Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinning-Up: The Case of the Symbiotic X-Ray Binary 3A 1954+319

We present a timing and spectral analysis of the variable X-ray source 3A 1954+319, Our analysis is mainly based on an outburst serendipitously observed during INTEGRAL Key Program observations of the Cygnus region in 2008 fall and on the Swift/BAT longterm light curve, Previous observations, though sparse, have identified the source to be one of only nine known symbiotic X-ray binaries, i.e., systems composed of an accreting neutron star orbiting in a highly inhomogeneous medium around an M-giant companion. The spectrum of 3A 1954+319 above 20 keY can be best described by a broken power law model. The extremely long pulse period of approx.5. 3 hours is clearly visible in the INTEGRAL/ISGRI light curve and confirmed through an epoch folding period search. Furthermore, the light curve allows us to determine a very strong spin up of -2x10(exp 4)h/h during the outburst. This spin up is confirmed by the pulse period evolution calculated from Swift/BAT data. The Swift/BAT data also show a long spin-down trend prior to the 2008 outburst. which is confirmed in archival INTEGRAL/ISGRI data. We discuss possible accretion models and geometries allowing for the transfer of such large amounts of angular momentum and investigate the harder spectrum of this outburst compared to previously published results.
WEIGHTED NORM INEQUALITIES ON ORLICZ-MORREY SPACES FOR THE MULTILINEAR FRACTIONAL INTEGRAL AND ORLICZ-FRACTIONAL MAXIMAL OPERATOR

We generalize Orlicz-Morrey spaces and Orlicz-fractional maximal operators to treat vector-valued functions, which extend to the multi-Morrey spaces and multilinear fractional maximal operators to the scale of the Orlicz spaces, respectively. In this article, we investigate the weighted norm inequalities for linear and multilinear fractional integrals and maximal operators and Orlicz-fractional maximal operators in Orlicz-Morrey spaces for multilinear version. One of the main results generalizes and improves the weighted estimate of the Adams inequality in multi-Morrey spaces. More-over, we extend the weighted estimates to endpoint cases.
Performance Analysis of Skin Cancer Detection Using GLCM and SVM

Cancer of the skin is the most prevalent and destructive form of the disease in humans. The most dangerous kind of skin cancer is called melanomas. If it is caught in its early stages, it is very easy to treat. The biopsy technique is the formal approach that is used for diagnosing the presence of melanoma. This technique may be rather uncomfortable and requires a lot of time to complete the procedure. As a result of this research, a computer-aided detection method for the early diagnosis of melanoma has been developed. Within the scope of this research, an effective method of diagnosis is developed via the use of image processing methods and the Support vector machine (SVM) algorithms. The picture of the damaged skin is captured, and then it is subjected to a number of different pre-processing methods in order to provide an improved image and a smoothed image. After that, the image goes through a process of segmentation that uses morphological and thresholding approaches to separate the various components of the picture. The photos of the skin have had some crucial aspects of its texture, color, and form removed. For the purpose of extracting texture characteristics, the Gray Level Co-occurrence Matrix (GLCM) approach is used. The GLCM, color, and form characteristics that were retrieved are the ones that are used as input by the SVM Model. It determines if the photograph in question depicts a benign melanoma or a malignant melanoma. When we integrate and use the shape, color, and GLCM characteristics to the classifier, we are able to reach a high level of precision that is equal to 96 percent.
Body condition of three autochthonous canid species from Serbia

: The body condition of three autochthonous canid species in Serbia (wolf– Canis lupus , jackal– Canis aureus, and fox– Vulpes vulpes ) was assessed based on the value of Kidney Fat Index (KFI). A total of 1232 specimens (147 wolves, 711 jackals, and 375 foxes) were collected during a ten years period. Data were analyzed according to multiple factors such as sex, age, seasons, species, and genus. The results indicate that the adult members of the Canis genus have a significantly better body conditional status than the yearlings as well as the representatives of the Vulpes genus (F (2, 900) = 71.465, p = 0.000). Significant differences in KFI across seasons are recorded in jackal (F (3, 542) = 6.912, p < 0.001) and fox (F (3, 371) = 2.675, p < 0.05) populations. Jackals have the highest KFI during winter when females are in significantly better body condition than males. None of these three canid species has shown significant difference in body condition between sexes, although females had slightly higher KFI. Among adult female jackals, a difference in KFI between nursing ones and other adult and pregnant females is observed (H (2, n = 220) = 8.339, p = 0.016). Food deprivation which occurs naturally during the winter months does not affect the body conditional status of these predators which are well prepared for the upcoming breeding season. Considered as a whole, in spite of the differences observed at the genus level, the body condition status of these autochthonous canids measured through KFI index indicates decent average fitness status in wolves, jackals, and foxes in Serbia.
Etudes de récepteurs MIMO-LDPC itératifs

