Miracle-dz/UniFAR-FTUs huggingface dataset:
First Evidence of Leatherback Movement from Africa to South America

Programme Kudu, Cellule de coordination ECOFAC, BP 15115, Libreville,Gabon (E-mail: protomac_lbv@yahoo.ie ), UICN-France, Muséum national d’Histoire naturelle, 36 rue Geoffroy Saint-Hilaire, 75005 Paris, France (E-mail: jfretey@imatech.fr), WWF-Gamba, Gabon (E-mails: sbverhage@yahoo.com & huijbregts_bas@hotmail.com), Projeto TAMAR IBAMA, Rua Antônio Athanasio no 273, Itaguá, Ubatuba SP / Brazil. Cep: 11680-000. (E-mail: bruno@tamar.org.br) Dpto. de Ecología Genética y Evolución, Facultad Cs. Exactas y Naturales, Universidad de Buenos Aires and Peyú Project – PRICTMA, Argentina (E-mail: lprosdo@yahoo.com.ar) Aquamarina PRICTMA and Buenos Aires Aquarium – PRICTMA, Argentina (E-mail: albareda@aquamarina.org & dalbareda@hotmail.com) Centre National de la Recherche Scientifique, Centre d’Ecologie et Physiologie Energétiques, CNRS UPR 9010, 23 rue Becquerel, 67087 Strasbourg, France (E-mail: jean-yves. georges@c-strasbourg.fr) Marine Turtle Research Program, NOAA-National Marine Fisheries Service, Southwest Fisheries Science Center, 8604 La Jolla Shores Drive, La Jolla, CA 92037, USA (E-mail : Manjula.Tiwari@noaa.gov)
Hubble Space Telescope Observations of the Very Low Mass Companion to Gliese 105A

Hubble Space Telescope images of the astrometric binary Gl 105A confirm the previous ground-based detection of a faint, very red companion (Gl 105C) located 3. ″39 from Gl 105A at P.A. 290°. The instrumental magnitudes of Gl 105C are V555 = 16.86 and I814 = 13.54. The observed position of Gl 105C differs significantly from the positions expected from current astrometric solutions. No other companions brighter than I814 = 20.3 are seen between 1'' and 13. ″5 from Gl 105A. Using the M dwarf model atmospheres of Allard and Hauschildt, we obtain for Gl 105C a standard color of V - I = 4.6, which suggests a spectral type of M7 V.
Hubble Space Telescope Observations of the Double Nucleus of NGC 4486B

Hubble Space Telescope WFPC2 images show that the low-luminosity elliptical galaxy companion to M87, NGC 4486B, has a double nucleus that resembles the one discovered in M31. The NGC 4486B nucleus comprises two peaks separated by ~0. ″15 or 12 pc. Neither peak is coincident with the galaxy photocenter, which falls between them. The nuclear morphology is independent of color; thus, the double structure is not likely to arise from dust absorption. It is also unlikely that the peaks are a binary stellar system (such as an ongoing merger of the nucleus of a less luminous system with the nucleus of NGC 4486B), since the decay timescale is short ( < 108 yr) and the present environment of NGC 4486B should inhibit mergers. We suggest that the nuclear morphology of NGC 4486B may be explained by the eccentric-disk model of Tremaine, which was originally advanced to account for the central structure of M31. This model requires that NGC 4486B contains a central massive dark object, which is suggested by the spectroscopic observations of Kormendy et al. The eccentric disk might be related to the symmetric disk seen at larger radii.
Functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is a relatively new technique for imaging changes in brain haemodynamics linked to local neuronal activity. It does not entail exposure to radioactivity and has other important technical advantages over previously available methods for mapping functional neuroanatomy, such as positron emission tomography. It is likely that fMRI will become an increasingly important and widely used technique for psychiatric research in the next 5 years; applications to clinical practice are also conceivable. Here we review the technical foundations of fMRI. We begin with a brief summary of the basic physical principles of magnetization and nuclear magnetic resonance. This is followed by a description of some commonly used procedures for magnetic resonance imaging in general; an introduction to exogenous and endogenous contrast agents; and a section on gradient echo echoplanar imaging for fMRI specifically. We describe common artefacts and essential hardware requirements. Then issues of experimental design and data analysis are discussed in conclusion. Given the rapid rate of scientific progress in this field, this review can be no more than introductory. References to some supplementary textbooks and specialist journals are provided as guides to further reading.
Reactivation/Acceleration of Landslides Caused by the 2018 Baige Landslide-Dammed Lake and its Breach Floods Revealed by InSAR and Optical Images

