Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional climate modeling activities in relation to the CLAVIER project

Observational records show that the global climate is changing and these changes are visible in the Central and Eastern European Countries (CEEC). Certainly negative impacts of climate change will involve significant economic losses in several regions of Europe, while others may bring health or welfare problems somewhere else. Within the EU funded project CLAVIER (Climate ChAnge and Variability: Impact on Central and Eastern EuRope) three representative CEEC are studied in detail: Hungary, Romania, and Bulgaria. Researchers from 6 countries and different disciplines identify linkages between climate change and its impact on weather patterns with consequences on air pollution, extreme events, and water resources. Furthermore, an evaluation of the economic impact on agriculture, tourism, energy supply, and public sector will be conducted. CLAVIER focuses on ongoing and future climate changes in CEEC using measurements and existing regional scenarios to determine possible developments of the climate and to address related uncertainties. Three regional climate models are used to simulate the climate evolution in CEEC for the period 1951 to 2050, the future regional climate projection being the first half of the 21st century. The issue of climate change uncertainties is addressed through the multi-model and multi-scenario ensemble approach. As a result, CLAVIER establishes a large data base, tools, and methodologies, which contribute to reasonable planning for a successful development of society and economy in CEEC under climate change conditions. Current regional climate projections show a strong warming and drying during the summer months, which seems partly due to a systematic error in model simulations. Detailed validation of the CLAVIER simulations, which goes much beyond this paper, is needed, and the results have to be related to possible climate changes projected for the region in future simulations.
The self-organization of capillary wave sources

A liquid drop can be kept bouncing at the surface of a bath of the same liquid for any length of time if the bath is kept oscillating vertically. Several regimes can be observed. For a liquid of moderate viscosity, the bouncing of the drop generates damped capillary waves with a wavelength corresponding to the forcing frequency. Therefore when several identical drops are placed on the oscillating surface, the interaction of their waves leads to the self-organization of the drops with a 2D triangular lattice. Another remarkable regime is observed when the forcing amplitude is increased close to the Faraday instability threshold: the drop starts moving in the horizontal plane at a constant velocity. We have studied the movement of one ‘walking drop’ as well as the possible interactions of several of these particular drops placed on the surface of the liquid, leading to their complex self-organization. These drops can collide via their waves and in certain situations attract each other and start orbiting.
Design and Development of DC-DC Converters Based on IoT Systems for Photovoltaic Applications in Egypt

Renewable energy sources are naturally pollution-free, and solar energy meets the world's energy demand. Photovoltaic (PV) systems are used to harvest solar energy, where a power converter is needed to regulate and control the harvested solar power and achieve the required output voltage for various applications. In this paper, four different DC-DC converters commonly used in research and industry are designed and developed to provide the required voltage of the PV systems, which are Buck, Synchronous Buck, Single-Ended Primary Inductor Converter (SEPIC), and Flyback converters. These DC-DC converters are modeled, simulated, and analyzed using LTspice software. The simulation studies and analysis quantify the system performance of these converters and the parameter values for the experiments. These converters are built and tested in different conditions using a standalone test bench to validate these converters for PV application systems. The test development sequences are conducted for open-loop and closed-loop systems of the power converters, where a variable power supply is used to mimic the solar panel output in the test bench. A proportional integral (PI) controller is designed to regulate the output voltage of the power converters to power load or charge the battery while solar radiation level, load, and other variables change. The Experimental test results show that the system performance profiles of these converters are different in ripple, efficiency, and other values. The maximum efficiency value achieved by the Synchronous Buck converter and less ripple output voltage is from the Flyback converter. Finally, the best proposed DC-DC converter is connected to a solar panel, where a customized Internet of Things (IoT) system is deployed into the PV system to supervise and monitor the parameters of the proposed PV system.
Parkinson’s from inside out: emerging and unexpected benefits of a long-term partnership

This article is a personal reflection on a long-standing patient and public involvement (PPI) partnership between a person with Parkinson’s and a cognitive neuroscience researcher. They describe how the partnership arose, was established and evolved to produce unexpected benefits to the research and more broadly. Initially, working together helped to communicate the purpose of the research to a lay audience and to make lab-based testing sessions for people with Parkinson’s as comfortable as possible. They then worked together on the steering group for a research project about Parkinson’s and imitation, which led to co-designing interventions using imitation and imagination of movements to improve movements, including a dance class. Further benefits were realised through co-teaching undergraduate students about Parkinson’s, establishing a broader culture of PPI within the research lab and sharing their expertise of PPI more broadly. They consider key ingredients for successful collaboration, including shared curiosity, open-mindedness and trust, as well as the importance of informal discussion and space. Challenges are also considered, including authorship of research articles and anonymity. Their account demonstrates the value of the collaboration to research itself, but also the broader (often unexpected) benefits that can emerge when a collaboration has space and time to flourish.
A primary roundness measuring machine

