Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising

The higher order singular value decomposition (HOSVD) of tensors is a generalization of matrix SVD. The perturbation analysis of HOSVD under random noise is more delicate than its matrix counterpart. Recently, polynomial time algorithms have been proposed where statistically optimal estimates of the singular subspaces and the low rank tensors are attainable in the Euclidean norm. In this article, we analyze the sup-norm perturbation bounds of HOSVD and introduce estimators of the singular subspaces with sharp deviation bounds in the sup-norm. We also investigate a low rank tensor denoising estimator and demonstrate its fast convergence rate with respect to the entry-wise errors. The sup-norm perturbation bounds reveal unconventional phase transitions for statistical learning applications such as the exact clustering in high dimensional Gaussian mixture model and the exact support recovery in sub-tensor localizations. In addition, the bounds established for HOSVD also elaborate the one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced (or fat) matrices.
Effect of Different Feeding Rates on Growth Performance and Survival Rate of Tilapia (Oreochromis niloticus L. 1758) Fingerlings Reared in Rectangular Hapas

The objective of the study was to determine the effect of feeding ration on the growth and survival of Oreochromis niloticus. Three experimental hapas (T1, T2 and T3) were studied for a total of 8 weeks. The hapas had a rectangular shape and the same size (0.0725 decimal). In each of the three experimental conditions, 420 fish/decimal were stocked. The fish were fed 8% of their body weight in T2 and 12% of their body weight in T3. In T1, the fish were reliant on food that occurred naturally in their environment. In T2 and T3, fishes were fed twice daily. The proximate composition of the experimental diet was as follows: 11% water, 30% protein, 6% fat, 10% ash, and 7% fiber. Throughout the trial period, it was determined that the range of water quality parameters (water temperature 28-32°C, dissolved oxygen 5.6-7.8 mg/l, and water pH 7.5-8.6) were suitable for Tilapia culture. The initial mean weight of Tilapia fry in each of the three treatments was 3g, and their final mean weight gain was 6.8±4.77 g for T1, 9.88±5.76 g for T2 and 19.2±9.9 g for T3, respectively. The average initial length for the three treatments was 5 cm, and the average final length gain was 8.2±6.15 cm, 9.86±7.36 cm, and 12.2±8.23 cm for T1, T2 and T3, respectively. The T3 group, which was fed at a rate of 12% of body weight, attained the highest length and weight, whereas the T1 group, with no supplemental nutrition revealed the least gain in terms of length and weight.
Oil-based mud reduces borehole problems

Esso Resources Canada Ltd. used a low-toxicity, oil-based drilling fluid to drill a directional delineation well in the Beaufort Sea in the Spring of 1985. Because of virtual elimination of severe hole problems that were encountered previously with water-based mud, the company drilled three more with oil-based muds. Experience to date indicates that, although significant hole condition improvements will be realized with oil mud, hole stability may continue to be a problem in Beaufort drilling. The case history of these wells drilled with oil-based mud is highlighted with insights into the regulatory process in this first of two articles on Beaufort drilling. The second will focus on a technical review of oil retention on cuttings and solids control equipment and operation.
Gardening Pocillopora spp. fragments and their potential for rebuilding reef systems in the southern Mexican Pacific

The degradation of coral reefs during the last decades has turned attention toward management and restoration interventions. This study seeks to operationalize coral gardening of Pocillopora spp. fragments in low profile bottom‐anchored nurseries and to compare survival and growth patterns between sites, time, and fragment size in the southern Mexican Pacific. After 357 days, fragments showed high survival (96.4%), growth rates (4.35 cm/year), and an increase in coral coverage from 3.62 ± 1.3% (mean ± SD) to 17.42 ± 4.8% (approximately 480%). Coral growth rate did not show differences between sites (pseudo‐F[1,635] = 0.21, p = 0.63), but corals grew more significantly during the upwelling season in the Gulf of Tehuantepec. According to linear and local regression analysis, the extension rate in nurseries was significantly higher when corals were smaller (<7 cm in diameter) being of relevance for operationalizing coral gardening from donor colonies; nevertheless, coral shrinkage (13.1%), when corals were smaller (i.e. during the early phases of the coral gardening protocol), calls for precaution and close monitoring. Operationalizing coral gardening of Pocillopora spp. fragments, including successful metrics in upwelling areas, are relevant for reef restoration in the eastern tropical Pacific; nevertheless, lessons regarding shrinkage and differential growth rates related to coral size should also be considered.
Efficient Trajectory Planning for Autonomous Vehicles Using Quadratic Programming With Weak Duality

