Miracle-dz/UniFAR-FTUs huggingface dataset:
Camouflage Design Method Combining Mean-Shift and Minimal Spanning Tree Algorithms

To solve the design problem of camouflage technology and realize high fusion of the camouflage image and the background environment,a method of image segmentation by combining mean shift and minimal spanning tree algorithm is presented.The method can accurately exact the camouflage spot pattern of background,and its operation speed was greatly improved in comparison with that of the others.It ensures the accuracy of the whole camouflage design and enhances the instantaneity of camouflage effect.Moreover,through the quantification of color histogram,the dominant background color is extracted as the display color of the camouflage pattern.The combination of plaque and color generates the final camouflage image.The experimental results confirm the validity of the method.
Sparsity Constraints and Regularization for Nonlinear Inverse Problems

In den letzten Jahren wurde Sparsity Regularisierung fur lineare und nichtlineare inverse Probleme untersucht. Bekanntermaen ist das Verfahren einfach zu verwenden und besitzt viele Vorteile fur Probleme mit sparsen Lsungen. Fur lineare inverse Probleme ist dieses Verfahren gut entwickelt, wohingegen nur wenige Ergebnisse fur nichtlineare inverse Probleme bekannt sind. Auch numerische Algorithmen wurden fr Sparsity Regularisierung entwickelt. Die meisten Algorithmen sind dafur bekannt, eine lineare Konvergenzrate zu besitzen und in der Praxis langsam zu sein, insbesondere fr nichtlineare inverse Probleme. Ziel dieser Dissertation ist es, Sparsity Regularisierung fur nichtlineare inverse Probleme zu untersuchen. Die Arbeit konzentriert sich auf die folgenden zwei Bereiche: Erstens: Sparsity Regularisierung fur das Diffusionskoeffizienten-Identifikationsproblem und elektrische Impedanz-Tomographie wird untersucht. In diesen Problemen wird der energiefunktionale Ansatz (unter Einbeziehung der Sparsity Regularisierung) anstelle des Kleinsten-Quadrate-Ansatzes gewahlt. Wir analysieren Vorteile des neuen Ansatzes, sowie die Gutgestelltheit und Konvergenzraten des Verfahrens fur jedes Problem. Zweitens: Numerische Algorithmen fur Minimierungsprobleme der Sparsity Regularisierung nichtlinearer inverser Probleme werden vorgeschlagen. Die Algorithmen sind ein GradientenAbstiegs-Verfahren, zwei beschleunigte Versionen des Gradienten-Abstiegs-Verfahrens, ein semi-smooth Newton Verfahren sowie ein semi-smooth Quasi-Newton Verfahren. Der Fokus liegt hierbei auf der Konvergenz der Verfahren. Bei einigen Algorithmen werden auch die Konvergenzrate sowie die Rate der Abnahme des Zielfunktionals untersucht. Die Algorithmen werden dann an den obigen zwei Parameteridentifikationsproblemen angewendet, die Effizienz der Algorithmen untersucht und an einigen spezifischen Beispielen veranschaulicht.
Fabrication of a Low-Cost Microfluidic Device for High-Throughput Drug Testing on Static and Dynamic Cancer Spheroid Culture Models

Drug development is a complex and expensive process from new drug discovery to product approval. Most drug screening and testing rely on in vitro 2D cell culture models; however, they generally lack in vivo tissue microarchitecture and physiological functionality. Therefore, many researchers have used engineering methods, such as microfluidic devices, to culture 3D cells in dynamic conditions. In this study, a simple and low-cost microfluidic device was fabricated using Poly Methyl Methacrylate (PMMA), a widely available material, and the total cost of the completed device was USD 17.75. Dynamic and static cell culture examinations were applied to monitor the growth of 3D cells. α-MG-loaded GA liposomes were used as the drug to test cell viability in 3D cancer spheroids. Two cell culture conditions (i.e., static and dynamic) were also used in drug testing to simulate the effect of flow on drug cytotoxicity. Results from all assays showed that with the velocity of 0.005 mL/min, cell viability was significantly impaired to nearly 30% after 72 h in a dynamic culture. This device is expected to improve in vitro testing models, reduce and eliminate unsuitable compounds, and select more accurate combinations for in vivo testing.
Mo-LDH-GO Hybrid Catalysts for Indigo Carmine Advanced Oxidation

