Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-momentum tensor near an evaporating black hole

We calculate the vacuum expectation value, T/sub . mu nu. /, of the energy--momentum tensor of a massless scalar field in a general two-dimensional spacetime and evaluate it in a two-dimensional model of gravitational collapse. In two dimensions, quantum radiation production is incompatible with a conserved and traceless T/sub . mu nu. /. We therefore resolve an ambiguity in our expression for T/sub . mu nu. /, regularized by a geodesic point-separation procedure, by demanding conservation but allowing a trace. In the collapse model, the results support that picture of black-hole evaporation in which pairs of particles are created outside the horizon (and not entirely in the collapsing matter), one of which carries negative energy into the future horizon of the black hole, while the other contributes to the thermal flux at infinity. (AIP)
Acquisition Comprimée intégrant une Connaissance Partielle du Modèle d'Observation: Analyse Asymptotique

– De nombreux contextes applicatifs d'importance presupposent une connaissance partielle des parametres du modele d'observation issus de mesures realisees au prealable. Le modele d'observation est alors compose d'un signal d'interet, d'un signal interference partiellement connu et d'un bruit additif. Ce travail propose dans le cadre de l'acquisition comprimee, une etude asymptotique des performances d'estimation du vecteur parcimonieux des amplitudes pour les estimateurs parcimonieux exploitant par deflation la connaissance a priori du sous-espace "interference". Sous ces hypotheses, nous proposons l'expression de la Borne de Cramer-Rao Moyennee (BCRM) dans un contexte asymptotique c'est-a-dire lorsque le nombre de mesures et le valeurs non-nulles d'amplitude croient avec un ratio asymptotiquement constant. L'expression qui en resulte est particulierement compacte en terme d'implementation et d'interpretation et est valable pour tout type de prior sur le vecteur d'amplitudes. Enfin, quelques simulations numeriques illustrent l'interet de la borne Bayesienne proposee au regard des estimateurs parcimonieux de l'etat de l'art. Abstract – Numbers of applicative contexts presuppose a partial knowledge of the observation model induced by post-processing acquisitions. The observation model is then composed of a signal of interest, a partially known interfering signal and an additive noise. Based on the compressed sensing theory, the presented work proposes an asymptotic performances analysis of the sparse amplitudes vector for sparse popular estimators straightforwardly extended in this article to exploitation of the a prior knowledge of the interfering subspace. With these assumptions, we propose closed-form expressions of the Expected Cramer-Rao Bound (ECRB) in an asymptotic regime, i.e. when the number of measurements and non-zero amplitude values increase with constant asymptotic ratio. The derived closed-form expressions are easily computable and easy to interpret. In addition, the proposed Bayesian lower is valid for any amplitudes vector priors. Finally, several numerical simulations illustrate the practical interest of the proposed Bayesian bound regarding the state-of-art sparse estimatiors.
Peran Learning Organization dan Knowledge Sharing Dalam Upaya Meningkatkan Organization Performance

Perubahan dalam dunia bisnis menuntut para pelaku bisnis maupun perusahaan untuk turut berbenah dan berubah. Perubahan ini dipercepat dengan kecanggihan teknologi yang perkembangannya semakin masif. Untuk itu dibutuhkan penyesuaian kerja baru, agar karyawan dan perusahaan mampu terus mempertahankan kinerjanya. Melalui Learning Organization diharapkan semua pengetahuan dapat terkumpul dan dimanfaatkan dengan baik melalui Knowladge Sharing. Kumpulan pengetahuan, baik yang baru ataupun perbaikan atas kesalahan yang telah dilakukan harus dibagikan kepada karyawan lain, sehingga dapat menjadi ajang pembelajaran dalam menjalankan pekerjannya. Tujuannya adalah untuk dapat mempertahankan dan bahkan meningkatkan Organization Performance. Selain itu, membagikan pengetahuan menjadi salah satu cara yang ditempuh perusahaan untuk dapat meningkatkan kinerja organisasinya dengan cepat. Terlebih dengan bantuan dari teknologi informasi proses pembelajaran karyawan akan dengan cepat dan mudah untuk dibagikan kepada semua karyawan dalam sebuah organisasi. Namun hal terpentingny adalah menumbuhkan pembelajar dan membiasakan diri untuk membagikan pengetahuan itu kepada karyawan lainnya sehingga tercipta satu system untuk mengumpulkan pengetahuan serta membagikannya yang tujuan akhirnya adalah untuk meningkatkan kinerja organisasi.
Explanation of ν=−12 fractional quantum Hall state in bilayer graphene

