Miracle-dz/UniFAR-FTUs huggingface dataset:
Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts

Short‐ to medium‐range flood forecasts are central to predicting and mitigating the impact of flooding across the world. However, producing reliable forecasts and reducing forecast uncertainties remains challenging, especially in poorly gauged river basins. The growing availability of synthetic aperture radar (SAR)‐derived flood image databases (e.g., generated from SAR sensors such as Envisat advanced synthetic aperture radar) provides opportunities to improve flood forecast quality. This study contributes to the development of more accurate global and near real‐time remote sensing‐based flood forecasting services to support flood management. We take advantage of recent algorithms for efficient and automatic delineation of flood extent using SAR images and demonstrate that near real‐time sequential assimilation of SAR‐derived flood extents can substantially improve flood forecasts. A case study based on four flood events of the River Severn (United Kingdom) is presented. The forecasting system comprises the SUPERFLEX hydrological model and the Lisflood‐FP hydraulic model. SAR images are assimilated using a particle filter. To quantify observation uncertainty as part of data assimilation, we use an image processing approach that assigns each pixel a probability of being flooded based on its backscatter values. Empirical results show that the sequential assimilation of SAR‐derived flood extent maps leads to a substantial improvement in water level forecasts. Forecast errors are reduced by as much as 50% at the assimilation time step, and improvements persist over subsequent time steps for 24 to 48 hr. The proposed approach holds promise for improving flood forecasts at locations where observed data availability is limited but satellite coverage exists.
Garnet‐controlled very low velocities in the lower mantle transition zone at sites of mantle upwelling

Deep mantle plumes and associated increased geotherms are expected to cause an upward deflection of the lower–upper mantle boundary and an overall thinning of the mantle transition zone between about 410 and 660 km depth. We use subsequent forward modelling of mineral assemblages, seismic velocities, and receiver functions to explain the common paucity of such observations in receiver function data. In the lower mantle transition zone, large horizontal differences in seismic velocities may result from temperature‐dependent assemblage variations. At this depth, primitive mantle compositions are dominated by majoritic garnet at high temperatures. Associated seismic velocities are expected to be much lower than for ringwoodite‐rich assemblages at undisturbed thermal conditions. Neglecting this ultralow‐velocity zone at upwelling sites can cause a miscalculation of the lower–upper mantle boundary on the order of 20 km.
Examining dispatching practices for Interagency Hotshot Crews to reduce seasonal travel distance and manage fatigue

Interagency Hotshot Crews (IHCs) are a crucial firefighting suppression resource in the United States. These crews travel substantial distances each year and work long and arduous assignments that can cause accumulated fatigue. Current dispatching practices for these crews are supposed to send the closest resource while adhering to existing fatigue-management policies. In this research, we designed a simulation process that repeatedly implements an optimisation model to assign crews to suppression requests. This study examines the potential effects of using an optimisation approach to shorten seasonal crew travel distances and mitigate fatigue. We also examine the potential benefits of coordinating crew-dispatch decisions to meet multiple requests. Results indicate there is substantial room for improvement in reducing travel distances while still balancing crew fatigue; coordinating crew dispatching for multiple requests can increase the assignment efficiency, particularly when both fatigue mitigation and travel distances are jointly optimised. This research indicates implementing an optimisation model for dispatching IHCs is promising.
Predictors of seizure outcomes in stereo-electroencephalography-guided radio-frequency thermocoagulation for MRI-negative epilepsy

Background: One-third of intractable epilepsy patients have no visually identifiable focus for neurosurgery based on imaging tests [magnetic resonance imaging (MRI)-negative cases]. Stereo-electroencephalography-guided radio-frequency thermocoagulation (SEEG-guided RF-TC) is utilized in the clinical treatment of epilepsy to lower the incidence of complications post-open surgery. Objective: This study aimed to identify prognostic factors and long-term seizure outcomes in SEEG-guided RF-TC for patients with MRI-negative epilepsy. Design: This was a single-center retrospective cohort study. Methods: We included 30 patients who had undergone SEEG-guided RF-TC at Sanbo Brain Hospital, Capital Medical University, from April 2015 to December 2019. The probability of remaining seizure-free and the plotted survival curves were analyzed. Prognostic factors were analyzed using log-rank tests in univariate analysis and the Cox regression model in multivariate analysis. Results: With a mean time of 31.07 ± 2.64 months (median 30.00, interquartile range: 18.00–40.00 months), 11 out of 30 patients (36.7%) were classified as International League Against Epilepsy class 1 in the last follow-up. The mean time of remaining seizure-free was 21.33 ± 4.55 months [95% confidence interval (CI) 12.41–30.25], and the median time was 3.00 ± 0.54 months (95% CI 1.94–4.06). Despite falling in the initial year, the probability of remaining seizure-free gradually stabilizes in the subsequent years. The patients were more likely to obtain seizure freedom when the epileptogenic zone was located in the insular lobe or with one focus on the limbic system (p = 0.034, hazard ratio 5.019, 95% CI 1.125–22.387). Conclusion: Our findings may be applied to guide individualized surgical interventions and help clinicians make better decisions.
Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Introduction Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic–clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic–clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.
Emotion recognition based on microstate analysis from temporal and spatial patterns of electroencephalogram