Highly autonomous driving technology is expected to improve driving safety and convenience, and collision avoidance technology is essential for fully autonomous driving. Planning a collision-free trajectory that includes velocity and path is one of the most challenging objectives. Optimization-based trajectory planners have been proposed in many previous studies because they offer a high degree of freedom and can handle various situations. However, most previous trajectory planners used nonlinear programming due to the nonlinearity or non-convexity of the optimization problem. These methods come with a high computational load. The trajectory planner requires the real-time ability to cope with dynamically changing environments. This article focuses on the trajectory planning of autonomous vehicles through quadratic programming (QP), which requires a low computational load. To achieve this, we introduce the longitudinal-lateral decomposition method. In addition, collision-free constraints are expressed as linear constraints through proposed ingenious dual functions. The proposed weak duality optimization problem has a QP form and optimized trajectory and obstacle avoidance timing through only one QP problem. This study verified that the proposed trajectory planner could plan smooth collision-free maneuvers for several driving situations by simulations.
IL-31—Pruritus Interleukin: Serum Values and Clinical Impact in Chronic Spontaneous Urticaria—A Romanian Retrospective Study

(1) Background: This study aimed to evaluate the implications of interleukin-31 (IL-31) in the pathogenesis of chronic spontaneous urticaria (CSU) and to assess the differences that occur between its serum values compared to controls. Additionally, the serum IL-31 levels were measured alongside other clinical and paraclinical parameters that were identified in the patients to understand its immunological importance in this skin disease and to determine if it could potentially serve as a therapeutic target in CSU in the future. (2) Methods: The serum levels of IL-31 were estimated in 50 patients diagnosed with CSU according to the accepted international guidelines. Additionally, 38 controls who had not experienced any episodes of urticaria during their lifetime were included. (3) Results: Significantly elevated serum IL-31 levels were observed in CSU patients compared to the controls (p < 0.0001). Although no direct correlations were found between IL-31 and inflammatory markers (erythrocyte sedimentation rate (ESR), C-reactive protein (CRP)), eosinophils, or total immunoglobulins E (IgE), significant differences in IL-31 levels were identified based on CSU severity, quality of life impact, itch intensity, and response to histamine H1 receptor antagonists (H1 antihistamines) (p < 0.05 for all). (4) Conclusions: Our findings underscore that IL-31 is not directly associated with general inflammation, eosinophilic response, or atopy in CSU. Nevertheless, its expression is influenced by key disease characteristics: severity, pruritus, and H1 antihistamine response. This investigation provides essential insights into CSU pathogenesis, potentially leading to novel therapeutic interventions. An enhanced understanding of these mechanisms is crucial due to the limitations of current treatment modalities in terms of fully managing CSU symptoms.
Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Autonomous vehicles (AVs) have moved from hype to reality as the penetration and acceptance rate continues to increase. As they are slowly integrated into traffic with human-driven vehicles (HDVs), it is necessary to predict the car-following behaviors of AVs and HDVs for better control of AV–HDV mixed traffic. This study extends a data-driven car-following model to incorporate drivers’ memory, and cooperation with the lead vehicle. The model predicts the following vehicle’s speed in AV–HDV mixed traffic. The effect of drivers’ cooperation on car-following behavior was modeled using prospect theory (PT), whereas the driver’s memory was incorporated using the memory cell of a long short-term memory (LSTM) neural network. This extended car-following model is called the “PT-LSTM model. ” Real-world vehicle trajectories of HDVs and AVs in the Waymo AV Open Dataset were used to calibrate and validate the PT-LSTM model. The PT-LSTM model demonstrated higher accuracy compared with the LSTM model that did not consider drivers’ cooperation, the multiple layer perceptron model, Gipps’ model, and the intelligent driver model that incorporated PT. The importance of variables in different time steps in the PT-LSTM model was also evaluated using SHapley Additive exPlanations (SHAP). The SHAP results showed that AV followers were more likely to cooperate with the lead HDV, whereas HDV followers were more likely to cooperate with the lead AV than the lead HDV. Thus, this study underscores the importance of considering drivers’ memory and cooperation with the lead vehicle for the prediction of car-following behaviors in AV–HDV mixed traffic.
APPLICATION OF STATISTICAL DOWNSCALING MODEL FOR LONG LEAD RAINFALL PREDICTION IN KURAU RIVER CATCHMENT OF MALAYSIA