Landslide-dammed lakes and their breaches can reactivate/accelerate landslides, causing potential damages. However, in the absence of displacement observations, the spatial-temporal deformation patterns of the landslide-dammed lakes and associated floods reactivated/accelerated landslides remain underexplored. In this article, we use the 2018 Baige landslide-dammed lake and associated floods reactivated/accelerated landslides to investigate the behaviors of such landslides. We first use an improved interferograms Stacking method to detect landslides, then utilize the multitemporal interferometric synthetic aperture radar to derive their deformation history. Through retrospective analysis of the deformation history and optical images, we find that the water level fluctuations and floods caused by the Baige landslide-dammed lake and its breaches reactivated/accelerated six landslides upstream and five landslides downstream. The area of the largest reactivated/accelerated landslide is about 5 km2. The maximum velocity change of these reactivated/accelerated landslides is about 20 cm/year. Landslide reactivation/acceleration occurs progressively from the toe to head, resulting in varying reactivation/acceleration times for different parts. The velocity change and acceleration area have a linear relationship, with larger landslides showing larger velocity changes and prolonged activity than smaller ones. Among these 11 reactivated/accelerated landslides, 4 are located in the river narrow sections and their volumes are all larger than that of the Baige landslide. Thus, their failure may cause larger damages than that caused by the Baige landslide. Our findings contribute to a better understanding of landslide-induced geological hazard chains and landslide behaviors.
Portable Surveillance System using AWS Kinesis Model

In this modern era, major security issues are faced specifically within personal spaces; consequently, several specially trained personnel are needed to attain the desired security. Development and innovation in the field of facial recognition play an important role in crime prevention in this century. The proposed solution to the aforementioned matter is a surveillance capable of facial recognition, which can be used to detect intruders in restricted or non-inclusive locations where surveillance of a CCTV camera is possible. The model is based on image processing concepts to match live images with the existing trained images of the person stored in the data set who is allowed in the CCTV camera zone. To detect intruders, the Raspberry Pi can be set up with a camera module and motion sensor. The chosen S3 bucket within the AWS environment receives the media that the microcamera has securely delivered. The media are stored in a robust and expandable manner in an S3 bucket, which also guarantees data integrity and high availability. If necessary, it also makes it simple to retrieve and analyse any photographs or videos that were captured. The model sends an SNS notification when an incursion is discovered or when particular circumstances are satisfied, like motion detection. The alert message comprising pertinent data, such as timestamps and S3 bucket references for the collected media, is delivered to subscribed endpoints, such as mobile devices or PCs. This framework offers developers and engineers a strong base on which to build a solution that is tailored to their unique use case. In general, connecting a Raspberry Pi with AWS for intruder alert offers a scalable and dependable home security system that can be tailored to the user’s demands. Users can feel secure in their homes knowing that they have the capacity to remotely monitor and get notifications in real time.
LAKE VERA REVISITED: PARITY AND SURVIVAL RATES OFANOPHELES PUNCTIPENNISAT THE SITE OF A MALARIA OUTBREAK IN THE SIERRA NEVADA

Parity and survival rates of Anopheles punctipennis were studied at the site of a 1952 outbreak of malaria in the Sierra Nevada foothills of California with the purpose of estimating blood feeding frequency and survivorship of such populations. Anopheles punctipenniswas the dominant species in landing collections conducted for 20 consecutive nights in August and September 1990. The mean parity rate of An. punctipennis was 0.82. The gonotrophic cycle was estimated to last three days based on time series analysis of the number of nulliparous and parous mosquitoes collected each day. Survivorship was estimated to be 0.79 per gonotrophic cycle and 0.92 per day. Ovarian dilatation data indicated that some females had completed eight gonotrophic cycles prior to being collected and 45% of the females completed two or more cycles. The high prevalence of multiparous individuals, high parity rate, long survivorship and a short three-day gonotrophic cycle estimate indicates that this population is long lived and has a capacity to be an efficient vector of malaria. In 1952, an outbreak of malaria occurred in visitors to summer camps and residences at Lake Vera in the foothills of the Sierra Nevada in California. This outbreak was re- markable in that a single infected individual spent a weekend at the lake and served as the source for 35 subsequent cases.
Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks

Brain–computer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with “yes” or “no” to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance.
Equivalent 3D Printed Perforated X-Band Horn Antenna Sparsed Wire-Grid Structures Using OCGA

The investigation of weight reduction in waveguide antennas is a relevant and important task, particularly for wideband applications. The optimal current grid approximation (OCGA) and its modifications offer promising approaches to achieve this objective while maintaining the electrical properties and mechanical fabricability of the antenna structures. By applying OCGA to a wire-grid antenna, it is possible to generate a sparse structure that operates equivalently to the original antenna while controlling the accuracy of its characteristics. In this paper, the application of OCGA and its modifications is demonstrated using a horn antenna as an example, which was previously investigated in another study. The characteristics of the antenna structures obtained after applying these approaches are compared to both measured results obtained for different 3D printed models and results obtained using another numerical method for a perforated model. The comparison demonstrates the effectiveness of OCGA in creating antenna structures with reduced mass compared to other techniques. Furthermore, the OCGA structures can be used in subsequent modeling, replacing the original antenna model, thus leading to computational cost savings. Overall, the paper highlights the potential of OCGA and its modifications and validate their suitability for practical applications, offering both weight reduction and computational efficiency advantages compared to other approaches.
Cancer-related fatigue: impact on patient quality of life and management approaches

: Cancer-related fatigue is a common and distressing symptom that is present in the majority of cancer patients at some point during the disease course. Cancer-related fatigue has a profound influence on patients, affecting functional performance, mood, and one’s overall quality of life. The etiology of cancer-related fatigue is multifactorial, involving a complex interplay of biological and body system factors. All cancer patients should be screened for fatigue, as it often coexists with other symptoms and its side effects may be underdiagnosed. Treatment modalities for cancer-related fatigue are still evolving. Current recommendations for management from national and international cancer experts and societies focus on patient and family education regarding cancer-related fatigue, exercise and physical activity, and psychosocial interventions
Development of a Network Computer Laboratory for Training in Road Traffic Simulation and Model Verification

The paper focuses on creating a network computer laboratory (NCL) for training in traffic simulation model verification using up-to-date web application technologies. The significance of this web development is growing with the extensive use of information technology, especially distance learning. The NCL is based on original mathematical models of traffic flows, particularly the macroscopic quasi-gas dynamic traffic model and multilane model of cellular automata. The platform is built using ASP.NET Core to create cross-platform web applications, using the Model-View-Controller (MVC) design pattern. The NCL includes a knowledge base of various models and numerical methods employed in the transportation modeling, alongside test tasks to control students ‘progress. Emphasis is given to visualizing calculation outcomes, where the cellular automata model is visualized using Unity 3D. NCL can act as an environment for laboratory works, enabling students to acquire essential professional skills and practical experience in researching current transportation systems.
Spatio-Temporal Forecasting of Air Pollution in Jakarta Using Deep Learning Methods

Air pollution has always been a prominent problem that can cause severe health problems for humanity. Fine particulate matter are particles that contribute directly to this phenomenon, the higher the value the more pollution it indicates. The Jakarta air quality index at the time of this study is very high and determined to be unhealthy by the World Health organization standard. Forecasting of this value can help humans to act accordingly to minimize the adverse effects. In this study, an air pollution forecast utilizing spatio-temporal data is developed to address the problem. Multiple deep learning techniques such as Gated Recurrent Unit, Long-Short Term Memory, and bidirectional Long-Short Term Memory were used in this study. The results from this study showed that Long-Short Term Memory performs best in air pollution forecasting using spatio-temporal data achieving the lowest Mean Absolute Percentage Error value of 1.25% percent and highest R-Square value of 0.998.
Holistic Exploration of Structural, Electronic, Magnetic, Transport, Mechanical, and Thermodynamic Characteristics, Including Curie Temperature Analysis