On a commercial roundness measurement instrument METAS has implemented several modifications offering an up-grade of the measurement capabilities and a full understanding of the measurement process. The Talyrond 73 instrument is equipped with a rotating spindle with an oil-hydrostatic bearing. The modifications include an incremental encoder on the spindle, a vibration free DC motor with variable rotation speed for driving the spindle, new amplifier electronics with selectable gain for the LVDT probe, new software for data acquisition and evaluation, and a heat protection shield. The application of error separation techniques, the complete characterization of all relevant parameters and the high level of precision achieved allow the attribute "primary" be given to this roundness measuring machine.
Combined Hyperthermia and Re-Irradiation in Non-Breast Cancer Patients: A Systematic Review

Simple Summary Hyperthermia in the range of 39–43 °C is associated with potent radiosensitization. The clinical benefit of combined radiotherapy and hyperthermia has been shown for a variety of indications. Regarding re-irradiation, added hyperthermia can be considered for locoregional breast cancer recurrence with isolated unresectable disease or as an adjuvant therapy after resection. For other cancer entities, the clinical evidence is scarce. This review aims to systematically summarize the currently available literature. The final search was performed on 29 August 2022. Abstract Purpose: This systematic literature review summarizes clinical studies and trials involving combined non-ablative hyperthermia and re-irradiation in locoregionally recurrent cancer except breast cancer. Methods: One database and one registry, MEDLINE and clinicaltrials.gov, respectively, were searched for studies on combined non-ablative hyperthermia and re-irradiation in non-breast cancer patients. Extracted study characteristics included treatment modalities and re-irradiation dose concepts. Outcomes of interest were tumor response, survival measures, toxicity data and palliation. Within-study bias assessment included the identification of conflict of interest (COI). The final search was performed on 29 August 2022. Results: Twenty-three articles were included in the final analysis, reporting on 603 patients with eight major tumor types. Twelve articles (52%) were retrospective studies. Only one randomized trial was identified. No COI statement was declared in 11 studies. Four of the remaining twelve studies exhibited significant COI. Low study and patient numbers, high heterogeneity in treatment modalities and endpoints, as well as significant within- and across-study bias impeded the synthesis of results. Conclusion: Outside of locoregionally recurrent breast cancer, the role of combined moderate hyperthermia and re-irradiation can so far not be established. This review underscores the necessity for more clinical trials to generate higher levels of clinical evidence for combined re-irradiation and hyperthermia.
Opposing Effects of ApoE2 and ApoE4 on Glycolytic Metabolism in Neuronal Aging Supports a Warburg Neuroprotective Cascade against Alzheimer’s Disease

Apolipoprotein E4 (ApoE4) is the most recognized genetic risk factor for late-onset Alzheimer’s disease (LOAD), whereas ApoE2 reduces the risk for LOAD. The underlying mechanisms are unclear but may include effects on brain energy metabolism. Here, we used neuro-2a (N2a) cells that stably express human ApoE isoforms (N2a-hApoE), differentiated N2a-hApoE neuronal cells, and humanized ApoE knock-in mouse models to investigate relationships among ApoE isoforms, glycolytic metabolism, and neuronal health and aging. ApoE2-expressing cells retained robust hexokinase (HK) expression and glycolytic activity, whereas these endpoints progressively declined with aging in ApoE4-expressing cells. These divergent ApoE2 and ApoE4 effects on glycolysis directly correlated with markers of cellular wellness. Moreover, ApoE4-expressing cells upregulated phosphofructokinase and pyruvate kinase with the apparent intent of compensating for the HK-dependent glycolysis reduction. The introduction of ApoE2 increased HK levels and glycolysis flux in ApoE4 cells. PI3K/Akt signaling was distinctively regulated by ApoE isoforms but was only partially responsible for the ApoE-mediated effects on HK. Collectively, our findings indicate that human ApoE isoforms differentially modulate neuronal glycolysis through HK regulation, with ApoE2 upregulating and ApoE4 downregulating, which markedly impacts neuronal health during aging. These findings lend compelling support to the emerging inverse-Warburg theory of AD and highlight a therapeutic opportunity for bolstering brain glycolytic resilience to prevent and treat AD.
``Force,'' ontology, and language