Introduction Recently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition. Methods We proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP). Results For the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network). Discussion In summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.
Future Projections of the Large‐Scale Meteorology Associated with California Heat Waves in CMIP5 Models

Previous work showed that climate models capture historical large‐scale meteorological patterns (LSMPs) associated with California Central Valley heat waves including both ways these heat waves form. This work examines what models predict under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios. Model performance varies, so a multimodel average weights each model based on its historical performance in four parameters. An LSMP index (LSMPi) defined using upper atmosphere variables captures dates of past extreme surface temperature maxima. LSMPi correlates well with all values of California Central Valley‐average surface maximum temperature. LSMPi distributions in future simulations shift ~0.6 standard deviations higher between 1961–2000 and 2061–2100 for RCP8.5 data. Based on the historical climatology, future scenarios show a large increase in the frequency and duration of heat waves in every model. Four times as many heat waves occur and their median duration doubles, using historical thresholds. Of the two ways heat waves form, Type 1 has similar frequency in the future. But, Type 2 becomes much more common because Type 2 has a preexisting hot anomaly in Southwestern Canada, much like the historical to future climatological change in that region (a “global warming” signal). The 20‐year return value anomaly increases by 30–40%. The average of the 50 hottest temperatures increases 3.5–6 K depending on the scenario. When extreme values are defined using the future climatology, the models and their average have no consistent increase or decrease of distribution properties such as shape, scale, and return values of the extremes compared to historical values.
Perbandingan Metode Ensemble Untuk Meningkatkan Akurasi Algoritm Machine Learning Dalam Memprediksi Penyakit Breast Cancer (Kanker Payudara)

Machine Learning merupakan suatu teknologi pembelajaran mesin yang dapat digunakan untuk memudahkan pekerjaan berbagai bidang, salah satunya yaitu pada bidang kesehatan. Machine Learning dalam bidang kesehatan dapat digunakan dalam memprediksi atau mendiagnosa suatu penyakit yang dihasilkan berdasarkan dataset. Kanker payudara (breast cancer) merupakan salah satu penyakit yang mematikan khususnya banyak diderita oleh wanita, oleh karena itu perlu adanya diagnosa dini terkait penyakit kanker payudara agar penanganan dapat dilakukan dengan tepat serta mencegah adanya penyebaran kanker pada tubuh. Penelitian sebelumnya telah membahas tentang diagnosa penyakit kanker payudara namun tingkat akurasi masih rendah sehingga perlu adanya teknik peningkatan akurasi untuk dapat memberikan informasi yang akurat. Tujuan dalam penelitian ini yaitu membandingkan metode Ensemble menggunakan algoritma Machine Learning yaitu Decision Tree, Naïve Bayes, dan K-Nearest Neighbor (KNN), untuk meningkatkan akurasi dalam memprediksi penyakit Kanker Payudara. Metode ensemble yang digunakan dalam penelitian ini yaitu Adaboost dan Bagging. Hasil penelitian menunjukan bahwa terdapat peningkatan pada algoritma klasifikasi menggunakan metode ensemble. Metode paling unggul yaitu Algoritma Decision Tree dan Metode ensamble yang menghasilkan akurasi sebesar yaitu 82.76%. Pada nilai AUC tertinggi diperoleh dari algoritma KNN yang dikombinasikan dengan metode Bagging yaitu sebesar 0.950 dengan kategori sangat baik.
The role of the Madden–Julian oscillation on the Amazon Basin intraseasonal rainfall variability