L’objectif de cette these est l’etude de recepteurs MIMO LDPC iteratifs. Les techniques MIMO permettent d’augmenter la capacite des reseaux sans fil sans la necessite de ressources frequentielles additives. Associees aux schemas de modulations multiporteuses CP-OFDM, les techniques MIMO sont ainsi devenues la pierre angulaire pour les systemes sans fil a haute efficacite spectrale. La reception optimale peut etre obtenue a l’aide d’une reception conjointe (Egalisation/Decodage). Etant tres complexe, la reception conjointe n’est pas envisagee et l’egalisation et le decodage sont realises disjointement au cout d’une degradation significative en performances. Entre ces deux solutions, la reception iterative trouve son interet pour sa capacite a s’approcher des performances optimales avec une complexite reduite. La conception de recepteurs iteratifs pour certaines applications, de type WiFi a titre d’exemple doit respecter la structure du code imposee par la norme. Ces codes ne sont pas optimises pour des recepteurs iteratifs. En observant l’effet du nombre d' iterations dans le processus iteratif, on montre par simulation que l’ordonnancement des iterations decodage LDPC/Turbo-egalisation joue un role important dans la complexite et le delai du recepteur. Nous proposons de definir des ordonnancements permettant de reduire la complexite globale du recepteur. Deux approches sont proposees, une approche statique ainsi qu'une autre dynamique. Ensuite nous considerons un systeme multi-utilisateur avec un acces multiple par repartition spatiale. Nous etudions l’interet de la reception iterative dans ce contexte tenant en compte la difference de puissance signale utile/interference.
5 CHARACTERIZING THE TACIT RELATIONSHIPS BETWEEN BIOLOGY TEACHERS’ CONTENT KNOWLEDGE (CK) AND OTHER PROFESSIONAL KNOWLEDGE COMPONENTS

Considerable effort has been made in the last three decades to construct a well-established conception of science teachers' professional knowledge. Both Content Knowledge (CK) and Pedagogical Content Knowledge (PCK) are considered as critical professional development resources for science teachers. Recently, the interconnectedness between PCK and CK as an integral part of teachers' knowledge for practice has been raised. Exploring the relationships between CK and other professional knowledge components is not a straightforward process due to their internal tacit nature. In-service teachers who develop expertise in teaching possess tacit or intuitive knowledge which is difficult to reveal. The teachers who hold tacit knowledge about something will be unable to verbalize it and will often be unaware of it. Here we examine the possible relations between CK and other professional knowledge components of in-service biology teachers using the repertory grid technique which has been used to elicit experts' personal tacit knowledge. Data analysis revealed that CK is a very important component of teachers' knowledge and that it is by and large distinct from other professional knowledge components. We therefore believe professional development programs should strengthen the relationships between biology teachers’ CK and other professional knowledge components instead of assuming that increasing CK will automatically lead to an improvement in teachers’ professional knowledge.
An Evaluation of the Compliance of Sri Lankan Listed Companies with Corporate Governance Principles and Best Practices

This study examines compliance with corporate governance principles and best practices by companies listed on the Colombo Stock Exchange by constructing a novel and comprehensive corporate governance index using a sample of 133 firms in 2009 and 2016. Overall, corporate governance improved during the sample period. Corporate governance also improved across all sub-areas of corporate governance, namely board of directors, shareholders, stakeholders, disclosure and transparency, and CEO and management. Moreover, corporate governance improved across all industries except for healthcare. Telecommunications, hotels, manufacturing, energy, and trading sectors show the largest increases while stores, banking, construction, services, and plantations have the lowest improvements. Services and stores sectors have relatively weaker corporate governance while telecommunications, diversified, banking, hotels, manufacturing, and energy show stronger governance. This study adds to the extant evidence on emerging markets and provides important evidence for policymakers to formulate and strengthen corporate governance in Sri Lanka.
The relationship between creative, cognitive, and emotional competences in Intellectual Disability. A case report.