This paper is focused on the utilization of hybrid catalysts obtained from layered double hydroxides containing molybdate as the compensation anion (Mo-LDH) and graphene oxide (GO) in advanced oxidation using environmentally friendly H2O2 as the oxidation agent for the removal of indigo carmine dye (IC) from wastewaters at 25 °C using 1 wt. % catalyst in the reaction mixture. Five samples of Mo-LDH-GO composites containing 5, 10, 15, 20, and 25 wt% GO labeled as HTMo-xGO (where HT is the abbreviation used for Mg/Al in the brucite type layer of the LDH and x stands for the concentration of GO) have been synthesized by coprecipitation at pH 10 and characterized by XRD, SEM, Raman, and ATR-FTIR spectroscopy, determination of the acid and base sites, and textural analysis by nitrogen adsorption/desorption. The XRD analysis confirmed the layered structure of the HTMo-xGO composites and GO incorporation in all samples has been proved by Raman spectroscopy. The most efficient catalyst was found to be the catalyst that contained 20%wt. GO, which allowed the removal of IC to reach 96.6%. The results of the catalytic tests indicated a strong correlation between catalytic activity and textural properties as well as the basicity of the catalysts.
Lightning Ground Flash Measurements over the Contiguous United States: 1995–97

Abstract Cloud-to-ground lightning data have been analyzed for the years 1995–97 for the contiguous United States for total flashes, positive flashes, the percentage of positive lightning, peak currents for negative and positive lightning, and for negative multiplicity. The authors examined a total of 75.8 million flashes divided among the three years, 22.7 million (1995), 26.2 million (1996), and 26.9 million (1997). The highest flash densities, uncorrected for detection efficiency, occur in Louisiana and Florida, typically exceeding 11 flashes km−2 on a grid scale of 0.2°. Positive flash densities exceed 1.1 flashes km−2 in Florida, Louisiana, and an area overlapped by the states of Tennessee, Mississippi, and Kentucky. The monthly percentage positive lightning ranges from 6.5% (July 1995) to 24.5% (January 1996). The annual percentage of positive lightning is 9.3% (1995), 10.2% (1996), and 10.1% (1997). Areas of positive lightning greater than 25% occur from the Canadian border as far south as Kansas, .
Self-Consistent Graph Neural Networks for Semi-Supervised Node Classification

Graph Neural Networks (GNNs), the powerful graph representation technique based on deep learning, have attracted great research interest in recent years. Although many GNNs have achieved the state-of-the-art accuracy on a set of standard benchmark datasets, they are still limited to traditional semi-supervised framework and lack of sufficient supervision information, especially for the large amount of unlabeled data. To overcome this issue, we propose a novel self-consistent graph neural networks (SCGNN) framework to enrich the supervision information from two aspects: the self-consistency of unlabeled data and the label information of labeled data. First, in order to extract the self-supervision information from the numerous unlabeled nodes, we perform graph data augmentation and leverage a self-consistent constraint to maximize the mutual information of the unlabeled nodes across different augmented graph views. The self-consistency can sufficiently utilize the intrinsic structural attributes of the graph to extract the self-supervision information from unlabeled data and improve the subsequent classification result. Second, to further extract supervision information from scarce labeled nodes, we introduce a fusion mechanism to obtain comprehensive node embeddings by fusing node representations of two positive graph views, and optimize the classification loss over labeled nodes to maximize the utilization of label information. We conduct comprehensive empirical studies on six public benchmark datasets in node classification task. In terms of accuracy, SCGNN improves by an average of 2.08% over the best baseline, and specifically by 5.8% on the Disease dataset.
High speed nanoindentation aided correlative study between local mechanical properties and chemical segregation in equiatomic MoNb and MoNbTi alloys

Chemical segregation at various length scales directly impacts the material behaviour during service, wherein homogeneous response is mostly desired. Our recent efforts on development of equiatomic MoNb and MoNbTi alloys have indicated propensity for chemical segregation that in turn influences the mechanical properties. In this regard, recent advances in high speed nanoindentation mapping have demonstrated the ability to accurately measure mechanical properties at the micrometer length scale. In this work, we extend this capability to establish a quantitative link between the local chemistry measured via energy dispersive spectroscopy (EDS) and nanoindentation mapping for as-cast and heat treated equiatomic MoNb and MoNbTi alloys. Excellent correlation between chemical composition and hardness is observed. This work provides a simple framework to study the effect of chemical segregation on the local mechanical properties of multi-principal element alloys, which can be gainfully used to tailor the microstructure and properties of these alloys. Graphical Abstract Correlation between chemical segregation and mechanical properties
The Fitting of Non-stationary Time-series Models with Time-dependent Parameters

SUMMARY We use the method of weighted least squares to estimate the timedependent parameters of non-stationary time-series models. Approximate expressions for the bias and variance of the estimates, which could be used to assess the merits of various weight functions on the basis of "mean square error" criterion, are obtained. In Section 5, it is shown that the estimates obtained by weighted least-squares method are the same as estimates obtained by weighted maximum-likelihood estimation procedure. The procedures are numerically illustrated in the last section.
Augmenting Organizational Change and Strategy Activities: Leveraging Generative Artificial Intelligence

