Miracle-dz/UniFAR-FTUs huggingface dataset:
Corporate governance and financial performance in Islamic banks: the role of the sharia supervisory board in multiple-layer management

This study aims to investigate the impact of Good Corporate Governance (GCG) on the financial performance of sharia banking. GCG is measured by the Board of Commissioners Performance, the Board of Commissioners Composition, the Number of Audit Committees, the Board of Directors, and the Sharia Supervisory Board Performance, whereas financial performance is proxied by Return on Assets, financing risk (Non-Performing Financing), and capital (Capital Adequacy Ratio). Sharia commercial banks registered by Bank Indonesia made the sample of this study. Annual reports and GCG reports of sharia commercial banks from 2014 to 2017 are used as a data source. The study uses a panel data regression approach to analyze the data; some interesting results have been obtained. The Sharia board positively affected financial performance of Islamic banks in terms of return on assets and capital adequacy ratio, and negatively as to non-performing financing. Similarly, the board of directors had a significant impact on the financial performance of Islamic banks in the same direction as the sharia supervisory board in terms of the three components. Meanwhile, the board of commissioners had a significant and positive impact only on the return on assets of Islamic banks in Indonesia. Darwanto (Indonesia), Anis Chariri (Indonesia)
Improved Electrochemical Performance of Li-Rich Cathode Materials via Spinel Li2MoO4 Coating

Li-rich manganese-based cathode materials (LRMs) are considered one of the most promising cathode materials for the next generation of lithium-ion batteries (LIBs) because of their high energy density. However, there are problems such as a capacity decay, poor rate performance, and continuous voltage drop, which seriously limit their large-scale commercial applications. In this work, Li1.2Mn0. 54Co0.13Ni0. 13O2 coated with Li2MoO4 with a unique spinel structure was prepared with the wet chemistry method and the subsequent calcination process. The Li2MoO4 coating layer with a spinel structure could provide a 3D Li+ transport channel, which is beneficial for improving rate performance, while protecting LRMs from electrolyte corrosion, suppressing interface side reactions, and improving cycling stability. The capacity retention rate of LRMs coated with 3 wt% Li2MoO4 increased from 69.25% to 81.85% after 100 cycles at 1 C, and the voltage attenuation decreased from 7.06 to 4.98 mV per cycle. The lower Rct also exhibited an improved rate performance. The results indicate that the Li2MoO4 coating effectively improves the cyclic stability and electrochemical performance of LRMs.
Attention-Based Multiscale Deep Neural Network for Diagnosis of Polycystic Ovary Syndrome Using Ovarian Ultrasound Images

Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that impacts a significant proportion of women in their reproductive years. It results in irregular menstrual cycles and elevated levels of androgens, known as male hormones. Women with PCOS often have ovaries that develop numerous small fluid-filled sacs called follicles, but they fail to release eggs regularly. While the precise cause of PCOS remains unknown, early identification and weight loss can help mitigate the risk of long-term complications. In this study, a novel attention-based multiscale convolutional neural network (AMCNN) is proposed for the detection of PCOS. The utilization of dilated convolution aids in preserving the multi-scale features with fewer parameters. The integration of multiscale characteristics is achieved by the attention mechanism, which enhances the importance of features within significant channels. The experimental results demonstrate the superior performance of the AMCNN, surpassing other prominent algorithms with an accuracy of 98.79%, proving its effectiveness for medical industrial applications.
Evaluation of the NDSI Threshold Value in Mapping Snow Cover of MODIS——A Case Study of Snow in the Middle Qilian Mountains

Currently,the MODIS(Moderate Resolution Imaging Spectroradiometer) snow cover products have being widely applied to many researching fields,such as NWP(numerical weather prediction),hydrology,climatology,and so on.The MODIS snow-mapping algorithm is essentially designed for the evaluation of the threshold value of the NDSI(Normalize Difference Snow Index) threshold value.The NDSI threshold of the MODIS snow cover products issued by the NSIDC(National Snow and Ice Data Center) is 0.40. The NDSI values of the MODIS scenes greater than or equal to 0.40 represent snow cover pixels.However,validation of the current NDSI threshold has being accomplished only by the measurements in the United States and Europe.In China there is not reliable NDSI threshold for the MODIS snow mapping and a credible threshold should be established.In the paper,the snow cover area in the middle Qilian Mountains was selected for studying.First,the Landsat-ETM+ snow cover maps were produced by the method of the SNOMAP.Then,the maps were compared with the ones derived by the manual photointerpretation classification technique.Overall agreement from a simple comparison of the number of snow pixels is high,about 96%. Thus,the Landsat-ETM+ snow cover maps can be served as the "groudtruth",with which the snow cover maps of the study area extracted from the MODIS 1B measurements by NDSI method were compared.For the MODSI snow cover maps of the study areas,the NDSI threshold value for snow increases gradually for 0.25 to 0.40 in steps of 0.01. At last,the comparisons focused on comparing the MODIS snow cover maps following with NDSI threshold value and the Landsat-ETM+ snow cover maps serving as absolute standard.The result suggests that the NDSI threshold for the MODIS snow cover products issued by the NSIDC,0.40,is underestimated in the snow-covered area of the study areas.In the study areas,the credible NDSI threshold value is approximately 0.33.
The APOSTEL recommendations for reporting quantitative optical coherence tomography studies.

