Miracle-dz/UniFAR-FTUs huggingface dataset:
1 3 1 α-Scattering Experiment on 64 Zn and the low energy α-nucleus optical potential

A. Ornelas∗a,b, D. Galavizb, Gy. Gyürkya, G. Kissa, Zs. Fülöpa, E. Somorjaia, T. Szücsa,1, M.P. Takácsa,1, P. Mohra,c, R.T. Gürayd , Z. Korkulua,d , N. Özkand , C. Yalçınd aATOMKI, H-4001 Debrecen, POB. 51, Hungary bCentro de Física Nuclear, University of Lisbon, 1649-003 Lisbon, Portugal cDiakonie-Klinikum, D-74523 Schwäbisch Hall, Germany dKocaeli University, Department of Physics, TR-41380 Umuttepe, Kocaeli, Turkey 1current adress: Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-01328 Dresden, Germany
Optimal unit-commitment generation scheduling using genetic algorithm: A case study of a 10-generator power system network

Generation Scheduling is a complex optimisation problem. The aim is to get an optimal combination of generating units for optimal operation. In this paper, Genetic Algorithm (GA) is presented as a viable optimisation tool to solve a fuel cost-based unit-commitment problem. The power system network adopted for the study is a 10-generator network. The prime objective here is to prepare the best economic start-up and shutdown schedules of the generators which meets the forecasted load demand plus reserve for a particular time interval while at the same time satisfying various system constraints. The implementation was done with the GA Toolbox in MATLAB 2018a. Results obtained were compared to the ones obtained with Lagrangian Relaxation optimisation technique and the comparison shows that Genetic Algorithm led to a slight reduction in fuel cost by ₦ 522,452.20 for the 24-hour period.
On the move Compton backscattering scanning

Compton backscattering X-rays imaging (CBI) allows the identification of hazardous organic materials by imaging the scattered photons returning from the body after this is illuminated with X-rays. Most of the commercial Compton scanners deployed in airports, base their functionality on a pixel-per-pixel sequential scanning, which is realized by the combination of a fan-beam collimator and a rotating chopper-wheel. This paper explores a simple and faster approach, coined compressive X-ray Compton backscattering imager (CXBI), where the body is illuminated with a static coded cone-beam pattern at once and several snapshots are acquired while the mask, or equivalently the body is translated at constant speed.
Recognition and Classification of Fast Food Images

Image processing is widely used for food recognition. A lot of different algorithms regarding food identification and classification has been proposed in recent research works. In this paper, we have use an easy and one of the most powerful machine learning technique from the field of deep learning to recognize and classify different categories of fast food images. We have used a pre trained Convolutional Neural Network (CNN) as a feature extractor to train an image category classifier. CNN’s can learn rich feature representations which often perform much better than other handcrafted features such as histogram of oriented gradients (HOG), Local binary patterns (LBP), or speeded up robust features (SURF). A multiclass linear Support Vector Machine (SVM) classifier trained with extracted CNN features is used to classify fast food images to ten different classes. After working on two different benchmark databases, we got the success rate of 99.5% which is higher than the accuracy achieved using bag of features (BoF) and SURF. RecognitionandClassificationofFastFoodImages Strictly as per the compliance and regulations of: Volume 18 Issue 1 Version 1.0 Year 2018 Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Publisher: Global Journals Type: Double Blind Peer Reviewed International Research Journal Graphics & vision Global Journal of Computer Science and Technology: F GJCST-F Classification: I.4. 0 © 2018. Amatul Bushra Akhi, Farzana Akter, Tania Khatun & Mohammad Shorif Uddin. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/bync/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited. Recognition and Classification of Fast Food Images Amatul Bushra Akhi , Farzana Akter , Tania Khatun ρ & Mohammad Shorif Uddin Ѡ © 2018 Global Journals G lo ba l Jo ur na l of C om pu te r Sc ie nc e an d Te ch no lo gy V ol um e X V III I ss ue I V er sio n I 7 Y e a r 20 18 ( ) F AbstractImage processing is widely used for food recognition. A lot of different algorithms regarding food identification and classification has been proposed in recent research works. In this paper, we have use an easy and one of the most powerful machine learning technique from the field of deep learning to recognize and classify different categories of fast food images. We have used a pre trained Convolutional Neural Network (CNN) as a feature extractor to train an image category classifier. CNN’s can learn rich feature representations which often perform much better than other handcrafted features such as histogram of oriented gradients (HOG), Local binary patterns (LBP), or speeded up robust features (SURF). A multiclass linear Support Vector Machine (SVM) classifier trained with extracted CNN features is used to classify fast food images to ten different classes. After working on two different benchmark databases, we got the success rate of 99.5% which is higher than the accuracy achieved using bag of features (BoF) and SURF.
Implications of Older Adults’ Attributions for Young Adults’ Attitudes to Aging: A Vignette Study