People with intellectual disabilities (ID) show cognitive deficits that are subject to an earlier deterioration and they experience difficulties in the social-emotional sphere. Various stimulation techniques exist to strengthen these skills that, however, are not usually proposed to individuals with ID because 1) the users are not necessarily in senile age, nor do they have an acquired impairment that requires rehabilitation. Consequently, it is erroneously assumed that their condition cannot be improved. 2) The standard pathways include socio-educational objectives, but not specifically neuro-cognitive ones. We believe that divergent thinking and creativity could be particularly suitable and proficient areas of intervention to promote a flexible, original, and autonomous thinking that can be extended in many different domains. To do so, we designed a research-intervention project named SoCraTEs (Social-emotional, Creative, and Thinking Enhancement), which is aimed at empowering these areas of interest, proposing creativity as an elective tool. In this paper we illustrate the results of a preliminary phase where we first explored the relationship between the aforementioned abilities to identify the most significant components that had the greatest potential. We found that creative abilities are strongly related to attentional, mnestic, and emotional skills, together with personal autonomy in everyday life. Implications for future research are discussed. application setting. The preliminary data we collected, in fact, indicates the importance of also considering some individual characteristics and in particular non-verbal intelligence, measured in our case through Raven's matrices. This tool provides an excellent assessment of abstract intelligence, which is not mediated by language and general knowledge, thus being particularly interesting in the context of intellectual disability. The existence of an inverse relationship between abstract reasoning and the performance on creativity tests implies that the activities performed, and related expectations, should be declined by virtue of the preserved cognitive resources, regardless of the emotional and social support that can be provided within the training. This does not mean that it is not possible to apply the same method, but that the strategies of involvement, the timing of application, the levels of difficulty, and the expectations of the results must be considered in relation to the starting cognitive level.
Software Fault Prediction using Machine Learning

— Software Fault Prediction assumes a critical job in the dynamic research regions of software building. A product fault is a blunder, bug, imperfection, blame, glitch or mix-up in programming that makes it make a wrong or startling result. The real hazard factors related with a product fault which isn't recognized amid the early period of programming improvement are time, quality, cost, exertion and wastage of assets. Faults may happen in any period of programming advancement. Booming programming organizations center fixation around programming quality, especially amid the early period of the product improvement. Thus, the key target of any association is to decide and address the imperfections in an early period of Software Development Life Cycle [SDLC]. To improve the nature of programming, machine learning strategies have been connected to assemble expectations regarding the disappointment of programming parts by misusing past information of programming segments and their deformities. This paper investigated the condition of craftsmanship in the field of programming imperfection the board and prediction and offered machine learning procedures in short.
Enhanced blue thermally activated delayed fluorescence with rigid adamantane as non-conjugated linker for space-confined donor-acceptor charge transfer: a theoretical study

A lack of control over the relative orientation of donor and acceptor moieties in solid films is the main reason for poor electroluminescence efficiency found in most D-σ-A patterns. In this study, adamantane was introduced as the linker for the construction of a D-σ-A system since its rigid but non-conjugated nature. For a systematic study, the same donor (9,9-dimethyl-10-phenyl-9,10-dihydroacridine, DMTPA) and acceptor (2,4,6-triphenyl-1,3,5-triazine, TPZ) block were attached at different sites of the adamantine molecule, forming three kinds of configuration including face-to-face (FF), head-to-tail (HT), and chair (CH) style. DFT/TD-DFT simulation was carried out to investigate the electronic structures, optical characteristics, and charge transfer (ICT) properties of these three designed molecules. Attributed to a non-conjugated adamantane linker, the conjugation between donor and acceptor was disrupted for either of these molecules, resulting in high triplet energy of 2.79, 2.99, and 3.10 eV, respectively. Among them, the face-to-face style molecule presents the smallest ΔEST and greatest charge transfer excitation from donor to acceptor, suggesting an excellent potential for thermal activated delayed fluorescence (TADF). Face-to-face type molecule is a good option for a high-performance blue organic light-emitting diode (OLED) host material based on these results. Our investigation may open a door for designing new host molecule with other donor and acceptor groups to further enhance the efficiency and longevity of OLEDs.
Research on system identification method of buffer system based on experimental signal pre-processing

In order to obtain the theoretical model of a gun buffer system, the dynamic vibration characteristics of the buffer system were studied by force hammer experiments, and the vibration acceleration response curves of the buffer system after being excited by the impact load were obtained. The acquired signals were processed by noise reduction, detrending, data smoothing and frequency domain integration, and the second-order free vibration model was used to identify the stiffness, damping and other parameters of the system, and compared with the experimental results. The deviation between the calculated results of the identification model and the experimental results is less than 4%, which compared with the general direct identification method, the error is reduced by about 10%. This method can provide a simple and fast technical way for the accurate identification of vibration system models.
Deep multimodal learning for residential building energy prediction