The climate impact studies in the hydrology are often relying on the climate change information at a fine spatial resolution. However, Global Climate Models (GCMs) which is regarded as the most advanced models yet for estimating the future climate change scenarios are operated on the coarse spatial resolution and not suitable for climate impact studies. Therefore, in this study, the Statistical Downscaling Model (SDSM) was applied to downscale rainfall from the GCMs. The data from single rainfall station located in the Kurau River were used as input of the SDSM model. The study included the calibration and validation with large-scale National Centers for Environmental Prediction (NCEP) reanalysis data, and the projection of future rainfall corresponding to the GCMs-variables (HadCM3 A2). The study results shows that during the calibration and the validation stage, the SDSM model can be well acceptable in regards to its performance in the downscaling of the daily and annual rainfall. For the future period (2010- 2099), the SDSM model estimates that there were increases in the total average annual rainfall and generally, the area of rainfall station become wetter.
Synthesis of high efficiency amidoxime polymer adsorbents for uranium extraction from seawater by radiation-induced polymerization

This study synthesized amidoxime polymer gel for uranium extraction from seawater through a polymerization process with gamma ray or UV-C radiation. The synthesis started with a mixture of acrylonitrile (AN) and methacrylic acid (MAA) monomers together with methylene bisacrylamide as a crosslinker and hydrogen peroxide in the case of UV radiation. The mixture was irradiated with gamma ray at various doses or UV-C for various durations. The resulting polymer gel was ground into fine particles and immersed in a hydroxylamine hydrochloride solution for 90 minutes in order to convert the chemical functional group from the cyano group into the amidoxime group. Results of laboratory-scale uranium extraction showed that for the case of amidoxime polymer gel prepared by gamma radiation, the ratio of monomer AN:MAA of 80:20, the amount of the crosslinker of 0.8 g/100 mL monomer, and 40 kGy gamma ray dose exhibited the highest uranium adsorption capacity. For the amidoxime polymer gel polymerized by UV-C radiation, the ratio of monomer AN:MAA of 80:20, the amount of the crosslinker of 1 g/100 mL monomer, hydrogen peroxide of 60 mL/100 mL monomer, and irradiation time of 8 hours exhibited the highest uranium adsorption capacity. The usage repeatability test indicated that after 8 cycles of repeated use, the polymer gel prepared from gamma ray did not show a decrease in the adsorption efficiency while the polymer gel prepared from UV-C showed a decrease in the adsorption efficiency of 50%. The field test results in natural seawater in front of Phuket Marine Biological Center (PMBC), Phuket province, Thailand for 8 weeks revealed that the polymer gel prepared from gamma ray exhibited the uranium adsorption of 0.05 mg/g while the polymer gel prepared from UV-C exhibited the uranium adsorption of 0.035 mg/g.
Fire in Mediterranean Ecosystems: Ecology, Evolution and Management