The present study was grounded in the revised communicative ecology model of successful aging (CEMSA) and examined whether brief, passing remarks made by older adults about aging influence both young adults’ aging efficacy and their expectations regarding aging. Young adult participants (n = 322) were randomly assigned to read a single vignette. In the vignette an older adult invoked either a negative age-based reason, a non-age-based reason, or a positive age-based reason to account for behavior that could be taken to reflect age-related stereotypes pertaining to physical decline, cognitive decline, or aversion to technology. Analyzes revealed that participants who read the positive age-based accounts perceived the aging process to have been portrayed more favorably. Consequently, they reported higher levels of positive affect about aging which translated into their feeling more efficacious about managing their own aging, and into having more positive expectations regarding aging in general.
Low resistance GaN/InGaN/GaN tunnel junctions

Enhanced interband tunnel injection of holes into a p-n junction is demonstrated using p-GaN/InGaN/n-GaN tunnel junctions with a specific resistivity of 1.2 × 10−4 Ω cm2. The design methodology and low-temperature characteristic of these tunnel junctions are discussed, and insertion into a p-n junction device is described. Applications of tunnel junctions in III-nitride optoelectronics devices are explained using energy band diagrams. The lower bandgap and polarization fields reduce tunneling barrier, eliminating the need for ohmic contacts to p-type GaN. This demonstration of efficient tunnel injection of carriers in III-nitrides can lead to a replacement of existing resistive p-type contact material in light emitters with tunneling contact layers requiring very little metal footprint on the surface, resulting in enhanced light extraction.
State of the Research & Literature Address : ACT with Children , Adolescents and Parents

Acceptance and Commitment Therapy (ACT; Hayes, Strosahl, & Wilson, 1999) has been found effective in treating a wide number of psychological conditions affecting adults. To date, however, little research has been done on the use of ACT with youth and parents. Few efforts have been made at summarizing the literature that does exist. This article, therefore, is a review of empirical and theoretical work with these populations. Online databases, ACT-related websites, and personal communication were used to collect information about published and unpublished, ongoing work. Recommendations for future research are also mentioned.
Investigation of the characteristics of the electromagnetic induction transparent-like spectrum with counter-propagating waves coupling mechanism for waveguide and micro-ring coupled system

In this paper, a new counter-propagation waves coupling mechanism is proposed which is expected to realize an electromagnetically induced transparency (EIT)-like effect. Comparing the travelling waves coupling mechanism (see J. Mod. Opt. 2015,62:313-320 [9]) with the counter-propagating waves coupling mechanism, we find out that the transparency window breadths of transmission spectra are greatly enhanced and the corresponding phase shift spectra possess a flat profile or a square profile. Our numerical simulated results are in good agreement with the theoretical analysis. The EIT-like effect can significantly reduce the group velocity near the edge of the square profile transparent window. We believe that the counter-propagating waves coupling mechanism is particularly beneficial for the realization of active manipulation of slow light devices (such as delay lines) required in the conventional EIT scheme. In the vicinity of the transparency peak, we can obtain a large group delay, may gain more significant potential applications in slow-light transmission and optical storage.
The risks of RELN polymorphisms and its expression in the development of otosclerosis