The commensurability condition is applied to determine the hierarchy of fractional filling of Landau levels for fractional quantum Hall effect (FQHE) in monolayer and bilayer graphene. Good agreement with experimental data is achieved. The presence of even-denominator filling fractions in the hierarchy of the FQHE in bilayer graphene is explained, including the state at ν=−12.
Recent Progress on Plasmon-Enhanced Fluorescence

Abstract The optically generated collective electron density waves on metal–dielectric boundaries known as surface plasmons have been of great scientific interest since their discovery. Being electromagnetic waves on gold or silver nanoparticle’s surface, localised surface plasmons (LSP) can strongly enhance the electromagnetic field. These strong electromagnetic fields near the metal surfaces have been used in various applications like surface enhanced spectroscopy (SES), plasmonic lithography, plasmonic trapping of particles, and plasmonic catalysis. Resonant coupling of LSPs to fluorophore can strongly enhance the emission intensity, the angular distribution, and the polarisation of the emitted radiation and even the speed of radiative decay, which is so-called plasmon enhanced fluorescence (PEF). As a result, more and more reports on surface-enhanced fluorescence have appeared, such as SPASER-s, plasmon assisted lasing, single molecule fluorescence measurements, surface plasmoncoupled emission (SPCE) in biological sensing, optical orbit designs etc. In this review, we focus on recent advanced reports on plasmon-enhanced fluorescence (PEF). First, the mechanism of PEF and early results of enhanced fluorescence observed by metal nanostructure will be introduced. Then, the enhanced substrates, including periodical and nonperiodical nanostructure, will be discussed and the most important factor of the spacer between molecule and surface and wavelength dependence on PEF is demonstrated. Finally, the recent progress of tipenhanced fluorescence and PEF from the rare-earth doped up-conversion (UC) and down-conversion (DC) nanoparticles (NPs) are also commented upon. This review provides an introduction to fundamentals of PEF, illustrates the current progress in the design of metallic nanostructures for efficient fluorescence signal amplification that utilises propagating and localised surface plasmons.
Ventajas de la estructura jerarquica del clustering en la interpretacion automatica de clasificaciones

En este articulo se presenta la aplicacion de la metodoloǵia Caracterizacion Conceptual por Condicionamientos Sucesivos, orientada a la generacion automatica de descripciones conceptuales de clasificaciones, que puedan dar soporte a la posterior toma de decisiones, aśi como su aplicacion a la interpretacion de las clases identificadas previamente en una planta depuradora de aguas residuales, las cuales caracterizan las distintas situaciones que se presentan en el proceso de depuracion. La particularidad del metodo es que interpreta una particion obtenida sobre un dominio poco estructurado a partir de una clasificacion jerarquica. La metodoloǵia parte del uso de algunas herramientas estadisticas (como el boxplot multiple, introducido por Tukey, y que en nuestro contexto supone una herramienta agil y potente con variables numericas) para conocer la estructura de los datos y extraer informacion util (utilizando el concepto de variables caracterizadoras introducido en trabajos anteriores por Gibert) para la generacion automatica de un sistema de reglas, que permita posteriormente identificar las clases obtenidas. Palabras Clave: clasificacion jerarquica, boxplot multiple, distribucion concidionada, variable caracterizadora, induccion de reglas,
Broadband unidirectional cloaks based on flat metasurface focusing lenses

Bandwidth extension and thickness reduction are now the two key issues of cloaks. In this paper, we propose to achieve broadband, thin uni-directional electromagnetic (EM) cloaks using metasurfaces. To this end, a wideband flat focusing lens is firstly devised based on high-efficiency transmissive metasurfaces. Due to the nearly dispersionless parabolic phase profile along the metasurface in the operating band, incident plane waves can be focused efficiently after passing through the metasurface. Broadband unidirectional EM cloaks were then designed by combining two identical flat lenses. Upon illumination, the incident plane waves are firstly focused by one lens and then are restored by the other lens, avoiding the cloaked region. Both simulation and experiment results verify the broadband unidirectional cloak. The broad bandwidth and small thickness of such cloaks have potential applications in achieving invisibility for electrically large objects.
ASYMMETRY OF THE HELIOSPHERIC MAGNETIC FIELD