We introduce a linguistic framework through which one can interpret systematically students’ understanding of and reasoning about force and motion. Some researchers have suggested that students have robust misconceptions or alternative frameworks grounded in everyday experience. Others have pointed out the inconsistency of students’ responses and presented a phenomenological explanation for what is observed, namely, knowledge in pieces. We wish to present a view that builds on and unifies aspects of this prior research. Our argument is that many students’ difficulties with force and motion are primarily due to a combination of linguistic and ontological difficulties. It is possible that students are primarily engaged in trying to define and categorize the meaning of the term “force” as spoken about by physicists. We found that this process of negotiation of meaning is remarkably similar to that engaged in by physicists in history. In this paper we will describe a study of the historical record that reveals an analogous process of meaning negotiation, spanning multiple centuries. Using methods from cognitive linguistics and systemic functional grammar, we will present an analysis of the force and motion literature, focusing on prior studies with interview data. We will then discuss the implications of our findings for physics instruction.
Sialyl LewisX/A and Cytokeratin Crosstalk in Triple Negative Breast Cancer

Simple Summary Triple-negative breast cancer (TNBC) is aggressive, highly metastatic, and associated with poor patient prognosis. Sialyl-Lewis X and A (sLeX/A) are sugars with important roles in cell signalling and metastasis. We aimed to describe the relevance of sLeX/A in TNBC patients and its association with other biomarkers. We identified that sLeX/A negatively correlated with cytokeratins, structural proteins present at the cell cytoskeleton, and are involved in cell attachment, by using patient tissues, cell lines, and datasets. Our data suggests that sLeX/A is decorating proteins such as integrin alpha 6, deregulating cell signalling responsible for hemidesmosome formation, impacting cell adhesion, and promoting metastatic behaviour. This work highlights sLeX/A as an important biomarker behind TNBC malignancy to target and treat this breast cancer type. Abstract Triple-negative breast cancer (TNBC) encompasses multiple entities and is generally highly aggressive and metastatic. We aimed to determine the clinical and biological relevance of Sialyl-Lewis X and A (sLeX/A)—a fucosylated glycan involved in metastasis—in TNBC. Here, we studied tissues from 50 TNBC patients, transcripts from a TNBC dataset from The Cancer Genome Atlas (TCGA) database, and a primary breast cancer cell line. All 50 TNBC tissue samples analysed expressed sLeX/A. Patients with high expression of sLeX/A had 3 years less disease-free survival than patients with lower expression. In tissue, sLeX/A negatively correlated with cytokeratins 5/6 (CK5/6, which was corroborated by the inverse correlation between fucosyltransferases and CK5/6 genes. Our observations were confirmed in vitro when inhibition of sLeX/A remarkably increased expression of CK5/6, followed by a decreased proliferation and invasion capacity. Among the reported glycoproteins bearing sLeX/A and based on the STRING tool, α6 integrin showed the highest interaction score with CK5/6. This is the first report on the sLeX/A expression in TNBC, highlighting its association with lower disease-free survival and its inverse crosstalk with CK5/6 with α6 integrin as a mediator. All in all, sLeX/A is critical for TNBC malignancy and a potential prognosis biomarker and therapeutic target.
Bayesian Ensembled Knowledge Extraction Strategy for Online Portfolio Selection

Online portfolio selection, one of the major fundamental problems in finance, has been explored quite extensively in recent years by machine learning and artificial intelligence communities. Recent state-of-the-art methods have focused on Mean Reversion significantly and have demonstrated outstanding performance. Another version of the same phenomenon, Median Reversion, has also performed well and demonstrated its ability to be robust against noises and outliers. Another important characteristic is Momentum. In this paper, a Bayesian ensembling approach to extract knowledge from both Mean Reversion and Median Reversion simultaneously based on the momentum associated with each one is proposed for the online portfolio selection task. The proposed method demonstrates its effectiveness by outperforming current state-of-the-art algorithms on several datasets.
Darknet Analysis-Based Early Detection Framework for Malware Activity: Issue and Potential Extension

Most packets arriving in the darknet (or network telescope), which is unused IP address space on the Internet, are related to indiscriminate scanning and attack activities. In recent years, the number of indiscriminate scanning attacks observed on the darknet has increased in diversity and quantity. In our earlier study, we proposed a framework called Dark-TRACER that detects anomalies in spatiotemporal pattern synchronization by using darknet data, with the aim being early detection of malware-caused indiscriminate scanning attacks. Although Dark-TRACER has achieved an average of 126.4 days earlier threat detection, we have not been able to determine whether there is a relationship between the early detections and the actual threats. Hence, in this paper, we perform a cross-checking analysis to identify if any information links the detections and the actual threats. As a result, we confirmed the validity of our early threat detection framework by showing, e.g., that more than 60% of unique hosts overlapped in large-scale threats. In addition, we outline four future studies to address the issue that the present Dark-TRACER has many false-positive alerts. Lastly, the darknet data used in our research has been made publicly available.
Renyi entropy-based classification of daily electrical load patterns