Part I. Introduction: 1. Mediterranean-type climate (MTC) ecosystems and fire 2. Fire and the fire regime framework 3. Fire related plant traits Part II. Regional Patterns: 4. Fire in the Mediterranean basin 5. Fire in California 6. Fire in Chile 7. Fire in the Cape region of South Africa 8. Fire in southern Australia Part III. Comparative Ecology, Evolution and Management: 9. Fire-adaptive trait evolution 10. Fire and the origins of Mediterranean-type vegetation 11. Plant diversity and fire 12. Alien species and fire 13. Fire management of Mediterranean landscapes 14. Climate, fire and geology in the convergence of Mediterranean-type climate ecosystems References Index.
PRODUKTIVITAS AYAM KAMPUNG SUPER PERIODE STARTER YANG DIBERI JENIS PAKAN KOMERSIAL

Penelitian ini bertujuan untuk mengetahui produktivitas ayam kampung super yang meliputi konsumsi ransum, pertambahan bobot badan dan konversi ransum ayam kampung super periode starter. Penelitian ini dilakukan di Desa Pantungo Kecamatan Telaga Biru Kabupaten Gorontalo, mulai dari bulan Agustus sampai dengan bulan September 2013. Ternak yang digunakan dalam penelitian ini sebanyak 100 ekor ayam kampung super umur satu hari (DOC). Ayam dipelihara dalam boks yang terbuat dari kawat ram dan akan diberi perlakuan setelah berumur satu minggu. Pakan yang digunakan adalah R1 (jenis pakan dengan kode BSG 101 produksi PT. Sierad Produced Tbk), R2 (jenis pakan dengan kode 511 produksi PT. Charoen Pokphan Indonesia Tbk), R3 (jenis pakan dengan kode PB1 produksi PT. Jadfa Comfeed Indonesia Tbk), R4 (jenis pakan kode 214C-BRS produksi PT. Gold Coin Indonesia Tbk), R5 (jenis pakan dengan kode BR-21E produksi PT. Sinta Prima Feedmill). Hasil penelitian ini diperoleh bahwa pengaruh tidak berbeda nyata terhadap konsumsi pakan, pertambahan berat badan dan konversi pakan. Jenis pakan yang baik yang digunakan untuk ayam kampung super adalah pakan R1 dan R4, karena konsumsi pakan ayam kampung super lebih sedikit, dengan pertambahan bobot badan gram/hari/ekor lebih tinggi dari perlakuann lainnya serta memiliki nilai konversi pakan yang lebih rendah dibanding dengan perlakuan lainnya. Kata Kunci : Ayam Kampung Super, Produktivitas, Pakan Komersial.
MetaParkinson: A Cyber-Physical Deep Meta-Learning Framework for n-Shot Diagnosis and Monitoring of Parkinson's Patients

Parkinson's disease (PD) is a neurodegenerative condition that affects the motor system and can result in tremors, limb rigidity, and gait and balance disorders. Currently, conventional tests consist of a series of spiral drawings, with the notion that the most prevalent PD symptoms directly impact the visual appearance of handwritten geometric dynamics. Current disease diagnosis methods rely heavily on a large amount of preexisting clinical data to train their models. However, the lack of sufficient labeled data samples in many diseases, including PD, makes it challenging to build robust predictive models, especially deep-learning ones. With measuring devices becoming more common and the continuous increase in processing power, industrial cyber-physical characterization is emerging as a key enabling technology for a wide range of fields. This article proposes an industrial cyber-physical deep meta-learning framework for diagnosing and monitoring PD patients based on metric learning that aims to train a reliable PD classifier with limited data. In particular, we present a component-based cyber-physical system architecture to combine contrastive learning with a CNN encoder to obtain unbiased feature embeddings and then use a Siamese network to finalize PD classification. Our technique outperforms pretrained models with few training instances on a publicly available PD dataset of hand-drawn spiral and wave images.
Assessing System Justification in Czech Population Using the System Justification Scale

