruct subsignals for complex signal with strong noise. Furthermore, the decomposition results verify that ATCCD can more accurately decompose complex AM–FM signals with strong noise containing crossing IFs, close Ifs, or fast changing IFs than current signal decomposition and TFT methods. Simultaneously, ATCCD can more precisely decompose complex experimental signals with strong noise and obtain higher quality time–frequency representations (TFRs).
On-board ephemeris representation for Topex/Poseidon

The Topex/Poseidon satellite requires real-time on-board knowledge of the satellite and TDRS ephemeris for attitude determination and control and High-Gain Antenna (HGA) pointing. The ephemeris representation concept for the MMS (Multimission Modular Spacecraft) satellites has shown that compressing the predicted ephemeris in a Fourier Power Series (FPS) before uplinking in conjunction with the On-Board Computer (OBC) ephemeris reconstruction algorithms is an efficient technique for ephemeris representation. As an MMS-based satellite, Topex/Poseidon has inherited the Landsat ephemeris representation concept including a daily FPS upload. This paper presents the Topex/Poseidon concept, analysis, and results including the conclusion that the ephemeris representation duration could be extended to 10 days or more and convenient weekly uploading is adopted without an increase in OBC memory requirements.
Improving Energy Demand Prediction in IoT Based Smart Grids through Hybrid CNN-LSTM Modelling With Modified Sea Lion Algorithm

- Efficient energy demand forecasting is pivotal for the reliable operation of modern IoT based smart grids, ensuring optimal resource allocation and grid stability. This study introduces a novel approach that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks within a hybrid framework for accurate energy demand prediction. An innovative modification of the Sea Lion Algorithm (SLA) is proposed to enhance the model's performance for optimal hyperparameter tuning. The hybrid CNN-LSTM architecture leverages the strengths of CNNs in feature extraction from sequential data and LSTM's proficiency in capturing temporal dependencies. By synergizing these capabilities, the model offers improved accuracy in predicting energy demand patterns, which is critical for effective energy management and distribution. The Modified Sea Lion Algorithm (MSLA) is employed to fine-tune the hybrid CNN-LSTM model's hyperparameters effectively. Inspired by the behaviour of sea lions in balancing exploration and exploitation during foraging, MSLA ensures an optimal configuration of model parameters, leading to enhanced forecasting accuracy. Extensive experiments use real-world energy consumption datasets to assess the proposed methodology's efficacy. Comparative analyses are conducted against conventional CNN-LSTM models with default settings, highlighting the superiority of the hybrid approach. The results demonstrate that integrating CNNs and LSTMs yields more accurate predictions, while the modified Sea Lion Algorithm provides optimal parameter values, further enhancing prediction accuracy
Video Compressed Sensing Reconstruction via an Untrained Network with Low-Rank Regularization

Deep image prior (DIP) is an emerging technology that indicates that the structure of an untrained network can serve as an excellent prior for image restoration. It bridges the gap between training-based and training-free methods and exhibits considerable potential in image compressed sensing (CS) reconstruction. In this article, we extend DIP and propose a novel Low-Rank Regularization Video Compressed Sensing Network for CS video reconstruction (dubbed LRR-VCSNet). We explore the application of a low-rank latent tensor with an untrained network for global low-rank regularization on video reconstruction, and the interframe low-rank approximation for framewise nonlocal low-rank regularization in the data space is also exploited. In addition, we design the structure of the untrained network based on the encoder-decoder architecture to improve the performance. Extensive experiments on six standard CIF video sequences show that LLR-VCSNet significantly outperforms traditional video CS methods and achieves competitive results when compared with the state-of-the-art training-based video CS method.
Sparse auto-encoder based micro-aneurysm detection