Objective: To develop consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results. Methods: A panel of experienced OCT researchers (including 11 neurologists, 2 ophthalmolo-gists, and 2 neuroscientists) discussed requirements for performing and reporting quantitative analyses of retinal morphology and developed a list of initial recommendations based on experi-ence and previous studies. The list of recommendations was subsequently revised during several meetings of the coordinating group. Results: We provide a 9-point checklist encompassing aspects deemed relevant when reporting quantitative OCT studies. The areas covered are study protocol, acquisition device, acquisition settings, scanning protocol, funduscopic imaging, postacquisition data selection, postacquisition data analysis, recommended nomenclature, and statistical analysis. Conclusions: The Advised Protocol for OCT Study Terminology and Elements recommendations include core items to standardize and improve quality of reporting in quantitative OCT studies. The recommendations will make reporting of quantitative OCT studies more consistent and in line with existing standards for reporting research in other biomedical areas. The recommendations originated from expert consensus and thus represent Class IV evidence. They will need to be reg-ularly adjusted according to new insights and practices. Neurology 2016;86:2303
Understanding Environmental Posts: Sentiment and Emotion Analysis of Social Media Data

Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments. Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader’s score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges.
MERCURY AT THE TERRESTRIAL-AQUATIC INTERFACE: THE BIOGEOCHEMICAL CYCLING OF MERCURY IN WETLANDS OF DIFFERENT HYDROGEOLOGIC SETTING, AND DURING SNOWMELT IN A FORESTED HEADWATER STREAM

Elevated atmospheric deposition of mercury since the mid-1800s industrial revolution has led to a large legacy of mercury accumulation in wetlands. However, the influence of different wetland types on the retention and biogeochemical cycling of mercury within wetlands remains unclear. In this study, mercury pool sizes and stoichiometry with C, N, and S are quantified across upland-wetland transects and among wetlands of different hydrogeologic setting in the Adirondack region of New York State. The pool size of mercury varied across the upland-wetland interface, among wetland types, across individual wetland transects, and along depth profiles in soils of forests and wetlands of the Sunday Lake watershed. Total mercury pool size was greater in upland soils than in wetland soils (to 50 cm depth, p<0.01). Shallow peat riparian wetlands had greater mercury pool size (to 50 cm depth) than deep peat riparian or headwater wetlands (p<0.099, p<0.046). There was greater accumulation of mercury at the upland-wetland interface in deep peat riparian and headwater wetlands as compared to the overall wetland transect (p=0.008). In mineral horizons of the uplands and shallow peat riparian wetlands, mercury was strongly correlated with carbon (p=0.002, r=0.73), and nitrogen (p<0.001, r=0.82), but not sulfur. In deep peat riparian and headwater wetlands, there was no relationship between mercury and carbon or nitrogen; however, there was a strong correlation between mercury and sulfur in the peat of headwater wetlands (p<0.0001, r=0.60). Hydrogeologic setting influences decomposition processes, biogeochemical cycling of mercury, and
WeightsandRobustnessofModel-based Seasonal Adjustment

Model-based seasonal adjustment implicitly defines a set of weights at the ends of series as well as in the middle. Until now, with the exception of very simple models, the weights have been obtained numerically. In this paper we give the analytical expressions for the weights for both the structural and the ARIMA framework for a model which contains trend, seasonal and irregular component. In the final part of the paper we address the question of robustness of model-based seasonal adjustment. We analyse practically, using real time series, and theoretically, through the analysis of the shape of the weights, how the fitting of diAerent specifications for the non-seasonal part aAects the extraction of the seasonal component. #1998 John Wiley & Sons, Ltd. J. Forecast. , Vol. 17, 19‐34 (1998)
Novel pattern recognition techniques for improved target detection in hyperspectral imagery