System justification (SJ) is an important construct in social psychology that has received considerable attention over the past three decades. At the empirical level, system justification is examined by means of a specially developed System Justification Scale (SJS), which is designed to explain how individuals accept justice, whether they consider a given social order to be fair, how they evaluate the conditions in the country in which they live, how they accept social change, or to what extent they express compliance with established rules. System justification involves not only those who benefit from the existing social order, but also those who are disadvantaged. In their case, system justification mitigates negative perceptions of objective inequalities and asymmetries. Empirical evidence suggests that system justification may also be associated with higher perceived quality of life. The present study translated and validated the SJS, providing complex and detailed information on the psychometric properties of the scale. In addition, the scale’s internal consistency, unidimensionality, and construct validity were examined. The conclusions presented are based on the results of exploratory factor analysis, internal consistency assessment, analysis of variance, correlation analysis, and confirmatory factor analysis. Results were obtained from face-to-face survey data collected from a sample of 1419 individuals representing the Czech population aged 18–79 years. Since the SJS showed high internal consistency, adequately discriminated levels of system justification, and had robust psychometric properties, it could be recommended for further use.
Multidirectional Graph Learning-Based Infrared Cirrus Detection With Local Texture Features

Infrared cirrus detection is extensively used in military and civil fields, but it poses several challenges to existing methods. These challenges include the complex and diverse shapes of the cirrus clouds, as well as their varying sizes. In addition, false alarms can be easily triggered by the shadows of cirrus clouds and strong edges. Furthermore, dim and weak cirrus clouds blend into the background and lack distinct features, leading to missed detections. This article presents an innovative infrared cirrus detection model based on multidirectional graph learning and local fractal feature prior weight mapping to overcome these challenges. Taking into account the structural characteristics of cirrus and background continuity, the proposed method utilizes graph learning-enhanced matrix decomposition to separate the cirrus clouds and background. In addition, to highlight cirrus clouds while suppressing strong edges in the background caused by mountains or rivers, weighted local fractal features are proposed as prior knowledge. To improve the detection of dim and small cirrus clouds and accelerate the convergence, a reweighting optimization scheme is proposed. The model is solved using the alternating direction method of multipliers (ADMM) framework. Extensive experiments demonstrate that the proposed scheme outperforms a variety of classic techniques in terms of detection performance.
A Distributed Coordination Approach for the Charge and Discharge of Electric Vehicles in Unbalanced Distribution Grids

Distributed-optimization-based approaches for electric vehicle (EV) charging coordination are becoming increasingly important to enable a massive-scale EV rollout without driving costly distribution network reinforcement. This article proposes an algorithm called Dis-Net-EVCD for distributedly coordinated, network-aware EV charging and discharging in unbalanced distribution grids, incorporating both EV customer economics (least cost charging) and distribution network awareness. The alternating direction method of multipliers underpins the development of Dis-Net-EVCD, wherein we seek to remove the need for central coordinating agents and allow EVs to iteratively determine their charge–discharge profiles locally via peer-to-peer communication. Numerical simulations carried out on the IEEE 13-node test feeder with 600 residential EVs demonstrate that EV customers implementing Dis-Net-EVCD yield a total operational cost reduction of 78% compared to uncoordinated EV charging, while conforming with the voltage regulatory requirements and fulfilling all of the EV charging demands ahead of their expected departure times. Moreover, Dis-Net-EVCD is shown to be approximately 60 times computationally faster than its centralized counterpart.
Weighty Matters

Previous work showed that concrete experiences of weight influence people’s judgments of how important certain issues are. In line with an embodied simulation account but contrary to a metaphor-enriched perspective, this work shows that perceived importance of an object influences perceptions of weight. Two studies manipulated information about a book’s importance, after which, participants estimated its weight. Importance information caused participants to perceive the book to be heavier. This was not merely a semantic association, because weight perceptions were affected only when participants physically held the book. Furthermore, importance information influenced weight perceptions but not perceptions of monetary value. These findings extend previous research by showing that the activation direction from weight to importance can be reversed, thus suggesting that the connection between importance and weight goes beyond metaphorical mappings. Implications for the debate on interpretation of findings on the interplay between bodily states and abstract information are discussed.