In this scoping essay, we discuss the potential for generative artificial intelligence (GAI) to shape the work of organizational change, development, or strategy implementation professionals. Using a case example of a culture change initiative, we illustrate how practitioners can benefit from using GAI tools to augment relevant change activities in planning initiative content and roll-out, mobilizing stakeholders, and monitoring initiative progress. We conclude with a reflection on the limitations of GAI systems and outline directions for future research related to (1) stakeholder responses to GAI, (2) GAI impact on the nature of change work, and (3) GAI value creation for change work and unintended consequences of GAI use.
Translation, Cultural Adaptation, and Reliability of the 15D Portuguese Version: A Generic Health-Related Quality of Life (HRQoL) Instrument

(1) Background: Purpose: The assessment of quality of life is essential to the human condition and can be measured through questionnaires. This study aims to translate and culturally adapt the 15D questionnaire to assess the population’s quality of life, as well as explore its relative reliability and internal consistency; (2) Methods: The translation and cultural adaptation of the 15D questionnaire was carried out independently, considering two translations. The synthesis version was applied to eight subjects, distributed by gender. Cognitive interviews were conducted to observe clarity, acceptability, and familiarity with the version of the questionnaire. The final version of the questionnaire, in Portuguese, was again translated into the official language by two translators who had never had contact with the questionnaire. To assess the test-retest reliability and internal consistency of the 15D questionnaire, 43 participants were interviewed; (3) Results: Participants indicated that they had some doubts about dimensions, breathing, and discomfort and symptoms; however, as there were no suggestions for change, the questionnaire had no changes. Items were clear and understandable. Internal consistency was observed using Cronbach’s alpha, with values between 0.76 and 0.98. The test-retest reliability values were between 0.77 and 0.97; and (4) Conclusions: The Portuguese version of the 15D questionnaire was proved to be equivalent to the English version and to be reliable for the Portuguese population. This instrument is easy to access and apply.
Formation of Ag-Fe Bimetallic Nano-Species on Mordenite Depending on the Initial Ratio of Components

The formation and properties of silver and iron nanoscale components in the Ag-Fe bimetallic system deposited on mordenite depend on several parameters during their preparation. Previously, it was shown that an important condition for optimizing nano-center properties in a bimetallic catalyst is to change the order of sequential deposition of components; the order “first Ag+, then Fe2+” was chosen as optimal. In this work, the influence of exact Ag/Fe atomic proportion on the system’s physicochemical properties was studied. This ratio has been confirmed to affect the stoichiometry of the reduction–oxidation processes involving Ag+ and Fe2+, as shown by XRD, DR UV-Vis, XPS, and XAFS data, while HRTEM, SBET and TPD-NH3 show little change. However, it was found the correlation between the occurrence and amount of the Fe3+ ions incorporated into the zeolite’s framework and the experimentally determined catalytic activities towards the model de-NOx reaction along the series of nanomaterials elucidated in this present paper.
Analysis of Urine Sediment Images for Detection and Classification of Cells

Urine sediment tests are important in the diagnosis of abnormal diseases related to the urinary tract. The formation of cells such as red blood cells and white blood cells in the urine of patients is important for the diagnosis of the disease. Therefore, cells need to be fully identified in clinical urinalysis. Urinalysis with human eyes; Since it is subjective, time consuming and causing errors, methods have been developed to automate microscopic analysis with the help of computer and software systems. In this study, the YOLO-v7 algorithm, which gives successful results in image processing technology, was used as a method and model. The dataset used in the study was created by using microscopic images of urine sediment taken from the Biochemistry Laboratory of the Faculty of Medicine, Selcuk University. Seven different cell segmentation and classification studies have been carried out, including WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles, which have clinical value for the diagnosis of the disease. Experimental studies were carried out with the YOLO-v7 algorithm and the results were presented. The contributions of this study can be summarized as follows. (1) In this study, which is proposed for segmentation of cells on the urine cell images in the Urine Sediment dataset, for the experimental studies carried out with the YOLO model, whose performance was evaluated; Precision, Recall, mAP(0.5) and F1-Score(%) segmentation performance metrics were calculated as 0.384, 0.759, 0.432 and 0.510, respectively. (2) A computer-aided support system to assist physicians in segmenting urine cells is presented as a secondary tool. Classification accuracy for WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles cells was calculated as 0.78, 0.94, 0.90, 0.57, 0.92, 0.68 and 0.97, respectively. A mean classification success of 0.822 was achieved for all classes. Thus, it has been seen that the Yolov7 model can be used by experts as a tool for recognizing cells in the urine sediment. As a result, it has been shown that suitable deep learning models can be used to recognize the biometric properties of urinary sediment cells. With the model created using deep learning libraries, urine sediment cells can be easily classified, and it is possible to define many different cells if there is a dataset with sufficient number of images.
Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79–13.2 mGy, 0.90–4.40 mGy, and 0.20–0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66–6.60 mSv, 0.50–0.80 mGy, and 0.39–0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2–4, whereas ULD reduced the dose reduction by a factor of 8–13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.
Improved diagonal interacting multiple model algorithm for manoeuvering target tracking based on H ∞ filter