The relationship between the Madden–Julian oscillation (MJO) and the seasonal cycle of the intraseasonal rainfall variability in the Amazon Basin (AB) are analysed using band‐pass‐filtered gauge‐based gridded rainfall data for the 1980–2009 period. Intraseasonal events (IE) have been defined and selected based on extreme values of the first principal component (PC1) time series, which comes from the empirical orthogonal function (EOF) analysis applied to the filtered rainfall data over the AB. A total of 132 IEs were identified with an average of approximately five events per year. About 25% of the total IEs in the Amazon region are not restricted to the eastwards propagating equatorially confined MJO and other mechanisms (e.g., through Rossby wave trains in the Southern Hemisphere) might play an important role. In addition, we find that the incomplete IEs (events that do not evolve through a complete life cycle) are associated with suppressed rainfall conditions over tropical South America. The development of the IEs over the AB, when compared with the different phases of the MJO index, shows a coherent relationship, where convective‐based indices are able to better account their evolution. On a global scale, the upper‐tropospheric patterns and the rainfall composites based on the PC1 time series show that the MJO is one of the main atmospheric modulator mechanisms of the intraseasonal rainfall variability over the Amazon region throughout the annual cycle. It is found that the intraseasonal variability is particularly important during the austral winter, when the percentage contribution with respect to the mean daily seasonal precipitation over some Amazon regions can reach 50%.
The nature of the Wathaman Batholith and its relationship to the Archean Peter Lake Domain along the Reindeer Lake transect, Saskatchewan

The Wathaman Batholith in Saskatchewan is a stitching pluton emplaced at the boundary between accreted volcano-sedimentary rocks of the La Ronge Domain and the Archean Hearne Province margin. Along the northern shore of Reindeer Lake, megacrystic granitoid rocks of the Wathaman suite intrude and host xenoliths of Archean gneiss of the Peter Lake Domain. The Swan River complex, which intrudes Archean gneiss of Peter Lake Domain, contains mafic-ultramafic sills including layered olivine gabbro and diorite. It locally mingles with megacrystic monzodiorite of the Wathaman Batholith, suggesting a co-genetic relationship. Along its southern margin, the batholith intrudes metasedimentary rocks of the Park Island assemblage, interpreted as the remnant of a foreland basin. The Swan River complex, as well as voluminous granitic to granodioritic plutons occurring in the La Ronge Domain, greatly increase the amount and compositional diversity of plutonic rocks believed to form part of the Wathaman suite. 1 Résumé : Le Batholite de Wathaman (Saskatchewan) est un pluton de suture qui a été mis en place à la limite entre les roches volcano-sédimentaires accrétées du Domaine de La Ronge et la marge archéenne de la Province de Hearne. Le long du littoral nord du lac Reindeer, des granitoïdes mégacristallins de la suite de Wathaman recoupent et contiennent des xénolites de gneiss archéen du Domaine de Peter Lake. Le complexe de Swan River, qui recoupe le gneiss archéen du Domaine de Peter Lake, contient des filons-couches mafiques-ultramafiques, y compris de la diorite et du gabbro à olivine stratiformes. Par endroits, il est mélangé à de la monzodiorite mégacristallin du Batholite de Wathaman, ce qui suggère une relation cogénétique. Le long de sa marge méridionale, le batholite pénètre des roches métasédimentaires de l’assemblage de Park Island, qui sont interprétées comme étant des vestiges d’un bassin d’avant pays. Le complexe de Swan River, aussi bien que les plutons granitiques à granodioritiques volumineux du Domaine de La Ronge, ont grandement augmenté la quantité et la diversité des compositions des roches plutoniques que l’on croit appartenir à la suite de Wathaman.
Optimal allocation of agricultural land for crop planning in Hirakud canal command area using swarm intelligence techniques