Novel Pattern Recognition Techniques for Improved T arget Detection in Hyperspectral Imagery. (December 2009) Wesam Adel Sakla, B.S., The University of South Ala bama; M.S., The University of South Alabama Chair of Advisory Committee: Dr. Andrew Chan A fundamental challenge in target detection in hyp ers ectral imagery is spectral variability. In target detection applications, we a r provided with a pure target signature; we do not have a collection of samples that charact e ize the spectral variability of the target. Another problem is that the performance of st chastic detection algorithms such as the spectral matched filter can be detrimentally ffected by the assumptions of multivariate normality of the data, which are often violated in practical situations. We address the challenge of lack of training samp les by creating two models to characterize the target class spectral variability— the first model makes no assumptions regarding inter-band correlation, while the second model uses a first-order Markovbased scheme to exploit correlation between bands. Using these models, we present two techniques for meeting these challenges—the kernelbased support vector data description (SVDD) and spectral fringe-adjusted joi nt transform correlation (SFJTC). We have developed an algorithm that uses the kerne l-bas d SVDD for use in full-pixel target detection scenarios. We have addr essed optimization of the SVDD
Exploring the Impact of Signal Quality Enhancement on Heart Sound Classification Models

Limited cardiology resources increase the urgency for automated heart disease screening for the general public. Heart sound diagnostic models have been recently employed as a cost-effective solution for the initial screening of heart disease. Noise in heart sound recordings, however, can reduce the performance of such data-driven models. Various quality enhancement approaches have been adopted to alleviate the destructive impact of noise on model performance. One approach is universal noise reduction which applies denoising techniques to recordings, irrespective of their noise level. The second approach is targeted noise reduction, which applies denoising solely to recordings deemed to need it, based on an assessment of signal noise level. The third approach is filtering where instead of noise reduction, the quality of recordings is assessed and the signals falling below a minimum threshold of quality are discarded. This study aims to understand which quality enhancement approach leads to a more accurate heart sound classification. We developed multiple data-driven models using different classifiers and feature representations and analyzed the impact of quality enhancement on the accuracy of those models. The results indicate that noise reduction is associated with an overall performance drop in classification models. We observe that both universal and targeted noise reduction have a destructive impact on models’ performance. However, filtering improves the accuracy of the models, in particular, for the clinically important abnormal class. The findings of this study can be leveraged to inform the design decisions for the pre-processing of heart sound recordings and consequently optimize downstream classification performance.
Wave Equation Inversion of Skeletonized Surface Waves

We present a surface-wave inversion method that inverts for the S-wave velocity from the Rayleigh dispersion curve for the fundamental-mode. We call this wave equation inversion of skeletonized surface waves because the dispersion curve for the fundamental-mode Rayleigh wave is inverted using finitedifference solutions to the wave equation. The best match between the predicted and observed dispersion curves provides the optimal S-wave velocity model. Results with synthetic and field data illustrate the benefits and limitations of this method.
Quantitative analysis of the complexity in seismic exploration of the high and steep structures in Kuqa depression

How a wave interacts with heterogeneous media has been pursued by many geophysicists.Based on seismic imaging operators,we conduct a quantitative assessment of the complexity of salt-related overthrust complex structures in Kuqa depression.We first use seismic data to compute the slowness spectrum and the dipping-angle spectrum for the Dabei,Bozi, Quele,Xiqiu 10,and Xiqiu 4 structures,respectively.These heterogeneity spectra express the geological complexity of subsurface structures as a spectrum to quantify lateral velocity variations and dipping-angle distributions.Through an interaction between the geological heterogeneity spectra and the migrator s angular spectra,we obtain imaging efficiencies and complexity coefficients for these complex structures in Kuqa depression,which quantitatively evaluate the geological complexity in terms of migrators.The results indicate that the Quele-Xiqiu structure zone is more complex than the Dabei-Bozi structure zone.Along Quele through Xiqiu 10 to Xiqiu 4,the complexity coefficient gradually increases because of stronger velocity contrasts,broader heterogeneity spectra,richer off-peak spectral contents in the slowness and dipping-angle spectra.These conclude that time migrations for complex Kuqa structures are prone to cause wrong imaging and migration noise.Depth migrations should be enhanced for Kuqa depression.
Modelling the migration of ice stream margins