This study is devoted to the problem of state estimate of discrete-time stochastic systems with Markov jump parameters. A robust algorithm – diagonal interacting multiple model algorithm based on H ∞ filtering is presented for manoeuvering target tracking when noise of measurement is of unknown statistics. Extensive Monte Carlo simulations show the effectiveness and superiority of the proposed algorithm.
A Four-Sided Matching Game for Energy-Efficient Scheduling in Narrowband-IoT Networks

This paper studies the uplink resource allocation problem in NB-IoT networks. We aim to maximize the network’s energy efficiency to serve devices in need for long battery lifetime. Also, the network energy efficiency is affected by the associated UEs to candidates, carrying the scheduling information for UEs. For this reason, we investigate the joint resource allocation and Candidate-UE association problem in the uplink (UL) in NB-IoT. The resource allocation problem allocates the UEs with the suitable scheduling delay value and subcarrier indication field. The Candidate-UE association problem selects the appropriate UE for each candidate. The optimization variables involved in the joint problem are divided into binary association variables and binary resource allocation variables. This makes it hard to solve the joint problem since its optimization variables are of combinatorial nature. To this end, we formulate the joint problem as a four-sided matching game that ranks the UEs, candidates, the scheduling delay values and subcarrier indication fields while considering maximizing the energy efficiency as a preference metric. We then describe a four-sided matching game algorithm to solve the problem. The simulation results highlight the proposed algorithm’s effectiveness compared to other algorithms in the obtained network energy efficiency.
Researchers on ice? How the COVID-19 pandemic has impacted Antarctic researchers

Abstract The COVID-19 pandemic and pandemic-related measures have impacted the lives and work-related activities of Antarctic researchers. To explore these impacts, we designed, piloted and disseminated an online survey in English, Russian, Spanish and Chinese in late 2020 and early 2021. The survey explored how the pandemic affected the productivity of Antarctic researchers, their career prospects and their mental wellbeing. Findings exposed patterns of inequities. For instance, of the 406 unique responses to the survey, women appeared to have been affected more adversely than men, especially in relation to mental health, and early-career researchers were disadvantaged more than their mid- or late-career colleagues. Overall, a third of the research participants reported at least one major negative impact from the pandemic on their mental health. Approximately half of the participants also mentioned that the COVID-19 pandemic had some positive effects, especially in terms of the advantages that working from home brought and opportunities to attend events, network or benefit from training workshops online. We conclude with a series of recommendations for science administrators and policymakers to mitigate the most serious adverse impacts of the pandemic on Antarctic research communities, with implications for other contexts where scientific activities are conducted under extreme circumstances.
Experimental and Numerical Investigation of Shock/Turbulence Interaction by Hot-wire Technique

In the present paper, an experimental investigation has been carried out to observe the amplification of turbulence intensity after shock/turbulence interaction by hot-wire technique. The hot wires are installed in the wake of turbulent grids to measure turbulence fluctuations before and after the reflected shock interaction with turbulence. It is observed that the turbulence fluctuations for less open area of the grid plate are higher than the turbulence fluctuations for more open area of the grid plate. For numerical computations, grid plate of 49.5 % open area is used. The average longitudinal velocity line obtained from experimental velocity data simulates with numerical results properly and in some places, 5-7 % deviations are observed with numerical results. All the simulation results indicate that the present code with turbulence model is working properly. The substantial amplification of pressure fluctuations obtained from experiment is observed after interaction. The dissipation rate of turbulent kinetic energy (TKE) and the levels of length scales are determined numerically. It is observed that the dissipation rate of TKE and the levels of length scales decrease after shock/turbulence interaction.
Gamma-Ray Bursts at high and very high energies

Gamma-Ray Bursts (GRBs) are extra-galactic and extremely energetic transient emissions of gamma rays, which are thought to be associated with the death of massive stars or the merger of compact objects in binary systems. Their huge luminosities involve the presence a newborn stellar-mass black hole emitting a relativistic collimated outow, which accelerates particles and produces non-thermal emissions from the radio domain to the highest energies. In this article, I review recent progresses in the understanding of GRB jet physics above 100 MeV, based on Fermi observations of bright GRBs. I discuss the physical implications of these observations and their impact on GRB modeling, and I present some prospects for GRB observation at very high energies in the near future. To cite this article: F. Piron, C. R. Physique XX (2015). R esum e