Otosclerosis (OTSC) is the primary form of conductive hearing loss characterized by abnormal bone remodelling within the otic capsule of the human middle ear. A genetic association of the RELN SNP rs3914132 with OTSC has been identified in European population. Previously, we showed a trend towards association of this polymorphism with OTSC and identified a rare variant rs74503667 in a familial case. Here, we genotyped these variants in an Indian cohort composed of 254 OTSC cases and 262 controls. We detected a significant association of rs3914132 with OTSC (OR = 0.569, 95%CI = 0.386–0.838, p = 0.0041). To confirm this finding, we completed a meta-analysis which revealed a significant association of the rs3914132 polymorphism with OTSC (Z = 6.707, p<0.0001) across different ethnic populations. Linkage analysis found the evidence of linkage at RELN locus (LOD score 2.1059) in the OTSC family which has shown the transmission of rare variant rs74503667 in the affected individuals. To understand the role of RELN and its receptors in the development of OTSC, we went further to perform a functional analysis of RELN/reelin. Here we detected a reduced RELN (p = 0.0068) and VLDLR (p = 0.0348) mRNA levels in the otosclerotic stapes tissues. Furthermore, a reduced reelin protein expression by immunohistochemistry was confirmed in the otosclerotic tissues. Electrophoretic mobility shift assays for rs3914132 and rs74503667 variants revealed an altered binding of transcription factors in the mutated sequences which indicates the regulatory role of these variations in the RELN gene regulation. Subsequently, we showed by scanning electron microscopy a change in stapes bone morphology of otosclerotic patients. In conclusion, this study evidenced that the rare variation rs74503667 and the common polymorphism rs3914132 in the RELN gene and its reduced expressions that were associated with OTSC.
Wireless Sensor System of UAV Infrared Image and Visible Light Image Registration Fusion

The application of multisource sensors to drones requires high-quality images to ensure it. In simultaneous interpreting two or more multisensor images based on the same scene or target, the image obtained by the UAV sensor is limited by the imaging time and the shooting angle. The images obtained may not be aligned in the spatial position, thus affecting the fusion effect. Therefore, different sensor images must be registered before image fusion. During the shooting process of the drone imaging sensor, imaging angle, and environmental conditions, the obtained various sensor images will have rotation, translation, and other deformations in the spatial position so that they do not reach the spatial position. Therefore, it is impossible to directly perform image fusion directly. Therefore, before the multisensor image fusion, the image registration process must be completed to ensure that the two images are aligned in space. This paper analyzes the principles; based on the principles of the Powell search algorithm and improved walking algorithm, an algorithm combining Powell and improved walking algorithm is proposed. This paper also studies several traditional image neutrosophic fusions. The algorithm combines the fusion optimization algorithm proposed in this paper greatly reduces the calculation speed and improves the performance of the optimization algorithm and success rate.
Contesting neoliberal governance. The case of Romanian Roma

Abstract The article delineates the shifting forms of minority governance that took shape after 1989 in solving Roma related affairs and its ambiguous effects on the ground. I argue that, after 1989, the new social and public policies adopted a more neoliberal trend in solving Romani affairs through processes of decentralization, public-private partnerships and mobilization of civil society (Roma) organizations as key tools for empowering and social inclusion of Romani communities, abandoning old governmental programs focused on discipline, control and policing. However, as we will see in the Romanian case, these processes and policies had ambiguous effects and often have gone together with a diminishing of democratic accountability and control of Roma related affairs by state/public institutions and with the devolution of responsibilities to non-governmental and human rights organizations, Roma representatives from public institutions and communities themselves (see also van Baar 2011a).
The evolution of regional unemployment in the EU. An analysis via the Gompertz diffusion process