The Siple Coast ice streams are long, narrow bands of ice within the Antarctic ice sheet. They move significantly faster than the surrounding ice ridges, and therefore discharge significantly more ice. Observations suggest that their fast flow is due to sliding along a water-saturated bed, while the bed of the neighboring ridges appears to be frozen. The ice stream velocities and widths vary on decadal to centennial time scales. These variations involve the migration of the ice stream margins, where the fast flow slows down to the speed of the surrounding ridges. In this talk we first show that conventional thin film models, which are used to calculate the evolution of ice sheets on continental scales, are only able to reproduce the inwards migration of ice stream margins and the subsequent shutdown of an ice stream. These processes are the result of insufficient heat dissipation and freezing at the bed. Conversely, we find that the widening of ice streams into regions where the bed is frozen can only be modeled by taking small-scale heat transfer processes in the ice stream margin into account. Previous research has shown that ice stream widening results from an interplay of heating through lateral shearing in the ice stream margin and inflow of cold ice from the adjacent ridges. However, the relative importance of the different effects on the migration speed has not yet been quantified. To account for these processes, we use a newly derived boundary layer model for ice stream margins. The numerical solution of this model provides us with the margin migration speed as a function of largescale ice stream properties such as ice stream width, ice thickness, and geothermal heat flux. By considering asymptotic solutions in the limit of high heat production rates and high advection velocities, a limit that likely applies in real ice stream margins, we derive a parameterization of ice stream widening that can be incorporated in continental-scale models.
Colloid-facilitated transport of cations in an unsaturated fractured soil under transient condition

Mohanty, Sanjay Kumar (Ph. D., Civil Engineering) Colloid-facilitated transport of cations in an unsaturated fractured soil under transient condition Thesis directed by Professor Joseph N. Ryan Rainfall experiments were conducted using intact soil cores and an instrumented soil pedon to examine the effect of physical heterogeneity and rainfall characteristics on the mobilization of colloids, organic matter, cesium, and strontium in a fractured soil. To measure the spatial variability of infiltration of colloids and contaminants, samples were collected through a 19-port grid placed below the soil core in laboratory study and in 27 samplers at multiple depths in the soil pedon in the field study. Cesium and strontium were applied to the soil cores and the soil pedon prior to mobilization experiments. Rainwater solutions of multiple ionic strengths and organic matter concentrations were applied to the soil cores and soil pedon to mobilize in situ colloids, cesium, and strontium. The mobilization of colloids and metal cations occurred through preferential flow paths in the soil cores. Compared to steady rainfall, greater amounts of colloids were mobilized during rainfall interrupted by pauses, which indicates that the supply of colloids to be mobilized was replenished during the pauses. A maximum in the amount of mobilized colloids were mobilized during a rainfall following a pause of 2.5 d. Pauses of shorter or longer duration resulted in less colloid mobilization. Freeze-thaw cycles, a transient condition in winter, enhanced colloid mobilization and colloid-facilitated transport of cesium and strontium in the soil cores. The exchange of solutes between the soil matrix and macropores caused a hysteretic mobilization of colloids, cesium, and strontium during changes in ionic strength. Colloid-facilitated mobilization of cesium and strontium was important at low ionic strength in
Sedimentation Analysis Using USLE Model with Modified Parameters

Soil erosion within watersheds results sedimentation reducing the useful life of reservoirs. Universal Soil Loss Equation (USLE) is a widely used field application model to compute soil erosion. Tools like GIS & Remote Sensing technique make it easy to implement the model at pixel accuracy. The modified procedures are adopted for evaluation of soil erodibility parameter, cover management parameter and conservation practice parameter. The refined analysis of these parameters is carried out using satellite imagery, topographic sheets and ground observations. The comparison of computed value of USLE model using modified conservation parameter, with the observed value of sedimentation shows fairly close association. Thus it can be concluded that modified procedure of evaluation of model parameter gives better estimate of sediment computations. The spatial distribution of soil erosion rates drawn from analysis can help in planning appropriate soil and water conservation measures in the study area.
Optimization of 2D location system for smartphones using a single WiFi acces point with Frequency-Scanned Antenna for Real Time Location System

This work studies a WiFi 2D Real Time Location System (RTLS) requiring only one Access Point. The precise two-dimensional position is computed with a combination of the Direction-of-Arrival (DoA) estimated from the RSSI acquired by a frequency-scanned antenna, and the range distance obtained with the 802.11mc Fine-Time Measurement (FTM) Round Trip Time (RTT) protocol. The DoA is computed by acquiring the RSSI from several WiFi channels, and a heuristic algorithm is employed to determine the best channel for measuring the RTT. In this work, we investigate the effect of reducing the number of used WiFi channels and the number of RTT samples, in order to minimize the acquisition time. It is shown that by reducing the number of WiFi channels from eleven to three and acquiring half of the RTT samples, the localization latency is reduced from 3.4 sec. to 1.2 sec. On the other hand, the 2D location mean error increases only from 1.18 m to 1.49 m for an outdoor experiment, and from 2.27 m to 2.67 m for an indoor experiment. This time reduction is of key importance for RTLS, such as tracking moving devices.