The residential sector has become the second-largest energy consumer since 1987 in the UK. Approximately 24 million existing dwellings in England made up over 32% of the overall energy consumption in 2020. A robust understanding of existing buildings’ energy performance is therefore critical in guiding proper home retrofit measures to accelerate towards meeting the UK’s climate targets. A substantial number of predictions at a city scale rely on available data, e.g., Energy Performance Certificates (EPCs) and GIS products, to develop statistical and machine learning models to estimate energy consumption. However, issues with existing data are not negligible. This work adopted the idea of deep multimodal learning to study the potential for using Google Street View (GSV) images as an additional input for residential building energy prediction. 20,031 GSV images of 5,933 residential buildings in central Barnsley, UK, have been selected for a case study. All images were pre-processed using a state-of-the-art object detection algorithm to minimise the noise caused by other elements that may appear nearby. Building specifications that cannot be easily determined by the appearance are extracted from existing EPC information as text-based inputs for prediction. A multimodal model was designed to jointly take images and texts as inputs. These inputs are first propagated through a convolutional neural network and multi-layer perceptron, respectively, before being combined into a connected network for final energy prediction. The multi-input model was trained and tested on the case study area and predicted an annual energy consumption with a mean absolute difference of 0.01kWh/m2 per annum on average compared with what is recorded in the EPC. The difference between the predicted results and the EPC may also provide some hints on the bias the certificates potentially contain.
Mathematical Modeling of Flow Control Using Magnetic Fluid and Field

In this paper some previous studies related to those for flow control aspects of the hydrodynamics of magnetic fluid and field are reviewed first and recent mathematical approaches for the corresponding systems of partial differential equations are discussed. Next, mathematical modeling of a two-fluid system, with one fluid being a non-conducting ferrofluid and another fluid being a regular non-ferrofluid is considered. A numerical method is used to solve the mathematical system for the problem, which, in particular, captures the flow structure in the two-fluid system. The convective flow velocities and the heat fluxes were determined for various values of the parameters of the problem. Certain aspects of the magnetic fluids and fields were found to be useful for convective flow control, which is important in many application areas including microgravity space applications. In particular, under some conditions, the surface force that can exist at the interface between the two fluid zones was found to reduce the magnitude of the flow velocity and instabilities that may occur in the two-fluid system.
Exploring long-term building stock strategies in Switzerland in line with IPCC carbon budgets

Stringent limits and reduction strategies paths on greenhouse gas (GHG) emissions are being defined at different levels for long-term temperature stabilization. Given the nearly linear relationship between warming and cumulative net emissions, a carbon budget approach is required to limit global warming, as stated by the IPCC. In this setting, the built environment, as a cross-sectorial and transnational area of activity, plays a crucial role in today’s carbon emissions and future reduction potentials. Previous research showed the need for effective and aligned carbon-targets to support and guide all actors in the construction sector towards these challenging global goals. In this context, previous research compared top-down derived carbon budgets for the Swiss built environment with a preliminary estimation of future cumulative emissions of the sector. Findings showed the misalignment of current best practices and the significant magnitude of effort that would be required to comply with such objectives. Nevertheless, limitations in the preliminary work emerged, such as the lack of dynamicity of the parameters included in the model restricting the representativity of its results. The current paper brings further this previous work by integrating the dynamic evolution of the energy supply, the materials’ production, and the renovation rate. Results are then presented by mean of a parallel coordinate interactive graph. This interactive component allows the parametric exploration of the compliance with limited global budgets by varying the input parameters. This way the influence of macro-level strategies to decarbonize the Swiss building stock can easily be visualized with reference to the IPCC carbon budgets. Ultimately, the available interactive tool might support policy makers in decisions taken at the building stock level.
Detection of the Sunyaev / Zeldovich Effect in ABELL:773

We have observed the cluster Abell 773 with the Ryle Telescope at 15 GHz, and have measured a decrement in the cosmic microwave background radiation (CMB) of −590±116 μJy beam −1 , due to the Sunyaev-Zel'dovich (S-Z) effect. On reasonable assumptions about the structure of the cluster atmosphere, this implies a central temperature decrement of between −790 and −990 μK. Together with the previous detection in A2218, which is significantly different from A773 as regards radiosource confusion, this result confirms the power of the method used, and emphasizes the advantages of interferometric methods over switched-beam telescopes for imaging structure in the CMB
Community detection based on competitive walking network embedding method

Currently, much of the information of the real world is network-structured, and extracting hidden information from network-structured data helps to understand the corresponding systems, but can also be a challenging problem. In recent years, network embedding has been an effective way to extract network information, which represents nodes in complex networks as low-dimensional space vectors, while preserving the properties of the network. Community attributes are an important property of networks, and in most network embedding algorithms, the community structure is usually ignored or cannot be explicitly preserved. In this paper, we propose a new network embedding framework that explicitly considers community structure feature extraction. The framework, called competitive walking network embedding (CWNE), extracts sample sequences by competitive walking and obtains node representation vectors by skip-gram training. Competitive walking allows the extracted sample sequences to be concentrated within the same community, effectively preserving the community structure features of the network. The results of testing the proposed method on artificial and real-world networks show that our model is more effective in detecting community structure in networks. In addition, visualization experiments show that the results of CWNE show that nodes from the same community are more tightly distributed in low-dimensional space.