At the end of Nineties, Danny Quah devoted several papers to the analysis of polarization and stratification in the convergence processes of economies, creating the image of the ``convergence clubs'' and suggesting the importance of studying the distribution dynamics of the macroeconomic variables. As for the labor markets, Overman and Puga (2002) showed that a progressive polarization of unemployment was in fact occurring among the European regions in 1986-1996, causing a phenomenon of cross-border clusterization of the European regions. Here we propose to analyze the evolution of the unemployment rates of the EU27 regions in the last two decades assuming that the unemployment rates evolve according to a Gompertz stochastic process. The estimated parameters of the process - intrinsic growth rate, deceleration factor, volatility - represent the evolutionary path of the unemployment rate and allow for estimating the steady state of the process. A cluster analysis is performed on the steady state values of the unemployment rates. The analysis confirms the emergence of several ``convergence clubs'' among the European regional labor markets, which are compared to the cluster resulting from the more traditional clusterization on the current unemployment rates.
Venture Capital and Private Equity Investment Strategies in Selected European Countries

Abstract Private equity and venture capital (PE/VC) funding is the provision of equity capital by financial investors to non-quoted companies with high growth potential. It has a particular emphasis on entrepreneurial activities rather than on mature businesses. PE/VC investors differ on several dimensions including: investment targets, screening evaluation methods, governance mechanisms, and objectives. The paper is a continuation of the discussion that concerns investment strategies of PE/VC funds. While studying the PE/VC market it is important to analyze the origin and structure of capital. The authors assumed that different types of investors have different investment strategies. Our research is an attempt to answer the following research question: whether the investor type, on the European PE/VC market, has an impact on the selection of industries. The paper presents results of statistical analysis of venture capital and private equity funds investment strategies in selected countries.
Mesoporous silica nano-adjuvant triggers pro-inflammatory responses in Caco-2/peripheral blood mononuclear cell (PBMC) co-cultures

The aim of this study was to evaluate the cytotoxicity and immune-stimulatory effect of Mesoporous silica nanoparticle (MSN) Nano-adjuvant on pro-inflammatory cytokines and pattern recognition receptors (PRR) genes expression in Caco-2/PBMC co-culture model. MSNs were synthesized and characterized by scanning electron microscope (SEM), Brunauer Emmett Teller (BET) and Barrett Joyner Halenda (BJH) techniques. The BET specific surface area of MSNs was around 947 m2/g and the total pore volume and average pore diameter were 1.5 cm3/g and 8.01 nm, respectively. At the concentration of 10 µg/mL, MSN showed a low and time-dependent cytotoxicity on Caco-2 cells, while no cytotoxic effect was observed for 0.1 and 1 µg/mL concentrations after 24, 48 and 72 h. The expression of pro-inflammatory cytokines genes (IL-1, IL-8 and TNF-α) in co-cultures treated with different concentrations of MSN showed a dose-dependent significant increase up to 17.44, 2.722 and 4.34 folds, respectively, while the expression augmentation of IL-1 gene was significantly higher than the others. This indicates slight stimulation of intestinal inflammation. Different concentrations of MSN significantly increased TLR4 and NOD2 expression to 4.14 and 2.14 folds, respectively. NOD1 was not affected significantly. It can be concluded that MSN might increase protective immune responses against antigens as a vaccine adjuvant candidate. It seems that stimulation of TNF-α, IL-1, and IL-8 expression in enterocytes probably transpires through the agonistic activity of MSN for TLRs including TLR4, while NOD2-associated signaling pathways are also involved. This study provides an overall picture of MSN as a novel and potent oral adjuvant for mucosal immunity.
Keep Calm and Aim for the Head: Biofeedback-Controlled Dynamic Difficulty Adjustment in a Horror Game