Through intricate calculations, the density functional theory (DFT) implemented in the Wien2k code was employed to comprehensively investigate a wide range of material characteristics. Our study encompasses an exhaustive analysis of structural stability, electronic properties, magnetic behaviors, transport phenomena, mechanical responses, and thermodynamic profiles of two notable instances of filled Skutterudites, namely, CeNi4P12 and DyCo4Sb12, which have been thoroughly explored. These computations were performed using the WIEN 2K code, combining local orbitals and the full-potential linearized augmented plane-wave approach. The findings provided insight into the wide range of properties of these materials. In this methodology, the exchange–correlation potential relies on the local-density approximation. We conducted the calculations with and without incorporating spin–orbit interactions. The results obtained provide information about the lattice constant, bulk modulus, and pressure derivative. The stability, as indicated by the P–V graphical plot, suggests that there are no structural phase transitions from the cubic symmetry structure. Notably, our work includes an examination of Curie temperatures, which are pivotal in understanding magnetic phase transitions. The validated elastic properties further support the material’s stability and corroborate its ductile nature. These alloys should be considered for spintronic and thermoelectric applications due to their estimated transport characteristics and the observed ductile nature. To enhance our understanding of the thermal stability of antimony-based compounds, we have made reliable estimations of the thermophysical characteristics. By integrating theoretical insights with practical implications, we bridge the gap between fundamental understanding and material design applications. Using DFT in the Wien2k framework, we discover connections and patterns among different properties, showing how to create materials with specific functions and better performance. This approach not only advances our fundamental comprehension of materials but also promises innovation across various technological domains.
Quantization Lobe Suppression in Binary Antenna Arrays with Large Partitioning Order

We investigate the efficiency of quantization lobe suppression in binary reconfigurable intelligent surfaces (RIS) consisting of several tiles (panels). The cases of partitioning the RIS into 4, 16 or 64 tiles (partition orders 1, 2 and 3 respectively) and shifting half of them along the direction perpendicular to the array aperture by 1/8 of the wavelength are investigated. The difference in the suppression of the quantization lobe was shown to make 17 percent (1.6 dB) or less between the first and the third partition orders for scan angles of up to 45 degrees.
The Sand-Table Deduction of Wind Power Feed-In Tariff Policy Optimization Under the CPSSE Framework

The long-term evolution of new energy power generation capacity in the physical dimension is affected by social factors such as new energy policies and investment behavior of power generation companies (GENCOs). Under the guidance of the Cyber-Physical-Social System in Energy (CPSSE) framework of energy, a hybrid simulation model that can simulate the dynamic interaction between wind power feed-in tariff policies, investment behaviors of GENCOs and long-term evolution pathways of new energy power generation capacity is established, and a sand-table deduction of feed-in tariff policy optimization is carried out using wind power as an example. A feed-in tariff policy optimization method based on the idea of “feedback” is realized, in which the feed-in tariff policy adjustment target is set to make up for the gap between the actual development pathway and the planned pathway of new energy, using the information of new energy power generation cost and GENCOs investment behavior. The results of sand-table deduction verify the effectiveness of the proposed method.
A "coarse-grained" model based on a cellular automaton for the study of diffusion in microporous materials.

It is well-known that in molecular liquids diffusion coefficients are in the range 10 -9 - 10 -8 m 2 s -1 , while in microporous materials such as zeolites this range is much larger, spanning from 10 -8 m 2 s -1 (for example methane in silicalite-1) to 10 -19 m 2 s -1 (for example benzene in zeolite Ca Y) or even smaller. The phenomenon is made complex if the concentration (or loading) is considered: high loadings make the diffusion coefficient smaller and smaller, following different trends. Classical Molecular Dynamics (MD) computer simulation technique made it possible to study diffusion of small molecules in zeolites for times not exceeding some tens of nanoseconds and for systems including not more than 10000 particles. These limitations stimulated the interest to develop models able to reach mesoscopic scales through a coarse-graining process of atomic scale models. Actually the coarse-graining scheme usually involves the evaluation of the free energy of an adsorbed molecules using detailed atomistic models to characterize adsorption sites and the possible presence of energy barriers to diffusion, and to use these results as an input for a lattice model of the considered system. This permits to study a much larger system (with a large number of adsorption sites) for a much larger time, because the calculation is greatly simplified as the integration of equations of motion is avoided.