ABSTRACT The present study investigates the prevailing cropping pattern, adopted by the farmers of the study area under consideration. It aims at improving the net benefits from the farming activities with present irrigation water allocation. To arrive at the optimal cropping pattern, various swarm intelligence techniques, genetic algorithm (GA), cuckoo search (CS) and particle swarm optimization (PSO) techniques are used to formulate an efficient cropping pattern for maximizing net return for the part of Hirakud command area, in India. Maximum available land area, water for irrigation and cropping area for different crops were considered as constraints. The results are compared with the output from linear programming (LP) to evaluate the efficiency of the models. The results reveal that the net economic return arrived at by adopting the optimal cropping pattern derived with the use of PSO works out to be 230.120 Billion Rupees, whereas it is 132.2 Billion Rupees from the prevailing crop pattern adopted by farmers; 199.271 Billion Rupees with the application of LP; 210.19 billion rupees with GA and 229.895 billion rupees with CS. Weightage is given to a given crop under consideration by allocating suitable land area as per the type of land and water availability.
Theoretical Considerations on Factors Confounding the Interpretation of the Oceanic Carbon Export Ratio

The fraction of primary production exported out of the surface ocean, known as the export ratio (ef ratio), is often used to assess how various factors, including temperature, primary production, phytoplankton size, and community structure, affect the export efficiency of an ecosystem. To investigate possible causes for reported discrepancies in the dominant factors influencing the export efficiency, we develop a metabolism‐based mechanistic model of the ef ratio. Consistent with earlier studies, we find based on theoretical considerations that the ef ratio is a negative function of temperature. We show that the ef ratio depends on the optical depth, defined as the physical depth times the light attenuation coefficient. As a result, varying light attenuation may confound the interpretation of ef ratio when measured at a fixed depth (e.g., 100 m) or at the base of the mixed layer. Finally, we decompose the contribution of individual factors on the seasonality of the ef ratio. Our results show that at high latitudes, the ef ratio at the base of mixed layer is strongly influenced by mixed layer depth and surface irradiation on seasonal time scales. Future studies should report the ef ratio at the base of the euphotic layer or account for the effect of varying light attenuation if measured at a different depth. Overall, our modeling study highlights the large number of factors confounding the interpretation of field observations of the ef ratio.
Perancangan Sistem Informasi Pada Pembelian Tiket Batam JET (Bengkalis – Batam) di Bandar Sri Laksamana Kabupaten Bengkalis

Penelitian ini bertujuan untuk mengkaji Pelabuhan Bandar Sri Laksamana Bengkalis 2003-2017.Hal ini karena menjadi pelabuhan penting di Kabupaten Bengkalis dan Provinsi Kepulauan Riau,Pelabuhan Bandar Sri Laksamana Bengkalis melayani rute melalui pesisir pantai timur sumatera, Demikian juga dengan adanya Pelabuhan Bandar Sri Laksamana telah berkontribusi terhadap pendapatan bagi daerah bengkalis. Penelitian ini menggunakan metode penelitian SDLC, badan perancangan pembangunan daerah (bappeda),badan pusat statistic,badan pendapatan daerah (bapenda), unit pelaksana teknis, syahbandar, dan untuk mempertajam penghasilan kajian ini telah dilakukan penelitian kepustakaan dan penelitian lapangan.Sedangkan sumber lisan diperoleh melalui wawancara dengan 10 orang informan dengan kategori pimpinan,staf dan masyarakat.
Partitioning of macroinvertebrate communities in a large New Zealand river highlights the role of multiple shore‐zone habitat types

Bank stabilization is increasing along large rivers as urban areas expand, and the need to protect infrastructure increases in the face of changing climate and flow patterns, but the cumulative effects of different stabilization approaches on reach‐scale biodiversity are not well understood. We investigated physical habitat characteristics and macroinvertebrate community composition and diversity for four shore‐zone habitat types across nested spatial scales over two sampling occasions. Distinct physical conditions were evident for riprap, beach and willow (mixed trees dominated by Salix spp.) habitats, reflecting variations in the combinations of shade, water velocity and substrate size/type, but there was wide variation in habitat conditions for mixed willow‐riprap sites. Additive biodiversity partitioning decomposed reach (γ) diversity into within (α) and among (β1) sample, among habitat (β2), and among site (β3) components, and highlighted significant effects of all spatial scales on macroinvertebrate diversity. Low autumn water levels led to truncated species accumulation curves at beach sites where macrophyte beds that supported macroinvertebrates became stranded, or elevated species accumulation curves for exposed willow‐riprap sites where the river benthos was sampled during hydrological disconnection of bank habitats. Spring and autumn differences in macroinvertebrate community composition were stronger than differences between habitat types. Our findings (a) highlight the interacting effect of river level with shore‐zone habitat function, and (b) underscore the importance of maintaining a diversity of bank habitat types at multiple sites along river shore‐zones to maximize macroinvertebrate diversity.