Who said that violent video games cannot promote calm behavior? Could we reverse engineer the difficulty system of a horror game to encourage the player to stay calm as opposed to feeling under constant pressure? To explore the feasibility of the approach and its effectiveness, we created a horror game, Caroline, which uses the player’s biometric data to adapt the difficulty of the game: if the player is too stressed, the difficulty increases, and the opposite happens if the player is relaxed. We explored the effect of such an approach on players’ cognitive, emotional, performative, and decision-making challenge as well as their intrinsic motivation by comparing it to the base game without any dynamic difficulty adjustment (DDA). Our results showed that players felt more motivated when the gameplay was adjusted according to their heart rate. However, out of the four types of challenge, the only one affected by the DDA was the decision-making challenge. We discuss what these findings mean for video game design and research into affective computing and provide suggestions for future research.
Sustainable Technology Supplier Selection in the Banking Sector

Sustainable supplier selection is a key strategic problem in supply chain management. The aim of this research is to provide a new hybrid multicriteria model for evaluating technology suppliers and validate it with a case study in the banking sector. This approach allows companies to perform qualification, selection, ranking and sorting of suppliers on a sustainable basis. Integration of several techniques is necessary to address this complex decision problem with conflicting economic, environmental and social criteria. Analytic hierarchy process (AHP) is useful for problem structuring and weighting criteria collaboratively. Multi-attribute utility theory (MAUT) is applied to obtain indicators for product quality and supplier risks, whose utility functions are derived by data-driven models that favour evaluation objectivity and transparency. Preference ranking organisation method for enrichment evaluation (PROMETHEE) is suitable for supplier selection due to its discriminant power among alternatives. Finally, FlowSort is proposed to classify suppliers into ordered groups and the outcomes are compared with results from MAUT. Results show its applicability by increasing process transparency and reducing operational risks in practice.
LSTM-RNN-FNN Model for Load Forecasting Based on Deleuze’s Assemblage Perspective

Reliable load forecasting is essential for electricity generation and even for people’s lives. However, the existing load forecasting theories cannot match the requirements of complex systems (e.g., smart grids). Deleuze’s metaphysical complexity theory is seen as the theoretical foundation for comprehending complex systems, and thus, a new perspective based on Deleuze’s assemblage concept is given. According to the assemblage perspective, the electrical load is a quantitative representation of the mutual becoming of people and electricity, and load forecasting is an attempt to control this continuous process of deterritorialization and reterritorialization. We built an LSTM-RNN-FNN model for load forecasting based on the assemblage perspective, and the assessment results demonstrated that the model has high prediction performance. Furthermore, the performance of adding the temperature parameter into the network is also tested, while the correlation between the temperature and load is not strong enough and may not be suitable for load prediction. The assemblage perspective has significant implications for future load forecasting and potentially smart grid research.
First-pass decoding with n-gram approximation of RNNLM: The problem of rare words

Recurrent Neural Network Language Models (RNNLMs) can be utilized in ﬁrst-pass decoding by approximating them to N gram models. Although these approximated RNNLMs have shown to improve the Word Error Rate (WER), our experiments show that the word-based N -gram approximation seems to be poor at predicting words that occur with low frequency. In our ongoing work, we plan to switch from words to subword units for building approximated RNNLMs to improve the rare word prediction without compromising the general WER. To support this aim, we outline the various challenges and discuss the important factors for building better RNNLM approximations for the ﬁrst-pass decoding.
Effects of exogenous salicylic acid and sodium nitroprusside on α-tocopherol and phytochelatin biosynthesis in zinc-stressed safflower plants

Shahram NAMDJOYAN*, Hossein KERMANIAN, Ali Abolhasani SOORKI, Sedigheh MODARRES TABATABAEI, Nazli ELYASI Department of Biology, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran Department of Pulp and Paper, Faculty of Energy and New Technology, Shahid Beheshti University, Tehran, Iran ACECR-Research Institute of Applied Sciences, Shahid Beheshti University, Tehran, Iran Iranian Department of the Environment, Tehran, Iran Department of Statistics, Trade Planning Office, Trade Promotion Organization, Tehran, Iran