This study illustrates and discusses an original approach to classify the electricity consumers according to their daily load patterns. This approach exploits the notion of entropy introduced by Renyi for setting up specific clustering procedures. The proposed procedures differ with respect to typical methods adopted for electricity consumer classification, based on the Euclidean distance notion. The algorithms tested include firstly a classical method based on the between-cluster entropy and its slight variation. Then, a novel procedure is presented, based on the calculation of the similarity between centroids, with successive refinement to allow effective identification of the outliers. The outcomes of the classification carried out by using the proposed procedure are compared to the results of other available techniques, using a set of clustering validity indicators for ranking the clustering methods. On the basis of these results, it emerges that the novel procedure exhibits better clustering performance with respect to both the literature approaches and the classical entropy-based method, for different numbers of clusters. The results obtained are of key relevance for assisting the electricity suppliers in identifying a reduced number of load pattern-dependent classes, to be associated with distinct consumer groups for load aggregation or tariff purposes.
Utilizing variational autoencoders in photoacoustic tomography

In the inverse problem of photoacoustic tomography (PAT), initial pressure distribution induced by the photoa-coustic effect is estimated from a set of measured ultrasound data. In the recent decade, utilization of various deep learning approaches for the inverse problem of PAT have been proposed. However, many of these approaches do not provide information of the uncertainties of the reconstructed images. In this work, we propose a deep learning based approach for the Bayesian inverse problem of PAT based on variational autoencoders. The approach is evaluated using numerical simulations and compared against posterior distribution obtained using a conventional Bayesian image reconstruction approach. The approach is shown to provide rapid and accurate reconstructions with reliability estimates.
Hydromagnetic flows from accretion discs and the production of radio jets

Summary. We examine the possibility that energy and angular momentum are removed magnetically from accretion discs, by field lines that leave the disc surface and extend to large distances. We illustrate this mechanism by solving the equations of magnetohydrodynamics, assuming infinite conductivity, for axially symmetric, self-similar, cold magnetospheric flow from a Keplerian accretion disc in which the field strength B scales with radius r as Bo: ,- 514• We show that a centrifugally driven outflow of matter from the disc is possible, if the poloidal component of the magnetic field makes an angle of less than 60° with the disc surface. At large distances from the disc, the toroidal com ponent of the magnetic field becomes important and collimates the outflow into a pair of anti-parallel jets moving perpendicular to the disc. Close to the disc, the flow is probably driven by gas pressure in a hot magnetically dominated corona. In this way, magnetic stresses can extract the angular momentum from a thin accretion disc and thus enable matter to be accreted, independently of the presence of viscosity. These jet solutions have the property that most of the power is concentrated within a central core, while most of the angular momentum and magnetic flux is carried near the jet walls.
A GENERALIZED ALGORITHM FOR SIMULATING CONTAMINANT DISTRIBUTION IN COMPLEX VENTILATION SYSTEMS WITH RECIRCULATION

In complex ventilation systems with air recirculation, the contaminant concentration at the supply inlets is generally unknown, which limits the application of computational fluid dynamics (CFD) tools to simulate the indoor contaminant distribution. In this article, we develop a new algorithm that can deal with different ventilation scenarios while it requires minimum computing time. This algorithm divides indoor contaminant distribution into two parts: the contribution from inlets and the contribution from sources. The former can be obtained with the concentrations at inlets and volume fraction for each inlet, which is defined as the relative concentration when the boundary concentrations are 1 for the inlet and 0 for other inlets. The latter is calculated by setting the concentrations at all inlets as 0. By combining the contaminant transportation characteristics in rooms and system ducts, the algorithm does not require iteration to solve the contaminant distribution in actual ventilation systems. The demonstration results of two case studies show the great influence of duct configuration on indoor contaminant distribution.
A low-diffusive and oscillation-free convection scheme

A higher-resolution and bounded discretization scheme is proposed for calculations of incompressible steady flows with finite-volume methods. The scheme combines a second-order upstream-weighted approximation with the first-order upwind differencing under the control of a convection boundedness criterion. It is easy to implement for calculations of multi-dimensional flows, and the resulting finite-difference coefficient matrix is always diagonally dominant. Applications to three test problems, two linear and one non-linear, and comparisons with two commonly used schemes, hybrid upwind/central differencing and QUICK, demonstrate the capability of the method in capturing steep gradients while maintaining the boundedness of solutions.
Using Machine Learning to Measure Financial Risk in China

We summarize the findings of our recent ECB working paper, which develops a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre - defined set of relevant newspaper articles is first selected using a specific constellation of risk - related keywords. Then, we employ topic modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. A structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad. Since