This thesis studies the structure and evolution of the large scale heliospheric magnetic field. The work covers the space age, the period when satellite measurements revolutionized our knowledge about space. Now, this period is known to be the declining phase of the grand modern maximum of solar activity. The thesis addresses how the hemispherical asymmetry of solar activity is seen in the photospheric magnetic field and how it appears in the corona and in the heliosphere until the termination shock. According to geomagnetic and heliospheric observations, the heliospheric current sheet has been southward shifted around the solar minima since 1930s. Using Ulysses probe observations, we derive an accurate estimate of 2◦ for the southward shift of the heliospheric current sheet during two very different solar minimum in the mid 1990s and 2000s. The overall structure of the heliospheric magnetic field has changed significantly now when the grand modern maximum has come to an end. During the present low solar activity the polar fields are weaker and the heliospheric current sheet covered a wide latitudinal range during the previous minimum. When the heliospheric current sheet is wide the asymmetry is less visible at the Earth’s orbit. We extend our study to the outer heliosphere using measurements made by Voyager and Pioneer probes and show that the hemispherical asymmetry in the coronal hole evolution, and the related southward shift of the heliospheric current sheet, are seen until the termination shock. In order to understand the origin of the hemispherical asymmetry, we complete a multipole analysis of the solar magnetic field since 1976. We find that the minimum time southward shift of the heliospheric current sheet is due to the quadrupole component of the coronal magnetic field. The quadrupole term exists because the generation and transport of the magnetic flux in the Sun tends to proceed differently in the northern and southern hemispheres. During this and the following decade the Sun is most likely going to be less active than it has been since 1920s. Therefore it is probable that the hemispherical asymmetry of the heliospheric magnetic field will be less visible in the ecliptic plane in the near future. Now, when the Sun seems to be at the maximum of cycle 24, we are looking forward to see how the polar fields and the heliospheric magnetic field are formed when approaching the following solar minimum. It is possible that, as the activity rises again after the present and future low cycles, the hemispherical asymmetry will be opposite to that of the 20th century and the minimum time heliospheric current sheet would be northward shifted.
SLOW AND FAST MAGNETO-OPTICAL RESPONSE OF MAGNETITE NANOPARTICLES SUSPENSION

DC magnetic field applied to Fe 3O4 nanoparticle suspension affects its light scattering. Time dependent variations in the light intensity transmitted through a suspen- sion are observed after the magnetic field is switched-on. Two types of variations can be distinguished. Fast response takes less than millisecond while slow variations occur at the time interval from seconds to hundreds of minutes. Possible mechanisms of these variations are discussed.
Research on Filling Method of Tidal Missing Data Based On Gated Recurrent Neural Network

Tidal data reflects the changes of coastal sea level, and plays an important role in many fields. Tidal data missing brings inconvenience to data usage. Based on the tidal data of Longhai and Xiamen Ocean Stations in 2020, this paper proposed a filling method based on Gated Recurrent Neural Network (GRU). Compared with traditional interpolation methods such as linear interpolation and spline interpolation, the GRU method is stable, accurate and convenient to use. Especially when the length of missing data is large, GRU method is obviously superior to the traditional interpolation method. At the same time, this method is also suitable for filling other missing time series data, such as water temperature.
Forward selection‐based ensemble of deep neural networks for melanoma classification in dermoscopy images

Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection‐based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size‐related various properties of dermoscopy images. To this end, EfficientNet models B3–B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross‐validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.
Nectin-4 as a Predictive Marker for Poor Prognosis of Endometrial Cancer with Mismatch Repair Impairment

Simple Summary Endometrial cancer has become increasingly common owing to the recent westernization of diet and lifestyle, with 1.7% of cancer patients dying annually. Furthermore, the 5-year endometrial cancer survival rate is barely 15%. Nectin-4 has emerged as a possible biomarker and therapeutic target. Nectin-4 is highly expressed in various cancers. However, no studies have been conducted to determine the clinical importance of Nectin-4 expression in endometrial cancer. Here, we examined 320 tissue samples from patients with endometrial cancer to determine the relevance of Nectin-4 expression in the diagnosis and prognosis of endometrial cancer. Our findings emphasize the importance of Nectin-4 as a novel diagnostic tool and screening marker for assessing endometrial cancer and improving the accuracy of approaches used to predict high-risk endometrial cancer. Abstract The adhesion molecule Nectin-4 is a new potential therapeutic target for different types of cancer; however, little is known about its diagnosis significance in endometrial cancer (EC). We found that Nectin-4 expression was significantly higher in EC tissues than in nonadjacent normal tissue. The area under the receiver operating characteristic curve value of 0.922 indicated good diagnostic accuracy for Nectin-4 expression in EC. Furthermore, Nectin-4 expression was associated with DNA mismatch repair (MMR) protein deficiency. Notably, the high Nectin-4 expression group of patients with MSH2/6-deficient EC had shorter progression-free survival than that of the low Nectin-4 expression group. The number of lymphovascular space invasion-positive patients in groups with MMR deficiency and high Nectin-4 expression was also increased compared with that in the low Nectin-4 expression group. Bioinformatics analysis revealed that alteration in Nectin-4 and MMR genes is associated with Nectin-4 expression in EC. To the best of our knowledge, this is the first study to show that Nectin-4 expression may be a potential biomarker for EC diagnosis and that high Nectin-4 expression in MMR-deficient patients with EC can predict short progression-free survival, thus providing clues to identify patients for adjuvant therapy.
Binary classification of malware by analyzing its behavior in the network using machine learning