Microaneurysms (MAs) are the early noticeable lesions in retina, their detection plays a crucial role in the diagnosis of diabetic retinopathy. Here the discriminative features are automatically learned in an unsupervised manner. The stacked sparse auto-encoder (SSAE) is effective at learning high-level features from overlapping image patches during training. Using 10-fold cross-validation and fine-tuning yield an improved F-measure of 97.3% and an average area under the ROC curve (AUC) 96.7% obtained. Experimental validation is performed, both quantitative and qualitative, in the public datasetDIARETDB1. The result achieved a better accuracy compared to other methods.
Deconvolving the sedimentary phases of barium using flow-through time-resolved analysis

approved: __________________________________________________ Marta E. Torres and Gary P. Klinkhammer The distribution of barite (BaSO4) in marine sediments has long been studied as a proxy for paleoproductivity. While pure barite is known to be a stable mineral in oxic sediments, it is also known that variations in the Sr/Ba ratio influence its solubility and liability during early diagenesis. To extract this information we developed Flow-Through Time-Resolved Analysis (FT-TRA) technique coupled with Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) to continuously monitor the dissolution process using sequential leaching with deionized water (DIW), acidic acid (HNO3), acidic hydroxylamine (NH2OH) and diethylene triamine pentaacetic acid (DTPA) at controlled flow rate and temperature. We applied this technique to the analyses of barite samples and sediments from two contrasting basins in the California borderlands. To further understand various barium-carrying phases we analyzed foraminifera recovered from core tops from various locations around the world, using FT-TRA coupled with Inductively Coupled Plasma Mass Spectrometry. Combined with available pore water data, our results provide a set of data that illuminates the nature (e.g. barite, carbonate, oxyhydroxide) and composition (e.g. relative concentrations of Sr, Ba and Ca) of barium-carrying phases to the sediment. We found the presence of a barium-carrying carbonate phase, hitherto unrecognized, which is present both as disseminated carbonates in the sediment as well as overgrowths in foraminifera. This phase has various compositions, but in general contains significantly more barium than biogenic carbonate, and may be responsible for early diagenetic remobilization of barium as the pH of the sediment is lowered during oxic respiration. The analytical technique and the results presented in this thesis provide the foundation for further investigations that can be used to constrain the barium geochemistry so as to fully exploit its application in a wide realm of studies. Copyright by Chih-Ting Hsieh June 5, 2009 All Rights Reserved Deconvolving the Sedimentary Phases of Barium Using Flow-Through TimeResolved Analysis
Predictors, Prevalence, and Clinical Outcomes of Out-of-Hospital Cardiac Arrests in Croatia: A Nationwide Study

Background: Out-of-hospital cardiac arrest (OHCA) remains a pivotal health challenge globally. In Croatia, there has been a knowledge gap regarding the prevalence, predictors, and outcomes of OHCA patients. This study aims to determine the prevalence, prediction, and outcomes of OHCA patients in Croatia. Methods: An extensive one-year analysis was performed on all OHCA treated by the Emergency Medical Service in Croatia, based on the Utstein recommendations. Data were extracted from Croatian Institute of Emergency Medicine databases, focusing on adult individuals who experienced sudden cardiac arrest in out-of-hospital settings in Croatia. Results: From 7773 OHCA cases, 9.5% achieved spontaneous circulation pre-hospital. Optimal outcomes corresponded to EMS intervention within ≤13 min post-arrest onset AUC = 0.577 (95% CI: 0.56–0.59; p < 0.001) and female gender OR = 1.81 (95% CI: 1.49–2.19; p < 0.001). Northern Croatia witnessed lower success rates relative to the capital city Zagreb OR = 0.68 (95% CI: 0.50–0.93; p = 0.015). Conclusions: Early intervention by EMS, specifically within a 13-min period following the onset of a cardiac arrest, significantly enhances the probability of achieving successful OHCA outcomes. Gender differences and specific initial heart rhythms further influenced the likelihood of successful outcomes. Regional disparities, with reduced success rates in northern Croatia compared to the City of Zagreb, were evident.
Biogas processes for sustainable development

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