Introduction. Every day we are exposed to all kinds of cyber-threats when we browse the internet, compromising the confidentiality, integrity, and availability of our devices. Cyber-attacks have become more sophisticated and cyber attackers require less technical knowledge to execute such attacks. An automated and well-defined process to counter these attacks becomes urgent. The study aim was to solve this problem. Methods. A model was developed to analyze the information in Packet Capture (PCAP) files and classify network connections as either benign or malicious (malware generated). This software used two methods: traditional machine learning algorithms and neural networks. Our experiments were carried out using the Intrusion Detection Evaluation Dataset (CICIDS2017), which contains labeled samples of PCAP files. We experimented using both raw and standardized data. The classification results were evaluated using recall, precision, F1-score, and accuracy metrics. Results. These were satisfactory for both methods, obtaining more than 95% in the F1-score and recall metric, indicating a low number of false negatives. Conclusion. It was found that data standardization had a favorable impact on all metrics and should be used carefully. Overall, our experiments showed that malicious network traffic can be successfully detected using automated methods achieving above 95% of F1-score in the K-Nearest Neighbors algorithm (K-NN) classifier.
Seasonal Changes in the Antioxidant Activity and Biochemical Parameters of Goat Milk

Simple Summary Goat milk is an excellent product, which contains a large amount of nutrients and biologically active compounds. The action of many of them also manifested in antioxidant activity. However, the composition of goat’s milk varies from season to season, which may affect its functional activity. To study this effect was the purpose of our work. The numerous significant changes in the biochemical parameters of goat milk and its antioxidant activity depending on the season of the year found. This main result of our work will be useful for goat physiology and biochemistry, as well as for the monitoring of the quality of goat’s milk. Abstract Goats are ubiquitous, including in hot and dry regions, while also being very sensitive to climate fluctuations, expressed in temperature differences. This affects their productivity and milk quality. Adaptation to heat requires high energy costs, affects “neurohumoral” regulation and is accompanied by oxidative stress with the increased production of free radicals. The aim was to study the main biochemical parameters of goat milk and its antioxidant activity depending on the season of the year. Sampling was carried out in April, June, August and October. Analysis of the biochemical components and antioxidant activity of goat milk was performed using modern analytical systems. From spring to autumn, the mass fraction of true or crude proteins in goat milk increased by 14.6–63.7% or by 12.3–52.1%, and the mass fraction of caseins also increased by 13.6–60.6%. For vitamin C level and the total amount of water-soluble antioxidants, a pronounced gradual decrease from spring to autumn was observed. In the summer period, a small increase in the carotene level in milk (by 3.0–6.1% compared to April) was established. Vitamin A content increased by 86.5% (June) or by 70.3% (October) compared to April. Thus, the numerous significant changes in the major parameters of goat’s milk depending on the season were revealed.
IoT based smart home automation using blockchain and deep learning models

For the past few years, the concept of the smart house has gained popularity. The major challenges concerning a smart home include data security, privacy issues, authentication, secure identification, and automated decision-making of Internet of Things (IoT) devices. Currently, existing home automation systems address either of these challenges, however, home automation that also involves automated decision-making systems and systematic features apart from being reliable and safe is an absolute necessity. The current study proposes a deep learning-driven smart home system that integrates a Convolutional neural network (CNN) for automated decision-making such as classifying the device as “ON” and “OFF” based on its utilization at home. Additionally, to provide a decentralized, secure, and reliable mechanism to assure the authentication and identification of the IoT devices we integrated the emerging blockchain technology into this study. The proposed system is fundamentally comprised of a variety of sensors, a 5 V relay circuit, and Raspberry Pi which operates as a server and maintains the database of each device being used. Moreover, an android application is developed which communicates with the Raspberry Pi interface using the Apache server and HTTP web interface. The practicality of the proposed system for home automation is tested and evaluated in the lab and in real-time to ensure its efficacy. The current study also assures that the technology and hardware utilized in the proposed smart house system are inexpensive, widely available, and scalable. Furthermore, the need for a more comprehensive security and privacy model to be incorporated into the design phase of smart homes is highlighted by a discussion of the risks analysis’ implications including cyber threats, hardware security, and cyber attacks. The experimental results emphasize the significance of the proposed system and validate its usability in the real world.
On the propagation of Rayleigh waves in a strongly elliptic thermoelastic material with microtemperatures

ABSTRACT This article is concerned with the linear theory of thermoelasticity with microtemperatures, which permits the transmission of heat as thermal waves of finite speed, following the theory of Green and Naghdi. We study the propagation of Rayleigh waves in an anisotropic and strongly elliptic thermoelastic half-space by the Stroh formulation.